From 0063936db7442b50d9c2268376f30885d0e3376f Mon Sep 17 00:00:00 2001 From: Denys Herasymuk Date: Fri, 13 Sep 2024 02:42:03 +0300 Subject: [PATCH 01/27] Adapted seed setting to different names and ability to set seed in the fit and predict methods --- README.md | 2 +- .../test_multiple_models_api.py | 48 +++++++++---------- .../abstract_overall_variance_analyzer.py | 20 ++++---- .../batch_overall_variance_analyzer.py | 28 +++++++++-- ...verall_variance_analyzer_postprocessing.py | 12 +++-- virny/utils/common_helpers.py | 5 ++ .../postprocessing_intervention_utils.py | 15 ++++-- 7 files changed, 84 insertions(+), 46 deletions(-) diff --git a/README.md b/README.md index 1f4c9d8a..aea30093 100644 --- a/README.md +++ b/README.md @@ -118,4 +118,4 @@ If Virny has been useful to you, and you would like to cite it in a scientific p ## 📝 License -**Virny** is free and open-source software licensed under the [3-clause BSD license](https://github.com/DataResponsibly/Virny/blob/main/LICENSE). \ No newline at end of file +**Virny** is free and open-source software licensed under the [3-clause BSD license](https://github.com/DataResponsibly/Virny/blob/main/LICENSE). diff --git a/tests/user_interfaces/test_multiple_models_api.py b/tests/user_interfaces/test_multiple_models_api.py index 53a05aad..14d1bb3e 100644 --- a/tests/user_interfaces/test_multiple_models_api.py +++ b/tests/user_interfaces/test_multiple_models_api.py @@ -68,27 +68,27 @@ def test_compute_metrics_with_config_none_seeds(law_school_dataset_1k_params): assert not compare_metric_dfs_v2(metrics_dct1['LogisticRegression'], metrics_dct2['LogisticRegression']) -# def test_compute_metrics_with_config_should_equal_prev_release_results(law_school_dataset_20k_params): -# base_flow_dataset, config, models_config, save_results_dir_path = law_school_dataset_20k_params -# -# config.random_state = 100 -# metrics_dct = compute_metrics_with_config(dataset=base_flow_dataset, -# config=config, -# models_config=copy.deepcopy(models_config), -# save_results_dir_path=save_results_dir_path) -# -# if sys.version_info.major == 3 and sys.version_info.minor >= 12: -# print("Python 3.12 or newer is installed.") -# metrics_path = str(pathlib.Path(__file__).parent.parent.joinpath('files_for_tests', 'law_school_dataset_20k', 'python_3_12+')) -# else: -# print("Older version of Python is installed.") -# metrics_path = str(pathlib.Path(__file__).parent.parent.joinpath('files_for_tests', 'law_school_dataset_20k', 'python_3_11-')) -# -# expected_metrics_dct = read_model_metric_dfs(metrics_path, model_names=['LogisticRegression', 'DecisionTreeClassifier']) -# -# # Drop technical columns -# metrics_dct['LogisticRegression'] = metrics_dct['LogisticRegression'].drop('Runtime_in_Mins', axis=1) -# metrics_dct['DecisionTreeClassifier'] = metrics_dct['DecisionTreeClassifier'].drop('Runtime_in_Mins', axis=1) -# -# assert compare_metric_dfs_with_tolerance(expected_metrics_dct['LogisticRegression'], metrics_dct['LogisticRegression']) -# assert compare_metric_dfs_with_tolerance(expected_metrics_dct['DecisionTreeClassifier'], metrics_dct['DecisionTreeClassifier']) +def test_compute_metrics_with_config_should_equal_prev_release_results(law_school_dataset_20k_params): + base_flow_dataset, config, models_config, save_results_dir_path = law_school_dataset_20k_params + + config.random_state = 100 + metrics_dct = compute_metrics_with_config(dataset=base_flow_dataset, + config=config, + models_config=copy.deepcopy(models_config), + save_results_dir_path=save_results_dir_path) + + if sys.version_info.major == 3 and sys.version_info.minor >= 12: + print("Python 3.12 or newer is installed.") + metrics_path = str(pathlib.Path(__file__).parent.parent.joinpath('files_for_tests', 'law_school_dataset_20k', 'python_3_12+')) + else: + print("Older version of Python is installed.") + metrics_path = str(pathlib.Path(__file__).parent.parent.joinpath('files_for_tests', 'law_school_dataset_20k', 'python_3_11-')) + + expected_metrics_dct = read_model_metric_dfs(metrics_path, model_names=['LogisticRegression', 'DecisionTreeClassifier']) + + # Drop technical columns + metrics_dct['LogisticRegression'] = metrics_dct['LogisticRegression'].drop('Runtime_in_Mins', axis=1) + metrics_dct['DecisionTreeClassifier'] = metrics_dct['DecisionTreeClassifier'].drop('Runtime_in_Mins', axis=1) + + assert compare_metric_dfs_with_tolerance(expected_metrics_dct['LogisticRegression'], metrics_dct['LogisticRegression']) + assert compare_metric_dfs_with_tolerance(expected_metrics_dct['DecisionTreeClassifier'], metrics_dct['DecisionTreeClassifier']) diff --git a/virny/analyzers/abstract_overall_variance_analyzer.py b/virny/analyzers/abstract_overall_variance_analyzer.py index 342ad955..047ee4fd 100644 --- a/virny/analyzers/abstract_overall_variance_analyzer.py +++ b/virny/analyzers/abstract_overall_variance_analyzer.py @@ -6,9 +6,9 @@ from copy import deepcopy from abc import ABCMeta, abstractmethod +from virny.utils.common_helpers import has_method from virny.custom_classes.custom_logger import get_logger -from virny.utils.stability_utils import generate_bootstrap -from virny.utils.stability_utils import count_prediction_metrics +from virny.utils.stability_utils import generate_bootstrap, count_prediction_metrics class AbstractOverallVarianceAnalyzer(metaclass=ABCMeta): @@ -74,15 +74,15 @@ def __init__(self, base_model, base_model_name: str, bootstrap_fraction: float, self.y_test = y_test @abstractmethod - def _fit_model(self, classifier, X_train, y_train): + def _fit_model(self, classifier, X_train, y_train, random_state: int): pass @abstractmethod - def _batch_predict(self, classifier, X_test): + def _batch_predict(self, classifier, X_test, random_state: int): pass @abstractmethod - def _batch_predict_proba(self, classifier, X_test): + def _batch_predict_proba(self, classifier, X_test, random_state: int): pass def compute_metrics(self, save_results: bool = True, with_fit: bool = True): @@ -148,26 +148,26 @@ def UQ_by_boostrap(self, boostrap_size: int, with_replacement: bool, with_fit: b # Train and test each estimator in models_predictions for idx in cycle_range: classifier = self.models_lst[idx] + classifier_random_state = self.random_state + idx + 1 if self.random_state is not None else None # If True, fit the classifier. Otherwise, use already fitted classifier. if with_fit: - classifier_random_state = self.random_state + idx + 1 if self.random_state is not None else None X_sample, y_sample = generate_bootstrap(features=self.X_train, labels=self.y_train, boostrap_size=boostrap_size, with_replacement=with_replacement, random_state=classifier_random_state) - if 'random_state' in classifier.get_params(): + if 'random_state' in classifier.get_params() and has_method(classifier, 'set_params'): classifier.set_params(random_state=classifier_random_state) - classifier = self._fit_model(classifier, X_sample, y_sample) + classifier = self._fit_model(classifier, X_sample, y_sample, random_state=classifier_random_state) # Use a predict_proba method if the classifier supports it. # Note that model predictions do not preserve X_test indexes. # Indexes of the predictions will be aligned with X_test later in the pipeline. if self.with_predict_proba: - models_predictions[idx] = self._batch_predict_proba(classifier, self.X_test) + models_predictions[idx] = self._batch_predict_proba(classifier, self.X_test, random_state=classifier_random_state) else: - models_predictions[idx] = self._batch_predict(classifier, self.X_test) + models_predictions[idx] = self._batch_predict(classifier, self.X_test, random_state=classifier_random_state) self.models_lst[idx] = classifier diff --git a/virny/analyzers/batch_overall_variance_analyzer.py b/virny/analyzers/batch_overall_variance_analyzer.py index c961b1b4..a0e0a02e 100644 --- a/virny/analyzers/batch_overall_variance_analyzer.py +++ b/virny/analyzers/batch_overall_variance_analyzer.py @@ -1,3 +1,4 @@ +import inspect import pandas as pd from virny.analyzers.abstract_overall_variance_analyzer import AbstractOverallVarianceAnalyzer @@ -61,20 +62,41 @@ def __init__(self, base_model, base_model_name: str, bootstrap_fraction: float, verbose=verbose) self.target_column = target_column - def _fit_model(self, classifier, X_train: pd.DataFrame, y_train: pd.DataFrame): + def _fit_model(self, classifier, X_train: pd.DataFrame, y_train: pd.DataFrame, random_state: int): """ Fit a classifier that is an instance of self.base_model """ + # Get the signature of the function + signature = inspect.signature(classifier.fit) + if 'random_state' in signature.parameters: + return classifier.fit(X_train, y_train.values.ravel(), random_state=random_state) + elif 'seed' in signature.parameters: + return classifier.fit(X_train, y_train.values.ravel(), seed=random_state) + return classifier.fit(X_train, y_train.values.ravel()) - def _batch_predict(self, classifier, X_test: pd.DataFrame): + def _batch_predict(self, classifier, X_test: pd.DataFrame, random_state: int): """ Predict with the classifier for X_test set and return predictions """ + # Get the signature of the function + signature = inspect.signature(classifier.predict) + if 'random_state' in signature.parameters: + return classifier.predict(X_test, random_state=random_state) + elif 'seed' in signature.parameters: + return classifier.predict(X_test, seed=random_state) + return classifier.predict(X_test) - def _batch_predict_proba(self, classifier, X_test: pd.DataFrame): + def _batch_predict_proba(self, classifier, X_test: pd.DataFrame, random_state: int): """ Predict with the classifier for X_test set and return probabilities for each class for each test point """ + # Get the signature of the function + signature = inspect.signature(classifier.predict_proba) + if 'random_state' in signature.parameters: + return classifier.predict_proba(X_test, random_state=random_state)[:, 0] + elif 'seed' in signature.parameters: + return classifier.predict_proba(X_test, seed=random_state)[:, 0] + return classifier.predict_proba(X_test)[:, 0] diff --git a/virny/analyzers/batch_overall_variance_analyzer_postprocessing.py b/virny/analyzers/batch_overall_variance_analyzer_postprocessing.py index 97d29a0e..899a654e 100644 --- a/virny/analyzers/batch_overall_variance_analyzer_postprocessing.py +++ b/virny/analyzers/batch_overall_variance_analyzer_postprocessing.py @@ -3,6 +3,7 @@ import pandas as pd from copy import deepcopy +from virny.utils.common_helpers import has_method from virny.utils.stability_utils import generate_bootstrap from virny.analyzers.batch_overall_variance_analyzer import BatchOverallVarianceAnalyzer from virny.utils.postprocessing_intervention_utils import (construct_binary_label_dataset_from_df, @@ -120,25 +121,26 @@ def UQ_by_boostrap(self, boostrap_size: int, with_replacement: bool, with_fit: b # Train and test each estimator in models_predictions for idx in cycle_range: + classifier_random_state = self.random_state + idx + 1 if self.random_state is not None else None classifier = self.models_lst[idx] postprocessor = self.postprocessors_lst[idx] if with_fit: - classifier_random_state = self.random_state + idx + 1 if self.random_state is not None else None X_sample, y_sample = generate_bootstrap(features=self.X_train, labels=self.y_train, boostrap_size=boostrap_size, with_replacement=with_replacement, random_state=classifier_random_state) - classifier.set_params(random_state=classifier_random_state) - classifier = self._fit_model(classifier, X_sample, y_sample) + if 'random_state' in classifier.get_params() and has_method(classifier, 'set_params'): + classifier.set_params(random_state=classifier_random_state) + classifier = self._fit_model(classifier, X_sample, y_sample, random_state=classifier_random_state) # Force garbage collection to avoid out of memory error if with_fit and ((idx + 1) % 10 == 0 or (idx + 1) == self.n_estimators): gc.collect() train_binary_label_dataset_sample = construct_binary_label_dataset_from_samples(X_sample, y_sample, self.X_train.columns, self.target_column, self.sensitive_attribute) - train_binary_label_dataset_sample_pred = predict_on_binary_label_dataset(classifier, train_binary_label_dataset_sample) - test_binary_label_dataset_pred = predict_on_binary_label_dataset(classifier, self.test_binary_label_dataset) + train_binary_label_dataset_sample_pred = predict_on_binary_label_dataset(classifier, train_binary_label_dataset_sample, random_state=classifier_random_state) + test_binary_label_dataset_pred = predict_on_binary_label_dataset(classifier, self.test_binary_label_dataset, random_state=classifier_random_state) postprocessor_fitted = postprocessor.fit(train_binary_label_dataset_sample, train_binary_label_dataset_sample_pred) # Note that model predictions do not preserve X_test indexes. diff --git a/virny/utils/common_helpers.py b/virny/utils/common_helpers.py index 6c44d255..33993737 100644 --- a/virny/utils/common_helpers.py +++ b/virny/utils/common_helpers.py @@ -96,6 +96,11 @@ def str_to_float(str_var: str, var_name: str): raise ValueError(f"{var_name} must be a float number with a '.' separator.") +# Check if the instance has a method with the specific name +def has_method(obj, method_name): + return hasattr(obj, method_name) and callable(getattr(obj, method_name)) + + def save_metrics_to_file(metrics_df, result_filename, save_dir_path): os.makedirs(save_dir_path, exist_ok=True) diff --git a/virny/utils/postprocessing_intervention_utils.py b/virny/utils/postprocessing_intervention_utils.py index 2cc54232..af60be73 100644 --- a/virny/utils/postprocessing_intervention_utils.py +++ b/virny/utils/postprocessing_intervention_utils.py @@ -1,4 +1,5 @@ import copy +import inspect import numpy as np import pandas as pd @@ -31,11 +32,19 @@ def construct_binary_label_dataset_from_df(X_sample, y_sample, target_column, se return binary_label_dataset -def predict_on_binary_label_dataset(model, orig_dataset, threshold=0.5): +def predict_on_binary_label_dataset(model, orig_dataset, random_state, threshold=0.5): orig_dataset_pred = copy.deepcopy(orig_dataset) - fav_idx = np.where(model.classes_ == orig_dataset.favorable_label)[0][0] - y_pred_prob = model.predict_proba(orig_dataset.features)[:, fav_idx] + + # Get the signature of the function + signature = inspect.signature(model.predict_proba) + if 'random_state' in signature.parameters: + y_pred_prob = model.predict_proba(orig_dataset.features, random_state=random_state)[:, fav_idx] + elif 'seed' in signature.parameters: + y_pred_prob = model.predict_proba(orig_dataset.features, seed=random_state)[:, fav_idx] + else: + y_pred_prob = model.predict_proba(orig_dataset.features)[:, fav_idx] + orig_dataset.scores = y_pred_prob.reshape(-1, 1) y_pred = np.zeros_like(orig_dataset.labels) From 10d808126db639cf0667edacafd564acd969b87e Mon Sep 17 00:00:00 2001 From: Denys Herasymuk Date: Fri, 13 Sep 2024 02:45:51 +0300 Subject: [PATCH 02/27] Adapted seed setting to different names and ability to set seed in the fit and predict methods --- virny/analyzers/abstract_overall_variance_analyzer.py | 4 +++- .../batch_overall_variance_analyzer_postprocessing.py | 4 +++- 2 files changed, 6 insertions(+), 2 deletions(-) diff --git a/virny/analyzers/abstract_overall_variance_analyzer.py b/virny/analyzers/abstract_overall_variance_analyzer.py index 047ee4fd..7cce7635 100644 --- a/virny/analyzers/abstract_overall_variance_analyzer.py +++ b/virny/analyzers/abstract_overall_variance_analyzer.py @@ -157,7 +157,9 @@ def UQ_by_boostrap(self, boostrap_size: int, with_replacement: bool, with_fit: b boostrap_size=boostrap_size, with_replacement=with_replacement, random_state=classifier_random_state) - if 'random_state' in classifier.get_params() and has_method(classifier, 'set_params'): + if (has_method(classifier, 'get_params') + and 'random_state' in classifier.get_params() + and has_method(classifier, 'set_params')): classifier.set_params(random_state=classifier_random_state) classifier = self._fit_model(classifier, X_sample, y_sample, random_state=classifier_random_state) diff --git a/virny/analyzers/batch_overall_variance_analyzer_postprocessing.py b/virny/analyzers/batch_overall_variance_analyzer_postprocessing.py index 899a654e..0c474cf2 100644 --- a/virny/analyzers/batch_overall_variance_analyzer_postprocessing.py +++ b/virny/analyzers/batch_overall_variance_analyzer_postprocessing.py @@ -130,7 +130,9 @@ def UQ_by_boostrap(self, boostrap_size: int, with_replacement: bool, with_fit: b boostrap_size=boostrap_size, with_replacement=with_replacement, random_state=classifier_random_state) - if 'random_state' in classifier.get_params() and has_method(classifier, 'set_params'): + if (has_method(classifier, 'get_params') + and 'random_state' in classifier.get_params() + and has_method(classifier, 'set_params')): classifier.set_params(random_state=classifier_random_state) classifier = self._fit_model(classifier, X_sample, y_sample, random_state=classifier_random_state) From d3737ad1706aea7fa95fe4666d81b478a6e21808 Mon Sep 17 00:00:00 2001 From: Denys Herasymuk Date: Fri, 13 Sep 2024 02:49:37 +0300 Subject: [PATCH 03/27] Adapted seed setting to different names and ability to set seed in the fit and predict methods --- virny/analyzers/batch_overall_variance_analyzer.py | 7 +++++-- 1 file changed, 5 insertions(+), 2 deletions(-) diff --git a/virny/analyzers/batch_overall_variance_analyzer.py b/virny/analyzers/batch_overall_variance_analyzer.py index