diff --git a/deckard/layers/afr.py b/deckard/layers/afr.py index 8312d67a..d9c42c76 100644 --- a/deckard/layers/afr.py +++ b/deckard/layers/afr.py @@ -1,9 +1,11 @@ import pandas as pd import numpy as np from pathlib import Path - +import logging +import yaml +import argparse +import seaborn as sns import matplotlib.pyplot as plt - from sklearn.model_selection import train_test_split from lifelines import ( WeibullAFTFitter, @@ -12,13 +14,13 @@ CoxPHFitter, ) from .clean_data import drop_frames_without_results -import matplotlib -import logging -import yaml -import argparse +from .plots import set_matplotlib_vars + logger = logging.getLogger(__name__) +sns.set_theme(style="whitegrid", font_scale=1.8, font="times new roman") + def plot_aft( df, @@ -30,12 +32,11 @@ def plot_aft( xlabel=None, ylabel=None, replacement_dict={}, - filetype=".eps", folder=".", legend={}, **kwargs, ): - file = Path(folder, file).with_suffix(filetype) + file = Path(folder, file) aft = fit_aft(df, event_col, duration_col, mtype, kwargs) columns = list(df.columns) columns.remove(event_col) @@ -58,7 +59,6 @@ def plot_aft( ax.get_figure().tight_layout() ax.get_figure().savefig(file) logger.info(f"Saved graph to {file}") - plt.show() plt.gcf().clear() return ax, aft @@ -102,11 +102,10 @@ def plot_partial_effects( replacement_dict={}, cmap="coolwarm", folder=".", - filetype=".eps", **kwargs, ): plt.gcf().clear() - file = Path(folder, file).with_suffix(filetype) + file = Path(folder, file) partial_effects = aft.plot_partial_effects_on_outcome( covariate_array, values_array, @@ -135,7 +134,6 @@ def score_model(aft, train, test): train_score = aft.score(train) test_score = aft.score(test) scores = {"train_score": train_score, "test_score": test_score} - plt.show() return scores @@ -175,7 +173,7 @@ def make_afr_table( aft_data.to_csv(folder / "aft_comparison.csv", na_rep="--") logger.info(f"Saved AFT comparison to {folder / 'aft_comparison.csv'}") aft_data.to_latex( - buf=folder / f"{filename}.tex", + buf=Path(folder / "aft_comparison.tex").as_posix(), float_format="%.3g", na_rep="--", label=label, @@ -203,16 +201,13 @@ def clean_data_for_aft( ), f"Target {target} not in dataframe with columns {subset.columns}" logger.info(f"Shape of dirty data: {subset.shape}") cleaned = pd.DataFrame() - covariate_list.append(target) - + if target not in covariate_list: + covariate_list.append(target) logger.info(f"Covariates : {covariate_list}") for kwarg in covariate_list: assert kwarg in subset.columns, f"{kwarg} not in data.columns" cleaned = pd.concat([cleaned, subset[kwarg]], axis=1) - cols = cleaned.columns - cleaned = pd.DataFrame(subset, columns=cols) - cleaned.index = subset.index - # remove rows with -1e10 or 1e10, which are placeholders for run-time errors depending on the direction of optimization + cols = list(cleaned.columns) for col in cols: cleaned = cleaned[cleaned[col] != -1e10] cleaned = cleaned[cleaned[col] != 1e10] @@ -249,8 +244,8 @@ def split_data_for_aft( assert ( duration_col in cleaned ), f"Duration {duration_col} not in dataframe with columns {cleaned.columns}" - X_train = X_train.dropna(axis=0, how="any") - X_test = X_test.dropna(axis=0, how="any") + # X_train = X_train.dropna(axis=0, how="any") + # X_test = X_test.dropna(axis=0, how="any") X_train = pd.DataFrame(X_train, columns=cleaned.columns) X_test = pd.DataFrame(X_test, columns=cleaned.columns) return X_train, X_test @@ -333,18 +328,6 @@ def render_all_afr_plots( print("*" * 80) -def set_matplotlib_vars(matplotlib_dict=None): - if matplotlib_dict is None: - matplotlib_dict = { - "font": { - "family": "Times New Roman", - "weight": "bold", - "size": 22, - }, - } - matplotlib.rc(**matplotlib_dict) - - def fillna(data, config): fillna = config.pop("fillna", {}) for k, v in fillna.items(): @@ -354,9 +337,19 @@ def fillna(data, config): if "__main__" == __name__: afr_parser = argparse.ArgumentParser() - afr_parser.add_argument("--target", type=str, default="adv_failures") - afr_parser.add_argument("--duration_col", type=str, default="adv_fit_time") - afr_parser.add_argument("--dataset", type=str, default="mnist") + afr_parser.add_argument( + "--target", + type=str, + help="Failure count column", + required=True, + ) + afr_parser.add_argument( + "--duration_col", + type=str, + help="Duration column", + required=True, + ) + afr_parser.add_argument("--dataset", type=str, help="Dataset name", required=True) afr_parser.add_argument("--data_file", type=str, default="data.csv") afr_parser.add_argument("--config_file", type=str, default="afr.yaml") afr_parser.add_argument("--plots_folder", type=str, default="plots") @@ -394,12 +387,8 @@ def fillna(data, config): covariates = config.get("covariates", []) assert len(covariates) > 0, "No covariates specified in config file" - # Cannot fit AFT models with missing values - logger.info(f"Shape of data before data before dropping na: {data.shape}") - data = drop_frames_without_results(data, covariates) - logger.info(f"Shape of data before data before dropping na: {data.shape}") # Converting accuracy to unnormalized count, if needed - if "adv_failures" in covariates and "adv_failures" in data.columns: + if "adv_failures" in covariates: logger.info("Adding adv_failures to data") assert "adv_accuracy" in data.columns, "adv_accuracy not in data" assert "attack.attack_size" in data.columns, "attack.attack_size not in data" @@ -415,6 +404,10 @@ def fillna(data, config): :, "data.sample.test_size", ] + # Cannot fit AFT models with missing values + logger.info(f"Shape of data before data before dropping na: {data.shape}") + data = drop_frames_without_results(data, covariates) + logger.info(f"Shape of data before data before dropping na: {data.shape}") # Plotting AFT models render_all_afr_plots( config, diff --git a/deckard/layers/clean_data.py b/deckard/layers/clean_data.py index 609ce48c..92e2646f 100644 --- a/deckard/layers/clean_data.py +++ b/deckard/layers/clean_data.py @@ -626,7 +626,6 @@ def main(args): if "adv_accuracy" in results.columns: results = calculate_failure_rate(results) - results = min_max_scaling(results, *min_max) output_file = save_results( results, diff --git a/deckard/layers/plots.py b/deckard/layers/plots.py index 99f77e52..7859baff 100644 --- a/deckard/layers/plots.py +++ b/deckard/layers/plots.py @@ -10,6 +10,21 @@ sns.set_theme(style="whitegrid", font_scale=1.8, font="times new roman") +def set_matplotlib_vars(matplotlib_dict=None): + if matplotlib_dict is None: + matplotlib_dict = { + "font": { + "family": "Times New Roman", + "weight": "bold", + "size": 22, + }, + } + else: + assert isinstance(matplotlib_dict, dict), "matplotlib_dict must be a dictionary" + for k, v in matplotlib_dict.items(): + plt.rc(k, **v) + + def cat_plot( data, x, @@ -266,6 +281,9 @@ def scatter_plot( file = Path(file).with_suffix(filetype) logger.info(f"Rendering graph {file}") data = data.sort_values(by=[hue, x, y]) + assert hue in data.columns, f"{hue} not in data columns" + assert x in data.columns, f"{x} not in data columns" + assert y in data.columns, f"{y} not in data columns" graph = sns.scatterplot( data=data, x=x, @@ -356,20 +374,16 @@ def main(args): logger.info(f"Creating folder {FOLDER}") FOLDER.mkdir(parents=True, exist_ok=True) - i = 0 cat_plot_list = big_dict.get("cat_plot", []) for dict_ in cat_plot_list: - i += 1 cat_plot(data, **dict_, folder=FOLDER, filetype=IMAGE_FILETYPE) line_plot_list = big_dict.get("line_plot", []) for dict_ in line_plot_list: - i += 1 line_plot(data, **dict_, folder=FOLDER, filetype=IMAGE_FILETYPE) scatter_plot_list = big_dict.get("scatter_plot", []) for dict_ in scatter_plot_list: - i += 1 scatter_plot(data, **dict_, folder=FOLDER, filetype=IMAGE_FILETYPE) diff --git a/deckard/layers/prometheus.py b/deckard/layers/prometheus.py deleted file mode 100644 index 68e20092..00000000 --- a/deckard/layers/prometheus.py +++ /dev/null @@ -1,188 +0,0 @@ -# import experiments.libs.functions -# from prometheus_api_client import PrometheusConnect -# from datetime import datetime -# from dataclasses import dataclass -# from pathlib import Path -# import yaml - - -# @dataclass -# class PromQuery: -# prom_host = "labumu.se" -# prom_port = "30090" -# prom_address = "http://" + prom_host + ":" + prom_port + "/" -# warmup = 9000 -# warmdown = 3000 -# step = 5 -# query = "" -# start = 0 -# end = 0 -# service = "" -# namespace = "" -# percentile = "" -# reporter = "source" -# response_code = "" - -# def query_prometheus(self): -# """ -# This function collects data in prometheus for a given query, in a given time interval, with a given -# warmup/warmdown time offset and a given step. -# :return: -# """ -# prom = PrometheusConnect(url=self.prom_address, disable_ssl=True) -# start = datetime.fromtimestamp((self.start + self.warmup) / 1000) -# end = datetime.fromtimestamp((self.end - self.warmdown) / 1000) - -# result = prom.custom_query_range( -# query=self.query, -# start_time=start, -# end_time=end, -# step=self.step, -# ) -# return result - -# def get_response_time(self, version=None): -# """ -# This function will get the response time for a given service in a given time interval and based on a given -# percentile -# :return: -# """ -# if version == None: -# version = "latest" -# if self.response_code == "": -# self.query = ( -# "(histogram_quantile(" -# + str(self.percentile) -# + ', sum(irate(istio_request_duration_milliseconds_bucket{reporter="' -# + self.reporter -# + '", destination_service=~"' -# + self.service -# + "." -# + self.namespace -# + '.svc.cluster.local", destination_canonical_revision="' -# + version -# + '"}[1m])) ' -# "by (le)) / 1000)" -# ) -# elif self.response_code == "200": -# self.query = ( -# "(histogram_quantile(" -# + str(self.percentile) -# + ', sum(irate(istio_request_duration_milliseconds_bucket{reporter="' -# + self.reporter -# + '", destination_service=~"' -# + self.service -# + "." -# + self.namespace -# + '.svc.cluster.local",' -# 'response_code="' -# + self.response_code -# + '", destination_canonical_revision="' -# + version -# + '"}[1m])) by (le)) / 1000)' -# ) -# else: -# self.query = ( -# "(histogram_quantile(" -# + str(self.percentile) -# + ', sum(irate(istio_request_duration_milliseconds_bucket{reporter="' -# + self.reporter -# + '", destination_service=~"' -# + self.service -# + "." -# + self.namespace -# + '.svc.cluster.local",' -# 'response_code!