a0e0a02e..78683eb0 100644 --- a/virny/analyzers/batch_overall_variance_analyzer.py +++ b/virny/analyzers/batch_overall_variance_analyzer.py @@ -69,10 +69,11 @@ def _fit_model(self, classifier, X_train: pd.DataFrame, y_train: pd.DataFrame, r # Get the signature of the function signature = inspect.signature(classifier.fit) if 'random_state' in signature.parameters: - return classifier.fit(X_train, y_train.values.ravel(), random_state=random_state) + return classifier.fit(X_train, y_train, random_state=random_state) elif 'seed' in signature.parameters: - return classifier.fit(X_train, y_train.values.ravel(), seed=random_state) + return classifier.fit(X_train, y_train, seed=random_state) + # Sklearn API return classifier.fit(X_train, y_train.values.ravel()) def _batch_predict(self, classifier, X_test: pd.DataFrame, random_state: int): @@ -86,6 +87,7 @@ def _batch_predict(self, classifier, X_test: pd.DataFrame, random_state: int): elif 'seed' in signature.parameters: return classifier.predict(X_test, seed=random_state) + # Sklearn API return classifier.predict(X_test) def _batch_predict_proba(self, classifier, X_test: pd.DataFrame, random_state: int): @@ -99,4 +101,5 @@ def _batch_predict_proba(self, classifier, X_test: pd.DataFrame, random_state: i elif 'seed' in signature.parameters: return classifier.predict_proba(X_test, seed=random_state)[:, 0] + # Sklearn API return classifier.predict_proba(X_test)[:, 0] From f81a964c25d6d0ff70dabf51cf910b6b111e0641 Mon Sep 17 00:00:00 2001 From: Denys Herasymuk Date: Fri, 13 Sep 2024 02:54:12 +0300 Subject: [PATCH 04/27] Adapted seed setting to different names and ability to set seed in the fit and predict methods --- virny/analyzers/batch_overall_variance_analyzer.py | 5 +++-- 1 file changed, 3 insertions(+), 2 deletions(-) diff --git a/virny/analyzers/batch_overall_variance_analyzer.py b/virny/analyzers/batch_overall_variance_analyzer.py index 78683eb0..f4c61efe 100644 --- a/virny/analyzers/batch_overall_variance_analyzer.py +++ b/virny/analyzers/batch_overall_variance_analyzer.py @@ -69,9 +69,10 @@ def _fit_model(self, classifier, X_train: pd.DataFrame, y_train: pd.DataFrame, r # Get the signature of the function signature = inspect.signature(classifier.fit) if 'random_state' in signature.parameters: - return classifier.fit(X_train, y_train, random_state=random_state) + return classifier.fit(X_train, y_train.values.ravel(), random_state=random_state) + # PyTorch Tabular API elif 'seed' in signature.parameters: - return classifier.fit(X_train, y_train, seed=random_state) + return classifier.fit(train=pd.concat([X_train, y_train], axis=1), seed=random_state) # Sklearn API return classifier.fit(X_train, y_train.values.ravel()) From 525ea7bf30e982b2097dcfda8ea392d82321e12c Mon Sep 17 00:00:00 2001 From: Denys Herasymuk Date: Fri, 13 Sep 2024 03:01:06 +0300 Subject: [PATCH 05/27] Adapted seed setting to different names and ability to set seed in the fit and predict methods --- .../batch_overall_variance_analyzer.py | 7 ++++++- .../utils/postprocessing_intervention_utils.py | 18 +++++++++++------- 2 files changed, 17 insertions(+), 8 deletions(-) diff --git a/virny/analyzers/batch_overall_variance_analyzer.py b/virny/analyzers/batch_overall_variance_analyzer.py index f4c61efe..7d97dfa3 100644 --- a/virny/analyzers/batch_overall_variance_analyzer.py +++ b/virny/analyzers/batch_overall_variance_analyzer.py @@ -1,6 +1,7 @@ import inspect import pandas as pd +from virny.utils.common_helpers import has_method from virny.analyzers.abstract_overall_variance_analyzer import AbstractOverallVarianceAnalyzer @@ -69,7 +70,7 @@ def _fit_model(self, classifier, X_train: pd.DataFrame, y_train: pd.DataFrame, r # Get the signature of the function signature = inspect.signature(classifier.fit) if 'random_state' in signature.parameters: - return classifier.fit(X_train, y_train.values.ravel(), random_state=random_state) + return classifier.fit(X_train, y_train.values.ravel() , random_state=random_state) # PyTorch Tabular API elif 'seed' in signature.parameters: return classifier.fit(train=pd.concat([X_train, y_train], axis=1), seed=random_state) @@ -95,6 +96,10 @@ def _batch_predict_proba(self, classifier, X_test: pd.DataFrame, random_state: i """ Predict with the classifier for X_test set and return probabilities for each class for each test point """ + # PyTorch Tabular API + if not has_method(classifier, 'predict_proba'): + return classifier.predict(X_test)[:, 0] + # Get the signature of the function signature = inspect.signature(classifier.predict_proba) if 'random_state' in signature.parameters: diff --git a/virny/utils/postprocessing_intervention_utils.py b/virny/utils/postprocessing_intervention_utils.py index af60be73..8f9954b9 100644 --- a/virny/utils/postprocessing_intervention_utils.py +++ b/virny/utils/postprocessing_intervention_utils.py @@ -1,10 +1,11 @@ import copy import inspect - import numpy as np import pandas as pd from aif360.datasets import BinaryLabelDataset +from virny.utils.common_helpers import has_method + def construct_binary_label_dataset_from_samples(X_sample, y_sample, column_names, target_column, sensitive_attribute): df = pd.DataFrame(X_sample, columns=column_names) @@ -37,13 +38,16 @@ def predict_on_binary_label_dataset(model, orig_dataset, random_state, threshold fav_idx = np.where(model.classes_ == orig_dataset.favorable_label)[0][0] # Get the signature of the function - signature = inspect.signature(model.predict_proba) - if 'random_state' in signature.parameters: - y_pred_prob = model.predict_proba(orig_dataset.features, random_state=random_state)[:, fav_idx] - elif 'seed' in signature.parameters: - y_pred_prob = model.predict_proba(orig_dataset.features, seed=random_state)[:, fav_idx] - else: + if not has_method(model, 'predict_proba'): y_pred_prob = model.predict_proba(orig_dataset.features)[:, fav_idx] + else: + signature = inspect.signature(model.predict_proba) + if 'random_state' in signature.parameters: + y_pred_prob = model.predict_proba(orig_dataset.features, random_state=random_state)[:, fav_idx] + elif 'seed' in signature.parameters: + y_pred_prob = model.predict_proba(orig_dataset.features, seed=random_state)[:, fav_idx] + else: + y_pred_prob = model.predict_proba(orig_dataset.features)[:, fav_idx] orig_dataset.scores = y_pred_prob.reshape(-1, 1) From 9ae561a7c937e8239458d54d9c6df447975df371 Mon Sep 17 00:00:00 2001 From: Denys Herasymuk Date: Fri, 13 Sep 2024 03:06:31 +0300 Subject: [PATCH 06/27] Adapted seed setting to different names and ability to set seed in the fit and predict methods --- virny/analyzers/batch_overall_variance_analyzer.py | 1 + 1 file changed, 1 insertion(+) diff --git a/virny/analyzers/batch_overall_variance_analyzer.py b/virny/analyzers/batch_overall_variance_analyzer.py index 7d97dfa3..6e13884d 100644 --- a/virny/analyzers/batch_overall_variance_analyzer.py +++ b/virny/analyzers/batch_overall_variance_analyzer.py @@ -98,6 +98,7 @@ def _batch_predict_proba(self, classifier, X_test: pd.DataFrame, random_state: i """ # PyTorch Tabular API if not has_method(classifier, 'predict_proba'): + print('type(classifier):', type(classifier)) return classifier.predict(X_test)[:, 0] # Get the signature of the function From 134507d4b00e09fb687fd73dc565ef1765f07a20 Mon Sep 17 00:00:00 2001 From: Denys Herasymuk Date: Fri, 13 Sep 2024 03:22:26 +0300 Subject: [PATCH 07/27] Adapted seed setting to different names and ability to set seed in the fit and predict methods --- virny/analyzers/batch_overall_variance_analyzer.py | 6 ++++-- virny/utils/postprocessing_intervention_utils.py | 5 +++-- 2 files changed, 7 insertions(+), 4 deletions(-) diff --git a/virny/analyzers/batch_overall_variance_analyzer.py b/virny/analyzers/batch_overall_variance_analyzer.py index 6e13884d..9ac23e7a 100644 --- a/virny/analyzers/batch_overall_variance_analyzer.py +++ b/virny/analyzers/batch_overall_variance_analyzer.py @@ -98,8 +98,10 @@ def _batch_predict_proba(self, classifier, X_test: pd.DataFrame, random_state: i """ # PyTorch Tabular API if not has_method(classifier, 'predict_proba'): - print('type(classifier):', type(classifier)) - return classifier.predict(X_test)[:, 0] + new_df = X_test + new_df['Diabetic'] = None + return classifier.predict(test=new_df, tta_seed=random_state)[:, 0] + # return classifier.predict(X_test, tta_seed=random_state)[:, 0] # Get the signature of the function signature = inspect.signature(classifier.predict_proba) diff --git a/virny/utils/postprocessing_intervention_utils.py b/virny/utils/postprocessing_intervention_utils.py index 8f9954b9..58a299af 100644 --- a/virny/utils/postprocessing_intervention_utils.py +++ b/virny/utils/postprocessing_intervention_utils.py @@ -37,10 +37,11 @@ def predict_on_binary_label_dataset(model, orig_dataset, random_state, threshold orig_dataset_pred = copy.deepcopy(orig_dataset) fav_idx = np.where(model.classes_ == orig_dataset.favorable_label)[0][0] - # Get the signature of the function + # PyTorch Tabular API if not has_method(model, 'predict_proba'): - y_pred_prob = model.predict_proba(orig_dataset.features)[:, fav_idx] + y_pred_prob = model.predict_proba(orig_dataset.features, tta_seed=random_state)[:, fav_idx] else: + # Get the signature of the function signature = inspect.signature(model.predict_proba) if 'random_state' in signature.parameters: y_pred_prob = model.predict_proba(orig_dataset.features, random_state=random_state)[:, fav_idx] From 1aae3e64aa124521db04f32a56225d7558f82ee8 Mon Sep 17 00:00:00 2001 From: Denys Herasymuk Date: Fri, 13 Sep 2024 03:28:32 +0300 Subject: [PATCH 08/27] Adapted seed setting to different names and ability to set seed in the fit and predict methods --- virny/analyzers/batch_overall_variance_analyzer.py | 6 ++---- 1 file changed, 2 insertions(+), 4 deletions(-) diff --git a/virny/analyzers/batch_overall_variance_analyzer.py b/virny/analyzers/batch_overall_variance_analyzer.py index 9ac23e7a..55825eec 100644 --- a/virny/analyzers/batch_overall_variance_analyzer.py +++ b/virny/analyzers/batch_overall_variance_analyzer.py @@ -98,10 +98,8 @@ def _batch_predict_proba(self, classifier, X_test: pd.DataFrame, random_state: i """ # PyTorch Tabular API if not has_method(classifier, 'predict_proba'): - new_df = X_test - new_df['Diabetic'] = None - return classifier.predict(test=new_df, tta_seed=random_state)[:, 0] - # return classifier.predict(X_test, tta_seed=random_state)[:, 0] + print('type(classifier):', type(classifier)) + return classifier.predict(X_test, tta_seed=random_state)[:, 0] # Get the signature of the function signature = inspect.signature(classifier.predict_proba) From 383f4f91071be841c95a684bf6f40edcb4a29810 Mon Sep 17 00:00:00 2001 From: Denys Herasymuk Date: Fri, 13 Sep 2024 03:33:04 +0300 Subject: [PATCH 09/27] Adapted seed setting to different names and ability to set seed in the fit and predict methods --- virny/analyzers/abstract_overall_variance_analyzer.py | 1 + virny/user_interfaces/multiple_models_api.py | 1 + 2 files changed, 2 insertions(+) diff --git a/virny/analyzers/abstract_overall_variance_analyzer.py b/virny/analyzers/abstract_overall_variance_analyzer.py index 7cce7635..3dd75a03 100644 --- a/virny/analyzers/abstract_overall_variance_analyzer.py +++ b/virny/analyzers/abstract_overall_variance_analyzer.py @@ -59,6 +59,7 @@ def __init__(self, base_model, base_model_name: str, bootstrap_fraction: float, self.dataset_name = dataset_name self.n_estimators = n_estimators self.models_lst = [deepcopy(base_model) for _ in range(n_estimators)] + print('type(self.models_lst[0]):', type(self.models_lst[0])) self.random_state = random_state self.with_predict_proba = with_predict_proba self.models_predictions = None diff --git a/virny/user_interfaces/multiple_models_api.py b/virny/user_interfaces/multiple_models_api.py index 084a5b88..e7b23fe3 100644 --- a/virny/user_interfaces/multiple_models_api.py +++ b/virny/user_interfaces/multiple_models_api.py @@ -161,6 +161,7 @@ def run_metrics_computation(dataset: BaseFlowDataset, bootstrap_fraction: float, print('#' * 30, f' [Model {model_idx + 1} / {num_models}] Analyze {model_name} ', '#' * 30) try: base_model = models_config[model_name] + print('type(base_model):', type(base_model)) computation_start_date_time = datetime.now() model_metrics_df, fitted_bootstrap = compute_one_model_metrics(base_model=base_model, n_estimators=n_estimators, From 3b47b1b5a5d6f92a5b39b938437cc36f1177ce3e Mon Sep 17 00:00:00 2001 From: Denys Herasymuk Date: Fri, 13 Sep 2024 03:40:06 +0300 Subject: [PATCH 10/27] Adapted seed setting to different names and ability to set seed in the fit and predict methods --- virny/analyzers/abstract_overall_variance_analyzer.py | 1 + virny/analyzers/batch_overall_variance_analyzer.py | 5 +++-- 2 files changed, 4 insertions(+), 2 deletions(-) diff --git a/virny/analyzers/abstract_overall_variance_analyzer.py b/virny/analyzers/abstract_overall_variance_analyzer.py index 3dd75a03..06dcc041 100644 --- a/virny/analyzers/abstract_overall_variance_analyzer.py +++ b/virny/analyzers/abstract_overall_variance_analyzer.py @@ -149,6 +149,7 @@ def UQ_by_boostrap(self, boostrap_size: int, with_replacement: bool, with_fit: b # Train and test each estimator in models_predictions for idx in cycle_range: classifier = self.models_lst[idx] + print('type(classifier)2:', type(classifier)) classifier_random_state = self.random_state + idx + 1 if self.random_state is not None else None # If True, fit the classifier. Otherwise, use already fitted classifier. diff --git a/virny/analyzers/batch_overall_variance_analyzer.py b/virny/analyzers/batch_overall_variance_analyzer.py index 55825eec..4684af63 100644 --- a/virny/analyzers/batch_overall_variance_analyzer.py +++ b/virny/analyzers/batch_overall_variance_analyzer.py @@ -70,10 +70,11 @@ def _fit_model(self, classifier, X_train: pd.DataFrame, y_train: pd.DataFrame, r # Get the signature of the function signature = inspect.signature(classifier.fit) if 'random_state' in signature.parameters: - return classifier.fit(X_train, y_train.values.ravel() , random_state=random_state) + return classifier.fit(X_train, y_train.values.ravel(), random_state=random_state) # PyTorch Tabular API elif 'seed' in signature.parameters: - return classifier.fit(train=pd.concat([X_train, y_train], axis=1), seed=random_state) + classifier.fit(train=pd.concat([X_train, y_train], axis=1), seed=random_state) + return classifier # Sklearn API return classifier.fit(X_train, y_train.values.ravel()) From 6a666a57f214b52989c502f3790c69411bae9fd0 Mon Sep 17 00:00:00 2001 From: Denys Herasymuk Date: Fri, 13 Sep 2024 03:42:28 +0300 Subject: [PATCH 11/27] Adapted seed setting to different names and ability to set seed in the fit and predict methods --- virny/analyzers/batch_overall_variance_analyzer.py | 3 +-- virny/utils/postprocessing_intervention_utils.py | 2 +- 2 files changed, 2 insertions(+), 3 deletions(-) diff --git a/virny/analyzers/batch_overall_variance_analyzer.py b/virny/analyzers/batch_overall_variance_analyzer.py index 4684af63..86e1277f 100644 --- a/virny/analyzers/batch_overall_variance_analyzer.py +++ b/virny/analyzers/batch_overall_variance_analyzer.py @@ -99,8 +99,7 @@ def _batch_predict_proba(self, classifier, X_test: pd.DataFrame, random_state: i """ # PyTorch Tabular API if not has_method(classifier, 'predict_proba'): - print('type(classifier):', type(classifier)) - return classifier.predict(X_test, tta_seed=random_state)[:, 0] + return classifier.predict(X_test, tta_seed=random_state)['0_probability'] # Get the signature of the function signature = inspect.signature(classifier.predict_proba) diff --git a/virny/utils/postprocessing_intervention_utils.py b/virny/utils/postprocessing_intervention_utils.py index 58a299af..f72dab59 100644 --- a/virny/utils/postprocessing_intervention_utils.py +++ b/virny/utils/postprocessing_intervention_utils.py @@ -39,7 +39,7 @@ def predict_on_binary_label_dataset(model, orig_dataset, random_state, threshold # PyTorch Tabular API if not has_method(model, 'predict_proba'): - y_pred_prob = model.predict_proba(orig_dataset.features, tta_seed=random_state)[:, fav_idx] + y_pred_prob = model.predict_proba(orig_dataset.features, tta_seed=random_state)[f'{fav_idx}_probability'] else: # Get the signature of the function signature = inspect.signature(model.predict_proba) From 53f2ecc01053a71a220668e340855d169d69c729 Mon Sep 17 00:00:00 2001 From: Denys Herasymuk Date: Fri, 13 Sep 2024 03:43:31 +0300 Subject: [PATCH 12/27] Adapted seed setting to different names and ability to set seed in the fit and predict methods --- virny/analyzers/abstract_overall_variance_analyzer.py | 2 -- virny/user_interfaces/multiple_models_api.py | 1 - 2 files changed, 3 deletions(-) diff --git a/virny/analyzers/abstract_overall_variance_analyzer.py b/virny/analyzers/abstract_overall_variance_analyzer.py index 06dcc041..7cce7635 100644 --- a/virny/analyzers/abstract_overall_variance_analyzer.py +++ b/virny/analyzers/abstract_overall_variance_analyzer.py @@ -59,7 +59,6 @@ def __init__(self, base_model, base_model_name: str, bootstrap_fraction: float, self.dataset_name = dataset_name self.n_estimators = n_estimators self.models_lst = [deepcopy(base_model) for _ in range(n_estimators)] - print('type(self.models_lst[0]):', type(self.models_lst[0])) self.random_state = random_state self.with_predict_proba = with_predict_proba self.models_predictions = None @@ -149,7 +148,6 @@ def UQ_by_boostrap(self, boostrap_size: int, with_replacement: bool, with_fit: b # Train and test each estimator in models_predictions for idx in cycle_range: classifier = self.models_lst[idx] - print('type(classifier)2:', type(classifier)) classifier_random_state = self.random_state + idx + 1 if self.random_state is not None else None # If True, fit the classifier. Otherwise, use already fitted classifier. diff --git a/virny/user_interfaces/multiple_models_api.py b/virny/user_interfaces/multiple_models_api.py index e7b23fe3..084a5b88 100644 --- a/virny/user_interfaces/multiple_models_api.py +++ b/virny/user_interfaces/multiple_models_api.py @@ -161,7 +161,6 @@ def run_metrics_computation(dataset: BaseFlowDataset, bootstrap_fraction: float, print('#' * 30, f' [Model {model_idx + 1} / {num_models}] Analyze {model_name} ', '#' * 30) try: base_model = models_config[model_name] - print('type(base_model):', type(base_model)) computation_start_date_time = datetime.now() model_metrics_df, fitted_bootstrap = compute_one_model_metrics(base_model=base_model, n_estimators=n_estimators, From 24c58974788e6e0755481b0ca48d6c828d53e3dd Mon Sep 17 00:00:00 2001 From: Denys Herasymuk Date: Fri, 13 Sep 2024 03:49:58 +0300 Subject: [PATCH 13/27] Adapted seed setting to different names and ability to set seed in the fit and predict methods --- virny/analyzers/batch_overall_variance_analyzer.py | 2 +- virny/utils/postprocessing_intervention_utils.py | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/virny/analyzers/batch_overall_variance_analyzer.py b/virny/analyzers/batch_overall_variance_analyzer.py index 86e1277f..66dbb012 100644 --- a/virny/analyzers/batch_overall_variance_analyzer.py +++ b/virny/analyzers/batch_overall_variance_analyzer.py @@ -99,7 +99,7 @@ def _batch_predict_proba(self, classifier, X_test: pd.DataFrame, random_state: i """ # PyTorch Tabular API if not has_method(classifier, 'predict_proba'): - return classifier.predict(X_test, tta_seed=random_state)['0_probability'] + return classifier.predict(X_test, tta_seed=random_state)['0_probability'].values # Get the signature of the function signature = inspect.signature(classifier.predict_proba) diff --git a/virny/utils/postprocessing_intervention_utils.py b/virny/utils/postprocessing_intervention_utils.py index f72dab59..b42cfe28 100644 --- a/virny/utils/postprocessing_intervention_utils.py +++ b/virny/utils/postprocessing_intervention_utils.py @@ -39,7 +39,7 @@ def predict_on_binary_label_dataset(model, orig_dataset, random_state, threshold # PyTorch Tabular API if not has_method(model, 'predict_proba'): - y_pred_prob = model.predict_proba(orig_dataset.features, tta_seed=random_state)[f'{fav_idx}_probability'] + y_pred_prob = model.predict_proba(orig_dataset.features, tta_seed=random_state)[f'{fav_idx}_probability'].values else: # Get the signature of the function signature = inspect.signature(model.predict_proba) From f1405fffd2783ec208a5d06e2ba40f9df079f9dc Mon Sep 17 00:00:00 2001 From: Denys Herasymuk Date: Fri, 13 Sep 2024 04:12:53 +0300 Subject: [PATCH 14/27] Adapted seed setting to different names and ability to set seed in the fit and predict methods --- virny/user_interfaces/multiple_models_api.py | 7 ++++++- 1 file changed, 6 insertions(+), 1 deletion(-) diff --git a/virny/user_interfaces/multiple_models_api.py b/virny/user_interfaces/multiple_models_api.py index 084a5b88..2bd28825 100644 --- a/virny/user_interfaces/multiple_models_api.py +++ b/virny/user_interfaces/multiple_models_api.py @@ -297,9 +297,14 @@ def compute_one_model_metrics(base_model, n_estimators: int, dataset: BaseFlowDa metrics_df = metrics_df.reset_index() metrics_df = metrics_df.rename(columns={"index": "Metric"}) metrics_df['Model_Name'] = base_model_name - metrics_df['Model_Params'] = str(base_model.get_params()) metrics_df['Virny_Random_State'] = random_state + # Pytorch Tabular API + if hasattr(base_model, 'config'): + metrics_df['Model_Params'] = str(base_model.config) + else: + metrics_df['Model_Params'] = str(base_model.get_params()) + if save_results: # Save metrics result_filename = f'Metrics_{dataset_name}_{base_model_name}' From 964cebb1fba63e4e45b5c23cbe482eb00746cf3d Mon Sep 17 00:00:00 2001 From: Denys Herasymuk Date: Fri, 13 Sep 2024 19:24:59 +0300 Subject: [PATCH 15/27] Improved an intergration with Pytorch Tabular --- .../abstract_overall_variance_analyzer.py | 3 +- .../batch_overall_variance_analyzer.py | 11 +----- ...verall_variance_analyzer_postprocessing.py | 3 +- virny/custom_classes/wrappers/__init__.py | 0 .../wrappers/pytorch_tabular_wrapper.py | 39 +++++++++++++++++++ virny/preprocessing/basic_preprocessing.py | 10 ++++- virny/user_interfaces/multiple_models_api.py | 9 ++--- ...iple_models_with_multiple_test_sets_api.py | 4 +- virny/utils/common_helpers.py | 6 ++- .../postprocessing_intervention_utils.py | 18 ++++----- 10 files changed, 69 insertions(+), 34 deletions(-) create mode 100644 virny/custom_classes/wrappers/__init__.py create mode 100644 virny/custom_classes/wrappers/pytorch_tabular_wrapper.py diff --git a/virny/analyzers/abstract_overall_variance_analyzer.py b/virny/analyzers/abstract_overall_variance_analyzer.py index 7cce7635..ee4cb192 100644 --- a/virny/analyzers/abstract_overall_variance_analyzer.py +++ b/virny/analyzers/abstract_overall_variance_analyzer.py @@ -157,8 +157,7 @@ def UQ_by_boostrap(self, boostrap_size: int, with_replacement: bool, with_fit: b boostrap_size=boostrap_size, with_replacement=with_replacement, random_state=classifier_random_state) - if (has_method(classifier, 'get_params') - and 'random_state' in classifier.get_params() + if ('random_state' in classifier.get_params() and has_method(classifier, 'set_params')): classifier.set_params(random_state=classifier_random_state) classifier = self._fit_model(classifier, X_sample, y_sample, random_state=classifier_random_state) diff --git a/virny/analyzers/batch_overall_variance_analyzer.py b/virny/analyzers/batch_overall_variance_analyzer.py index 66dbb012..5f14e82e 100644 --- a/virny/analyzers/batch_overall_variance_analyzer.py +++ b/virny/analyzers/batch_overall_variance_analyzer.py @@ -71,12 +71,9 @@ def _fit_model(self, classifier, X_train: pd.DataFrame, y_train: pd.DataFrame, r signature = inspect.signature(classifier.fit) if 'random_state' in signature.parameters: return classifier.fit(X_train, y_train.values.ravel(), random_state=random_state) - # PyTorch Tabular API elif 'seed' in signature.parameters: - classifier.fit(train=pd.concat([X_train, y_train], axis=1), seed=random_state) - return classifier + return classifier.fit(X_train, y_train, seed=random_state) - # Sklearn API return classifier.fit(X_train, y_train.values.ravel()) def _batch_predict(self, classifier, X_test: pd.DataFrame, random_state: int): @@ -90,17 +87,12 @@ def _batch_predict(self, classifier, X_test: pd.DataFrame, random_state: int): elif 'seed' in signature.parameters: return classifier.predict(X_test, seed=random_state) - # Sklearn API return classifier.predict(X_test) def _batch_predict_proba(self, classifier, X_test: pd.DataFrame, random_state: int): """ Predict with the classifier for X_test set and return probabilities for each class for each test point """ - # PyTorch Tabular API - if not has_method(classifier, 'predict_proba'): - return classifier.predict(X_test, tta_seed=random_state)['0_probability'].values - # Get the signature of the function signature = inspect.signature(classifier.predict_proba) if 'random_state' in signature.parameters: @@ -108,5 +100,4 @@ def _batch_predict_proba(self, classifier, X_test: pd.DataFrame, random_state: i elif 'seed' in signature.parameters: return classifier.predict_proba(X_test, seed=random_state)[:, 0] - # Sklearn API return classifier.predict_proba(X_test)[:, 0] diff --git a/virny/analyzers/batch_overall_variance_analyzer_postprocessing.py b/virny/analyzers/batch_overall_variance_analyzer_postprocessing.py index 0c474cf2..da657882 100644 --- a/virny/analyzers/batch_overall_variance_analyzer_postprocessing.py +++ b/virny/analyzers/batch_overall_variance_analyzer_postprocessing.py @@ -130,8 +130,7 @@ def UQ_by_boostrap(self, boostrap_size: int, with_replacement: bool, with_fit: b boostrap_size=boostrap_size, with_replacement=with_replacement, random_state=classifier_random_state) - if (has_method(classifier, 'get_params') - and 'random_state' in classifier.get_params() + if ('random_state' in classifier.get_params() and has_method(classifier, 'set_params')): classifier.set_params(random_state=classifier_random_state) classifier = self._fit_model(classifier, X_sample, y_sample, random_state=classifier_random_state) diff --git a/virny/custom_classes/wrappers/__init__.py b/virny/custom_classes/wrappers/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/virny/custom_classes/wrappers/pytorch_tabular_wrapper.py b/virny/custom_classes/wrappers/pytorch_tabular_wrapper.py new file mode 100644 index 00000000..8b82245a --- /dev/null +++ b/virny/custom_classes/wrappers/pytorch_tabular_wrapper.py @@ -0,0 +1,39 @@ +import copy +import numpy as np +import pandas as pd + +from virny.utils.postprocessing_intervention_utils import construct_binary_label_dataset_from_df + + +class PytorchTabularWrapper: + """ + A wrapper for Pytorch Tabular models. The wrapper aligns fit, predict, and predict_proba methods + to be compatible with sklearn models. + """ + + def __init__(self, estimator): + self.estimator = estimator + + def __copy__(self): + new_estimator = copy.copy(self.estimator) + return PytorchTabularWrapper(estimator=new_estimator) + + def __deepcopy__(self, memo): + new_estimator = copy.deepcopy(self.estimator) + return PytorchTabularWrapper(estimator=new_estimator) + + def get_params(self): + return eval(str(self.estimator.config)) + + def set_params(self, random_state: int): + pass + + def fit(self, X: pd.DataFrame, y: pd.DataFrame, seed: int): + self.estimator.fit(train=pd.concat([X, y], axis=1), seed=seed) + return self + + def predict_proba(self, X, seed: int): + return self.estimator.predict(X, tta_seed=seed).values + + def predict(self, X, seed: int): + return self.estimator.predict(X, tta_seed=seed) diff --git a/virny/preprocessing/basic_preprocessing.py b/virny/preprocessing/basic_preprocessing.py index 05aa7cf6..4a183acd 100644 --- a/virny/preprocessing/basic_preprocessing.py +++ b/virny/preprocessing/basic_preprocessing.py @@ -3,9 +3,11 @@ from sklearn.preprocessing import StandardScaler from sklearn.model_selection import train_test_split -from virny.configs.constants import INTERSECTION_SIGN from virny.datasets.base import BaseDataLoader +from virny.configs.constants import INTERSECTION_SIGN +from virny.custom_classes.wrappers.pytorch_tabular_wrapper import PytorchTabularWrapper from virny.custom_classes.base_dataset import BaseFlowDataset +from virny.utils.common_helpers import get_source_library_name def preprocess_dataset(data_loader: BaseDataLoader, column_transformer: ColumnTransformer, sensitive_attributes_dct: dict, @@ -54,6 +56,12 @@ def preprocess_dataset(data_loader: BaseDataLoader, column_transformer: ColumnTr categorical_columns=data_loader.categorical_columns) +def preprocess_base_model(base_model): + if get_source_library_name(base_model) == 'pytorch_tabular': + return PytorchTabularWrapper(estimator=base_model) + return base_model + + def get_dummies(data: pd.DataFrame, categorical_columns: list, numerical_columns: list): """ Return a dataset made by one-hot encoding for categorical columns and concatenate with numerical columns. diff --git a/virny/user_interfaces/multiple_models_api.py b/virny/user_interfaces/multiple_models_api.py index 2bd28825..8e017859 100644 --- a/virny/user_interfaces/multiple_models_api.py +++ b/virny/user_interfaces/multiple_models_api.py @@ -6,6 +6,7 @@ from virny.configs.constants import ModelSetting from virny.custom_classes.base_dataset import BaseFlowDataset +from virny.preprocessing.basic_preprocessing import preprocess_base_model from virny.analyzers.subgroup_variance_analyzer import SubgroupVarianceAnalyzer from virny.analyzers.subgroup_error_analyzer import SubgroupErrorAnalyzer from virny.utils.protected_groups_partitioning import create_test_protected_groups @@ -161,6 +162,7 @@ def run_metrics_computation(dataset: BaseFlowDataset, bootstrap_fraction: float, print('#' * 30, f' [Model {model_idx + 1} / {num_models}] Analyze {model_name} ', '#' * 30) try: base_model = models_config[model_name] + base_model = preprocess_base_model(base_model) computation_start_date_time = datetime.now() model_metrics_df, fitted_bootstrap = compute_one_model_metrics(base_model=base_model, n_estimators=n_estimators, @@ -298,12 +300,7 @@ def compute_one_model_metrics(base_model, n_estimators: int, dataset: BaseFlowDa metrics_df = metrics_df.rename(columns={"index": "Metric"}) metrics_df['Model_Name'] = base_model_name metrics_df['Virny_Random_State'] = random_state - - # Pytorch Tabular API - if hasattr(base_model, 'config'): - metrics_df['Model_Params'] = str(base_model.config) - else: - metrics_df['Model_Params'] = str(base_model.get_params()) + metrics_df['Model_Params'] = str(base_model.get_params()) if save_results: # Save metrics diff --git a/virny/user_interfaces/multiple_models_with_multiple_test_sets_api.py b/virny/user_interfaces/multiple_models_with_multiple_test_sets_api.py index 29aa4c85..cda7becb 100644 --- a/virny/user_interfaces/multiple_models_with_multiple_test_sets_api.py +++ b/virny/user_interfaces/multiple_models_with_multiple_test_sets_api.py @@ -5,6 +5,7 @@ from virny.configs.constants import ModelSetting from virny.utils.protected_groups_partitioning import create_test_protected_groups +from virny.preprocessing.basic_preprocessing import preprocess_base_model from virny.custom_classes.base_dataset import BaseFlowDataset from virny.analyzers.subgroup_variance_analyzer import SubgroupVarianceAnalyzer from virny.analyzers.subgroup_error_analyzer import SubgroupErrorAnalyzer @@ -168,6 +169,7 @@ def run_metrics_computation_with_multiple_test_sets(dataset: BaseFlowDataset, bo print('#' * 30, f' [Model {model_idx + 1} / {num_models}] Analyze {model_name} ', '#' * 30) try: base_model = models_config[model_name] + base_model = preprocess_base_model(base_model) computation_start_date_time = datetime.now() model_metrics_dfs_lst, fitted_bootstrap =\ compute_one_model_metrics_with_multiple_test_sets(base_model=base_model, @@ -313,8 +315,8 @@ def compute_one_model_metrics_with_multiple_test_sets(base_model, n_estimators: metrics_df = metrics_df.reset_index() metrics_df = metrics_df.rename(columns={"index": "Metric"}) metrics_df['Model_Name'] = base_model_name - metrics_df['Model_Params'] = str(base_model.get_params()) metrics_df['Virny_Random_State'] = random_state + metrics_df['Model_Params'] = str(base_model.get_params()) all_test_sets_metrics_lst.append(metrics_df) diff --git a/virny/utils/common_helpers.py b/virny/utils/common_helpers.py index 33993737..6a3a7c2b 100644 --- a/virny/utils/common_helpers.py +++ b/virny/utils/common_helpers.py @@ -96,11 +96,15 @@ def str_to_float(str_var: str, var_name: str): raise ValueError(f"{var_name} must be a float number with a '.' separator.") -# Check if the instance has a method with the specific name def has_method(obj, method_name): + # Check if the instance has a method with the specific name return hasattr(obj, method_name) and callable(getattr(obj, method_name)) +def get_source_library_name(obj): + return obj.__class__.__module__.split('.')