="200", destination_canonical_revision="' -# + version -# + '"}[1m])) by (le)) / 1000)' -# ) - -# result = self.query_prometheus() -# return result - -# def get_status_codes(self, version=None): -# """ -# This function will get the request status codes for agiven service, in agiven time interval with a given -# warmup/warmdown time offset and a given step - -# """ -# if version == None: -# version = "latest" -# self.query = ( -# 'round(sum(irate(istio_requests_total{destination_service=~"' -# + self.service -# + "" -# "." -# + self.namespace -# + '.svc.cluster.local", reporter="source", destination_canonical_revision="' -# + version -# + '"}[1m])) by (response_code, response_flags), 0.001)' -# ) -# result = self.query_prometheus() -# return result - -# def get_retried_requests(self, port, version=""): -# """ -# This function gets the number of retried requests for a given service, in given time interval with a given -# warmup/warmdown time offset and a given step -# """ -# self.query = ( -# 'round(sum(irate(envoy_cluster_upstream_rq_retry{cluster_name="outbound|' -# + str(port) -# + "|" -# + version -# + "|" -# + self.service -# + '.default.svc.cluster.local"}[1m])) by (), 0.001)' -# ) -# result = self.query_prometheus() -# return result - -# def get_requests_in_queue(self): -# """ -# This function will get the request in the queue for a given service -# """ -# self.query = ( -# 'round(sum(irate(envoy_http_inbound_0_0_0_0_5000_downstream_rq_active{app=~"' -# + self.service -# + '"}[1m])) by (service_istio_io_canonical_name), 0.001)' -# ) -# result = self.query_prometheus() -# return result - -# def get_current_queue_size(self, job="istio"): -# """ -# This function will get the current queue size which is pushed in pushgateway (HTTP2MaxRequests) -# """ -# self.query = 'destination_rule_http2_max_requests{exported_job="' + job + '"}' -# result = self.query_prometheus() -# return result - -# def get_retry_attempt(self): -# """ -# This function get the retry attempt which is pushed in pushgateway (attempts) -# """ -# self.query = "retry_attempts_" + self.service -# result = self.query_prometheus() -# return result - -# def __call__(self, config_file, output_file, output_folder) -> None: -# """ -# This function will call the prometheus query function and write the result in a given file -# """ -# # Available metrics: -# # train_time, train_start_time, train_end_time, -# # predict_proba_time, predict_proba_start_time, predict_proba_end_time, -# # adv_train_time, adv_train_start_time, adv_train_end_time, -# # adv_predict_proba_time, adv_predict_proba_start_time, adv_predict_proba_end_time, -# # Find all output_file recursively inside output_folder -# with open(config_file, "r") as f: -# config = yaml.load(f, Loader=yaml.FullLoader) -# files = Path(output_folder).rglob(output_file) -# # Each file will have train_start_time train_end_time, predict_proba_start_time predict_predict_proba_end_time, adv_ -# # Query Prometheus -# # Do calulations -# # Write to file -# # Use a lambda function so that this will be parallelized across all the files in the files iterator and across each entry of the config -# # Return None -# None diff --git a/examples/power/conf/afr.yaml b/examples/power/conf/afr.yaml index 35f112d8..35b94189 100644 --- a/examples/power/conf/afr.yaml +++ b/examples/power/conf/afr.yaml @@ -7,6 +7,7 @@ covariates: - data.sample.random_state - adv_fit_time - attack.init.eps + - adv_failures fillna: model.trainer.nb_epoch: 20 model.trainer.batch_size: 1024 @@ -35,8 +36,8 @@ weibull: "covariate_array": "model.trainer.nb_epoch" "values_array": [1,10,25,50] "title": "$S(t)$ for Weibull AFR" - "ylabel": "Expectation of $S(t)$" - "xlabel": "Time $T$ (seconds)" + "ylabel": "$\\mathbb{P}~(T>t)$" + "xlabel": "Time $t$ (seconds)" "legend_kwargs": { "title": "Epochs", "labels": ["1", "10", "25", "50"] @@ -65,8 +66,8 @@ cox: "covariate_array": "model.trainer.nb_epoch" "values_array": [1,10,25,50] "title": "$S(t)$ for Cox AFR" - "ylabel": "Expectation of $S(t)$" - "xlabel": "Time $T$ (seconds)" + "ylabel": "$\\mathbb{P}~(T>t)$" + "xlabel": "Time $t$ (seconds)" "legend_kwargs": { "title": "Epochs", "labels": ["1", "10", "25", "50"] @@ -95,8 +96,8 @@ log_logistic: "covariate_array": "model.trainer.nb_epoch" "values_array": [1,10,25,50] "title": "$S(t)$ for Log-Logistic AFR" - "ylabel": "Expectation of $S(t)$" - "xlabel": "Time $T$ (seconds)" + "ylabel": "$\\mathbb{P}~(T>t)$" + "xlabel": "Time $t$ (seconds)" "legend_kwargs": { "title": "Epochs", "labels": ["1", "10", "25", "50"] @@ -125,8 +126,8 @@ log_normal: "covariate_array": "model.trainer.nb_epoch" "values_array": [1,10,25,50] "title": "$S(t)$ for Log-Normal AFR" - "ylabel": "Expectation of $S(t)$" - "xlabel": "Time $T$ (seconds)" + "ylabel": "$\\mathbb{P}~(T>t)$" + "xlabel": "Time $t$ (seconds)" "legend_kwargs": { "title": "Epochs", "labels": ["1", "10", "25", "50"] diff --git a/examples/power/conf/attack/default.yaml b/examples/power/conf/attack/default.yaml index 7d528793..6971e74b 100644 --- a/examples/power/conf/attack/default.yaml +++ b/examples/power/conf/attack/default.yaml @@ -6,7 +6,6 @@ init: _target_: deckard.base.attack.AttackInitializer name: art.attacks.evasion.FastGradientMethod eps: .99 - # eps_step : ${eval:'(.1)*${.eps}'} batch_size : ${model.trainer.batch_size} targeted : false minimal : true diff --git a/examples/power/conf/clean.yaml b/examples/power/conf/clean.yaml index 7a715ad0..c8f2b5c7 100644 --- a/examples/power/conf/clean.yaml +++ b/examples/power/conf/clean.yaml @@ -1,12 +1,18 @@ attacks: FastGradientMethod: FGM + ProjectedGradientDescent: PGD + HopSkipJump: HSJ + DeepFool: Deep defences: Control: Control FeatureSqueezing: FSQ - nb_epoch: Epochs + Epochs: Epochs model_layers: Control params: FGM: attack.init.eps + PGD: attack.init.eps + HSJ: attack.init.eps + DeepFool: attack.init.eps FSQ: model.art.preprocessor.bit_depth Control: model_layers Epochs: model.trainer.nb_epoch diff --git a/examples/power/conf/combined_afr.yaml b/examples/power/conf/combined_afr.yaml index 6595542a..4665f3bc 100644 --- a/examples/power/conf/combined_afr.yaml +++ b/examples/power/conf/combined_afr.yaml @@ -7,11 +7,7 @@ covariates: - attack.init.eps - data.sample.random_state - adv_fit_time - # - model.art.preprocessor.bit_depth - # - train_power - # - predict_power - # - n_pixels - # - n_channels + - adv_failures fillna: model.trainer.nb_epoch: 20 model.trainer.batch_size: 1024 @@ -40,8 +36,8 @@ weibull: "covariate_array": "model.trainer.nb_epoch" "values_array": [1,10,25,50, 100] "title": "$S(t)$ for Weibull AFR" - "ylabel": "Expectation of $S(t)$" - "xlabel": "Time $T$ (seconds)" + "ylabel": "$\\mathbb{P}~(T>t)$" + "xlabel": "Time $t$ (seconds)" "legend_kwargs": { "title": "Epochs", "labels": ["1", "10", "25", "50"] @@ -50,8 +46,8 @@ weibull: "covariate_array": "model.trainer.batch_size" "values_array": [1,10,100, 1000] "title": "$S(t)$ for Weibull AFR" - "ylabel": "Expectation of $S(t)$" - "xlabel": "Time $T$ (seconds)" + "ylabel": "$\\mathbb{P}~(T>t)$" + "xlabel": "Time $t$ (seconds)" "legend_kwargs": { "title": "Batch Size", "labels": ["10", "100", "1000", "10000"] @@ -60,8 +56,8 @@ weibull: "covariate_array": "model.trainer.batch_size" "values_array": [1, 10, 100, 1000] "title": "$S(t)$ for Weibull AFR" - "ylabel": "Expectation of $S(t)$" - "xlabel": "Time $T$ (seconds)" + "ylabel": "$\\mathbb{P}~(T>t)$" + "xlabel": "Time $t$ (seconds)" "legend_kwargs": { "title": "$T_t$", "labels": ["1", "10", "100", "1000"] @@ -70,8 +66,8 @@ weibull: "covariate_array" : predict_proba_time "values_array" : [1e-4, 1e-3, 1e-2, 1e-1, 1] "title": "$S(t)$ for Weibull AFR" - "ylabel": "Expectation of $S(t)$" - "xlabel": "Time $T$ (seconds)" + "ylabel": "$\\mathbb{P}~(T>t)$" + "xlabel": "Time $t$ (seconds)" "legend_kwargs": { "title": "$t_i$", "labels": ["1e-4", "1e-3", "1e-2", '1e-1', '1'] @@ -80,8 +76,8 @@ weibull: "covariate_array" : predict_proba_time "values_array" : [1e-4, 1e-3, 1e-2, 1e-1, 1] "title": "$S(t)$ for Weibull AFR" - "ylabel": "Expectation of $S(t)$" - "xlabel": "Time $T$ (seconds)" + "ylabel": "$\\mathbb{P}~(T>t)$" + "xlabel": "Time $t$ (seconds)" "legend_kwargs": { "title": "$\\varepsilon$", "labels": ["1e-4", "1e-3", "1e-2", '1e-1', '1'] @@ -110,8 +106,8 @@ cox: "covariate_array": "model.trainer.nb_epoch" "values_array": [1,10,25,50] "title": "$S(t)$ for Cox AFR" - "ylabel": "Expectation of $S(t)$" - "xlabel": "Time $T$ (seconds)" + "ylabel": "$\\mathbb{P}~(T>t)$" + "xlabel": "Time $t$ (seconds)" "legend_kwargs": { "title": "Epochs", "labels": ["1", "10", "25", "50"] @@ -120,8 +116,8 @@ cox: "covariate_array": "model.trainer.batch_size" "values_array": [1,10,25,50] "title": "$S(t)$ for Cox AFR" - "ylabel": "Expectation of $S(t)$" - "xlabel": "Time $T$ (seconds)" + "ylabel": "$\\mathbb{P}~(T>t)$" + "xlabel": "Time $t$ (seconds)" "legend_kwargs": { "title": "Batch Size", "labels": ["10", "100", "1000", "10000"] @@ -130,8 +126,8 @@ cox: "covariate_array": "model.trainer.batch_size" "values_array": [1, 10, 100, 1000] "title": "$S(t)$ for Cox AFR" - "ylabel": "Expectation of $S(t)$" - "xlabel": "Time $T$ (seconds)" + "ylabel": "$\\mathbb{P}~(T>t)$" + "xlabel": "Time $t$ (seconds)" "legend_kwargs": { "title": "$T_t$", "labels": ["1", "10", "100", "1000"] @@ -140,8 +136,8 @@ cox: "covariate_array" : predict_proba_time "values_array" : [1e-4, 1e-3, 1e-2, 1e-1, 1] "title": "$S(t)$ for Cox AFR" - "ylabel": "Expectation of $S(t)$" - "xlabel": "Time $T$ (seconds)" + "ylabel": "$\\mathbb{P}~(T>t)$" + "xlabel": "Time $t$ (seconds)" "legend_kwargs": { "title": "$t_i$", "labels": ["1e-4", "1e-3", "1e-2", '1e-1', '1'] @@ -150,8 +146,8 @@ cox: "covariate_array" : predict_proba_time "values_array" : [1e-4, 1e-3, 1e-2, 1e-1, 1] "title": "$S(t)$ for Cox AFR" - "ylabel": "Expectation of $S(t)$" - "xlabel": "Time $T$ (seconds)" + "ylabel": "$\\mathbb{P}~(T>t)$" + "xlabel": "Time $t$ (seconds)" "legend_kwargs": { "title": "$\\varepsilon$", "labels": ["1e-4", "1e-3", "1e-2", '1e-1', '1'] @@ -180,8 +176,8 @@ log_logistic: "covariate_array": "model.trainer.nb_epoch" "values_array": [1,10,25,50] "title": "$S(t)$ for Log-Logistic AFR" - "ylabel": "Expectation of $S(t)$" - "xlabel": "Time $T$ (seconds)" + "ylabel": "$\\mathbb{P}~(T>t)$" + "xlabel": "Time $t$ (seconds)" "legend_kwargs": { "title": "Epochs", "labels": ["1", "10", "25", "50"] @@ -190,8 +186,8 @@ log_logistic: "covariate_array": "model.trainer.batch_size" "values_array": [1,10,25,50] "title": "$S(t)$ for Log-Logistic AFR" - "ylabel": "Expectation of $S(t)$" - "xlabel": "Time $T$ (seconds)" + "ylabel": "$\\mathbb{P}~(T>t)$" + "xlabel": "Time $t$ (seconds)" "legend_kwargs": { "title": "Batch Size", "labels": ["10", "100", "1000", "10000"] @@ -200,8 +196,8 @@ log_logistic: "covariate_array": "model.trainer.batch_size" "values_array": [1, 10, 100, 1000] "title": "$S(t)$ for Log-Logistic AFR" - "ylabel": "Expectation of $S(t)$" - "xlabel": "Time $T$ (seconds)" + "ylabel": "$\\mathbb{P}~(T>t)$" + "xlabel": "Time $t$ (seconds)" "legend_kwargs": { "title": "$T_t$", "labels": ["1", "10", "100", "1000"] @@ -210,8 +206,8 @@ log_logistic: "covariate_array" : predict_proba_time "values_array" : [1e-4, 1e-3, 1e-2, 1e-1, 1] "title": "$S(t)$ for Log-Logistic AFR" - "ylabel": "Expectation of $S(t)$" - "xlabel": "Time $T$ (seconds)" + "ylabel": "$\\mathbb{P}~(T>t)$" + "xlabel": "Time $t$ (seconds)" "legend_kwargs": { "title": "$t_i$", "labels": ["1e-4", "1e-3", "1e-2", '1e-1', '1'] @@ -220,8 +216,8 @@ log_logistic: "covariate_array" : predict_proba_time "values_array" : [1e-4, 1e-3, 1e-2, 1e-1, 1] "title": "$S(t)$ for Log-Logistic AFR" - "ylabel": "Expectation of $S(t)$" - "xlabel": "Time $T$ (seconds)" + "ylabel": "$\\mathbb{P}~(T>t)$" + "xlabel": "Time $t$ (seconds)" "legend_kwargs": { "title": "$\\varepsilon$", "labels": ["1e-4", "1e-3", "1e-2", '1e-1', '1'] @@ -250,8 +246,8 @@ log_normal: "covariate_array": "model.trainer.nb_epoch" "values_array": [1,10,25,50] "title": "$S(t)$ for Log-Normal AFR" - "ylabel": "Expectation of $S(t)$" - "xlabel": "Time $T$ (seconds)" + "ylabel": "$\\mathbb{P}~(T>t)$" + "xlabel": "Time $t$ (seconds)" "legend_kwargs": { "title": "Epochs", "labels": ["1", "10", "25", "50"] @@ -260,8 +256,8 @@ log_normal: "covariate_array": "model.trainer.batch_size" "values_array": [1,10,25,50] "title": "$S(t)$ for Log-Normal AFR" - "ylabel": "Expectation of $S(t)$" - "xlabel": "Time $T$ (seconds)" + "ylabel": "$\\mathbb{P}~(T>t)$" + "xlabel": "Time $t$ (seconds)" "legend_kwargs": { "title": "Batch Size", "labels": ["10", "100", "1000", "10000"] @@ -270,8 +266,8 @@ log_normal: "covariate_array": "model.trainer.batch_size" "values_array": [1, 10, 100, 1000] "title": "$S(t)$ for Log-Normal AFR" - "ylabel": "Expectation of $S(t)$" - "xlabel": "Time $T$ (seconds)" + "ylabel": "$\\mathbb{P}~(T>t)$" + "xlabel": "Time $t$ (seconds)" "legend_kwargs": { "title": "$T_t$", "labels": ["1", "10", "100", "1000"] @@ -280,8 +276,8 @@ log_normal: "covariate_array" : predict_proba_time "values_array" : [1e-4, 1e-3, 1e-2, 1e-1, 1] "title": "$S(t)$ for Log-Normal AFR" - "ylabel": "Expectation of $S(t)$" - "xlabel": "Time $T$ (seconds)" + "ylabel": "$\\mathbb{P}~(T>t)$" + "xlabel": "Time $t$ (seconds)" "legend_kwargs": { "title": "$t_i$", "labels": ["1e-4", "1e-3", "1e-2", '1e-1', '1'] @@ -290,8 +286,8 @@ log_normal: "covariate_array" : predict_proba_time "values_array" : [1e-4, 1e-3, 1e-2, 1e-1, 1] "title": "$S(t)$ for Log-Normal AFR" - "ylabel": "Expectation of $S(t)$" - "xlabel": "Time $T$ (seconds)" + "ylabel": "$\\mathbb{P}~(T>t)$" + "xlabel": "Time $t$ (seconds)" "legend_kwargs": { "title": "$\\varepsilon$", "labels": ["1e-4", "1e-3", "1e-2", '1e-1', '1'] diff --git a/examples/power/conf/plots.yaml b/examples/power/conf/plots.yaml index f8043284..cb501f1f 100644 --- a/examples/power/conf/plots.yaml +++ b/examples/power/conf/plots.yaml @@ -26,8 +26,9 @@ line_plot: y_scale: ylabel: $Ben. Accuracy$ hue_order: - - nvidia-tesla-v100 - - nvidia-tesla-p100 + - "Nvidia Tesla V100" + - "Nvidia Tesla P100" + - "Nvidia L4" scatter_plot: - x: train_time_per_sample y: adv_failure_rate @@ -42,8 +43,8 @@ scatter_plot: title: Hardware Device bbox_to_anchor: [1.05, 1] hue_order: - - nvidia-tesla-v100 - - nvidia-tesla-p100 + - "Nvidia Tesla V100" + - "Nvidia Tesla P100" - x: train_time_per_sample y: failure_rate hue: device_id @@ -57,5 +58,6 @@ scatter_plot: title: Hardware Device bbox_to_anchor: [1.05, 1] hue_order: - - nvidia-tesla-v100 - - nvidia-tesla-p100 + - "Nvidia Tesla V100" + - "Nvidia Tesla P100" + - diff --git a/examples/power/dvc.lock b/examples/power/dvc.lock deleted file mode 100644 index e365ba47..00000000 --- a/examples/power/dvc.lock +++ /dev/null @@ -1,278 +0,0 @@ -schema: '2.0' -stages: - install_deckard: - cmd: python -m pip install -e ../../ && python -m pip install torch==1.8.1+cu101 - torchvision==0.9.1+cu101 torchaudio==0.8.1 -f https://download.pytorch.org/whl/torch_stable.html - deps: - - path: ../../setup.py - md5: 4da6845155a1ec0b5a5d1add2e985068 - size: 4920 - outs: - - path: ../../deckard.egg-info - md5: 5f080ee95597cf407fdc12d442214ed5.dir - size: 6608 - nfiles: 5 - parse_params: - cmd: python -m deckard.layers.parse --config_file torch_mnist.yaml - deps: - - path: conf/ - md5: 6866c6a54a32f2f84a97c72b14cf2bc7.dir - size: 138029 - nfiles: 35 - outs: - - path: params.yaml - md5: fb9682b00807fe1b37aa04052897ef23 - size: 6826 - attack: - cmd: python -m deckard.layers.experiment attack --config_file torch_mnist.yaml - deps: - - path: params.yaml - md5: fb9682b00807fe1b37aa04052897ef23 - size: 6826 - params: - params.yaml: - attack: - _target_: deckard.base.attack.Attack - attack_size: 10 - data: - _target_: deckard.base.data.Data - generate: - _target_: deckard.base.data.generator.DataGenerator - name: torch_mnist - sample: - _target_: deckard.base.data.sampler.SklearnDataSampler - random_state: 0 - stratify: true - sklearn_pipeline: - _target_: deckard.base.data.sklearn_pipeline.SklearnDataPipeline - preprocessor: - name: sklearn.preprocessing.StandardScaler - with_mean: true - with_std: true - init: - _target_: deckard.base.attack.AttackInitializer - batch_size: 1024 - eps: 0.99 - minimal: true - model: - _target_: deckard.base.model.Model - art: - _target_: deckard.base.model.art_pipeline.ArtPipeline - data: - _target_: deckard.base.data.Data - generate: - _target_: deckard.base.data.generator.DataGenerator - name: torch_mnist - sample: - _target_: deckard.base.data.sampler.SklearnDataSampler - random_state: 0 - stratify: true - sklearn_pipeline: - _target_: deckard.base.data.sklearn_pipeline.SklearnDataPipeline - preprocessor: - name: sklearn.preprocessing.StandardScaler - with_mean: true - with_std: true - initialize: - clip_values: - - 0 - - 255 - criterion: - name: torch.nn.CrossEntropyLoss - optimizer: - lr: 0.01 - momentum: 0.9 - name: torch.optim.SGD - library: pytorch - data: - _target_: deckard.base.data.Data - generate: - _target_: deckard.base.data.generator.DataGenerator - name: torch_mnist - sample: - _target_: deckard.base.data.sampler.SklearnDataSampler - random_state: 0 - stratify: true - sklearn_pipeline: - _target_: deckard.base.data.sklearn_pipeline.SklearnDataPipeline - preprocessor: - name: sklearn.preprocessing.StandardScaler - with_mean: true - with_std: true - init: - _target_: deckard.base.model.ModelInitializer - name: torch_example.ResNet18 - num_channels: 1 - library: pytorch - trainer: - batch_size: 1024 - nb_epoch: 1 - name: art.attacks.evasion.FastGradientMethod - targeted: false - method: evasion - model: - _target_: deckard.base.model.Model - art: - _target_: deckard.base.model.art_pipeline.ArtPipeline - data: - _target_: deckard.base.data.Data - generate: - _target_: deckard.base.data.generator.DataGenerator - name: torch_mnist - sample: - _target_: deckard.base.data.sampler.SklearnDataSampler - random_state: 0 - stratify: true - sklearn_pipeline: - _target_: deckard.base.data.sklearn_pipeline.SklearnDataPipeline - preprocessor: - name: sklearn.preprocessing.StandardScaler - with_mean: true - with_std: true - initialize: - clip_values: - - 0 - - 255 - criterion: - name: torch.nn.CrossEntropyLoss - optimizer: - lr: 0.01 - momentum: 0.9 - name: torch.optim.SGD - library: pytorch - data: - _target_: deckard.base.data.Data - generate: - _target_: deckard.base.data.generator.DataGenerator - name: torch_mnist - sample: - _target_: deckard.base.data.sampler.SklearnDataSampler - random_state: 0 - stratify: true - sklearn_pipeline: - _target_: deckard.base.data.sklearn_pipeline.SklearnDataPipeline - preprocessor: - name: sklearn.preprocessing.StandardScaler - with_mean: true - with_std: true - init: - _target_: deckard.base.model.ModelInitializer - name: torch_example.ResNet18 - num_channels: 1 - library: pytorch - trainer: - batch_size: 1024 - nb_epoch: 1 - data: - _target_: deckard.base.data.Data - generate: - _target_: deckard.base.data.generator.DataGenerator - name: torch_mnist - sample: - _target_: deckard.base.data.sampler.SklearnDataSampler - random_state: 0 - stratify: true - sklearn_pipeline: - _target_: deckard.base.data.sklearn_pipeline.SklearnDataPipeline - preprocessor: - name: sklearn.preprocessing.StandardScaler - with_mean: true - with_std: true - device_id: cpu - files: - _target_: deckard.base.files.FileConfig - adv_predictions_file: adv_predictions.json - attack_dir: attacks - attack_file: attack - attack_type: .pkl - data_dir: data - data_file: data - data_type: .pkl - directory: /result/mnist/ - model_dir: - model_file: - model_type: - name: default - params_file: params.yaml - predictions_file: predictions.json - reports: reports - score_dict_file: score_dict.json - model: - _target_: deckard.base.model.Model - art: - _target_: deckard.base.model.art_pipeline.ArtPipeline - data: - _target_: deckard.base.data.Data - generate: - _target_: deckard.base.data.generator.DataGenerator - name: torch_mnist - sample: - _target_: deckard.base.data.sampler.SklearnDataSampler - random_state: 0 - stratify: true - sklearn_pipeline: - _target_: deckard.base.data.sklearn_pipeline.SklearnDataPipeline - preprocessor: - name: sklearn.preprocessing.StandardScaler - with_mean: true - with_std: true - initialize: - clip_values: - - 0 - - 255 - criterion: - name: torch.nn.CrossEntropyLoss - optimizer: - lr: 0.01 - momentum: 0.9 - name: torch.optim.SGD - library: pytorch - data: - _target_: deckard.base.data.Data - generate: - _target_: deckard.base.data.generator.DataGenerator - name: torch_mnist - sample: - _target_: deckard.base.data.sampler.SklearnDataSampler - random_state: 0 - stratify: true - sklearn_pipeline: - _target_: deckard.base.data.sklearn_pipeline.SklearnDataPipeline - preprocessor: - name: sklearn.preprocessing.StandardScaler - with_mean: true - with_std: true - init: - _target_: deckard.base.model.ModelInitializer - name: torch_example.ResNet18 - num_channels: 1 - library: pytorch - trainer: - batch_size: 1024 - nb_epoch: 1 - scorers: - _target_: deckard.base.scorer.ScorerDict - accuracy: - _target_: deckard.base.scorer.ScorerConfig - direction: maximize - name: sklearn.metrics.accuracy_score - log_loss: - _target_: deckard.base.scorer.ScorerConfig - direction: minimize - name: sklearn.metrics.log_loss - outs: - - path: /result/mnist//attacks/attack.pkl - md5: a1df3a75fbad7c46a8b85353fc107bd1 - size: 31517 - - path: /result/mnist//data/data.pkl - md5: de934a5f5157970e5f30b8f3f1856a68 - size: 222320311 - - path: /result/mnist//reports/attack/default/adv_predictions.json - md5: 5f725a27f10b29f4e44bb62b3e58ff71 - size: 2140 - - path: /result/mnist//reports/attack/default/predictions.json - md5: 6226ebd48f2073fd0e6021ef18a2c6cf - size: 2884815 - - path: /result/mnist//reports/attack/default/score_dict.json - md5: 76066289ba570a3db6f3d55e537bf1da - size: 1342 diff --git a/examples/power/dvc.yaml b/examples/power/dvc.yaml index 712e1f1a..8f64f0fe 100644 --- a/examples/power/dvc.yaml +++ b/examples/power/dvc.yaml @@ -57,10 +57,6 @@ stages: cache : false - ${files.directory}/${files.data_dir}/${files.data_file}${files.data_type}: cache : false - # - ${files.directory}/${files.model_dir}/${files.model_file}${files.model_type}: - # cache : false - # - ${files.directory}/${files.model_dir}/${files.model_file}.optimizer${files.model_type}: - # cache : false - ${files.directory}/${files.reports}/attack/${files.name}/${files.predictions_file}: # logit outputs for our model cache : false deps: @@ -76,14 +72,3 @@ stages: # This outputs a database file for each model - ${files.directory}/${data.generate.name}.db: cache: false - plot_all_data_and_hardware: - deps: - - plots/dvc.yaml - - conf/afr.yaml - - conf/clean.yaml - - conf/combined_afr.yaml - - conf/plots.yaml - cmd: cd plots && dvc repro - outs: - - plots/data - - plots/plots diff --git a/examples/power/plots/combined_plots.py b/examples/power/plots/combined_plots.py index a705cdb5..a15d78f6 100644 --- a/examples/power/plots/combined_plots.py +++ b/examples/power/plots/combined_plots.py @@ -2,12 +2,18 @@ from pathlib import Path import seaborn as sns import matplotlib.pyplot as plt +from deckard.layers.plots import set_matplotlib_vars + +set_matplotlib_vars() + + +sns.set_theme(style="whitegrid", font_scale=1.8, font="times new roman") normal_dir = "data" datasets = ["mnist", "cifar", "cifar100"] extra_data_dir = "bit_depth" - +# big_df = pd.DataFrame() for data in datasets: df = pd.read_csv( @@ -16,7 +22,6 @@ low_memory=False, ) df["dataset"] = data - print(f"Shape of {data} is {df.shape}") big_df = pd.concat([big_df, df], axis=0) if Path(normal_dir, extra_data_dir, data, "power.csv").exists(): extra_df = pd.read_csv( @@ -25,7 +30,6 @@ low_memory=False, ) extra_df["dataset"] = data - print(f"Shape of {extra_data_dir}/{data} is {extra_df.shape}") big_df = pd.concat([big_df, extra_df], axis=0) @@ -46,9 +50,6 @@ big_df["adv_fit_power"] = big_df["adv_fit_power"] / big_df["adv_pred_samples"] -plt.hist(big_df["predict_time"]) - - memory_bandwith = { "nvidia-tesla-p100": 732, "nvidia-tesla-v100": 900, @@ -79,9 +80,9 @@ bit_depth = "model.art.preprocessor.bit_depth" resolution = "n_pixels" - +# Add Metadata for device in big_df.device_id.unique(): - big_df.loc[big_df.device_id == device, "peak_memory_bandwith"] = float( + big_df.loc[big_df.device_id == device, "peak_memory_bandwidth"] = float( memory_bandwith[device], ) big_df.loc[big_df.device_id == device, "cost"] = float(cost[device]) @@ -95,22 +96,17 @@ big_df.loc[big_df.dataset == dataset, "n_channels"] = int(dataset_channels[dataset]) big_df.loc[big_df.dataset == dataset, "n_classes"] = int(dataset_classes[dataset]) -big_df["peak_memory_bandwith"] = big_df["peak_memory_bandwith"].astype(float) big_df.loc[:, "memory_per_batch"] = ( big_df[batch_size] * big_df[resolution] * big_df[resolution] * big_df[bit_depth] / 8 ).values big_df["Device"] = big_df["device_id"].str.replace("-", " ").str.title() big_df = big_df.reset_index(drop=True) Path("data/combined").mkdir(parents=True, exist_ok=True) +Path("plots/combined").mkdir(parents=True, exist_ok=True) big_df.to_csv("data/combined/combined.csv") big_df = pd.read_csv("data/combined/combined.csv", index_col=0, low_memory=False) - -# acc_melt = pd.melt(big_df, id_vars=['name', 'device_id', 'dataset'], value_vars=['accuracy', 'adv_accuracy'], var_name='accuracy_type', value_name='accuracy_melt') -# pow_melt = pd.melt(big_df, id_vars=['name'], value_vars=['predict_power', 'train_power', 'adv_fit_power', 'adv_predict_power'], var_name='power_type', value_name='power_melt') -# time_melt = pd.melt(big_df, id_vars=['name'], value_vars=['predict_time', 'train_time', 'adv_fit_time', 'adv_predict_time'], var_name='time_type', value_name='time_melt') - - +# Accuracy Plot fig, ax = plt.subplots(1, 2, figsize=(8, 5)) ben_acc = sns.boxenplot( data=big_df, @@ -118,11 +114,11 @@ y="accuracy", hue="Device", ax=ax[0], - legend=False, ) -ben_acc.set_title("Average Accuracy on Benign Samples") +ben_acc.set_title("") ben_acc.set_ylabel("Ben. Accuracy") ben_acc.set_xlabel("Dataset") +ben_acc.legend().remove() adv_acc = sns.boxenplot( data=big_df, x="dataset", @@ -130,14 +126,16 @@ hue="Device", ax=ax[1], ) -adv_acc.set_title("Average Accuracy on Adversarial Samples") +adv_acc.set_title("") adv_acc.set_ylabel("Adv. Accuracy") adv_acc.set_xlabel("Dataset") - -Path("plots/combined").mkdir(parents=True, exist_ok=True) +adv_acc.legend(bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.0) +for _, ax in enumerate(fig.axes): + ax.set_xticklabels(ax.get_xticklabels(), rotation=90) +fig.tight_layout() fig.savefig("plots/combined/acc.pdf") - +# Time Plot fig, ax = plt.subplots(1, 3, figsize=(16, 5)) train_time = sns.boxenplot( data=big_df, @@ -146,9 +144,10 @@ hue="Device", ax=ax[0], ) -train_time.set_title("Average Training Time per Sample") +train_time.set_title("") train_time.set_ylabel("$t_{t}$ (seconds)") train_time.set_xlabel("Dataset") +train_time.legend().remove() predict_time = sns.boxenplot( data=big_df, x="dataset", @@ -156,9 +155,10 @@ hue="Device", ax=ax[1], ) -predict_time.set_title("Average Inference Time per Sample") +predict_time.set_title("") predict_time.set_ylabel("$t_{i}$ (seconds)") predict_time.set_xlabel("Dataset") +predict_time.legend().remove() adv_fit_time = sns.boxenplot( data=big_df, x="dataset", @@ -166,12 +166,14 @@ hue="Device", ax=ax[2], ) -adv_fit_time.set_title("Average Attack Time per Sample") +adv_fit_time.set_title("") adv_fit_time.set_ylabel("$t_{a}$ (seconds)") adv_fit_time.set_xlabel("Dataset") +adv_fit_time.legend(bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.0) +fig.tight_layout() fig.savefig("plots/combined/time.pdf") - +# Power Plot fig, ax = plt.subplots(1, 3, figsize=(18, 5)) train_time = sns.boxenplot( data=big_df, @@ -180,9 +182,10 @@ hue="Device", ax=ax[0], ) -train_time.set_title("Average Training Power per Sample") +train_time.set_title("") train_time.set_ylabel("$P_{t}$ (Watts)") train_time.set_xlabel("Dataset") +train_time.legend().remove() predict_time = sns.boxenplot( data=big_df, x="dataset", @@ -190,9 +193,10 @@ hue="Device", ax=ax[1], ) -predict_time.set_title("Average Inference Power per Sample") +predict_time.set_title("") predict_time.set_ylabel("$P_{i}$ (Watts)") predict_time.set_xlabel("Dataset") +predict_time.legend().remove() adv_fit_time = sns.boxenplot( data=big_df, x="dataset", @@ -200,12 +204,14 @@ hue="Device", ax=ax[2], ) -adv_fit_time.set_title("Average Attack Power per Sample") +adv_fit_time.set_title("") adv_fit_time.set_ylabel("$P_{a}$ (Watts)") adv_fit_time.set_xlabel("Dataset") +adv_fit_time.legend(bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.0) +fig.tight_layout() fig.savefig("plots/combined/power.pdf") - +# Cost Plot fig, ax = plt.subplots(1, 3, figsize=(18, 5)) train_cost = sns.boxenplot( data=big_df, @@ -214,20 +220,21 @@ hue="Device", ax=ax[0], ) -train_cost.set_title("Average Training Cost per Sample") +train_cost.set_title("") train_cost.set_ylabel("$C_{t}$ (USD)") train_cost.set_xlabel("Dataset") +train_cost.legend().remove() predict_cost = sns.boxenplot( data=big_df, x="dataset", y="predict_cost", hue="Device", ax=ax[1], - legend=False, ) -predict_cost.set_title("Average Inference Cost per Sample") +predict_cost.set_title("") predict_cost.set_ylabel("$C_{i}$ (USD)") predict_cost.set_xlabel("Dataset") +predict_cost.legend().remove() adv_fit_cost = sns.boxenplot( data=big_df, x="dataset", @@ -235,7 +242,9 @@ hue="Device", ax=ax[2], ) -adv_fit_cost.set_title("Average Attack Cost per Sample") +adv_fit_cost.set_title("") adv_fit_cost.set_ylabel("$C_{a}$ (USD)") adv_fit_cost.set_xlabel("Dataset") +adv_fit_cost.legend(bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.0) +fig.tight_layout() fig.savefig("plots/combined/cost.pdf") diff --git a/examples/power/plots/data/bit_depth/cifar/raw.csv b/examples/power/plots/data/bit_depth/cifar/raw.csv deleted file mode 100644 index 84f48afb..00000000 --- a/examples/power/plots/data/bit_depth/cifar/raw.csv +++ /dev/null @@ -1,1020 +0,0 @@ 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diff --git a/examples/power/plots/data/mnist/raw.csv b/examples/power/plots/data/mnist/raw.csv index 157c0b5c..bb66dc62 100644 --- a/examples/power/plots/data/mnist/raw.csv +++ b/examples/power/plots/data/mnist/raw.csv @@ -1,2009 +1,2009 @@ 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,1701431163.2222643 ,cuda:0 ,5.659668e-05 , 0.005659668 , 1701431163.8156242 , 1701431163.8212872 ,cuda:0 , 0.48 ,0.9833571428571428 , 1.1423801037165269 , 0.12 ,15.273131429553032 ,/result/mnist/reports/attack/ffb94bf202cfac1669446613d2dc7a3f/predictions.json ,/result/mnist/reports/attack/ffb94bf202cfac1669446613d2dc7a3f/adv_predictions.json , ,attack ,deckard.base.experiment.Experiment ,deckard.base.attack.Attack , 100 ,evasion ,deckard.base.data.Data ,deckard.base.data.generator.DataGenerator ,torch_mnist ,deckard.base.data.sampler.SklearnDataSampler , 6 ,True ,deckard.base.data.sklearn_pipeline.SklearnDataPipeline ,sklearn.preprocessing.StandardScaler ,True ,True ,deckard.base.attack.AttackInitializer , 5377 ,0.5826222855779957 ,True ,deckard.base.model.Model ,deckard.base.model.art_pipeline.ArtPipeline ,deckard.base.data.Data ,deckard.base.data.generator.DataGenerator ,torch_mnist ,deckard.base.data.sampler.SklearnDataSampler , 6 ,True ,deckard.base.data.sklearn_pipeline.SklearnDataPipeline ,sklearn.preprocessing.StandardScaler ,True ,True ,"[0, 255]" ,torch.nn.CrossEntropyLoss ,0.01775535591717737 , 0.9 ,torch.optim.SGD ,pytorch ,deckard.base.data.Data ,deckard.base.data.generator.DataGenerator ,torch_mnist ,deckard.base.data.sampler.SklearnDataSampler , 6 ,True ,deckard.base.data.sklearn_pipeline.SklearnDataPipeline ,sklearn.preprocessing.StandardScaler ,True ,True ,deckard.base.model.ModelInitializer ,torch_example.ResNet18 , 1 ,pytorch , 5377 , 1 ,art.attacks.evasion.FastGradientMethod ,False ,deckard.base.model.Model ,deckard.base.model.art_pipeline.ArtPipeline ,deckard.base.data.Data ,deckard.base.data.generator.DataGenerator ,torch_mnist ,deckard.base.data.sampler.SklearnDataSampler , 6 ,True ,deckard.base.data.sklearn_pipeline.SklearnDataPipeline ,sklearn.preprocessing.StandardScaler ,True ,True ,"[0, 255]" ,torch.nn.CrossEntropyLoss ,0.01775535591717737 , 0.9 ,torch.optim.SGD ,pytorch ,deckard.base.data.Data ,deckard.base.data.generator.DataGenerator ,torch_mnist ,deckard.base.data.sampler.SklearnDataSampler , 6 ,True ,deckard.base.data.sklearn_pipeline.SklearnDataPipeline ,sklearn.preprocessing.StandardScaler ,True ,True ,deckard.base.model.ModelInitializer ,torch_example.ResNet18 , 1 ,pytorch , 5377 , 1 ,deckard.base.data.Data ,deckard.base.data.generator.DataGenerator ,torch_mnist ,deckard.base.data.sampler.SklearnDataSampler , 6 ,True ,deckard.base.data.sklearn_pipeline.SklearnDataPipeline ,sklearn.preprocessing.StandardScaler ,True ,True ,nvidia-tesla-v100 ,deckard.base.files.FileConfig ,adv_predictions.json ,attacks ,43c1fc2a94586b4be5ac250243594d27 ,.pkl ,data ,4314322d36d2ded753c7481a69808fbb ,.pkl ,/result/mnist/ ,ffb94bf202cfac1669446613d2dc7a3f ,predictions.json ,reports ,score_dict.json ,attack , , , , ,deckard.base.model.Model ,pytorch ,deckard.base.model.art_pipeline.ArtPipeline ,deckard.base.data.Data ,deckard.base.data.generator.DataGenerator ,torch_mnist ,deckard.base.data.sampler.SklearnDataSampler , 6 ,True ,deckard.base.data.sklearn_pipeline.SklearnDataPipeline ,sklearn.preprocessing.StandardScaler ,True ,True ,"[0, 255]" ,torch.nn.CrossEntropyLoss ,0.01775535591717737 , 0.9 ,torch.optim.SGD ,pytorch ,deckard.base.data.Data ,deckard.base.data.generator.DataGenerator ,torch_mnist ,deckard.base.data.sampler.SklearnDataSampler , 6 ,True ,deckard.base.data.sklearn_pipeline.SklearnDataPipeline ,sklearn.preprocessing.StandardScaler ,True ,True ,deckard.base.model.ModelInitializer ,torch_example.ResNet18 , 1 , 5377 , 1 ,ffb94bf202cfac1669446613d2dc7a3f ,deckard.base.scorer.ScorerDict ,deckard.base.scorer.ScorerConfig ,maximize ,sklearn.metrics.accuracy_score ,deckard.base.scorer.ScorerConfig ,minimize ,sklearn.metrics.log_loss , 40.149600669 ,0.0007169571548035714 ,1701431117.4995723 ,1701431158.6913888 ,cuda:0 , 0.735992227 ,1.3142718339285714e-05 ,1701431158.693424 ,1701431159.4315705 ,cuda:0 , 0.592530021 ,1.0580893232142857e-05 , 1701431159.9411275 , 1701431160.5494967 ,cuda:0 , 0.578435289 ,1.0329201589285714e-05 , 1701431160.5495715 , 1701431161.1452973 ,cuda:0 , 0.00058332435 , 0.058332435 ,1701431163.1637359 ,1701431163.2222643 ,cuda:0 ,5.659668e-05 , 0.005659668 , 1701431163.8156242 , 1701431163.8212872 ,cuda:0 , 0.48 ,0.9833571428571428 , 1.1423801037165269 , 0.12 ,15.273131429553032 ,/result/mnist/reports/attack/ffb94bf202cfac1669446613d2dc7a3f/predictions.json ,/result/mnist/reports/attack/ffb94bf202cfac1669446613d2dc7a3f/adv_predictions.json , +ffc7bc33bd1805c71a7be3d29fc6c39c ,attack ,deckard.base.experiment.Experiment ,ffc7bc33bd1805c71a7be3d29fc6c39c ,ffc7bc33bd1805c71a7be3d29fc6c39c ,nvidia-tesla-v100 ,ffc7bc33bd1805c71a7be3d29fc6c39c ,ffc7bc33bd1805c71a7be3d29fc6c39c ,ffc7bc33bd1805c71a7be3d29fc6c39c ,ffc7bc33bd1805c71a7be3d29fc6c39c , 41.357164932 ,0.0007385208023571429 ,1701451870.2849886 ,1701451912.6666586 ,cuda:0 , 0.582499547 ,1.0401777625e-05 ,1701451912.6690974 ,1701451913.2655857 ,cuda:0 , 0.56339867 ,1.0060690535714286e-05 , 1701451913.5619385 , 1701451914.1257887 ,cuda:0 , 0.604873002 ,1.0801303607142857e-05 , 1701451914.1258614 , 1701451914.74013 ,cuda:0 , 0.00033602495 , 0.033602495 ,1701451916.4948106 ,1701451916.5285835 ,cuda:0 ,5.215592e-05 , 0.005215592 , 1701451917.1266174 , 1701451917.131862 ,cuda:0 , 0.0 ,0.4837142857142857 , 2.5091268205216952 , 0.03 ,27.023939659297465 ,/result/mnist/reports/attack/ffc7bc33bd1805c71a7be3d29fc6c39c/predictions.json ,/result/mnist/reports/attack/ffc7bc33bd1805c71a7be3d29fc6c39c/adv_predictions.json , ,attack ,deckard.base.experiment.Experiment ,deckard.base.attack.Attack , 100 ,evasion ,deckard.base.data.Data ,deckard.base.data.generator.DataGenerator ,torch_mnist ,deckard.base.data.sampler.SklearnDataSampler , 7 ,True ,deckard.base.data.sklearn_pipeline.SklearnDataPipeline ,sklearn.preprocessing.StandardScaler ,True ,True ,deckard.base.attack.AttackInitializer , 9323 ,0.08748795849524267 ,True ,deckard.base.model.Model ,deckard.base.model.art_pipeline.ArtPipeline ,deckard.base.data.Data ,deckard.base.data.generator.DataGenerator ,torch_mnist ,deckard.base.data.sampler.SklearnDataSampler , 7 ,True ,deckard.base.data.sklearn_pipeline.SklearnDataPipeline ,sklearn.preprocessing.StandardScaler ,True ,True ,"[0, 255]" ,torch.nn.CrossEntropyLoss ,0.5122029914829485 , 0.9 ,torch.optim.SGD ,pytorch ,deckard.base.data.Data ,deckard.base.data.generator.DataGenerator ,torch_mnist ,deckard.base.data.sampler.SklearnDataSampler , 7 ,True ,deckard.base.data.sklearn_pipeline.SklearnDataPipeline ,sklearn.preprocessing.StandardScaler ,True ,True ,deckard.base.model.ModelInitializer ,torch_example.ResNet18 , 1 ,pytorch , 9323 , 41 ,art.attacks.evasion.FastGradientMethod ,False ,deckard.base.model.Model ,deckard.base.model.art_pipeline.ArtPipeline ,deckard.base.data.Data ,deckard.base.data.generator.DataGenerator ,torch_mnist ,deckard.base.data.sampler.SklearnDataSampler , 7 ,True 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,predictions.json ,reports ,score_dict.json ,attack , , , , ,deckard.base.model.Model ,pytorch ,deckard.base.model.art_pipeline.ArtPipeline ,deckard.base.data.Data ,deckard.base.data.generator.DataGenerator ,torch_mnist ,deckard.base.data.sampler.SklearnDataSampler , 7 ,True ,deckard.base.data.sklearn_pipeline.SklearnDataPipeline ,sklearn.preprocessing.StandardScaler ,True ,True ,"[0, 255]" ,torch.nn.CrossEntropyLoss ,0.5122029914829485 , 0.9 ,torch.optim.SGD ,pytorch ,deckard.base.data.Data ,deckard.base.data.generator.DataGenerator ,torch_mnist ,deckard.base.data.sampler.SklearnDataSampler , 7 ,True ,deckard.base.data.sklearn_pipeline.SklearnDataPipeline ,sklearn.preprocessing.StandardScaler ,True ,True ,deckard.base.model.ModelInitializer ,torch_example.ResNet18 , 1 , 9323 , 41 ,ffc7bc33bd1805c71a7be3d29fc6c39c ,deckard.base.scorer.ScorerDict ,deckard.base.scorer.ScorerConfig ,maximize ,sklearn.metrics.accuracy_score ,deckard.base.scorer.ScorerConfig ,minimize ,sklearn.metrics.log_loss , 41.357164932 ,0.0007385208023571429 ,1701451870.2849886 ,1701451912.6666586 ,cuda:0 , 0.582499547 ,1.0401777625e-05 ,1701451912.6690974 ,1701451913.2655857 ,cuda:0 , 0.56339867 ,1.0060690535714286e-05 , 1701451913.5619385 , 1701451914.1257887 ,cuda:0 , 0.604873002 ,1.0801303607142857e-05 , 1701451914.1258614 , 1701451914.74013 ,cuda:0 , 0.00033602495 , 0.033602495 ,1701451916.4948106 ,1701451916.5285835 ,cuda:0 ,5.215592e-05 , 0.005215592 , 1701451917.1266174 , 1701451917.131862 ,cuda:0 , 0.0 ,0.4837142857142857 , 2.5091268205216952 , 0.03 ,27.023939659297465 ,/result/mnist/reports/attack/ffc7bc33bd1805c71a7be3d29fc6c39c/predictions.json ,/result/mnist/reports/attack/ffc7bc33bd1805c71a7be3d29fc6c39c/adv_predictions.json , +ffd5c9d766af1977a8e4030dc1f24336 ,attack ,deckard.base.experiment.Experiment ,ffd5c9d766af1977a8e4030dc1f24336 ,ffd5c9d766af1977a8e4030dc1f24336 ,nvidia-tesla-v100 ,ffd5c9d766af1977a8e4030dc1f24336 ,ffd5c9d766af1977a8e4030dc1f24336 ,ffd5c9d766af1977a8e4030dc1f24336 ,ffd5c9d766af1977a8e4030dc1f24336 , 43.100318499 ,0.000769648544625 ,1701444560.2505903 ,1701444604.7715735 ,cuda:0 , 0.598240742 ,1.0682870392857142e-05 ,1701444604.7738552 ,1701444605.3800747 ,cuda:0 , 0.565044465 ,1.0090079732142858e-05 , 1701444605.7005029 , 1701444606.267815 ,cuda:0 , 0.556154853 ,9.931336660714286e-06 , 1701444606.2678866 , 1701444606.823892 ,cuda:0 , 0.00052987729 , 0.052987729 ,1701444608.5051024 ,1701444608.5581918 ,cuda:0 ,5.463192e-05 , 0.005463192 , 1701444609.151924 , 1701444609.1573992 ,cuda:0 , 0.97 ,0.19942857142857143 ,14.213199379632316 , 0.14 ,13.641899617314339 ,/result/mnist/reports/attack/ffd5c9d766af1977a8e4030dc1f24336/predictions.json ,/result/mnist/reports/attack/ffd5c9d766af1977a8e4030dc1f24336/adv_predictions.json , ,attack ,deckard.base.experiment.Experiment ,deckard.base.attack.Attack , 100 ,evasion ,deckard.base.data.Data ,deckard.base.data.generator.DataGenerator ,torch_mnist ,deckard.base.data.sampler.SklearnDataSampler , 0 ,True ,deckard.base.data.sklearn_pipeline.SklearnDataPipeline ,sklearn.preprocessing.StandardScaler ,True ,True ,deckard.base.attack.AttackInitializer , 1021 ,0.41619476630156443 ,True ,deckard.base.model.Model ,deckard.base.model.art_pipeline.ArtPipeline ,deckard.base.data.Data ,deckard.base.data.generator.DataGenerator ,torch_mnist ,deckard.base.data.sampler.SklearnDataSampler , 0 ,True ,deckard.base.data.sklearn_pipeline.SklearnDataPipeline ,sklearn.preprocessing.StandardScaler ,True ,True ,"[0, 255]" ,torch.nn.CrossEntropyLoss ,1.637943744480656e-06 , 0.9 ,torch.optim.SGD ,pytorch ,deckard.base.data.Data ,deckard.base.data.generator.DataGenerator ,torch_mnist ,deckard.base.data.sampler.SklearnDataSampler , 0 ,True ,deckard.base.data.sklearn_pipeline.SklearnDataPipeline ,sklearn.preprocessing.StandardScaler ,True ,True ,deckard.base.model.ModelInitializer ,torch_example.ResNet18 , 1 ,pytorch , 1021 , 4 ,art.attacks.evasion.FastGradientMethod ,False ,deckard.base.model.Model ,deckard.base.model.art_pipeline.ArtPipeline ,deckard.base.data.Data ,deckard.base.data.generator.DataGenerator ,torch_mnist ,deckard.base.data.sampler.SklearnDataSampler , 0 ,True ,deckard.base.data.sklearn_pipeline.SklearnDataPipeline ,sklearn.preprocessing.StandardScaler ,True ,True ,"[0, 255]" ,torch.nn.CrossEntropyLoss ,1.637943744480656e-06 , 0.9 ,torch.optim.SGD ,pytorch ,deckard.base.data.Data ,deckard.base.data.generator.DataGenerator ,torch_mnist ,deckard.base.data.sampler.SklearnDataSampler , 0 ,True ,deckard.base.data.sklearn_pipeline.SklearnDataPipeline ,sklearn.preprocessing.StandardScaler ,True ,True ,deckard.base.model.ModelInitializer ,torch_example.ResNet18 , 1 ,pytorch , 1021 , 4 ,deckard.base.data.Data ,deckard.base.data.generator.DataGenerator ,torch_mnist ,deckard.base.data.sampler.SklearnDataSampler , 0 ,True ,deckard.base.data.sklearn_pipeline.SklearnDataPipeline ,sklearn.preprocessing.StandardScaler ,True ,True ,nvidia-tesla-v100 ,deckard.base.files.FileConfig ,adv_predictions.json ,attacks ,1eb38c57888e314006bdf7fba3f2fc97 ,.pkl ,data ,72a811dfeeaf6f45e0831671d538f57a ,.pkl ,/result/mnist/ ,ffd5c9d766af1977a8e4030dc1f24336 ,predictions.json ,reports ,score_dict.json ,attack , , , , ,deckard.base.model.Model ,pytorch ,deckard.base.model.art_pipeline.ArtPipeline ,deckard.base.data.Data ,deckard.base.data.generator.DataGenerator ,torch_mnist ,deckard.base.data.sampler.SklearnDataSampler , 0 ,True ,deckard.base.data.sklearn_pipeline.SklearnDataPipeline ,sklearn.preprocessing.StandardScaler ,True ,True ,"[0, 255]" ,torch.nn.CrossEntropyLoss ,1.637943744480656e-06 , 0.9 ,torch.optim.SGD ,pytorch ,deckard.base.data.Data ,deckard.base.data.generator.DataGenerator ,torch_mnist ,deckard.base.data.sampler.SklearnDataSampler , 0 ,True ,deckard.base.data.sklearn_pipeline.SklearnDataPipeline ,sklearn.preprocessing.StandardScaler ,True ,True ,deckard.base.model.ModelInitializer ,torch_example.ResNet18 , 1 , 1021 , 4 ,ffd5c9d766af1977a8e4030dc1f24336 ,deckard.base.scorer.ScorerDict ,deckard.base.scorer.ScorerConfig ,maximize ,sklearn.metrics.accuracy_score ,deckard.base.scorer.ScorerConfig ,minimize ,sklearn.metrics.log_loss , 43.100318499 ,0.000769648544625 ,1701444560.2505903 ,1701444604.7715735 ,cuda:0 , 0.598240742 ,1.0682870392857142e-05 ,1701444604.7738552 ,1701444605.3800747 ,cuda:0 , 0.565044465 ,1.0090079732142858e-05 , 1701444605.7005029 , 1701444606.267815 ,cuda:0 , 0.556154853 ,9.931336660714286e-06 , 1701444606.2678866 , 1701444606.823892 ,cuda:0 , 0.00052987729 , 0.052987729 ,1701444608.5051024 ,1701444608.5581918 ,cuda:0 ,5.463192e-05 , 0.005463192 , 1701444609.151924 , 1701444609.1573992 ,cuda:0 , 0.97 ,0.19942857142857143 ,14.213199379632316 , 0.14 ,13.641899617314339 ,/result/mnist/reports/attack/ffd5c9d766af1977a8e4030dc1f24336/predictions.json ,/result/mnist/reports/attack/ffd5c9d766af1977a8e4030dc1f24336/adv_predictions.json , diff --git a/examples/power/plots/dvc.lock b/examples/power/plots/dvc.lock index 6d08c98d..8efdadc0 100644 --- a/examples/power/plots/dvc.lock +++ b/examples/power/plots/dvc.lock @@ -1,147 +1,52 @@ schema: '2.0' stages: - compile@mnist: - cmd: python -m deckard.layers.compile --report_folder /result/mnist/reports/attack/ - --results_file raw.csv --results_folder data/mnist --config "../conf/compile.yaml" - deps: - - path: /result/mnist/reports/attack/ - hash: md5 - md5: 2fd9e6eb909c65078ded91f85c5938d1.dir - size: 5846593866 - nfiles: 8024 - outs: - - path: data/mnist/raw.csv - hash: md5 - md5: 49a0f8e54e50ce0f68929842fe150bb4 - size: 9741090 - compile@cifar: - cmd: python -m deckard.layers.compile --report_folder /result/cifar/reports/attack/ - --results_file raw.csv --results_folder data/cifar --config "../conf/compile.yaml" - deps: - - path: /result/cifar/reports/attack/ - hash: md5 - md5: ed8b007df2788a65643c2c0b99a87a4d.dir - size: 5035287644 - nfiles: 8031 - outs: - - path: data/cifar/raw.csv - hash: md5 - md5: b0bc8f836427dfd15154db3eb951c770 - size: 9768945 - compile@cifar100: - cmd: python -m deckard.layers.compile --report_folder /result/cifar100/reports/attack/ - --results_file raw.csv --results_folder data/cifar100 --config "../conf/compile.yaml" - deps: - - path: /result/cifar100/reports/attack/ - hash: md5 - md5: 98cca8dcf77af04243bb18055c5f9bd6.dir - size: 73162490540 - nfiles: 12469 - outs: - - path: data/cifar100/raw.csv - hash: md5 - md5: b60317375748c88064e0fbc7648664ad - size: 15729259 - compile@bit_depth/mnist/: - cmd: python -m deckard.layers.compile --report_folder /result/bit_depth/mnist//reports/attack/ - --results_file raw.csv --results_folder data/bit_depth/mnist/ --config "../conf/compile.yaml" - deps: - - path: /result/bit_depth/mnist//reports/attack/ - hash: md5 - md5: 0e98e6497ec11efe7d15d6d26d48e373.dir - size: 3045516686 - nfiles: 4182 - outs: - - path: data/bit_depth/mnist//raw.csv - hash: md5 - md5: 931d5f4dbd17abe4c31689f81dadf8a3 - size: 5302969 - compile@bit_depth/cifar/: - cmd: python -m deckard.layers.compile --report_folder /result/bit_depth/cifar//reports/attack/ - --results_file raw.csv --results_folder data/bit_depth/cifar/ --config "../conf/compile.yaml" - deps: - - path: /result/bit_depth/cifar//reports/attack/ - hash: md5 - md5: 8bb22698afd785bc1f0f3c2a6e59da3f.dir - size: 2549717549 - nfiles: 4064 - outs: - - path: data/bit_depth/cifar//raw.csv - hash: md5 - md5: f8af91790f0fabe6bf17878379ea5541 - size: 5174817 - compile@bit_depth/cifar100/: - cmd: python -m deckard.layers.compile --report_folder /result/bit_depth/cifar100//reports/attack/ - --results_file raw.csv --results_folder data/bit_depth/cifar100/ --config "../conf/compile.yaml" - deps: - - path: /result/bit_depth/cifar100//reports/attack/ - hash: md5 - md5: d87f6fb72a177d6cef4e687bb74202fa.dir - size: 16843406378 - nfiles: 2985 - outs: - - path: data/bit_depth/cifar100//raw.csv - hash: md5 - md5: 37b87d60ad6e609c8c5a42c8c5623ee5 - size: 3900609 - clean@mnist: - cmd: python -m deckard.layers.clean_data --i plots/mnist/merged.csv --o plots/mnist/clean.csv - --config "../conf/clean.yaml" + merge@cifar100: + cmd: python merge.py --big_dir data/bit_depth/cifar100 --little_dir data/cifar100 + --config ../conf/afr.yaml --data_file power.csv --output_folder plots/cifar100/ + --output_file merged.csv deps: - path: ../conf/clean.yaml - hash: md5 - md5: eba3dd17dd820ad8273a37ba57ea73b7 - size: 373 - - path: plots/mnist/merged.csv - hash: md5 - md5: 2d6929af603db1b4f0ae106fc31ae713 - size: 15053785 + md5: 3b23a3d656ad56e49e69f8624e227852 + size: 538 + - path: data/bit_depth/cifar100/power.csv + md5: d52080413e3ba7bd86ef8d92ebd00507 + size: 3988195 + - path: data/cifar100/power.csv + md5: f51f0d4ebd105f64829af90a0212de00 + size: 16085014 params: ../conf/clean.yaml: - attacks: - FastGradientMethod: FGM - defences: - Control: Control - FeatureSqueezing: FSQ - nb_epoch: Epochs - model_layers: Control fillna: Epochs: 20 FGM: 0.0 FSQ: 32 Control: 18 Batch_Size: 1024 - params: - FGM: attack.init.eps - FSQ: model.art.preprocessor.bit_depth - Control: model_layers - Epochs: model.trainer.nb_epoch - Batch_Size: model.trainer.batch_size outs: - - path: plots/mnist/clean.csv - hash: md5 - md5: b8947e66cc080181238a0aa6acc2d295 - size: 4760564 - clean@cifar: - cmd: python -m deckard.layers.clean_data --i plots/cifar/merged.csv --o plots/cifar/clean.csv + - path: plots/cifar100/merged.csv + md5: f2bf33451a3c6a27e8b37dafc2a30ace + size: 20103676 + clean@cifar100: + cmd: python -m deckard.layers.clean_data --i plots/cifar100/merged.csv --o plots/cifar100/clean.csv --config "../conf/clean.yaml" deps: - path: ../conf/clean.yaml - hash: md5 - md5: eba3dd17dd820ad8273a37ba57ea73b7 - size: 373 - - path: plots/cifar/merged.csv - hash: md5 - md5: d391c352c3777885f066ddb922105832 - size: 14952800 + md5: 3b23a3d656ad56e49e69f8624e227852 + size: 538 + - path: plots/cifar100/merged.csv + md5: f2bf33451a3c6a27e8b37dafc2a30ace + size: 20103676 params: ../conf/clean.yaml: attacks: FastGradientMethod: FGM + ProjectedGradientDescent: PGD + HopSkipJump: HSJ + DeepFool: Deep defences: Control: Control FeatureSqueezing: FSQ - nb_epoch: Epochs + Epochs: Epochs model_layers: Control fillna: Epochs: 20 @@ -151,111 +56,132 @@ stages: Batch_Size: 1024 params: FGM: attack.init.eps + PGD: attack.init.eps + HSJ: attack.init.eps + DeepFool: attack.init.eps FSQ: model.art.preprocessor.bit_depth Control: model_layers Epochs: model.trainer.nb_epoch Batch_Size: model.trainer.batch_size outs: - - path: plots/cifar/clean.csv - hash: md5 - md5: f6506c50252d904308f67f55f281c0e8 - size: 4638245 - clean@cifar100: - cmd: python -m deckard.layers.clean_data --i plots/cifar100/merged.csv --o plots/cifar100/clean.csv - --config "../conf/clean.yaml" + - path: plots/cifar100/clean.csv + md5: 697750ab677d3969845ee84dc0c12a22 + size: 9261307 + afr@cifar100: + cmd: python -m deckard.layers.afr --dataset cifar100 --data_file plots/cifar100/clean.csv + --config_file "../conf/afr.yaml" --plots_folder plots/cifar100/ --target adv_failures + --duration_col adv_fit_time + deps: + - path: ../conf/afr.yaml + md5: ba48c8059bc4d6b9822853c6b923c764 + size: 4853 + - path: plots/cifar100/clean.csv + md5: 697750ab677d3969845ee84dc0c12a22 + size: 9261307 + outs: + - path: plots/cifar100/aft_comparison.csv + md5: 3af00791562aea95acc6857504a8d6a3 + size: 465 + - path: plots/cifar100/aft_comparison.tex + md5: 34272f06716f4853d4e43e25103d6a37 + size: 681 + - path: plots/cifar100/cox_aft.pdf + md5: 541f6d1ab36a76f922703284635478da + size: 20683 + - path: plots/cifar100/cox_epochs_partial_effect.pdf + md5: 4d095777f3f3058bbd78513adcd0d942 + size: 32509 + - path: plots/cifar100/log_logistic_aft.pdf + md5: c4f24a6186191fdc221a99a931e8b02c + size: 22688 + - path: plots/cifar100/log_logistic_epochs_partial_effect.pdf + md5: 1b72dcb1fad4eb8fc39837a51858e7af + size: 27428 + - path: plots/cifar100/log_normal_aft.pdf + md5: 3fb7329d26bd6b6cdd2bcb11275ff597 + size: 23091 + - path: plots/cifar100/log_normal_epochs_partial_effect.pdf + md5: 60c3322a25fb54b58bf878eeaa1f573a + size: 28269 + - path: plots/cifar100/weibull_aft.pdf + md5: 2953f8fbde0fdeeac933409cafa5249d + size: 29752 + - path: plots/cifar100/weibull_epochs_partial_effect.pdf + md5: 70657c85b91844a8cf32c20a019f14d1 + size: 27176 + merge@cifar: + cmd: python merge.py --big_dir data/bit_depth/cifar --little_dir data/cifar --config + ../conf/afr.yaml --data_file power.csv --output_folder plots/cifar/ --output_file + merged.csv deps: - path: ../conf/clean.yaml - hash: md5 - md5: eba3dd17dd820ad8273a37ba57ea73b7 - size: 373 - - path: plots/cifar100/merged.csv - hash: md5 - md5: 37d9468e287e2d8409334e2d771a371e - size: 19653431 + md5: 3b23a3d656ad56e49e69f8624e227852 + size: 538 + - path: data/bit_depth/cifar/power.csv + md5: b9ebbe01b2bd93f1c4af02fb8abd910b + size: 5292031 + - path: data/cifar/power.csv + md5: a8e056a8c4008ce37006237f2e44b2bf + size: 9999301 params: ../conf/clean.yaml: - attacks: - FastGradientMethod: FGM - defences: - Control: Control - FeatureSqueezing: FSQ - nb_epoch: Epochs - model_layers: Control fillna: Epochs: 20 FGM: 0.0 FSQ: 32 Control: 18 Batch_Size: 1024 - params: - FGM: attack.init.eps - FSQ: model.art.preprocessor.bit_depth - Control: model_layers - Epochs: model.trainer.nb_epoch - Batch_Size: model.trainer.batch_size outs: - - path: plots/cifar100/clean.csv - hash: md5 - md5: d8c937ae54b9f6d8558863ead2e19a6a - size: 3498981 - clean@bit_depth/mnist/: - cmd: python -m deckard.layers.clean_data --i bit_depth/mnist//attack.csv --o bit_depth/mnist//clean.csv - --config "../conf/clean.yaml" + - path: plots/cifar/merged.csv + md5: 2d2cbcb913a61d304950c910bd487b9b + size: 15307632 + merge@mnist: + cmd: python merge.py --big_dir data/bit_depth/mnist --little_dir data/mnist --config + ../conf/afr.yaml --data_file power.csv --output_folder plots/mnist/ --output_file + merged.csv deps: - path: ../conf/clean.yaml - hash: md5 - md5: 8abf5440006041306a4bc2f66926dbe0 - size: 382 - - path: bit_depth/mnist//attack.csv - hash: md5 - md5: 931d5f4dbd17abe4c31689f81dadf8a3 - size: 5302969 + md5: 3b23a3d656ad56e49e69f8624e227852 + size: 538 + - path: data/bit_depth/mnist/power.csv + md5: ed40bca679961759527ca94ca4cbd984 + size: 5422584 + - path: data/mnist/power.csv + md5: 6c84e8e91d81504e86db933bab0e9ae2 + size: 9970090 params: ../conf/clean.yaml: - attacks: - FastGradientMethod: FGM - defences: - Control: Control - FeatureSqueezing: FSQ - nb_epoch: Epochs - model_layers: Control fillna: Epochs: 20 FGM: 0.0 FSQ: 32 Control: 18 Batch_Size: 1024 - params: - FGM: attack.init.eps - FSQ: model.art.pipeline.preprocessor.bit_depth - Control: model_layers - Epochs: model.trainer.nb_epoch - Batch_Size: model.trainer.batch_size outs: - - path: bit_depth/mnist//clean.csv - hash: md5 - md5: be9c19e571e973494d1ec9d5e5c296aa - size: 4616360 - clean@bit_depth/cifar/: - cmd: python -m deckard.layers.clean_data --i bit_depth/cifar//attack.csv --o bit_depth/cifar//clean.csv + - path: plots/mnist/merged.csv + md5: 8f227012f75f50d6f01745b54d2dc5a5 + size: 15411588 + clean@mnist: + cmd: python -m deckard.layers.clean_data --i plots/mnist/merged.csv --o plots/mnist/clean.csv --config "../conf/clean.yaml" deps: - path: ../conf/clean.yaml - hash: md5 - md5: 8abf5440006041306a4bc2f66926dbe0 - size: 382 - - path: bit_depth/cifar//attack.csv - hash: md5 - md5: f8af91790f0fabe6bf17878379ea5541 - size: 5174817 + md5: 3b23a3d656ad56e49e69f8624e227852 + size: 538 + - path: plots/mnist/merged.csv + md5: 8f227012f75f50d6f01745b54d2dc5a5 + size: 15411588 params: ../conf/clean.yaml: attacks: FastGradientMethod: FGM + ProjectedGradientDescent: PGD + HopSkipJump: HSJ + DeepFool: Deep defences: Control: Control FeatureSqueezing: FSQ - nb_epoch: Epochs + Epochs: Epochs model_layers: Control fillna: Epochs: 20 @@ -265,35 +191,80 @@ stages: Batch_Size: 1024 params: FGM: attack.init.eps - FSQ: model.art.pipeline.preprocessor.bit_depth + PGD: attack.init.eps + HSJ: attack.init.eps + DeepFool: attack.init.eps + FSQ: model.art.preprocessor.bit_depth Control: model_layers Epochs: model.trainer.nb_epoch Batch_Size: model.trainer.batch_size outs: - - path: bit_depth/cifar//clean.csv - hash: md5 - md5: 87925c04208c680ca5f74ff9b06771cc - size: 4499337 - clean@bit_depth/cifar100/: - cmd: python -m deckard.layers.clean_data --i bit_depth/cifar100//attack.csv --o - bit_depth/cifar100//clean.csv --config "../conf/clean.yaml" + - path: plots/mnist/clean.csv + md5: 1cb59603927ff95aaceccdd3425feadb + size: 9236530 + afr@mnist: + cmd: python -m deckard.layers.afr --dataset mnist --data_file plots/mnist/clean.csv + --config_file "../conf/afr.yaml" --plots_folder plots/mnist/ --target adv_failures + --duration_col adv_fit_time + deps: + - path: ../conf/afr.yaml + md5: ba48c8059bc4d6b9822853c6b923c764 + size: 4853 + - path: plots/mnist/clean.csv + md5: 1cb59603927ff95aaceccdd3425feadb + size: 9236530 + outs: + - path: plots/mnist/aft_comparison.csv + md5: de519adac05bb1c1d97599656e9c7b22 + size: 466 + - path: plots/mnist/aft_comparison.tex + md5: e9d0235d6761e11914d4fa7451f8ba7b + size: 675 + - path: plots/mnist/cox_aft.pdf + md5: 3d0d011c8e6e406e21b717ffdaf6536d + size: 20317 + - path: plots/mnist/cox_epochs_partial_effect.pdf + md5: ff624f52f5270751a40b6ae46ac6a094 + size: 31744 + - path: plots/mnist/log_logistic_aft.pdf + md5: 53905f954e86a007b85b48edaa237c31 + size: 22750 + - path: plots/mnist/log_logistic_epochs_partial_effect.pdf + md5: 81c56575f5e5f77465faea7b13a24486 + size: 27423 + - path: plots/mnist/log_normal_aft.pdf + md5: a3035ba654305bc1c242b3e81290ea4d + size: 23155 + - path: plots/mnist/log_normal_epochs_partial_effect.pdf + md5: 38ec8e960f4429de53da463da2638e40 + size: 28313 + - path: plots/mnist/weibull_aft.pdf + md5: 3245c7814e6a734642e11046243994e9 + size: 29797 + - path: plots/mnist/weibull_epochs_partial_effect.pdf + md5: e9715946b06c1c1d9bec5b76d30b8622 + size: 27068 + clean@cifar: + cmd: python -m deckard.layers.clean_data --i plots/cifar/merged.csv --o plots/cifar/clean.csv + --config "../conf/clean.yaml" deps: - path: ../conf/clean.yaml - hash: md5 - md5: 8abf5440006041306a4bc2f66926dbe0 - size: 382 - - path: bit_depth/cifar100//attack.csv - hash: md5 - md5: eb5f127dea09691fe7a082aa1f8c66e0 - size: 2732950 + md5: 3b23a3d656ad56e49e69f8624e227852 + size: 538 + - path: plots/cifar/merged.csv + md5: 2d2cbcb913a61d304950c910bd487b9b + size: 15307632 params: ../conf/clean.yaml: attacks: FastGradientMethod: FGM + ProjectedGradientDescent: PGD + HopSkipJump: HSJ + DeepFool: Deep defences: Control: Control FeatureSqueezing: FSQ - nb_epoch: Epochs + Epochs: Epochs model_layers: Control fillna: Epochs: 20 @@ -303,488 +274,111 @@ stages: Batch_Size: 1024 params: FGM: attack.init.eps - FSQ: model.art.pipeline.preprocessor.bit_depth + PGD: attack.init.eps + HSJ: attack.init.eps + DeepFool: attack.init.eps + FSQ: model.art.preprocessor.bit_depth Control: model_layers Epochs: model.trainer.nb_epoch Batch_Size: model.trainer.batch_size outs: - - path: bit_depth/cifar100//clean.csv - hash: md5 - md5: 9b824fca1d0efc263cb84f051005e232 - size: 2403993 - plot@mnist: - cmd: python -m deckard.layers.plots --path plots/mnist --file plots/mnist/clean.csv -c - "../conf/plots.yaml" - deps: - - path: ../conf/plots.yaml - hash: md5 - md5: f874ee0b44e22fa0a7759900ad1b2221 - size: 1351 - - path: plots/mnist/clean.csv - hash: md5 - md5: b8947e66cc080181238a0aa6acc2d295 - size: 4760564 - outs: - - path: plots/mnist/adv_failure_rate_vs_train_time.pdf - hash: md5 - md5: df8a31ae70c24be3250443da741cee28 - size: 16818 - - path: plots/mnist/ben_failure_rate_vs_train_time.pdf - hash: md5 - md5: f712e0db55124868c7bb93c5de97fe6f - size: 17704 - - path: plots/mnist/train_time_vs_accuracy.pdf - hash: md5 - md5: 6a987f0377e030e499fc8a7cbcc0cdce - size: 20906 - plot@cifar: - cmd: python -m deckard.layers.plots --path plots/cifar --file plots/cifar/clean.csv -c - "../conf/plots.yaml" - deps: - - path: ../conf/plots.yaml - hash: md5 - md5: f874ee0b44e22fa0a7759900ad1b2221 - size: 1351 - path: plots/cifar/clean.csv - hash: md5 - md5: f6506c50252d904308f67f55f281c0e8 - size: 4638245 - outs: - - path: plots/cifar/adv_failure_rate_vs_train_time.pdf - hash: md5 - md5: 06aaaa3589a9f532e305305b79a22541 - size: 16854 - - path: plots/cifar/ben_failure_rate_vs_train_time.pdf - hash: md5 - md5: 6ccdc462f896990964afe64a5040465a - size: 17697 - - path: plots/cifar/train_time_vs_accuracy.pdf - hash: md5 - md5: 6701e6a2ab57fd8b2883e14b6ddf935c - size: 20906 - plot@cifar100: - cmd: python -m deckard.layers.plots --path plots/cifar100 --file plots/cifar100/clean.csv -c - "../conf/plots.yaml" - deps: - - path: ../conf/plots.yaml - hash: md5 - md5: f874ee0b44e22fa0a7759900ad1b2221 - size: 1351 - - path: plots/cifar100/clean.csv - hash: md5 - md5: d8c937ae54b9f6d8558863ead2e19a6a - size: 3498981 - outs: - - path: plots/cifar100/adv_failure_rate_vs_train_time.pdf - hash: md5 - md5: cf24209da78b8ecd0b7d63119799e84b - size: 23111 - - path: plots/cifar100/ben_failure_rate_vs_train_time.pdf - hash: md5 - md5: ba5509d06a3d9bdfcee3cce52dcbccd3 - size: 19387 - - path: plots/cifar100/train_time_vs_accuracy.pdf - hash: md5 - md5: 752d765b369a4f335360c36f8efd49d6 - size: 20906 - plot@bit_depth/mnist/: - cmd: python -m deckard.layers.plots --path bit_depth/mnist/ --file bit_depth/mnist//clean.csv - -o plot_data.csv -c "../conf/plots.yaml" - deps: - - path: ../conf/plots.yaml - hash: md5 - md5: f874ee0b44e22fa0a7759900ad1b2221 - size: 1351 - - path: bit_depth/mnist//clean.csv - hash: md5 - md5: be9c19e571e973494d1ec9d5e5c296aa - size: 4616360 - outs: - - path: bit_depth/mnist//adv_failure_rate_vs_train_time.pdf - hash: md5 - md5: 064c4d6b49cf6acde5dfe41b4999c924 - size: 16818 - - path: bit_depth/mnist//ben_failure_rate_vs_train_time.pdf - hash: md5 - md5: 3c377d887eba2fbdef68374cb592c51a - size: 17704 - - path: bit_depth/mnist//train_time_vs_accuracy.pdf - hash: md5 - md5: 187c4e2e61190791829f0a33417b6416 - size: 20906 - plot@bit_depth/cifar/: - cmd: python -m deckard.layers.plots --path bit_depth/cifar/ --file bit_depth/cifar//clean.csv - -o plot_data.csv -c "../conf/plots.yaml" - deps: - - path: ../conf/plots.yaml - hash: md5 - md5: f874ee0b44e22fa0a7759900ad1b2221 - size: 1351 - - path: bit_depth/cifar//clean.csv - hash: md5 - md5: 87925c04208c680ca5f74ff9b06771cc - size: 4499337 - outs: - - path: bit_depth/cifar//adv_failure_rate_vs_train_time.pdf - hash: md5 - md5: 264f9f4f08d3297059564d67d33f02cd - size: 16854 - - path: bit_depth/cifar//ben_failure_rate_vs_train_time.pdf - hash: md5 - md5: ad073ee47703296e7cd9d719d681570e - size: 17697 - - path: bit_depth/cifar//train_time_vs_accuracy.pdf - hash: md5 - md5: 498dc613bf96aa721afbfc1c15d92a19 - size: 20906 - merge@mnist: - cmd: python merge.py --big_dir data/bit_depth/mnist --little_dir data/mnist --config - ../conf/afr.yaml --data_file raw.csv --output_folder plots/mnist/ --output_file - merged.csv - deps: - - path: ../conf/clean.yaml - hash: md5 - md5: eba3dd17dd820ad8273a37ba57ea73b7 - size: 373 - - path: data/bit_depth/mnist/raw.csv - hash: md5 - md5: 931d5f4dbd17abe4c31689f81dadf8a3 - size: 5302969 - - path: data/mnist/raw.csv - hash: md5 - md5: 49a0f8e54e50ce0f68929842fe150bb4 - size: 9741090 - params: - ../conf/clean.yaml: - fillna: - Epochs: 20 - FGM: 0.0 - FSQ: 32 - Control: 18 - Batch_Size: 1024 - outs: - - path: plots/mnist/merged.csv - hash: md5 - md5: 2d6929af603db1b4f0ae106fc31ae713 - size: 15053785 - merge@cifar: - cmd: python merge.py --big_dir data/bit_depth/cifar --little_dir data/cifar --config - ../conf/afr.yaml --data_file raw.csv --output_folder plots/cifar/ --output_file - merged.csv - deps: - - path: ../conf/clean.yaml - hash: md5 - md5: eba3dd17dd820ad8273a37ba57ea73b7 - size: 373 - - path: data/bit_depth/cifar/raw.csv - hash: md5 - md5: f8af91790f0fabe6bf17878379ea5541 - size: 5174817 - - path: data/cifar/raw.csv - hash: md5 - md5: b0bc8f836427dfd15154db3eb951c770 - size: 9768945 - params: - ../conf/clean.yaml: - fillna: - Epochs: 20 - FGM: 0.0 - FSQ: 32 - Control: 18 - Batch_Size: 1024 - outs: - - path: plots/cifar/merged.csv - hash: md5 - md5: d391c352c3777885f066ddb922105832 - size: 14952800 - merge@cifar100: - cmd: python merge.py --big_dir data/bit_depth/cifar100 --little_dir data/cifar100 - --config ../conf/afr.yaml --data_file raw.csv --output_folder plots/cifar100/ - --output_file merged.csv - deps: - - path: ../conf/clean.yaml - hash: md5 - md5: eba3dd17dd820ad8273a37ba57ea73b7 - size: 373 - - path: data/bit_depth/cifar100/raw.csv - hash: md5 - md5: 37b87d60ad6e609c8c5a42c8c5623ee5 - size: 3900609 - - path: data/cifar100/raw.csv - hash: md5 - md5: b60317375748c88064e0fbc7648664ad - size: 15729259 - params: - ../conf/clean.yaml: - fillna: - Epochs: 20 - FGM: 0.0 - FSQ: 32 - Control: 18 - Batch_Size: 1024 - outs: - - path: plots/cifar100/merged.csv - hash: md5 - md5: 37d9468e287e2d8409334e2d771a371e - size: 19653431 - afr@mnist: - cmd: python -m deckard.layers.afr --dataset mnist --data_file plots/mnist/clean.csv - --config_file "../conf/afr.yaml" --plots_folder plots/mnist/ - deps: - - path: ../conf/afr.yaml - hash: md5 - md5: b58971bd4b1502dc2934b2b5abca19fe - size: 4709 - - path: plots/mnist/clean.csv - hash: md5 - md5: b8947e66cc080181238a0aa6acc2d295 - size: 4760564 - outs: - - path: plots/mnist/aft_comparison.csv - hash: md5 - md5: b05cdfb77b6cee64a96e9e80367f5b98 - size: 440 - - path: plots/mnist/aft_comparison.tex - hash: md5 - md5: d8cbf5fbde74dcfcf6ee573a2803c0a6 - size: 439 - - path: plots/mnist/cox_aft.pdf - hash: md5 - md5: c6b726a742ef9794df1c67d427a2a5b7 - size: 19538 - - path: plots/mnist/cox_epochs_partial_effect.pdf - hash: md5 - md5: a4db1a3fa696fbd62ee25ae0001b5125 - size: 31190 - - path: plots/mnist/log_logistic_aft.pdf - hash: md5 - md5: a8b9875697b0ed5278f4e16a8577d874 - size: 22628 - - path: plots/mnist/log_logistic_epochs_partial_effect.pdf - hash: md5 - md5: cb2e485070e14700e59a2d705df62e9b - size: 28008 - - path: plots/mnist/log_normal_aft.pdf - hash: md5 - md5: f134f518b812b3c60f4eb7208d207de6 - size: 23686 - - path: plots/mnist/log_normal_epochs_partial_effect.pdf - hash: md5 - md5: 986ef0401119d046d269ed098db64033 - size: 28441 - - path: plots/mnist/weibull_aft.pdf - hash: md5 - md5: c40018e1260efdf8e5b41ffeded68daa - size: 29747 - - path: plots/mnist/weibull_epochs_partial_effect.pdf - hash: md5 - md5: 322b99521a3dfde930486234a0ec9777 - size: 28206 + md5: 747305ca7d3e92c73c92985a35c5e664 + size: 9247427 afr@cifar: cmd: python -m deckard.layers.afr --dataset cifar --data_file plots/cifar/clean.csv - --config_file "../conf/afr.yaml" --plots_folder plots/cifar/ + --config_file "../conf/afr.yaml" --plots_folder plots/cifar/ --target adv_failures + --duration_col adv_fit_time deps: - path: ../conf/afr.yaml - hash: md5 - md5: b58971bd4b1502dc2934b2b5abca19fe - size: 4709 + md5: ba48c8059bc4d6b9822853c6b923c764 + size: 4853 - path: plots/cifar/clean.csv - hash: md5 - md5: f6506c50252d904308f67f55f281c0e8 - size: 4638245 + md5: 747305ca7d3e92c73c92985a35c5e664 + size: 9247427 outs: - path: plots/cifar/aft_comparison.csv - hash: md5 - md5: 21f0505016d6a25481fd253e511b3598 - size: 433 + md5: 4bc7aa6c0459bcf73d4dea6474085b39 + size: 462 - path: plots/cifar/aft_comparison.tex - hash: md5 - md5: 74823059e6a493d2470b11c6471830a9 - size: 439 + md5: b11c4e3119a67789a2e2a02dce05d1ed + size: 675 - path: plots/cifar/cox_aft.pdf - hash: md5 - md5: 0b728da06ebee527e909979d774b6dc6 - size: 19520 + md5: dbd2bc33ddb8a9c244e4054c7096f8b3 + size: 20691 - path: plots/cifar/cox_epochs_partial_effect.pdf - hash: md5 - md5: 2b753070e3b3e4e8e7ee63e7f50bdc8e - size: 31699 + md5: d3b2390367df0a2e591b07712c9c69fc + size: 32672 - path: plots/cifar/log_logistic_aft.pdf - hash: md5 - md5: e4146060062db9bd789dfc268024b4bf - size: 22603 + md5: 3c178d071eb728dcbb041349f0f6cd05 + size: 22399 - path: plots/cifar/log_logistic_epochs_partial_effect.pdf - hash: md5 - md5: 8662542b7c61cc6d08b25afbcc80e09d - size: 28410 + md5: 5b4f2fd197a17c0cc81648322dd3edfa + size: 27909 - path: plots/cifar/log_normal_aft.pdf - hash: md5 - md5: 6eb833b3888e73f568da7f31a5070e42 - size: 23316 + md5: c9f10cee72a88b33d06bdc983945e61c + size: 23492 - path: plots/cifar/log_normal_epochs_partial_effect.pdf - hash: md5 - md5: 55e0901b39b0f1593ada1850bd68d39e - size: 28823 + md5: c6354f4dcfca4e6d3cb09a4cda1862c7 + size: 28673 - path: plots/cifar/weibull_aft.pdf - hash: md5 - md5: 679002dd6de1230743cef0fca78a5089 - size: 29738 + md5: b930bdf51394fa4182a9ceddd6452bcd + size: 29449 - path: plots/cifar/weibull_epochs_partial_effect.pdf - hash: md5 - md5: 54d1c03a6bac5f6ef84a9cf2cd125afd - size: 28153 - afr@cifar100: - cmd: python -m deckard.layers.afr --dataset cifar100 --data_file plots/cifar100/clean.csv - --config_file "../conf/afr.yaml" --plots_folder plots/cifar100/ - deps: - - path: ../conf/afr.yaml - hash: md5 - md5: b58971bd4b1502dc2934b2b5abca19fe - size: 4709 - - path: plots/cifar100/clean.csv - hash: md5 - md5: d8c937ae54b9f6d8558863ead2e19a6a - size: 3498981 - outs: - - path: plots/cifar100/aft_comparison.csv - hash: md5 - md5: a37ca79fd85dda3e77e6ee2414063ab7 - size: 441 - - path: plots/cifar100/aft_comparison.tex - hash: md5 - md5: 8cd9cce7cde73b02c3b463d0327a5c12 - size: 445 - - path: plots/cifar100/cox_aft.pdf - hash: md5 - md5: e11bd988593a24836514cb0ea4967fd6 - size: 19926 - - path: plots/cifar100/cox_epochs_partial_effect.pdf - hash: md5 - md5: 246929db8bfbf5bbf8ebd8f7d6b1b4d7 - size: 30316 - - path: plots/cifar100/log_logistic_aft.pdf - hash: md5 - md5: e1ef3c2d3937cd76399faeaa79811e82 - size: 22604 - - path: plots/cifar100/log_logistic_epochs_partial_effect.pdf - hash: md5 - md5: 96ea80ede8d77a00f22836704611d874 - size: 28014 - - path: plots/cifar100/log_normal_aft.pdf - hash: md5 - md5: ecf1e497b584572e7b988afb805d6c36 - size: 23681 - - path: plots/cifar100/log_normal_epochs_partial_effect.pdf - hash: md5 - md5: c0edaacdee94a52a545c3a6054f9b0b5 - size: 28572 - - path: plots/cifar100/weibull_aft.pdf - hash: md5 - md5: 5f55f202fed3b85342da68c25545b2d5 - size: 29732 - - path: plots/cifar100/weibull_epochs_partial_effect.pdf - hash: md5 - md5: 6d53d27982d4528d250d8aedbdf43548 - size: 27984 - get_power_data@mnist: - cmd: python -m deckard.layers.query_kepler --input_file data/bit_depth/mnist/raw.csv - --output_file data/bit_depth/mnist/power.csv - deps: - - path: data/bit_depth/mnist/raw.csv - hash: md5 - md5: 931d5f4dbd17abe4c31689f81dadf8a3 - size: 5302969 - outs: - - path: data/bit_depth/mnist/power.csv - hash: md5 - md5: ed40bca679961759527ca94ca4cbd984 - size: 5421537 - get_power_data@cifar: - cmd: python -m deckard.layers.query_kepler --input_file data/bit_depth/cifar/raw.csv - --output_file data/bit_depth/cifar/power.csv - deps: - - path: data/bit_depth/cifar/raw.csv - hash: md5 - md5: f8af91790f0fabe6bf17878379ea5541 - size: 5174817 - outs: - - path: data/bit_depth/cifar/power.csv - hash: md5 - md5: b9ebbe01b2bd93f1c4af02fb8abd910b - size: 5291011 - get_power_data@cifar100: - cmd: python -m deckard.layers.query_kepler --input_file data/cifar100/raw.csv - --output_file data/cifar100/power.csv - deps: - - path: data/bit_depth/cifar100/raw.csv - hash: md5 - md5: 37b87d60ad6e609c8c5a42c8c5623ee5 - size: 3900609 - - path: data/cifar100/raw.csv - hash: md5 - md5: b60317375748c88064e0fbc7648664ad - size: 15729259 - outs: - - path: data/cifar100/power.csv - hash: md5 - md5: f51f0d4ebd105f64829af90a0212de00 - size: 16081846 + md5: ad5920a2dd199ef30dec2ee01d60c296 + size: 27534 combined_plots: cmd: python combined_plots.py deps: + - path: combined_plots.py + md5: 4fadfe07d2fc51e1c5edb694d4e92866 + size: 7438 - path: plots/cifar/aft_comparison.csv - hash: md5 - md5: 21f0505016d6a25481fd253e511b3598 - size: 433 + md5: 4bc7aa6c0459bcf73d4dea6474085b39 + size: 462 - path: plots/cifar100/aft_comparison.csv - hash: md5 - md5: a37ca79fd85dda3e77e6ee2414063ab7 - size: 441 + md5: 3af00791562aea95acc6857504a8d6a3 + size: 465 - path: plots/mnist/aft_comparison.csv - hash: md5 - md5: b05cdfb77b6cee64a96e9e80367f5b98 - size: 440 + md5: de519adac05bb1c1d97599656e9c7b22 + size: 466 outs: - path: data/combined/combined.csv - hash: md5 - md5: f52c78b3fe9ecf7d7724dbb2f24c5d42 - size: 52354570 + md5: 493c48e49102ccf2dcc55e6513c2d617 + size: 52364568 - path: plots/combined/acc.pdf - hash: md5 - md5: 7c6ef314e97dd016124e5c55d4f76652 - size: 26830 + md5: cf612187ee4fc7c1f81edbff3766aa9b + size: 32490 - path: plots/combined/cost.pdf - hash: md5 - md5: 43b93cd107e9e59a7476e8d48a0f86a8 - size: 40883 + md5: 4ef2481be399e11a8c7e36ffda8f99ca + size: 41752 - path: plots/combined/power.pdf - hash: md5 - md5: f16b7ffa34ebfe70b71a41891805fc57 - size: 41106 + md5: 50e3a38e3bc0c8d83758f5cd0ff79f84 + size: 41605 - path: plots/combined/time.pdf - hash: md5 - md5: 8a5695a782aeab504ae58c0b0085ac34 - size: 39788 + md5: c14c9557c84dab0b7f972803d50d52ba + size: 41600 combined_clean: cmd: python -m deckard.layers.clean_data --i data/combined/combined.csv --o plots/combined/clean.csv --config "../conf/clean.yaml" deps: - path: ../conf/clean.yaml - hash: md5 - md5: eba3dd17dd820ad8273a37ba57ea73b7 - size: 373 + md5: 3b23a3d656ad56e49e69f8624e227852 + size: 538 - path: data/combined/combined.csv - hash: md5 - md5: f52c78b3fe9ecf7d7724dbb2f24c5d42 - size: 52354570 + md5: 493c48e49102ccf2dcc55e6513c2d617 + size: 52364568 params: ../conf/clean.yaml: attacks: FastGradientMethod: FGM + ProjectedGradientDescent: PGD + HopSkipJump: HSJ + DeepFool: Deep defences: Control: Control FeatureSqueezing: FSQ - nb_epoch: Epochs + Epochs: Epochs model_layers: Control fillna: Epochs: 20 @@ -794,129 +388,104 @@ stages: Batch_Size: 1024 params: FGM: attack.init.eps + PGD: attack.init.eps + HSJ: attack.init.eps + DeepFool: attack.init.eps FSQ: model.art.preprocessor.bit_depth Control: model_layers Epochs: model.trainer.nb_epoch Batch_Size: model.trainer.batch_size outs: - path: plots/combined/clean.csv - hash: md5 - md5: 2d858268caf5e5ff75814c51be22c7fb - size: 13640407 + md5: 65e04fe33bd6a5a4607236b0a48340c7 + size: 13643214 combined_afr: cmd: python -m deckard.layers.afr --dataset Combined --data_file plots/combined/clean.csv - --config_file "../conf/combined_afr.yaml" --plots_folder plots/combined/ + --config_file "../conf/combined_afr.yaml" --plots_folder plots/combined/ --target + adv_failures --duration_col adv_fit_time deps: - path: ../conf/combined_afr.yaml - hash: md5 - md5: e8a50ab268c55b8c8966b9c5180dcc49 - size: 10971 + md5: 26e5e54acb8aec5ef5c1eb10d0e38184 + size: 11134 - path: plots/combined/clean.csv - hash: md5 - md5: 2d858268caf5e5ff75814c51be22c7fb - size: 13640407 + md5: 65e04fe33bd6a5a4607236b0a48340c7 + size: 13643214 outs: - path: plots/combined/aft_comparison.csv - hash: md5 - md5: e8300f052060ffea23a9be9fefb218f1 - size: 453 + md5: e74ba56582be7132de90ba39cdd3f739 + size: 473 - path: plots/combined/aft_comparison.tex - hash: md5 - md5: 8a7d7e784f024eca7b0540f50e7622fc - size: 488 + md5: 8f7d034613f99e5a508ead54b6d3d72c + size: 681 - path: plots/combined/cox_aft.pdf - hash: md5 - md5: 98de86517842d9005e13daf3aa141c13 - size: 19855 + md5: 8331a07b824a6d17f659f60ffb1c2801 + size: 20681 - path: plots/combined/cox_attack_eps_partial_effect.pdf - hash: md5 - md5: 50864180a77352e33761f7b1c66cb71b - size: 35694 + md5: db3edc9c18f90e52b8899b3514d4d1ae + size: 35669 - path: plots/combined/cox_batch_size_partial_effect.pdf - hash: md5 - md5: cf76996a611b9c985a3e238e47a9f4f2 - size: 39699 + md5: eca28da18f398d997c3a28191f1c629e + size: 39577 - path: plots/combined/cox_epochs_partial_effect.pdf - hash: md5 - md5: 0e3d1cdf8fdf0351156bf30706b5d2db - size: 38421 + md5: 4530df08dc3b86727fc93e68e850a872 + size: 38326 - path: plots/combined/cox_predict_time_partial_effect.pdf - hash: md5 - md5: 36c4e71d56630aca15e7c9dc0dc623fc - size: 35342 + md5: f49e41ff7406f126ecf3b9fbad8c1e25 + size: 35320 - path: plots/combined/cox_train_time_partial_effect.pdf - hash: md5 - md5: d1cf67c7b9e592fd8a2a10cc6305d5f5 - size: 36106 + md5: 382f0b19a041e8309189f63f3057d87b + size: 35794 - path: plots/combined/log_logistic_aft.pdf - hash: md5 - md5: 4b10017a8c0b50c3913aea32b26c09b2 - size: 22303 + md5: d2244674d38f9d4264e8cee091e0bb5b + size: 22601 - path: plots/combined/log_logistic_attack_eps_partial_effect.pdf - hash: md5 - md5: bbe923ad9711b8dea6f74465133fc24c - size: 26588 + md5: cea58b47c36202c29c502ccfaf68a29e + size: 27073 - path: plots/combined/log_logistic_batch_size_partial_effect.pdf - hash: md5 - md5: 02a4a125adefd0482e7bbad595d58237 - size: 28146 + md5: a3250c94e62ddb0c5b7be13a4059b28e + size: 28670 - path: plots/combined/log_logistic_epochs_partial_effect.pdf - hash: md5 - md5: 14c79dc688d53002af3db8a79fc0b716 - 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plots/combined/log_normal_epochs_partial_effect.pdf - hash: md5 - md5: 242cfcd936715c3bcd35d118b809954e - size: 29042 + md5: 7612c12795a4c1e1196a63f180eaa46f + size: 29076 - path: plots/combined/log_normal_predict_time_partial_effect.pdf - hash: md5 - md5: 64e17a3f49cedd99ccc4909688b98770 - size: 26653 + md5: 271102c4f7b0f7db05dcff4382be61db + size: 27465 - path: plots/combined/log_normal_train_time_partial_effect.pdf - hash: md5 - md5: 60eba3b232d1abdc8926a5bd9f734b7e - size: 25157 + md5: 73deaf8666f7277809c29b33382e0b20 + size: 25972 - path: plots/combined/weibull_aft.pdf - hash: md5 - md5: 09c06b42e875a5f29fdc7d8181f96ae9 - size: 29679 + md5: 1f23952c7a4ea97ecd39b489e32e017f + size: 29658 - path: plots/combined/weibull_attack_eps_partial_effect.pdf - hash: md5 - md5: 4585b0e516c242ad177f992a363389b8 - size: 26555 + md5: 9b95a846244858f4c5f401fa7b73e535 + size: 26856 - path: plots/combined/weibull_batch_size_partial_effect.pdf - hash: md5 - md5: fa43145c2fef3e2e20ef197fa7e89281 - size: 28446 + md5: 87137c44ba2a7080c054d0ebb8be5c73 + size: 28814 - path: plots/combined/weibull_epochs_partial_effect.pdf - hash: md5 - md5: dfa10172d285db441a145d14bf4a5a7a - size: 28713 + md5: 83299fd58a523139425b7d9863ce3ff3 + size: 28053 - path: plots/combined/weibull_predict_time_partial_effect.pdf - hash: md5 - md5: 1c08e87a515c8045eafd4ce4172f613d - size: 26206 + md5: 5ef04caefd298d3bd9c253fe76588867 + size: 26524 - path: plots/combined/weibull_train_time_partial_effect.pdf - hash: md5 - md5: 63b54cbb477b09a1038e357c6285497c - size: 24609 + md5: 8210a64e4e764d8aa43342663f262edd + size: 24804 diff --git a/examples/power/plots/dvc.yaml b/examples/power/plots/dvc.yaml index be102705..3fa5f2c0 100644 --- a/examples/power/plots/dvc.yaml +++ b/examples/power/plots/dvc.yaml @@ -8,19 +8,19 @@ vars: - ../conf/clean.yaml:fillna stages: # compile: - # foreach: # iterates through each stage - # - mnist - # - cifar - # - cifar100 - # - bit_depth/mnist/ - # - bit_depth/cifar/ - # - bit_depth/cifar100/ - # do: - # cmd: python -m deckard.layers.compile --report_folder /result/${item}/reports/attack/ --results_file raw.csv --results_folder data/${item} --config "../conf/compile.yaml" - # deps: - # - /result/${item}/reports/attack/ - # outs: - # - data/${item}/raw.csv + # foreach: # iterates through each stage + # - mnist + # - cifar + # - cifar100 + # - bit_depth/mnist/ + # - bit_depth/cifar/ + # - bit_depth/cifar100/ + # do: + # cmd: python -m deckard.layers.compile --report_folder /result/${item}/reports/attack/ --results_file raw.csv --results_folder data/${item} --config "../conf/compile.yaml" + # deps: + # - /result/${item}/reports/attack/ + # outs: + # - data/${item}/raw.csv # get_power_data: # foreach: # - mnist @@ -28,25 +28,24 @@ stages: # - cifar100 # - bit_depth/mnist/ # - bit_depth/cifar/ + # - bit_depth/cifar100/ # do: # cmd: python -m deckard.layers.query_kepler --input_file data/${item}/raw.csv --output_file data/${item}/power.csv # deps: # - data/bit_depth/${item}/raw.csv - # # - data/${item}/raw.csv + # - data/${item}/raw.csv # outs: - # # - data/${item}/power.csv - # - data/bit_depth/${item}/power.csv - + # - data/${item}/power.csv merge: foreach: - mnist - cifar - cifar100 do: - cmd : python merge.py --big_dir data/bit_depth/${item} --little_dir data/${item} --config ../conf/afr.yaml --data_file raw.csv --output_folder plots/${item}/ --output_file merged.csv + cmd : python merge.py --big_dir data/bit_depth/${item} --little_dir data/${item} --config ../conf/afr.yaml --data_file power.csv --output_folder plots/${item}/ --output_file merged.csv deps: - - data/bit_depth/${item}/raw.csv - - data/${item}/raw.csv + - data/bit_depth/${item}/power.csv + - data/${item}/power.csv - ../conf/clean.yaml outs: - plots/${item}/merged.csv @@ -71,28 +70,13 @@ stages: - defences - params - fillna - plot: - foreach: - - mnist - - cifar - - cifar100 - do: - cmd: python -m deckard.layers.plots --path plots/${item} --file plots/${item}/clean.csv -c "../conf/plots.yaml" - deps: - - plots/${item}/clean.csv - - "../conf/plots.yaml" - plots: - - plots/${item}/${scatter_plot[0].file} # You can use the file specified in each list entry from the parameters specified above in vars - - plots/${item}/${scatter_plot[1].file} - - plots/${item}/${line_plot[0].file} - # - plots/${item}/${cat_plot[0].file} afr: foreach: - mnist - cifar - cifar100 do: - cmd: python -m deckard.layers.afr --dataset ${item} --data_file plots/${item}/clean.csv --config_file "../conf/afr.yaml" --plots_folder plots/${item}/ + cmd: python -m deckard.layers.afr --dataset ${item} --data_file plots/${item}/clean.csv --config_file "../conf/afr.yaml" --plots_folder plots/${item}/ --target adv_failures --duration_col adv_fit_time deps: - plots/${item}/clean.csv - ../conf/afr.yaml @@ -115,6 +99,7 @@ stages: - plots/mnist/aft_comparison.csv - plots/cifar/aft_comparison.csv - plots/cifar100/aft_comparison.csv + - combined_plots.py outs: - data/combined/combined.csv plots: @@ -136,7 +121,7 @@ stages: - params - fillna combined_afr: - cmd: python -m deckard.layers.afr --dataset Combined --data_file plots/combined/clean.csv --config_file "../conf/combined_afr.yaml" --plots_folder plots/combined/ + cmd: python -m deckard.layers.afr --dataset Combined --data_file plots/combined/clean.csv --config_file "../conf/combined_afr.yaml" --plots_folder plots/combined/ --target adv_failures --duration_col adv_fit_time deps: - plots/combined/clean.csv - ../conf/combined_afr.yaml diff --git a/examples/power/plots/plots/combined/.gitignore b/examples/power/plots/plots/combined/.gitignore index d5595206..fada685b 100644 --- a/examples/power/plots/plots/combined/.gitignore +++ b/examples/power/plots/plots/combined/.gitignore @@ -1,29 +1,29 @@ -/acc.pdf -/time.pdf -/cost.pdf -/power.pdf /aft_comparison.tex /weibull_aft.pdf /weibull_epochs_partial_effect.pdf -/cox_epochs_partial_effect.pdf -/cox_aft.pdf -/log_logistic_aft.pdf -/log_logistic_epochs_partial_effect.pdf -/log_normal_aft.pdf -/log_normal_epochs_partial_effect.pdf /weibull_batch_size_partial_effect.pdf /weibull_train_time_partial_effect.pdf /weibull_predict_time_partial_effect.pdf +/weibull_attack_eps_partial_effect.pdf +/cox_aft.pdf +/cox_epochs_partial_effect.pdf /cox_batch_size_partial_effect.pdf /cox_train_time_partial_effect.pdf /cox_predict_time_partial_effect.pdf +/cox_attack_eps_partial_effect.pdf +/log_logistic_aft.pdf +/log_logistic_epochs_partial_effect.pdf /log_logistic_batch_size_partial_effect.pdf /log_logistic_train_time_partial_effect.pdf /log_logistic_predict_time_partial_effect.pdf +/log_logistic_attack_eps_partial_effect.pdf +/log_normal_aft.pdf +/log_normal_epochs_partial_effect.pdf /log_normal_batch_size_partial_effect.pdf /log_normal_train_time_partial_effect.pdf /log_normal_predict_time_partial_effect.pdf -/weibull_attack_eps_partial_effect.pdf -/cox_attack_eps_partial_effect.pdf -/log_logistic_attack_eps_partial_effect.pdf /log_normal_attack_eps_partial_effect.pdf +/acc.pdf +/time.pdf +/cost.pdf +/power.pdf