[0] + + def save_metrics_to_file(metrics_df, result_filename, save_dir_path): os.makedirs(save_dir_path, exist_ok=True) diff --git a/virny/utils/postprocessing_intervention_utils.py b/virny/utils/postprocessing_intervention_utils.py index b42cfe28..717b0918 100644 --- a/virny/utils/postprocessing_intervention_utils.py +++ b/virny/utils/postprocessing_intervention_utils.py @@ -37,18 +37,14 @@ def predict_on_binary_label_dataset(model, orig_dataset, random_state, threshold orig_dataset_pred = copy.deepcopy(orig_dataset) fav_idx = np.where(model.classes_ == orig_dataset.favorable_label)[0][0] - # PyTorch Tabular API - if not has_method(model, 'predict_proba'): - y_pred_prob = model.predict_proba(orig_dataset.features, tta_seed=random_state)[f'{fav_idx}_probability'].values + # Get the signature of the function + signature = inspect.signature(model.predict_proba) + if 'random_state' in signature.parameters: + y_pred_prob = model.predict_proba(orig_dataset.features, random_state=random_state)[:, fav_idx] + elif 'seed' in signature.parameters: + y_pred_prob = model.predict_proba(orig_dataset.features, seed=random_state)[:, fav_idx] else: - # Get the signature of the function - signature = inspect.signature(model.predict_proba) - if 'random_state' in signature.parameters: - y_pred_prob = model.predict_proba(orig_dataset.features, random_state=random_state)[:, fav_idx] - elif 'seed' in signature.parameters: - y_pred_prob = model.predict_proba(orig_dataset.features, seed=random_state)[:, fav_idx] - else: - y_pred_prob = model.predict_proba(orig_dataset.features)[:, fav_idx] + y_pred_prob = model.predict_proba(orig_dataset.features)[:, fav_idx] orig_dataset.scores = y_pred_prob.reshape(-1, 1) From b8809dffb549a96014e79f8eaf3bead691538364 Mon Sep 17 00:00:00 2001 From: Denys Herasymuk Date: Fri, 13 Sep 2024 23:37:34 +0300 Subject: [PATCH 16/27] Improved an intergration with Pytorch Tabular --- virny/analyzers/abstract_overall_variance_analyzer.py | 9 ++++++--- virny/analyzers/batch_overall_variance_analyzer.py | 1 - .../batch_overall_variance_analyzer_postprocessing.py | 9 ++++++--- virny/custom_classes/wrappers/pytorch_tabular_wrapper.py | 3 --- 4 files changed, 12 insertions(+), 10 deletions(-) diff --git a/virny/analyzers/abstract_overall_variance_analyzer.py b/virny/analyzers/abstract_overall_variance_analyzer.py index ee4cb192..9454b6a2 100644 --- a/virny/analyzers/abstract_overall_variance_analyzer.py +++ b/virny/analyzers/abstract_overall_variance_analyzer.py @@ -157,9 +157,12 @@ def UQ_by_boostrap(self, boostrap_size: int, with_replacement: bool, with_fit: b boostrap_size=boostrap_size, with_replacement=with_replacement, random_state=classifier_random_state) - if ('random_state' in classifier.get_params() - and has_method(classifier, 'set_params')): - classifier.set_params(random_state=classifier_random_state) + if has_method(classifier, 'set_params'): + if 'random_state' in classifier.get_params(): + classifier.set_params(random_state=classifier_random_state) + elif 'seed' in classifier.get_params(): + classifier.set_params(seed=classifier_random_state) + classifier = self._fit_model(classifier, X_sample, y_sample, random_state=classifier_random_state) # Use a predict_proba method if the classifier supports it. diff --git a/virny/analyzers/batch_overall_variance_analyzer.py b/virny/analyzers/batch_overall_variance_analyzer.py index 5f14e82e..ed01ade2 100644 --- a/virny/analyzers/batch_overall_variance_analyzer.py +++ b/virny/analyzers/batch_overall_variance_analyzer.py @@ -1,7 +1,6 @@ import inspect import pandas as pd -from virny.utils.common_helpers import has_method from virny.analyzers.abstract_overall_variance_analyzer import AbstractOverallVarianceAnalyzer diff --git a/virny/analyzers/batch_overall_variance_analyzer_postprocessing.py b/virny/analyzers/batch_overall_variance_analyzer_postprocessing.py index da657882..56ea0bed 100644 --- a/virny/analyzers/batch_overall_variance_analyzer_postprocessing.py +++ b/virny/analyzers/batch_overall_variance_analyzer_postprocessing.py @@ -130,9 +130,12 @@ def UQ_by_boostrap(self, boostrap_size: int, with_replacement: bool, with_fit: b boostrap_size=boostrap_size, with_replacement=with_replacement, random_state=classifier_random_state) - if ('random_state' in classifier.get_params() - and has_method(classifier, 'set_params')): - classifier.set_params(random_state=classifier_random_state) + if has_method(classifier, 'set_params'): + if 'random_state' in classifier.get_params(): + classifier.set_params(random_state=classifier_random_state) + elif 'seed' in classifier.get_params(): + classifier.set_params(seed=classifier_random_state) + classifier = self._fit_model(classifier, X_sample, y_sample, random_state=classifier_random_state) # Force garbage collection to avoid out of memory error diff --git a/virny/custom_classes/wrappers/pytorch_tabular_wrapper.py b/virny/custom_classes/wrappers/pytorch_tabular_wrapper.py index 8b82245a..70040dba 100644 --- a/virny/custom_classes/wrappers/pytorch_tabular_wrapper.py +++ b/virny/custom_classes/wrappers/pytorch_tabular_wrapper.py @@ -1,9 +1,6 @@ import copy -import numpy as np import pandas as pd -from virny.utils.postprocessing_intervention_utils import construct_binary_label_dataset_from_df - class PytorchTabularWrapper: """ From f4cd643da493b64c592f16d18ead986123a7463b Mon Sep 17 00:00:00 2001 From: Denys Herasymuk Date: Fri, 13 Sep 2024 23:50:41 +0300 Subject: [PATCH 17/27] Improved an intergration with Pytorch Tabular --- virny/custom_classes/wrappers/pytorch_tabular_wrapper.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/virny/custom_classes/wrappers/pytorch_tabular_wrapper.py b/virny/custom_classes/wrappers/pytorch_tabular_wrapper.py index 70040dba..9cb8f712 100644 --- a/virny/custom_classes/wrappers/pytorch_tabular_wrapper.py +++ b/virny/custom_classes/wrappers/pytorch_tabular_wrapper.py @@ -22,7 +22,7 @@ def __deepcopy__(self, memo): def get_params(self): return eval(str(self.estimator.config)) - def set_params(self, random_state: int): + def set_params(self, **params): pass def fit(self, X: pd.DataFrame, y: pd.DataFrame, seed: int): From ae579b0c074342d66170559b250dfd9c9a21742d Mon Sep 17 00:00:00 2001 From: Denys Herasymuk Date: Sat, 14 Sep 2024 00:07:59 +0300 Subject: [PATCH 18/27] Improved versions of dependencies to be more compatible --- lib_base_packages.txt | 26 +++++++++++++------------- requirements.txt | 35 +++++++++++++++++------------------ 2 files changed, 30 insertions(+), 31 deletions(-) diff --git a/lib_base_packages.txt b/lib_base_packages.txt index 30327cd0..dad008ec 100644 --- a/lib_base_packages.txt +++ b/lib_base_packages.txt @@ -1,14 +1,14 @@ -setuptools~=74.1.0 -aif360[Reductions]~=0.6.1 -matplotlib~=3.6.2 -pandas~=1.5.2 -altair~=4.2.0 +setuptools>=74.1.0 +aif360[Reductions]>=0.6.1 +matplotlib>=3.6.2 +pandas>=1.5.2 +altair>=4.2.0 scikit-learn>=1.2.0 -tqdm~=4.64.1 -gradio==4.10.0 -seaborn~=0.12.1 -folktables~=0.0.11 -munch~=2.5.0 -PyYAML~=6.0 -requests-toolbelt==1.0.0 -colorama~=0.4.6 \ No newline at end of file +tqdm>=4.64.1 +gradio>=4.10.0 +seaborn>=0.12.1 +folktables>=0.0.11 +munch>=2.5.0 +PyYAML>=6.0 +requests-toolbelt>=1.0.0 +colorama>=0.4.6 \ No newline at end of file diff --git a/requirements.txt b/requirements.txt index 56a0c2ac..a5471475 100644 --- a/requirements.txt +++ b/requirements.txt @@ -1,20 +1,19 @@ -wheel~=0.38.4 -twine~=4.0.2 -aif360~=0.6.1 -numpy==1.23.5 -fairlearn~=0.9.0 -matplotlib~=3.6.2 -pandas~=1.5.2 -altair~=4.2.0 +wheel>=0.43.0 +aif360>=0.6.1 +numpy>=1.23.5 +fairlearn>=0.9.0 +matplotlib>=3.6.2 +pandas>=1.5.2 +altair>=4.2.0 scikit-learn>=1.2.0 -tqdm~=4.64.1 -seaborn~=0.12.1 -folktables~=0.0.11 -xgboost~=1.7.2 -munch~=2.5.0 -PyYAML~=6.0 -python-dotenv~=1.0.0 -pytest~=7.2.2 +tqdm>=4.64.1 +seaborn>=0.12.1 +folktables>=0.0.11 +xgboost>=1.7.2 +munch>=2.5.0 +PyYAML>=6.0 +python-dotenv>=1.0.0 +pytest>=7.2.2 pymongo==4.3.3 -requests-toolbelt==1.0.0 -colorama~=0.4.6 \ No newline at end of file +requests-toolbelt>=1.0.0 +colorama>=0.4.6 \ No newline at end of file From d869bd4615f3e4b14c1f68bd01128f4c435f8738 Mon Sep 17 00:00:00 2001 From: Denys Herasymuk Date: Sat, 14 Sep 2024 01:00:26 +0300 Subject: [PATCH 19/27] Fixed tests --- tests/__init__.py | 18 + .../folk_employment_NY_2018.csv | 20001 ++++++++++++++++ .../test_protected_groups_partitioning.py | 17 +- 3 files changed, 20027 insertions(+), 9 deletions(-) create mode 100644 tests/files_for_tests/folk_employment_NY_2018.csv diff --git a/tests/__init__.py b/tests/__init__.py index 18206444..def2cb71 100644 --- a/tests/__init__.py +++ b/tests/__init__.py @@ -1,5 +1,6 @@ import os import pytest +import pathlib import numpy as np import pandas as pd @@ -139,6 +140,23 @@ def models_config(): } +@pytest.fixture(scope='package') +def folk_employment_NY_2018_loader(): + df_path = pathlib.Path(__file__).parent.joinpath('files_for_tests').joinpath('folk_employment_NY_2018.csv') + full_df = pd.read_csv(df_path, header=0) + + target = 'ESR' + numerical_columns = ['AGEP'] + categorical_columns = ['MAR', 'MIL', 'ESP', 'MIG', 'DREM', 'NATIVITY', 'DIS', 'DEAR', 'DEYE', 'SEX', 'RAC1P', + 'RELP', 'CIT', 'ANC', 'SCHL'] + + full_df[categorical_columns] = full_df[categorical_columns].astype('str') + return BaseDataLoader(full_df=full_df, + target=target, + numerical_columns=numerical_columns, + categorical_columns=categorical_columns) + + @pytest.fixture(scope='package') def compas_dataset_class(): dataset_path = os.path.join(ROOT_DIR, 'virny', 'datasets', 'data', 'COMPAS.csv') diff --git a/tests/files_for_tests/folk_employment_NY_2018.csv b/tests/files_for_tests/folk_employment_NY_2018.csv new file mode 100644 index 00000000..8731c5e0 --- /dev/null +++ b/tests/files_for_tests/folk_employment_NY_2018.csv @@ -0,0 +1,20001 @@ +MAR,MIL,ESP,MIG,DREM,NATIVITY,DIS,DEAR,DEYE,SEX,RAC1P,RELP,CIT,ANC,SCHL,AGEP,ESR +5,4,0,1,2,1,2,2,2,1,1,2,1,1,15,42,1 +1,4,0,1,2,1,2,2,2,2,1,0,1,2,21,59,1 +2,4,0,1,2,2,2,2,2,2,1,0,4,4,19,78,1 +5,4,0,1,2,1,2,2,2,1,1,10,1,4,14,19,0 +1,2,0,1,2,1,2,2,2,1,1,0,1,1,17,87,0 +1,4,0,1,2,1,2,2,2,1,1,0,1,4,19,61,1 +1,2,0,1,2,1,1,1,2,1,1,0,1,2,22,69,0 +1,4,0,1,2,2,2,2,2,1,6,6,4,4,15,70,0 +5,4,0,1,2,1,2,2,2,2,1,2,1,1,20,21,1 +2,4,0,1,2,1,2,2,2,2,3,0,1,1,19,41,0 +1,4,0,1,2,2,2,2,2,1,1,1,5,1,21,50,1 +5,4,0,3,2,1,2,2,2,1,1,13,1,1,19,36,0 +1,4,0,1,2,1,2,2,2,1,1,0,1,1,16,59,1 +1,4,0,1,2,1,2,2,2,2,1,1,1,1,21,63,0 +3,4,0,1,2,1,2,2,2,2,1,0,1,1,18,66,0 +1,4,0,1,2,1,2,2,2,1,1,1,1,4,16,52,0 +3,4,0,1,2,2,1,2,2,2,2,0,4,4,16,70,0 +2,4,0,1,2,1,1,2,2,2,1,0,1,1,16,61,0 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+5,4,0,1,2,1,2,2,2,1,1,17,1,1,16,59,0 +2,4,0,1,1,1,1,2,2,2,1,0,1,2,16,65,0 +1,4,0,1,2,1,2,2,2,2,1,0,1,2,16,44,1 +5,4,0,1,2,1,2,2,2,2,1,2,1,2,19,20,1 +1,2,0,1,2,1,2,2,2,1,1,0,1,1,22,83,0 +1,4,0,1,2,2,2,2,2,2,1,0,5,1,16,49,0 +1,4,0,3,2,2,2,2,2,2,8,1,5,1,16,55,1 diff --git a/tests/utils/test_protected_groups_partitioning.py b/tests/utils/test_protected_groups_partitioning.py index ed9b6f57..d15617f3 100644 --- a/tests/utils/test_protected_groups_partitioning.py +++ b/tests/utils/test_protected_groups_partitioning.py @@ -1,8 +1,10 @@ +import pandas as pd from sklearn.model_selection import train_test_split from virny.datasets import ACSEmploymentDataset from virny.utils.protected_groups_partitioning import check_sensitive_attrs_in_columns, create_test_protected_groups -from tests import config_params, folk_emp_config_params, compas_dataset_class, compas_without_sensitive_attrs_dataset_class +from tests import (config_params, folk_emp_config_params, compas_dataset_class, + compas_without_sensitive_attrs_dataset_class, folk_employment_NY_2018_loader) def test_check_sensitive_attrs_in_columns_true(config_params): @@ -81,13 +83,11 @@ def test_create_test_protected_groups_true2(compas_without_sensitive_attrs_datas assert actual_test_protected_groups['race_dis'].shape[0] == 642 -def test_create_test_protected_groups_folk_true1(folk_emp_config_params): - data_loader = ACSEmploymentDataset(state=['NY'], year=2018, with_nulls=False, - subsample_size=20_000, subsample_seed=42) +def test_create_test_protected_groups_folk_true1(folk_emp_config_params, folk_employment_NY_2018_loader): + data_loader = folk_employment_NY_2018_loader seed = 100 - X_train, X_test, y_train, y_test = train_test_split(data_loader.X_data, - data_loader.y_data, + X_train, X_test, y_train, y_test = train_test_split(data_loader.X_data, data_loader.y_data, test_size=folk_emp_config_params.test_set_fraction, random_state=seed) actual_test_protected_groups = create_test_protected_groups(X_test, @@ -104,9 +104,8 @@ def test_create_test_protected_groups_folk_true1(folk_emp_config_params): assert actual_test_protected_groups['SEX&RAC1P_dis'].shape[0] == X_test[(X_test.SEX == '2') & (X_test.RAC1P == '2')].shape[0] -def test_create_test_protected_groups_folk_true2(folk_emp_config_params): - data_loader = ACSEmploymentDataset(state=['NY'], year=2018, with_nulls=False, - subsample_size=20_000, subsample_seed=42) +def test_create_test_protected_groups_folk_true2(folk_emp_config_params, folk_employment_NY_2018_loader): + data_loader = folk_employment_NY_2018_loader new_sensitive_attributes_dct = {'SEX': '2', 'RAC1P': ['2', '3', '4', '5', '6', '7', '8', '9']} seed = 100 From 8fe5c0d4ef6b2bb555599e0e7d9d5f0eb91e8512 Mon Sep 17 00:00:00 2001 From: Denys Herasymuk Date: Sat, 14 Sep 2024 02:35:40 +0300 Subject: [PATCH 20/27] Fixed tests --- ...reeClassifier_50_Estimators_20240902__220132.csv | 0 ...ticRegression_50_Estimators_20240902__220132.csv | 0 ...reeClassifier_50_Estimators_20240423__010106.csv | 0 ...ticRegression_50_Estimators_20240423__010106.csv | 0 tests/user_interfaces/test_multiple_models_api.py | 13 +++++++++++-- 5 files changed, 11 insertions(+), 2 deletions(-) rename tests/files_for_tests/law_school_dataset_20k/{python_3_12+ => python_3_12}/Metrics_law_school_DecisionTreeClassifier_50_Estimators_20240902__220132.csv (100%) rename tests/files_for_tests/law_school_dataset_20k/{python_3_12+ => python_3_12}/Metrics_law_school_LogisticRegression_50_Estimators_20240902__220132.csv (100%) rename tests/files_for_tests/law_school_dataset_20k/{python_3_11- => python_3_8}/Metrics_law_school_DecisionTreeClassifier_50_Estimators_20240423__010106.csv (100%) rename tests/files_for_tests/law_school_dataset_20k/{python_3_11- => python_3_8}/Metrics_law_school_LogisticRegression_50_Estimators_20240423__010106.csv (100%) diff --git a/tests/files_for_tests/law_school_dataset_20k/python_3_12+/Metrics_law_school_DecisionTreeClassifier_50_Estimators_20240902__220132.csv b/tests/files_for_tests/law_school_dataset_20k/python_3_12/Metrics_law_school_DecisionTreeClassifier_50_Estimators_20240902__220132.csv similarity index 100% rename from tests/files_for_tests/law_school_dataset_20k/python_3_12+/Metrics_law_school_DecisionTreeClassifier_50_Estimators_20240902__220132.csv rename to tests/files_for_tests/law_school_dataset_20k/python_3_12/Metrics_law_school_DecisionTreeClassifier_50_Estimators_20240902__220132.csv diff --git a/tests/files_for_tests/law_school_dataset_20k/python_3_12+/Metrics_law_school_LogisticRegression_50_Estimators_20240902__220132.csv b/tests/files_for_tests/law_school_dataset_20k/python_3_12/Metrics_law_school_LogisticRegression_50_Estimators_20240902__220132.csv similarity index 100% rename from tests/files_for_tests/law_school_dataset_20k/python_3_12+/Metrics_law_school_LogisticRegression_50_Estimators_20240902__220132.csv rename to tests/files_for_tests/law_school_dataset_20k/python_3_12/Metrics_law_school_LogisticRegression_50_Estimators_20240902__220132.csv diff --git a/tests/files_for_tests/law_school_dataset_20k/python_3_11-/Metrics_law_school_DecisionTreeClassifier_50_Estimators_20240423__010106.csv b/tests/files_for_tests/law_school_dataset_20k/python_3_8/Metrics_law_school_DecisionTreeClassifier_50_Estimators_20240423__010106.csv similarity index 100% rename from tests/files_for_tests/law_school_dataset_20k/python_3_11-/Metrics_law_school_DecisionTreeClassifier_50_Estimators_20240423__010106.csv rename to tests/files_for_tests/law_school_dataset_20k/python_3_8/Metrics_law_school_DecisionTreeClassifier_50_Estimators_20240423__010106.csv diff --git a/tests/files_for_tests/law_school_dataset_20k/python_3_11-/Metrics_law_school_LogisticRegression_50_Estimators_20240423__010106.csv b/tests/files_for_tests/law_school_dataset_20k/python_3_8/Metrics_law_school_LogisticRegression_50_Estimators_20240423__010106.csv similarity index 100% rename from tests/files_for_tests/law_school_dataset_20k/python_3_11-/Metrics_law_school_LogisticRegression_50_Estimators_20240423__010106.csv rename to tests/files_for_tests/law_school_dataset_20k/python_3_8/Metrics_law_school_LogisticRegression_50_Estimators_20240423__010106.csv diff --git a/tests/user_interfaces/test_multiple_models_api.py b/tests/user_interfaces/test_multiple_models_api.py index 14d1bb3e..b2412b5d 100644 --- a/tests/user_interfaces/test_multiple_models_api.py +++ b/tests/user_interfaces/test_multiple_models_api.py @@ -79,10 +79,19 @@ def test_compute_metrics_with_config_should_equal_prev_release_results(law_schoo if sys.version_info.major == 3 and sys.version_info.minor >= 12: print("Python 3.12 or newer is installed.") - metrics_path = str(pathlib.Path(__file__).parent.parent.joinpath('files_for_tests', 'law_school_dataset_20k', 'python_3_12+')) + metrics_path = str(pathlib.Path(__file__).parent.parent.joinpath('files_for_tests', 'law_school_dataset_20k', 'python_3_12')) + elif sys.version_info.major == 3 and sys.version_info.minor == 11: + print("Python 3.11 or newer is installed.") + metrics_path = str(pathlib.Path(__file__).parent.parent.joinpath('files_for_tests', 'law_school_dataset_20k', 'python_3_11')) + elif sys.version_info.major == 3 and sys.version_info.minor == 10: + print("Python 3.10 or newer is installed.") + metrics_path = str(pathlib.Path(__file__).parent.parent.joinpath('files_for_tests', 'law_school_dataset_20k', 'python_3_10')) + elif sys.version_info.major == 3 and sys.version_info.minor == 9: + print("Python 3.9 or newer is installed.") + metrics_path = str(pathlib.Path(__file__).parent.parent.joinpath('files_for_tests', 'law_school_dataset_20k', 'python_3_9')) else: print("Older version of Python is installed.") - metrics_path = str(pathlib.Path(__file__).parent.parent.joinpath('files_for_tests', 'law_school_dataset_20k', 'python_3_11-')) + metrics_path = str(pathlib.Path(__file__).parent.parent.joinpath('files_for_tests', 'law_school_dataset_20k', 'python_3_8')) expected_metrics_dct = read_model_metric_dfs(metrics_path, model_names=['LogisticRegression', 'DecisionTreeClassifier']) From c03b5d5fe52cd4376edef3d47deeac0083c3760a Mon Sep 17 00:00:00 2001 From: Denys Herasymuk Date: Sat, 14 Sep 2024 02:36:01 +0300 Subject: [PATCH 21/27] Fixed tests --- requirements.txt | 1 + ...nTreeClassifier_50_Estimators_20240913.csv | 19 +++++++++++++++++++ ...ression_50_Estimators_202409013_220132.csv | 19 +++++++++++++++++++ ...nTreeClassifier_50_Estimators_20240913.csv | 19 +++++++++++++++++++ ...ression_50_Estimators_202409013_220132.csv | 19 +++++++++++++++++++ ...nTreeClassifier_50_Estimators_20240913.csv | 19 +++++++++++++++++++ ...ression_50_Estimators_202409013_220132.csv | 19 +++++++++++++++++++ 7 files changed, 115 insertions(+) create mode 100644 tests/files_for_tests/law_school_dataset_20k/python_3_10/Metrics_law_school_DecisionTreeClassifier_50_Estimators_20240913.csv create mode 100644 tests/files_for_tests/law_school_dataset_20k/python_3_10/Metrics_law_school_LogisticRegression_50_Estimators_202409013_220132.csv create mode 100644 tests/files_for_tests/law_school_dataset_20k/python_3_11/Metrics_law_school_DecisionTreeClassifier_50_Estimators_20240913.csv create mode 100644 tests/files_for_tests/law_school_dataset_20k/python_3_11/Metrics_law_school_LogisticRegression_50_Estimators_202409013_220132.csv create mode 100644 tests/files_for_tests/law_school_dataset_20k/python_3_9/Metrics_law_school_DecisionTreeClassifier_50_Estimators_20240913.csv create mode 100644 tests/files_for_tests/law_school_dataset_20k/python_3_9/Metrics_law_school_LogisticRegression_50_Estimators_202409013_220132.csv diff --git a/requirements.txt b/requirements.txt index d25e7a70..1a35fdff 100644 --- a/requirements.txt +++ b/requirements.txt @@ -7,6 +7,7 @@ pandas>=1.5.2 altair>=4.2.0 scikit-learn>=1.2.0 tqdm>=4.64.1 +gradio>=4.10.0 seaborn>=0.12.1 folktables>=0.0.11 xgboost>=1.7.2 diff --git a/tests/files_for_tests/law_school_dataset_20k/python_3_10/Metrics_law_school_DecisionTreeClassifier_50_Estimators_20240913.csv b/tests/files_for_tests/law_school_dataset_20k/python_3_10/Metrics_law_school_DecisionTreeClassifier_50_Estimators_20240913.csv new file mode 100644 index 00000000..d55844a6 --- /dev/null +++ b/tests/files_for_tests/law_school_dataset_20k/python_3_10/Metrics_law_school_DecisionTreeClassifier_50_Estimators_20240913.csv @@ -0,0 +1,19 @@ +Metric,overall,male_priv,male_dis,race_priv,race_dis,male&race_priv,male&race_dis,Model_Name,Virny_Random_State,Model_Params +Statistical_Bias,0.14871459501539216,0.13922538692994388,0.1612539056997345,0.12087562594490028,0.3035414166913454,0.13498160237084172,0.3081005399506289,DecisionTreeClassifier,100,"{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 20, 'max_features': 0.6, 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 0.1, 'min_weight_fraction_leaf': 0.0, 'monotonic_cst': None, 'random_state': None, 'splitter': 'best'}" +Mean_Prediction,0.10730163589001154,0.10127383420207314,0.11526694526335872,0.08243941922626423,0.2455732067991173,0.0941339892140465,0.26012614125045447,DecisionTreeClassifier,100,"{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 20, 'max_features': 0.6, 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 0.1, 'min_weight_fraction_leaf': 0.0, 'monotonic_cst': None, 'random_state': None, 'splitter': 'best'}" +IQR,0.053505982148091664,0.05084500766436651,0.05702226985872847,0.044968952602027774,0.10098479315664254,0.049091273172528614,0.1047433620765962,DecisionTreeClassifier,100,"{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 20, 'max_features': 0.6, 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 0.1, 'min_weight_fraction_leaf': 0.0, 'monotonic_cst': None, 'random_state': None, 'splitter': 'best'}" +Aleatoric_Uncertainty,0.35318189936459926,0.3413540927984995,0.368811500898374,0.3031037696893525,0.6316921284417605,0.3271516798716977,0.6552902043882751,DecisionTreeClassifier,100,"{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 20, 'max_features': 0.6, 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 0.1, 'min_weight_fraction_leaf': 0.0, 'monotonic_cst': None, 'random_state': None, 'splitter': 'best'}" +Std,0.041588229003281706,0.04014981475236609,0.04348899069199162,0.035707999117036614,0.07429121098892864,0.03859272201556965,0.0763542646485458,DecisionTreeClassifier,100,"{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 20, 'max_features': 0.6, 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 0.1, 'min_weight_fraction_leaf': 0.0, 'monotonic_cst': None, 'random_state': None, 'splitter': 'best'}" +Overall_Uncertainty,0.37102753012884837,0.35860883223592943,0.3874379523444912,0.3189445800260836,0.6606875964732465,0.3439739430925971,0.6850131008829163,DecisionTreeClassifier,100,"{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 20, 'max_features': 0.6, 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 0.1, 'min_weight_fraction_leaf': 0.0, 'monotonic_cst': None, 'random_state': None, 'splitter': 'best'}" +Epistemic_Uncertainty,0.01784563076424911,0.017254739437429945,0.018626451446117187,0.015840810336731126,0.028995468031486005,0.016822263220899414,0.029722896494641216,DecisionTreeClassifier,100,"{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 20, 'max_features': 0.6, 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 0.1, 'min_weight_fraction_leaf': 0.0, 'monotonic_cst': None, 'random_state': None, 'splitter': 'best'}" +Jitter,0.03490286499215079,0.027672021511307245,0.044457908163265336,0.013944459235764642,0.15146333612309273,0.023045132413278664,0.1725244279529997,DecisionTreeClassifier,100,"{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 20, 'max_features': 0.6, 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 0.1, 'min_weight_fraction_leaf': 0.0, 'monotonic_cst': None, 'random_state': None, 'splitter': 'best'}" +Label_Stability,0.9493557692307693,0.9616385135135135,0.9331249999999999,0.9821100397050483,0.767192429022082,0.9684804177545692,0.7273939393939394,DecisionTreeClassifier,100,"{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 20, 'max_features': 0.6, 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 0.1, 'min_weight_fraction_leaf': 0.0, 'monotonic_cst': None, 'random_state': None, 'splitter': 'best'}" +TPR,0.9959763948497854,0.9986046511627907,0.9923954372623575,1.0,0.967741935483871,0.9991389207807119,0.9508196721311475,DecisionTreeClassifier,100,"{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 20, 'max_features': 0.6, 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 0.1, 'min_weight_fraction_leaf': 0.0, 'monotonic_cst': None, 'random_state': None, 'splitter': 'best'}" +TNR,0.1111111111111111,0.06880733944954129,0.1542056074766355,0.0,0.28402366863905326,0.04335260115606936,0.38372093023255816,DecisionTreeClassifier,100,"{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 20, 'max_features': 0.6, 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 0.1, 'min_weight_fraction_leaf': 0.0, 'monotonic_cst': None, 'random_state': None, 'splitter': 'best'}" +PPV,0.9062728825970222,0.9136170212765957,0.8963938179736691,0.9254112308564946,0.7880910683012259,0.9131689401888772,0.8140350877192982,DecisionTreeClassifier,100,"{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 20, 'max_features': 0.6, 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 0.1, 'min_weight_fraction_leaf': 0.0, 'monotonic_cst': None, 'random_state': None, 'splitter': 'best'}" +FNR,0.004023605150214592,0.0013953488372093023,0.0076045627376425855,0.0,0.03225806451612903,0.0008610792192881745,0.04918032786885246,DecisionTreeClassifier,100,"{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 20, 'max_features': 0.6, 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 0.1, 'min_weight_fraction_leaf': 0.0, 'monotonic_cst': None, 'random_state': None, 'splitter': 'best'}" +FPR,0.8888888888888888,0.9311926605504587,0.8457943925233645,1.0,0.7159763313609467,0.9566473988439307,0.6162790697674418,DecisionTreeClassifier,100,"{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 20, 'max_features': 0.6, 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 0.1, 'min_weight_fraction_leaf': 0.0, 'monotonic_cst': None, 'random_state': None, 'splitter': 'best'}" +Accuracy,0.9040865384615384,0.9130067567567568,0.8922991071428571,0.9254112308564946,0.7854889589905363,0.9127937336814621,0.803030303030303,DecisionTreeClassifier,100,"{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 20, 'max_features': 0.6, 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 0.1, 'min_weight_fraction_leaf': 0.0, 'monotonic_cst': None, 'random_state': None, 'splitter': 'best'}" +F1,0.9490095846645368,0.9542222222222222,0.9419548872180451,0.9612608631609957,0.8687258687258688,0.9542214912280702,0.8771266540642723,DecisionTreeClassifier,100,"{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 20, 'max_features': 0.6, 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 0.1, 'min_weight_fraction_leaf': 0.0, 'monotonic_cst': None, 'random_state': None, 'splitter': 'best'}" +Selection-Rate,0.9848557692307692,0.9923986486486487,0.9748883928571429,1.0,0.9006309148264984,0.9953002610966057,0.8636363636363636,DecisionTreeClassifier,100,"{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 20, 'max_features': 0.6, 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 0.1, 'min_weight_fraction_leaf': 0.0, 'monotonic_cst': None, 'random_state': None, 'splitter': 'best'}" +Sample_Size,4160.0,2368.0,1792.0,3526.0,634.0,3830.0,330.0,DecisionTreeClassifier,100,"{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 20, 'max_features': 0.6, 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 0.1, 'min_weight_fraction_leaf': 0.0, 'monotonic_cst': None, 'random_state': None, 'splitter': 'best'}" diff --git a/tests/files_for_tests/law_school_dataset_20k/python_3_10/Metrics_law_school_LogisticRegression_50_Estimators_202409013_220132.csv b/tests/files_for_tests/law_school_dataset_20k/python_3_10/Metrics_law_school_LogisticRegression_50_Estimators_202409013_220132.csv new file mode 100644 index 00000000..823681c2 --- /dev/null +++ b/tests/files_for_tests/law_school_dataset_20k/python_3_10/Metrics_law_school_LogisticRegression_50_Estimators_202409013_220132.csv @@ -0,0 +1,19 @@ +Metric,overall,male_priv,male_dis,race_priv,race_dis,male&race_priv,male&race_dis,Model_Name,Virny_Random_State,Model_Params +Statistical_Bias,0.13885050770015747,0.12866008788771274,0.15231641959517375,0.11307644203241121,0.28219333978923217,0.12620325356436243,0.2856353057004457,LogisticRegression,100,"{'C': 0.1, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'deprecated', 'n_jobs': None, 'penalty': 'l2', 'random_state': None, 'solver': 'lbfgs', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}" +Mean_Prediction,0.10452067758387495,0.09387206767499487,0.11859205496346649,0.07599895190606157,0.2631446598235752,0.0885215884645407,0.29020707554463304,LogisticRegression,100,"{'C': 0.1, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'deprecated', 'n_jobs': None, 'penalty': 'l2', 'random_state': None, 'solver': 'lbfgs', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}" +IQR,0.012301095217005956,0.011473328744497403,0.013394929484249399,0.009755741419230564,0.02645711649611641,0.011003116163415115,0.027365518778378423,LogisticRegression,100,"{'C': 0.1, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'deprecated', 'n_jobs': None, 'penalty': 'l2', 'random_state': None, 'solver': 'lbfgs', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}" +Aleatoric_Uncertainty,0.34187640247836215,0.32240015679448136,0.3676128699892046,0.2923757999920252,0.6171747058960655,0.31529238636428736,0.6504121046508058,LogisticRegression,100,"{'C': 0.1, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'deprecated', 'n_jobs': None, 'penalty': 'l2', 'random_state': None, 'solver': 'lbfgs', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}" +Std,0.009203351829509116,0.008639388543288133,0.00994858902915827,0.007305676473074892,0.019757300262927203,0.008266836789368464,0.02007260214386879,LogisticRegression,100,"{'C': 0.1, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'deprecated', 'n_jobs': None, 'penalty': 'l2', 'random_state': None, 'solver': 'lbfgs', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}" +Overall_Uncertainty,0.3427925391226378,0.3232693942336032,0.3685909805831478,0.29307883438932947,0.6192760137119832,0.3161116384666409,0.6524526891604193,LogisticRegression,100,"{'C': 0.1, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'deprecated', 'n_jobs': None, 'penalty': 'l2', 'random_state': None, 'solver': 'lbfgs', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}" +Epistemic_Uncertainty,0.0009161366442756447,0.0008692374391218172,0.0009781105939432044,0.0007030343973042918,0.0021013078159176635,0.0008192521023535626,0.00204058450961353,LogisticRegression,100,"{'C': 0.1, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'deprecated', 'n_jobs': None, 'penalty': 'l2', 'random_state': None, 'solver': 'lbfgs', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}" +Jitter,0.00885832025117741,0.00783645890788747,0.010208637026239083,0.0053438596085059135,0.028404043005214732,0.006967762561943823,0.030800247371675905,LogisticRegression,100,"{'C': 0.1, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'deprecated', 'n_jobs': None, 'penalty': 'l2', 'random_state': None, 'solver': 'lbfgs', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}" +Label_Stability,0.987423076923077,0.9890878378378378,0.9852232142857142,0.9923539421440726,0.9599999999999999,0.9902558746736293,0.9545454545454546,LogisticRegression,100,"{'C': 0.1, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'deprecated', 'n_jobs': None, 'penalty': 'l2', 'random_state': None, 'solver': 'lbfgs', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}" +TPR,0.9847103004291845,0.9897674418604652,0.9778200253485425,0.9944836040453571,0.9161290322580645,0.9911021814006888,0.8934426229508197,LogisticRegression,100,"{'C': 0.1, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'deprecated', 'n_jobs': None, 'penalty': 'l2', 'random_state': None, 'solver': 'lbfgs', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}" +TNR,0.24305555555555555,0.17889908256880735,0.308411214953271,0.11787072243346007,0.4378698224852071,0.16473988439306358,0.5581395348837209,LogisticRegression,100,"{'C': 0.1, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'deprecated', 'n_jobs': None, 'penalty': 'l2', 'random_state': None, 'solver': 'lbfgs', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}" +PPV,0.9182091045522761,0.9224100563502384,0.9124778237729154,0.9332758124820247,0.817658349328215,0.9227685729556387,0.8515625,LogisticRegression,100,"{'C': 0.1, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'deprecated', 'n_jobs': None, 'penalty': 'l2', 'random_state': None, 'solver': 'lbfgs', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}" +FNR,0.01528969957081545,0.010232558139534883,0.022179974651457542,0.0055163959546429666,0.08387096774193549,0.008897818599311137,0.10655737704918032,LogisticRegression,100,"{'C': 0.1, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'deprecated', 'n_jobs': None, 'penalty': 'l2', 'random_state': None, 'solver': 'lbfgs', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}" +FPR,0.7569444444444444,0.8211009174311926,0.6915887850467289,0.8821292775665399,0.5621301775147929,0.8352601156069365,0.4418604651162791,LogisticRegression,100,"{'C': 0.1, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'deprecated', 'n_jobs': None, 'penalty': 'l2', 'random_state': None, 'solver': 'lbfgs', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}" +Accuracy,0.9076923076923077,0.9151182432432432,0.8978794642857143,0.9290981281905842,0.7886435331230284,0.9164490861618799,0.806060606060606,LogisticRegression,100,"{'C': 0.1, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'deprecated', 'n_jobs': None, 'penalty': 'l2', 'random_state': None, 'solver': 'lbfgs', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}" +F1,0.9502976960911209,0.9549024007179717,0.9440195778525543,0.9629080118694362,0.8640973630831643,0.9557154719070025,0.872,LogisticRegression,100,"{'C': 0.1, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'deprecated', 'n_jobs': None, 'penalty': 'l2', 'random_state': None, 'solver': 'lbfgs', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}" +Selection-Rate,0.9610576923076923,0.9742398648648649,0.9436383928571429,0.9861032331253545,0.8217665615141956,0.977023498694517,0.7757575757575758,LogisticRegression,100,"{'C': 0.1, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'deprecated', 'n_jobs': None, 'penalty': 'l2', 'random_state': None, 'solver': 'lbfgs', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}" +Sample_Size,4160.0,2368.0,1792.0,3526.0,634.0,3830.0,330.0,LogisticRegression,100,"{'C': 0.1, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'deprecated', 'n_jobs': None, 'penalty': 'l2', 'random_state': None, 'solver': 'lbfgs', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}" diff --git a/tests/files_for_tests/law_school_dataset_20k/python_3_11/Metrics_law_school_DecisionTreeClassifier_50_Estimators_20240913.csv b/tests/files_for_tests/law_school_dataset_20k/python_3_11/Metrics_law_school_DecisionTreeClassifier_50_Estimators_20240913.csv new file mode 100644 index 00000000..6e242bfa --- /dev/null +++ b/tests/files_for_tests/law_school_dataset_20k/python_3_11/Metrics_law_school_DecisionTreeClassifier_50_Estimators_20240913.csv @@ -0,0 +1,19 @@ +Metric,overall,male_priv,male_dis,race_priv,race_dis,male&race_priv,male&race_dis,Model_Name,Virny_Random_State,Model_Params +Std,0.041588229003281706,0.04014981475236609,0.04348899069199162,0.035707999117036614,0.07429121098892864,0.03859272201556965,0.0763542646485458,DecisionTreeClassifier,100,"{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 20, 'max_features': 0.6, 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 0.1, 'min_weight_fraction_leaf': 0.0, 'random_state': None, 'splitter': 'best'}" +IQR,0.053505982148091664,0.05084500766436651,0.05702226985872847,0.044968952602027774,0.10098479315664254,0.049091273172528614,0.1047433620765962,DecisionTreeClassifier,100,"{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 20, 'max_features': 0.6, 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 0.1, 'min_weight_fraction_leaf': 0.0, 'random_state': None, 'splitter': 'best'}" +Aleatoric_Uncertainty,0.35318189936459926,0.3413540927984995,0.368811500898374,0.3031037696893525,0.6316921284417605,0.3271516798716977,0.6552902043882751,DecisionTreeClassifier,100,"{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 20, 'max_features': 0.6, 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 0.1, 'min_weight_fraction_leaf': 0.0, 'random_state': None, 'splitter': 'best'}" +Statistical_Bias,0.14871459501539216,0.13922538692994388,0.1612539056997345,0.12087562594490028,0.3035414166913454,0.13498160237084172,0.3081005399506289,DecisionTreeClassifier,100,"{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 20, 'max_features': 0.6, 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 0.1, 'min_weight_fraction_leaf': 0.0, 'random_state': None, 'splitter': 'best'}" +Mean_Prediction,0.10730163589001154,0.10127383420207314,0.11526694526335872,0.08243941922626423,0.2455732067991173,0.0941339892140465,0.26012614125045447,DecisionTreeClassifier,100,"{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 20, 'max_features': 0.6, 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 0.1, 'min_weight_fraction_leaf': 0.0, 'random_state': None, 'splitter': 'best'}" +Overall_Uncertainty,0.37102753012884837,0.35860883223592943,0.3874379523444912,0.3189445800260836,0.6606875964732465,0.3439739430925971,0.6850131008829163,DecisionTreeClassifier,100,"{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 20, 'max_features': 0.6, 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 0.1, 'min_weight_fraction_leaf': 0.0, 'random_state': None, 'splitter': 'best'}" +Epistemic_Uncertainty,0.01784563076424911,0.017254739437429945,0.018626451446117187,0.015840810336731126,0.028995468031486005,0.016822263220899414,0.029722896494641216,DecisionTreeClassifier,100,"{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 20, 'max_features': 0.6, 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 0.1, 'min_weight_fraction_leaf': 0.0, 'random_state': None, 'splitter': 'best'}" +Jitter,0.03490286499215079,0.027672021511307245,0.044457908163265336,0.013944459235764642,0.15146333612309273,0.023045132413278664,0.1725244279529997,DecisionTreeClassifier,100,"{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 20, 'max_features': 0.6, 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 0.1, 'min_weight_fraction_leaf': 0.0, 'random_state': None, 'splitter': 'best'}" +Label_Stability,0.9493557692307693,0.9616385135135135,0.9331249999999999,0.9821100397050483,0.767192429022082,0.9684804177545692,0.7273939393939394,DecisionTreeClassifier,100,"{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 20, 'max_features': 0.6, 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 0.1, 'min_weight_fraction_leaf': 0.0, 'random_state': None, 'splitter': 'best'}" +TPR,0.9959763948497854,0.9986046511627907,0.9923954372623575,1.0,0.967741935483871,0.9991389207807119,0.9508196721311475,DecisionTreeClassifier,100,"{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 20, 'max_features': 0.6, 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 0.1, 'min_weight_fraction_leaf': 0.0, 'random_state': None, 'splitter': 'best'}" +TNR,0.1111111111111111,0.06880733944954129,0.1542056074766355,0.0,0.28402366863905326,0.04335260115606936,0.38372093023255816,DecisionTreeClassifier,100,"{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 20, 'max_features': 0.6, 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 0.1, 'min_weight_fraction_leaf': 0.0, 'random_state': None, 'splitter': 'best'}" +PPV,0.9062728825970222,0.9136170212765957,0.8963938179736691,0.9254112308564946,0.7880910683012259,0.9131689401888772,0.8140350877192982,DecisionTreeClassifier,100,"{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 20, 'max_features': 0.6, 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 0.1, 'min_weight_fraction_leaf': 0.0, 'random_state': None, 'splitter': 'best'}" +FNR,0.004023605150214592,0.0013953488372093023,0.0076045627376425855,0.0,0.03225806451612903,0.0008610792192881745,0.04918032786885246,DecisionTreeClassifier,100,"{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 20, 'max_features': 0.6, 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 0.1, 'min_weight_fraction_leaf': 0.0, 'random_state': None, 'splitter': 'best'}" +FPR,0.8888888888888888,0.9311926605504587,0.8457943925233645,1.0,0.7159763313609467,0.9566473988439307,0.6162790697674418,DecisionTreeClassifier,100,"{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 20, 'max_features': 0.6, 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 0.1, 'min_weight_fraction_leaf': 0.0, 'random_state': None, 'splitter': 'best'}" +Accuracy,0.9040865384615384,0.9130067567567568,0.8922991071428571,0.9254112308564946,0.7854889589905363,0.9127937336814621,0.803030303030303,DecisionTreeClassifier,100,"{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 20, 'max_features': 0.6, 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 0.1, 'min_weight_fraction_leaf': 0.0, 'random_state': None, 'splitter': 'best'}" +F1,0.9490095846645368,0.9542222222222222,0.9419548872180451,0.9612608631609957,0.8687258687258688,0.9542214912280702,0.8771266540642723,DecisionTreeClassifier,100,"{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 20, 'max_features': 0.6, 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 0.1, 'min_weight_fraction_leaf': 0.0, 'random_state': None, 'splitter': 'best'}" +Selection-Rate,0.9848557692307692,0.9923986486486487,0.9748883928571429,1.0,0.9006309148264984,0.9953002610966057,0.8636363636363636,DecisionTreeClassifier,100,"{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 20, 'max_features': 0.6, 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 0.1, 'min_weight_fraction_leaf': 0.0, 'random_state': None, 'splitter': 'best'}" +Sample_Size,4160.0,2368.0,1792.0,3526.0,634.0,3830.0,330.0,DecisionTreeClassifier,100,"{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 20, 'max_features': 0.6, 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 0.1, 'min_weight_fraction_leaf': 0.0, 'random_state': None, 'splitter': 'best'}" diff --git a/tests/files_for_tests/law_school_dataset_20k/python_3_11/Metrics_law_school_LogisticRegression_50_Estimators_202409013_220132.csv b/tests/files_for_tests/law_school_dataset_20k/python_3_11/Metrics_law_school_LogisticRegression_50_Estimators_202409013_220132.csv new file mode 100644 index 00000000..f9a516f8 --- /dev/null +++ b/tests/files_for_tests/law_school_dataset_20k/python_3_11/Metrics_law_school_LogisticRegression_50_Estimators_202409013_220132.csv @@ -0,0 +1,19 @@ +Metric,overall,male_priv,male_dis,race_priv,race_dis,male&race_priv,male&race_dis,Model_Name,Virny_Random_State,Model_Params +Std,0.00917147943187975,0.008607050335196386,0.00991733216678277,0.007286748976074028,0.01965343461669202,0.008239377682845418,0.01998950882218732,LogisticRegression,100,"{'C': 0.1, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': None, 'solver': 'lbfgs', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}" +IQR,0.012256803023578407,0.0114453771719569,0.013329044327506827,0.009739371815789648,0.026257532422100754,0.010976514379656195,0.027115910618190745,LogisticRegression,100,"{'C': 0.1, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': None, 'solver': 'lbfgs', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}" +Aleatoric_Uncertainty,0.34187256444638636,0.3224331953447354,0.3675603021878538,0.2923502561200833,0.6172915852011884,0.3152813861742172,0.6504913910597441,LogisticRegression,100,"{'C': 0.1, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': None, 'solver': 'lbfgs', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}" +Statistical_Bias,0.13884987532254198,0.12866752312909457,0.15230512643531174,0.11307241003118768,0.28221161446657245,0.12620186366971659,0.28564346511139427,LogisticRegression,100,"{'C': 0.1, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': None, 'solver': 'lbfgs', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}" +Mean_Prediction,0.10451562164187415,0.09389099362293032,0.11855530866690708,0.07598923254310201,0.26316553956343663,0.08851874474140387,0.2901763444564233,LogisticRegression,100,"{'C': 0.1, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': None, 'solver': 'lbfgs', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}" +Overall_Uncertainty,0.3427815142556397,0.3232951664439477,0.3685313310068042,0.2930493994498858,0.6193673767242337,0.31609460569791553,0.6525113923649841,LogisticRegression,100,"{'C': 0.1, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': None, 'solver': 'lbfgs', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}" +Epistemic_Uncertainty,0.0009089498092533232,0.0008619710992122664,0.0009710288189503924,0.0006991433298024763,0.0020757915230452673,0.0008132195236983386,0.002020001305240049,LogisticRegression,100,"{'C': 0.1, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': None, 'solver': 'lbfgs', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}" +Jitter,0.008828885400313994,0.007884376723662437,0.010076986151603502,0.005247780337319282,0.02874525204403535,0.006921084883039359,0.030970933828076638,LogisticRegression,100,"{'C': 0.1, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': None, 'solver': 'lbfgs', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}" +Label_Stability,0.9874423076923078,0.9889695945945947,0.9854241071428572,0.9924333522404991,0.9596845425867508,0.9902872062663186,0.9544242424242425,LogisticRegression,100,"{'C': 0.1, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': None, 'solver': 'lbfgs', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}" +TPR,0.9849785407725322,0.9897674418604652,0.9784537389100126,0.9947900704872816,0.9161290322580645,0.9913892078071183,0.8934426229508197,LogisticRegression,100,"{'C': 0.1, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': None, 'solver': 'lbfgs', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}" +TNR,0.24074074074074073,0.17889908256880735,0.3037383177570093,0.11787072243346007,0.4319526627218935,0.16473988439306358,0.5465116279069767,LogisticRegression,100,"{'C': 0.1, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': None, 'solver': 'lbfgs', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}" +PPV,0.918,0.9224100563502384,0.9119905493207324,0.9332949971247844,0.8160919540229885,0.9227892065188351,0.8482490272373541,LogisticRegression,100,"{'C': 0.1, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': None, 'solver': 'lbfgs', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}" +FNR,0.015021459227467811,0.010232558139534883,0.021546261089987327,0.005209929512718358,0.08387096774193549,0.008610792192881744,0.10655737704918032,LogisticRegression,100,"{'C': 0.1, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': None, 'solver': 'lbfgs', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}" +FPR,0.7592592592592593,0.8211009174311926,0.6962616822429907,0.8821292775665399,0.5680473372781065,0.8352601156069365,0.45348837209302323,LogisticRegression,100,"{'C': 0.1, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': None, 'solver': 'lbfgs', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}" +Accuracy,0.9076923076923077,0.9151182432432432,0.8978794642857143,0.9293817356778219,0.7870662460567823,0.9167101827676241,0.803030303030303,LogisticRegression,100,"{'C': 0.1, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': None, 'solver': 'lbfgs', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}" +F1,0.9503105590062112,0.9549024007179717,0.9440538061754815,0.9630618602581219,0.8632218844984803,0.9558599695585996,0.8702594810379242,LogisticRegression,100,"{'C': 0.1, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': None, 'solver': 'lbfgs', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}" +Selection-Rate,0.9615384615384616,0.9742398648648649,0.9447544642857143,0.9863868406125922,0.8233438485804416,0.9772845953002611,0.7787878787878788,LogisticRegression,100,"{'C': 0.1, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': None, 'solver': 'lbfgs', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}" +Sample_Size,4160.0,2368.0,1792.0,3526.0,634.0,3830.0,330.0,LogisticRegression,100,"{'C': 0.1, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': None, 'solver': 'lbfgs', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}" diff --git a/tests/files_for_tests/law_school_dataset_20k/python_3_9/Metrics_law_school_DecisionTreeClassifier_50_Estimators_20240913.csv b/tests/files_for_tests/law_school_dataset_20k/python_3_9/Metrics_law_school_DecisionTreeClassifier_50_Estimators_20240913.csv new file mode 100644 index 00000000..e3e92fef --- /dev/null +++ b/tests/files_for_tests/law_school_dataset_20k/python_3_9/Metrics_law_school_DecisionTreeClassifier_50_Estimators_20240913.csv @@ -0,0 +1,19 @@ +Metric,overall,male_priv,male_dis,race_priv,race_dis,male&race_priv,male&race_dis,Model_Name,Virny_Random_State,Model_Params +Std,0.041588229003281706,0.04014981475236609,0.04348899069199162,0.035707999117036614,0.07429121098892864,0.03859272201556965,0.0763542646485458,DecisionTreeClassifier,100,"{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 20, 'max_features': 0.6, 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 0.1, 'min_weight_fraction_leaf': 0.0, 'monotonic_cst': None, 'random_state': None, 'splitter': 'best'}" +Statistical_Bias,0.14871459501539216,0.13922538692994388,0.1612539056997345,0.12087562594490028,0.3035414166913454,0.13498160237084172,0.3081005399506289,DecisionTreeClassifier,100,"{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 20, 'max_features': 0.6, 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 0.1, 'min_weight_fraction_leaf': 0.0, 'monotonic_cst': None, 'random_state': None, 'splitter': 'best'}" +Mean_Prediction,0.10730163589001154,0.10127383420207314,0.11526694526335872,0.08243941922626423,0.2455732067991173,0.0941339892140465,0.26012614125045447,DecisionTreeClassifier,100,"{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 20, 'max_features': 0.6, 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 0.1, 'min_weight_fraction_leaf': 0.0, 'monotonic_cst': None, 'random_state': None, 'splitter': 'best'}" +Overall_Uncertainty,0.37102753012884837,0.35860883223592943,0.3874379523444912,0.3189445800260836,0.6606875964732465,0.3439739430925971,0.6850131008829163,DecisionTreeClassifier,100,"{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 20, 'max_features': 0.6, 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 0.1, 'min_weight_fraction_leaf': 0.0, 'monotonic_cst': None, 'random_state': None, 'splitter': 'best'}" +IQR,0.053505982148091664,0.05084500766436651,0.05702226985872847,0.044968952602027774,0.10098479315664254,0.049091273172528614,0.1047433620765962,DecisionTreeClassifier,100,"{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 20, 'max_features': 0.6, 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 0.1, 'min_weight_fraction_leaf': 0.0, 'monotonic_cst': None, 'random_state': None, 'splitter': 'best'}" +Aleatoric_Uncertainty,0.35318189936459926,0.3413540927984995,0.368811500898374,0.3031037696893525,0.6316921284417605,0.3271516798716977,0.6552902043882751,DecisionTreeClassifier,100,"{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 20, 'max_features': 0.6, 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 0.1, 'min_weight_fraction_leaf': 0.0, 'monotonic_cst': None, 'random_state': None, 'splitter': 'best'}" +Epistemic_Uncertainty,0.01784563076424911,0.017254739437429945,0.018626451446117187,0.015840810336731126,0.028995468031486005,0.016822263220899414,0.029722896494641216,DecisionTreeClassifier,100,"{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 20, 'max_features': 0.6, 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 0.1, 'min_weight_fraction_leaf': 0.0, 'monotonic_cst': None, 'random_state': None, 'splitter': 'best'}" +Jitter,0.03490286499215079,0.027672021511307245,0.044457908163265336,0.013944459235764642,0.15146333612309273,0.023045132413278664,0.1725244279529997,DecisionTreeClassifier,100,"{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 20, 'max_features': 0.6, 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 0.1, 'min_weight_fraction_leaf': 0.0, 'monotonic_cst': None, 'random_state': None, 'splitter': 'best'}" +Label_Stability,0.9493557692307693,0.9616385135135135,0.9331249999999999,0.9821100397050483,0.767192429022082,0.9684804177545692,0.7273939393939394,DecisionTreeClassifier,100,"{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 20, 'max_features': 0.6, 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 0.1, 'min_weight_fraction_leaf': 0.0, 'monotonic_cst': None, 'random_state': None, 'splitter': 'best'}" +TPR,0.9959763948497854,0.9986046511627907,0.9923954372623575,1.0,0.967741935483871,0.9991389207807119,0.9508196721311475,DecisionTreeClassifier,100,"{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 20, 'max_features': 0.6, 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 0.1, 'min_weight_fraction_leaf': 0.0, 'monotonic_cst': None, 'random_state': None, 'splitter': 'best'}" +TNR,0.1111111111111111,0.06880733944954129,0.1542056074766355,0.0,0.28402366863905326,0.04335260115606936,0.38372093023255816,DecisionTreeClassifier,100,"{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 20, 'max_features': 0.6, 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 0.1, 'min_weight_fraction_leaf': 0.0, 'monotonic_cst': None, 'random_state': None, 'splitter': 'best'}" +PPV,0.9062728825970222,0.9136170212765957,0.8963938179736691,0.9254112308564946,0.7880910683012259,0.9131689401888772,0.8140350877192982,DecisionTreeClassifier,100,"{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 20, 'max_features': 0.6, 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 0.1, 'min_weight_fraction_leaf': 0.0, 'monotonic_cst': None, 'random_state': None, 'splitter': 'best'}" +FNR,0.004023605150214592,0.0013953488372093023,0.0076045627376425855,0.0,0.03225806451612903,0.0008610792192881745,0.04918032786885246,DecisionTreeClassifier,100,"{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 20, 'max_features': 0.6, 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 0.1, 'min_weight_fraction_leaf': 0.0, 'monotonic_cst': None, 'random_state': None, 'splitter': 'best'}" +FPR,0.8888888888888888,0.9311926605504587,0.8457943925233645,1.0,0.7159763313609467,0.9566473988439307,0.6162790697674418,DecisionTreeClassifier,100,"{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 20, 'max_features': 0.6, 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 0.1, 'min_weight_fraction_leaf': 0.0, 'monotonic_cst': None, 'random_state': None, 'splitter': 'best'}" +Accuracy,0.9040865384615384,0.9130067567567568,0.8922991071428571,0.9254112308564946,0.7854889589905363,0.9127937336814621,0.803030303030303,DecisionTreeClassifier,100,"{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 20, 'max_features': 0.6, 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 0.1, 'min_weight_fraction_leaf': 0.0, 'monotonic_cst': None, 'random_state': None, 'splitter': 'best'}" +F1,0.9490095846645368,0.9542222222222222,0.9419548872180451,0.9612608631609957,0.8687258687258688,0.9542214912280702,0.8771266540642723,DecisionTreeClassifier,100,"{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 20, 'max_features': 0.6, 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 0.1, 'min_weight_fraction_leaf': 0.0, 'monotonic_cst': None, 'random_state': None, 'splitter': 'best'}" +Selection-Rate,0.9848557692307692,0.9923986486486487,0.9748883928571429,1.0,0.9006309148264984,0.9953002610966057,0.8636363636363636,DecisionTreeClassifier,100,"{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 20, 'max_features': 0.6, 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 0.1, 'min_weight_fraction_leaf': 0.0, 'monotonic_cst': None, 'random_state': None, 'splitter': 'best'}" +Sample_Size,4160.0,2368.0,1792.0,3526.0,634.0,3830.0,330.0,DecisionTreeClassifier,100,"{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 20, 'max_features': 0.6, 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 0.1, 'min_weight_fraction_leaf': 0.0, 'monotonic_cst': None, 'random_state': None, 'splitter': 'best'}" diff --git a/tests/files_for_tests/law_school_dataset_20k/python_3_9/Metrics_law_school_LogisticRegression_50_Estimators_202409013_220132.csv b/tests/files_for_tests/law_school_dataset_20k/python_3_9/Metrics_law_school_LogisticRegression_50_Estimators_202409013_220132.csv new file mode 100644 index 00000000..15e2dd82 --- /dev/null +++ b/tests/files_for_tests/law_school_dataset_20k/python_3_9/Metrics_law_school_LogisticRegression_50_Estimators_202409013_220132.csv @@ -0,0 +1,19 @@ +Metric,overall,male_priv,male_dis,race_priv,race_dis,male&race_priv,male&race_dis,Model_Name,Virny_Random_State,Model_Params +Std,0.009203351829509116,0.008639388543288133,0.00994858902915827,0.007305676473074892,0.019757300262927203,0.008266836789368464,0.02007260214386879,LogisticRegression,100,"{'C': 0.1, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'deprecated', 'n_jobs': None, 'penalty': 'l2', 'random_state': None, 'solver': 'lbfgs', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}" +Statistical_Bias,0.13885050770015747,0.12866008788771274,0.15231641959517375,0.11307644203241121,0.28219333978923217,0.12620325356436243,0.2856353057004457,LogisticRegression,100,"{'C': 0.1, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'deprecated', 'n_jobs': None, 'penalty': 'l2', 'random_state': None, 'solver': 'lbfgs', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}" +Mean_Prediction,0.10452067758387495,0.09387206767499487,0.11859205496346649,0.07599895190606157,0.2631446598235752,0.0885215884645407,0.29020707554463304,LogisticRegression,100,"{'C': 0.1, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'deprecated', 'n_jobs': None, 'penalty': 'l2', 'random_state': None, 'solver': 'lbfgs', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}" +Overall_Uncertainty,0.3427925391226378,0.3232693942336032,0.3685909805831478,0.29307883438932947,0.6192760137119832,0.3161116384666409,0.6524526891604193,LogisticRegression,100,"{'C': 0.1, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'deprecated', 'n_jobs': None, 'penalty': 'l2', 'random_state': None, 'solver': 'lbfgs', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}" +IQR,0.012301095217005956,0.011473328744497403,0.013394929484249399,0.009755741419230564,0.02645711649611641,0.011003116163415115,0.027365518778378423,LogisticRegression,100,"{'C': 0.1, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'deprecated', 'n_jobs': None, 'penalty': 'l2', 'random_state': None, 'solver': 'lbfgs', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}" +Aleatoric_Uncertainty,0.34187640247836215,0.32240015679448136,0.3676128699892046,0.2923757999920252,0.6171747058960655,0.31529238636428736,0.6504121046508058,LogisticRegression,100,"{'C': 0.1, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'deprecated', 'n_jobs': None, 'penalty': 'l2', 'random_state': None, 'solver': 'lbfgs', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}" +Epistemic_Uncertainty,0.0009161366442756447,0.0008692374391218172,0.0009781105939432044,0.0007030343973042918,0.0021013078159176635,0.0008192521023535626,0.00204058450961353,LogisticRegression,100,"{'C': 0.1, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'deprecated', 'n_jobs': None, 'penalty': 'l2', 'random_state': None, 'solver': 'lbfgs', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}" +Jitter,0.00885832025117741,0.00783645890788747,0.010208637026239083,0.0053438596085059135,0.028404043005214732,0.006967762561943823,0.030800247371675905,LogisticRegression,100,"{'C': 0.1, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'deprecated', 'n_jobs': None, 'penalty': 'l2', 'random_state': None, 'solver': 'lbfgs', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}" +Label_Stability,0.987423076923077,0.9890878378378378,0.9852232142857142,0.9923539421440726,0.9599999999999999,0.9902558746736293,0.9545454545454546,LogisticRegression,100,"{'C': 0.1, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'deprecated', 'n_jobs': None, 'penalty': 'l2', 'random_state': None, 'solver': 'lbfgs', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}" +TPR,0.9847103004291845,0.9897674418604652,0.9778200253485425,0.9944836040453571,0.9161290322580645,0.9911021814006888,0.8934426229508197,LogisticRegression,100,"{'C': 0.1, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'deprecated', 'n_jobs': None, 'penalty': 'l2', 'random_state': None, 'solver': 'lbfgs', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}" +TNR,0.24305555555555555,0.17889908256880735,0.308411214953271,0.11787072243346007,0.4378698224852071,0.16473988439306358,0.5581395348837209,LogisticRegression,100,"{'C': 0.1, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'deprecated', 'n_jobs': None, 'penalty': 'l2', 'random_state': None, 'solver': 'lbfgs', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}" +PPV,0.9182091045522761,0.9224100563502384,0.9124778237729154,0.9332758124820247,0.817658349328215,0.9227685729556387,0.8515625,LogisticRegression,100,"{'C': 0.1, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'deprecated', 'n_jobs': None, 'penalty': 'l2', 'random_state': None, 'solver': 'lbfgs', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}" +FNR,0.01528969957081545,0.010232558139534883,0.022179974651457542,0.0055163959546429666,0.08387096774193549,0.008897818599311137,0.10655737704918032,LogisticRegression,100,"{'C': 0.1, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'deprecated', 'n_jobs': None, 'penalty': 'l2', 'random_state': None, 'solver': 'lbfgs', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}" +FPR,0.7569444444444444,0.8211009174311926,0.6915887850467289,0.8821292775665399,0.5621301775147929,0.8352601156069365,0.4418604651162791,LogisticRegression,100,"{'C': 0.1, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'deprecated', 'n_jobs': None, 'penalty': 'l2', 'random_state': None, 'solver': 'lbfgs', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}" +Accuracy,0.9076923076923077,0.9151182432432432,0.8978794642857143,0.9290981281905842,0.7886435331230284,0.9164490861618799,0.806060606060606,LogisticRegression,100,"{'C': 0.1, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'deprecated', 'n_jobs': None, 'penalty': 'l2', 'random_state': None, 'solver': 'lbfgs', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}" +F1,0.9502976960911209,0.9549024007179717,0.9440195778525543,0.9629080118694362,0.8640973630831643,0.9557154719070025,0.872,LogisticRegression,100,"{'C': 0.1, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'deprecated', 'n_jobs': None, 'penalty': 'l2', 'random_state': None, 'solver': 'lbfgs', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}" +Selection-Rate,0.9610576923076923,0.9742398648648649,0.9436383928571429,0.9861032331253545,0.8217665615141956,0.977023498694517,0.7757575757575758,LogisticRegression,100,"{'C': 0.1, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'deprecated', 'n_jobs': None, 'penalty': 'l2', 'random_state': None, 'solver': 'lbfgs', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}" +Sample_Size,4160.0,2368.0,1792.0,3526.0,634.0,3830.0,330.0,LogisticRegression,100,"{'C': 0.1, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'deprecated', 'n_jobs': None, 'penalty': 'l2', 'random_state': None, 'solver': 'lbfgs', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}" From e043c38bbde903eb54f8bbe76973361b55a455f9 Mon Sep 17 00:00:00 2001 From: Denys Herasymuk Date: Sat, 14 Sep 2024 12:15:33 +0300 Subject: [PATCH 22/27] Fixed tests for Python 3.11 --- ...nTreeClassifier_50_Estimators_20240913.csv | 19 ------------------- ...ression_50_Estimators_202409013_220132.csv | 19 ------------------- .../test_multiple_models_api.py | 5 +++-- 3 files changed, 3 insertions(+), 40 deletions(-) delete mode 100644 tests/files_for_tests/law_school_dataset_20k/python_3_11/Metrics_law_school_DecisionTreeClassifier_50_Estimators_20240913.csv delete mode 100644 tests/files_for_tests/law_school_dataset_20k/python_3_11/Metrics_law_school_LogisticRegression_50_Estimators_202409013_220132.csv diff --git a/tests/files_for_tests/law_school_dataset_20k/python_3_11/Metrics_law_school_DecisionTreeClassifier_50_Estimators_20240913.csv b/tests/files_for_tests/law_school_dataset_20k/python_3_11/Metrics_law_school_DecisionTreeClassifier_50_Estimators_20240913.csv deleted file mode 100644 index 6e242bfa..00000000 --- a/tests/files_for_tests/law_school_dataset_20k/python_3_11/Metrics_law_school_DecisionTreeClassifier_50_Estimators_20240913.csv +++ /dev/null @@ -1,19 +0,0 @@ -Metric,overall,male_priv,male_dis,race_priv,race_dis,male&race_priv,male&race_dis,Model_Name,Virny_Random_State,Model_Params -Std,0.041588229003281706,0.04014981475236609,0.04348899069199162,0.035707999117036614,0.07429121098892864,0.03859272201556965,0.0763542646485458,DecisionTreeClassifier,100,"{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 20, 'max_features': 0.6, 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 0.1, 'min_weight_fraction_leaf': 0.0, 'random_state': None, 'splitter': 'best'}" -IQR,0.053505982148091664,0.05084500766436651,0.05702226985872847,0.044968952602027774,0.10098479315664254,0.049091273172528614,0.1047433620765962,DecisionTreeClassifier,100,"{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 20, 'max_features': 0.6, 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 0.1, 'min_weight_fraction_leaf': 0.0, 'random_state': None, 'splitter': 'best'}" -Aleatoric_Uncertainty,0.35318189936459926,0.3413540927984995,0.368811500898374,0.3031037696893525,0.6316921284417605,0.3271516798716977,0.6552902043882751,DecisionTreeClassifier,100,"{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 20, 'max_features': 0.6, 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 0.1, 'min_weight_fraction_leaf': 0.0, 'random_state': None, 'splitter': 'best'}" -Statistical_Bias,0.14871459501539216,0.13922538692994388,0.1612539056997345,0.12087562594490028,0.3035414166913454,0.13498160237084172,0.3081005399506289,DecisionTreeClassifier,100,"{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 20, 'max_features': 0.6, 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 0.1, 'min_weight_fraction_leaf': 0.0, 'random_state': None, 'splitter': 'best'}" -Mean_Prediction,0.10730163589001154,0.10127383420207314,0.11526694526335872,0.08243941922626423,0.2455732067991173,0.0941339892140465,0.26012614125045447,DecisionTreeClassifier,100,"{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 20, 'max_features': 0.6, 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 0.1, 'min_weight_fraction_leaf': 0.0, 'random_state': None, 'splitter': 'best'}" -Overall_Uncertainty,0.37102753012884837,0.35860883223592943,0.3874379523444912,0.3189445800260836,0.6606875964732465,0.3439739430925971,0.6850131008829163,DecisionTreeClassifier,100,"{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 20, 'max_features': 0.6, 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 0.1, 'min_weight_fraction_leaf': 0.0, 'random_state': None, 'splitter': 'best'}" -Epistemic_Uncertainty,0.01784563076424911,0.017254739437429945,0.018626451446117187,0.015840810336731126,0.028995468031486005,0.016822263220899414,0.029722896494641216,DecisionTreeClassifier,100,"{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 20, 'max_features': 0.6, 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 0.1, 'min_weight_fraction_leaf': 0.0, 'random_state': None, 'splitter': 'best'}" -Jitter,0.03490286499215079,0.027672021511307245,0.044457908163265336,0.013944459235764642,0.15146333612309273,0.023045132413278664,0.1725244279529997,DecisionTreeClassifier,100,"{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 20, 'max_features': 0.6, 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 0.1, 'min_weight_fraction_leaf': 0.0, 'random_state': None, 'splitter': 'best'}" -Label_Stability,0.9493557692307693,0.9616385135135135,0.9331249999999999,0.9821100397050483,0.767192429022082,0.9684804177545692,0.7273939393939394,DecisionTreeClassifier,100,"{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 20, 'max_features': 0.6, 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 0.1, 'min_weight_fraction_leaf': 0.0, 'random_state': None, 'splitter': 'best'}" -TPR,0.9959763948497854,0.9986046511627907,0.9923954372623575,1.0,0.967741935483871,0.9991389207807119,0.9508196721311475,DecisionTreeClassifier,100,"{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 20, 'max_features': 0.6, 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 0.1, 'min_weight_fraction_leaf': 0.0, 'random_state': None, 'splitter': 'best'}" -TNR,0.1111111111111111,0.06880733944954129,0.1542056074766355,0.0,0.28402366863905326,0.04335260115606936,0.38372093023255816,DecisionTreeClassifier,100,"{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 20, 'max_features': 0.6, 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 0.1, 'min_weight_fraction_leaf': 0.0, 'random_state': None, 'splitter': 'best'}" -PPV,0.9062728825970222,0.9136170212765957,0.8963938179736691,0.9254112308564946,0.7880910683012259,0.9131689401888772,0.8140350877192982,DecisionTreeClassifier,100,"{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 20, 'max_features': 0.6, 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 0.1, 'min_weight_fraction_leaf': 0.0, 'random_state': None, 'splitter': 'best'}" -FNR,0.004023605150214592,0.0013953488372093023,0.0076045627376425855,0.0,0.03225806451612903,0.0008610792192881745,0.04918032786885246,DecisionTreeClassifier,100,"{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 20, 'max_features': 0.6, 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 0.1, 'min_weight_fraction_leaf': 0.0, 'random_state': None, 'splitter': 'best'}" -FPR,0.8888888888888888,0.9311926605504587,0.8457943925233645,1.0,0.7159763313609467,0.9566473988439307,0.6162790697674418,DecisionTreeClassifier,100,"{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 20, 'max_features': 0.6, 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 0.1, 'min_weight_fraction_leaf': 0.0, 'random_state': None, 'splitter': 'best'}" -Accuracy,0.9040865384615384,0.9130067567567568,0.8922991071428571,0.9254112308564946,0.7854889589905363,0.9127937336814621,0.803030303030303,DecisionTreeClassifier,100,"{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 20, 'max_features': 0.6, 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 0.1, 'min_weight_fraction_leaf': 0.0, 'random_state': None, 'splitter': 'best'}" -F1,0.9490095846645368,0.9542222222222222,0.9419548872180451,0.9612608631609957,0.8687258687258688,0.9542214912280702,0.8771266540642723,DecisionTreeClassifier,100,"{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 20, 'max_features': 0.6, 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 0.1, 'min_weight_fraction_leaf': 0.0, 'random_state': None, 'splitter': 'best'}" -Selection-Rate,0.9848557692307692,0.9923986486486487,0.9748883928571429,1.0,0.9006309148264984,0.9953002610966057,0.8636363636363636,DecisionTreeClassifier,100,"{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 20, 'max_features': 0.6, 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 0.1, 'min_weight_fraction_leaf': 0.0, 'random_state': None, 'splitter': 'best'}" -Sample_Size,4160.0,2368.0,1792.0,3526.0,634.0,3830.0,330.0,DecisionTreeClassifier,100,"{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 20, 'max_features': 0.6, 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 0.1, 'min_weight_fraction_leaf': 0.0, 'random_state': None, 'splitter': 'best'}" diff --git a/tests/files_for_tests/law_school_dataset_20k/python_3_11/Metrics_law_school_LogisticRegression_50_Estimators_202409013_220132.csv b/tests/files_for_tests/law_school_dataset_20k/python_3_11/Metrics_law_school_LogisticRegression_50_Estimators_202409013_220132.csv deleted file mode 100644 index f9a516f8..00000000 --- a/tests/files_for_tests/law_school_dataset_20k/python_3_11/Metrics_law_school_LogisticRegression_50_Estimators_202409013_220132.csv +++ /dev/null @@ -1,19 +0,0 @@ -Metric,overall,male_priv,male_dis,race_priv,race_dis,male&race_priv,male&race_dis,Model_Name,Virny_Random_State,Model_Params -Std,0.00917147943187975,0.008607050335196386,0.00991733216678277,0.007286748976074028,0.01965343461669202,0.008239377682845418,0.01998950882218732,LogisticRegression,100,"{'C': 0.1, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': None, 'solver': 'lbfgs', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}" -IQR,0.012256803023578407,0.0114453771719569,0.013329044327506827,0.009739371815789648,0.026257532422100754,0.010976514379656195,0.027115910618190745,LogisticRegression,100,"{'C': 0.1, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': None, 'solver': 'lbfgs', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}" -Aleatoric_Uncertainty,0.34187256444638636,0.3224331953447354,0.3675603021878538,0.2923502561200833,0.6172915852011884,0.3152813861742172,0.6504913910597441,LogisticRegression,100,"{'C': 0.1, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': None, 'solver': 'lbfgs', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}" -Statistical_Bias,0.13884987532254198,0.12866752312909457,0.15230512643531174,0.11307241003118768,0.28221161446657245,0.12620186366971659,0.28564346511139427,LogisticRegression,100,"{'C': 0.1, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': None, 'solver': 'lbfgs', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}" -Mean_Prediction,0.10451562164187415,0.09389099362293032,0.11855530866690708,0.07598923254310201,0.26316553956343663,0.08851874474140387,0.2901763444564233,LogisticRegression,100,"{'C': 0.1, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': None, 'solver': 'lbfgs', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}" -Overall_Uncertainty,0.3427815142556397,0.3232951664439477,0.3685313310068042,0.2930493994498858,0.6193673767242337,0.31609460569791553,0.6525113923649841,LogisticRegression,100,"{'C': 0.1, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': None, 'solver': 'lbfgs', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}" -Epistemic_Uncertainty,0.0009089498092533232,0.0008619710992122664,0.0009710288189503924,0.0006991433298024763,0.0020757915230452673,0.0008132195236983386,0.002020001305240049,LogisticRegression,100,"{'C': 0.1, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': None, 'solver': 'lbfgs', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}" -Jitter,0.008828885400313994,0.007884376723662437,0.010076986151603502,0.005247780337319282,0.02874525204403535,0.006921084883039359,0.030970933828076638,LogisticRegression,100,"{'C': 0.1, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': None, 'solver': 'lbfgs', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}" -Label_Stability,0.9874423076923078,0.9889695945945947,0.9854241071428572,0.9924333522404991,0.9596845425867508,0.9902872062663186,0.9544242424242425,LogisticRegression,100,"{'C': 0.1, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': None, 'solver': 'lbfgs', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}" -TPR,0.9849785407725322,0.9897674418604652,0.9784537389100126,0.9947900704872816,0.9161290322580645,0.9913892078071183,0.8934426229508197,LogisticRegression,100,"{'C': 0.1, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': None, 'solver': 'lbfgs', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}" -TNR,0.24074074074074073,0.17889908256880735,0.3037383177570093,0.11787072243346007,0.4319526627218935,0.16473988439306358,0.5465116279069767,LogisticRegression,100,"{'C': 0.1, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': None, 'solver': 'lbfgs', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}" -PPV,0.918,0.9224100563502384,0.9119905493207324,0.9332949971247844,0.8160919540229885,0.9227892065188351,0.8482490272373541,LogisticRegression,100,"{'C': 0.1, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': None, 'solver': 'lbfgs', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}" -FNR,0.015021459227467811,0.010232558139534883,0.021546261089987327,0.005209929512718358,0.08387096774193549,0.008610792192881744,0.10655737704918032,LogisticRegression,100,"{'C': 0.1, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': None, 'solver': 'lbfgs', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}" -FPR,0.7592592592592593,0.8211009174311926,0.6962616822429907,0.8821292775665399,0.5680473372781065,0.8352601156069365,0.45348837209302323,LogisticRegression,100,"{'C': 0.1, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': None, 'solver': 'lbfgs', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}" -Accuracy,0.9076923076923077,0.9151182432432432,0.8978794642857143,0.9293817356778219,0.7870662460567823,0.9167101827676241,0.803030303030303,LogisticRegression,100,"{'C': 0.1, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': None, 'solver': 'lbfgs', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}" -F1,0.9503105590062112,0.9549024007179717,0.9440538061754815,0.9630618602581219,0.8632218844984803,0.9558599695585996,0.8702594810379242,LogisticRegression,100,"{'C': 0.1, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': None, 'solver': 'lbfgs', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}" -Selection-Rate,0.9615384615384616,0.9742398648648649,0.9447544642857143,0.9863868406125922,0.8233438485804416,0.9772845953002611,0.7787878787878788,LogisticRegression,100,"{'C': 0.1, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': None, 'solver': 'lbfgs', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}" -Sample_Size,4160.0,2368.0,1792.0,3526.0,634.0,3830.0,330.0,LogisticRegression,100,"{'C': 0.1, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': None, 'solver': 'lbfgs', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}" diff --git a/tests/user_interfaces/test_multiple_models_api.py b/tests/user_interfaces/test_multiple_models_api.py index b2412b5d..5d34de34 100644 --- a/tests/user_interfaces/test_multiple_models_api.py +++ b/tests/user_interfaces/test_multiple_models_api.py @@ -99,5 +99,6 @@ def test_compute_metrics_with_config_should_equal_prev_release_results(law_schoo metrics_dct['LogisticRegression'] = metrics_dct['LogisticRegression'].drop('Runtime_in_Mins', axis=1) metrics_dct['DecisionTreeClassifier'] = metrics_dct['DecisionTreeClassifier'].drop('Runtime_in_Mins', axis=1) - assert compare_metric_dfs_with_tolerance(expected_metrics_dct['LogisticRegression'], metrics_dct['LogisticRegression']) - assert compare_metric_dfs_with_tolerance(expected_metrics_dct['DecisionTreeClassifier'], metrics_dct['DecisionTreeClassifier']) + tolerance = 1e-4 if sys.version_info.major == 3 and sys.version_info.minor == 11 else 1e-6 + assert compare_metric_dfs_with_tolerance(expected_metrics_dct['LogisticRegression'], metrics_dct['LogisticRegression'], tolerance) + assert compare_metric_dfs_with_tolerance(expected_metrics_dct['DecisionTreeClassifier'], metrics_dct['DecisionTreeClassifier'], tolerance) From f5dd82d4693bdd39b9eb820d9c0415b4cc9de1f0 Mon Sep 17 00:00:00 2001 From: Denys Herasymuk Date: Sat, 14 Sep 2024 12:50:07 +0300 Subject: [PATCH 23/27] Fixed tests for Python 3.11 --- ...nTreeClassifier_50_Estimators_20240914.csv | 19 +++++++++++++++++++ ...ression_50_Estimators_202409014_220132.csv | 19 +++++++++++++++++++ 2 files changed, 38 insertions(+) create mode 100644 tests/files_for_tests/law_school_dataset_20k/python_3_11/Metrics_law_school_DecisionTreeClassifier_50_Estimators_20240914.csv create mode 100644 tests/files_for_tests/law_school_dataset_20k/python_3_11/Metrics_law_school_LogisticRegression_50_Estimators_202409014_220132.csv diff --git a/tests/files_for_tests/law_school_dataset_20k/python_3_11/Metrics_law_school_DecisionTreeClassifier_50_Estimators_20240914.csv b/tests/files_for_tests/law_school_dataset_20k/python_3_11/Metrics_law_school_DecisionTreeClassifier_50_Estimators_20240914.csv new file mode 100644 index 00000000..dc32ea2b --- /dev/null +++ b/tests/files_for_tests/law_school_dataset_20k/python_3_11/Metrics_law_school_DecisionTreeClassifier_50_Estimators_20240914.csv @@ -0,0 +1,19 @@ +Metric,overall,male_priv,male_dis,race_priv,race_dis,male&race_priv,male&race_dis,Model_Name,Virny_Random_State,Model_Params +Statistical_Bias,0.14871459501539216,0.13922538692994388,0.1612539056997345,0.12087562594490028,0.3035414166913454,0.13498160237084172,0.3081005399506289,DecisionTreeClassifier,100,"{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 20, 'max_features': 0.6, 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 0.1, 'min_weight_fraction_leaf': 0.0, 'monotonic_cst': None, 'random_state': None, 'splitter': 'best'}" +Mean_Prediction,0.10730163589001154,0.10127383420207314,0.11526694526335872,0.08243941922626423,0.2455732067991173,0.0941339892140465,0.26012614125045447,DecisionTreeClassifier,100,"{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 20, 'max_features': 0.6, 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 0.1, 'min_weight_fraction_leaf': 0.0, 'monotonic_cst': None, 'random_state': None, 'splitter': 'best'}" +IQR,0.053505982148091664,0.05084500766436651,0.05702226985872847,0.044968952602027774,0.10098479315664254,0.049091273172528614,0.1047433620765962,DecisionTreeClassifier,100,"{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 20, 'max_features': 0.6, 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 0.1, 'min_weight_fraction_leaf': 0.0, 'monotonic_cst': None, 'random_state': None, 'splitter': 'best'}" +Overall_Uncertainty,0.37102753012884837,0.35860883223592943,0.3874379523444912,0.3189445800260836,0.6606875964732465,0.3439739430925971,0.6850131008829163,DecisionTreeClassifier,100,"{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 20, 'max_features': 0.6, 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 0.1, 'min_weight_fraction_leaf': 0.0, 'monotonic_cst': None, 'random_state': None, 'splitter': 'best'}" +Aleatoric_Uncertainty,0.35318189936459926,0.3413540927984995,0.368811500898374,0.3031037696893525,0.6316921284417605,0.3271516798716977,0.6552902043882751,DecisionTreeClassifier,100,"{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 20, 'max_features': 0.6, 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 0.1, 'min_weight_fraction_leaf': 0.0, 'monotonic_cst': None, 'random_state': None, 'splitter': 'best'}" +Std,0.041588229003281706,0.04014981475236609,0.04348899069199162,0.035707999117036614,0.07429121098892864,0.03859272201556965,0.0763542646485458,DecisionTreeClassifier,100,"{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 20, 'max_features': 0.6, 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 0.1, 'min_weight_fraction_leaf': 0.0, 'monotonic_cst': None, 'random_state': None, 'splitter': 'best'}" +Epistemic_Uncertainty,0.01784563076424911,0.017254739437429945,0.018626451446117187,0.015840810336731126,0.028995468031486005,0.016822263220899414,0.029722896494641216,DecisionTreeClassifier,100,"{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 20, 'max_features': 0.6, 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 0.1, 'min_weight_fraction_leaf': 0.0, 'monotonic_cst': None, 'random_state': None, 'splitter': 'best'}" +Jitter,0.03490286499215079,0.027672021511307245,0.044457908163265336,0.013944459235764642,0.15146333612309273,0.023045132413278664,0.1725244279529997,DecisionTreeClassifier,100,"{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 20, 'max_features': 0.6, 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 0.1, 'min_weight_fraction_leaf': 0.0, 'monotonic_cst': None, 'random_state': None, 'splitter': 'best'}" +Label_Stability,0.9493557692307693,0.9616385135135135,0.9331249999999999,0.9821100397050483,0.767192429022082,0.9684804177545692,0.7273939393939394,DecisionTreeClassifier,100,"{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 20, 'max_features': 0.6, 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 0.1, 'min_weight_fraction_leaf': 0.0, 'monotonic_cst': None, 'random_state': None, 'splitter': 'best'}" +TPR,0.9959763948497854,0.9986046511627907,0.9923954372623575,1.0,0.967741935483871,0.9991389207807119,0.9508196721311475,DecisionTreeClassifier,100,"{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 20, 'max_features': 0.6, 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 0.1, 'min_weight_fraction_leaf': 0.0, 'monotonic_cst': None, 'random_state': None, 'splitter': 'best'}" +TNR,0.1111111111111111,0.06880733944954129,0.1542056074766355,0.0,0.28402366863905326,0.04335260115606936,0.38372093023255816,DecisionTreeClassifier,100,"{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 20, 'max_features': 0.6, 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 0.1, 'min_weight_fraction_leaf': 0.0, 'monotonic_cst': None, 'random_state': None, 'splitter': 'best'}" +PPV,0.9062728825970222,0.9136170212765957,0.8963938179736691,0.9254112308564946,0.7880910683012259,0.9131689401888772,0.8140350877192982,DecisionTreeClassifier,100,"{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 20, 'max_features': 0.6, 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 0.1, 'min_weight_fraction_leaf': 0.0, 'monotonic_cst': None, 'random_state': None, 'splitter': 'best'}" +FNR,0.004023605150214592,0.0013953488372093023,0.0076045627376425855,0.0,0.03225806451612903,0.0008610792192881745,0.04918032786885246,DecisionTreeClassifier,100,"{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 20, 'max_features': 0.6, 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 0.1, 'min_weight_fraction_leaf': 0.0, 'monotonic_cst': None, 'random_state': None, 'splitter': 'best'}" +FPR,0.8888888888888888,0.9311926605504587,0.8457943925233645,1.0,0.7159763313609467,0.9566473988439307,0.6162790697674418,DecisionTreeClassifier,100,"{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 20, 'max_features': 0.6, 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 0.1, 'min_weight_fraction_leaf': 0.0, 'monotonic_cst': None, 'random_state': None, 'splitter': 'best'}" +Accuracy,0.9040865384615384,0.9130067567567568,0.8922991071428571,0.9254112308564946,0.7854889589905363,0.9127937336814621,0.803030303030303,DecisionTreeClassifier,100,"{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 20, 'max_features': 0.6, 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 0.1, 'min_weight_fraction_leaf': 0.0, 'monotonic_cst': None, 'random_state': None, 'splitter': 'best'}" +F1,0.9490095846645368,0.9542222222222222,0.9419548872180451,0.9612608631609957,0.8687258687258688,0.9542214912280702,0.8771266540642723,DecisionTreeClassifier,100,"{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 20, 'max_features': 0.6, 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 0.1, 'min_weight_fraction_leaf': 0.0, 'monotonic_cst': None, 'random_state': None, 'splitter': 'best'}" +Selection-Rate,0.9848557692307692,0.9923986486486487,0.9748883928571429,1.0,0.9006309148264984,0.9953002610966057,0.8636363636363636,DecisionTreeClassifier,100,"{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 20, 'max_features': 0.6, 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 0.1, 'min_weight_fraction_leaf': 0.0, 'monotonic_cst': None, 'random_state': None, 'splitter': 'best'}" +Sample_Size,4160.0,2368.0,1792.0,3526.0,634.0,3830.0,330.0,DecisionTreeClassifier,100,"{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 20, 'max_features': 0.6, 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 0.1, 'min_weight_fraction_leaf': 0.0, 'monotonic_cst': None, 'random_state': None, 'splitter': 'best'}" diff --git a/tests/files_for_tests/law_school_dataset_20k/python_3_11/Metrics_law_school_LogisticRegression_50_Estimators_202409014_220132.csv b/tests/files_for_tests/law_school_dataset_20k/python_3_11/Metrics_law_school_LogisticRegression_50_Estimators_202409014_220132.csv new file mode 100644 index 00000000..17912b1c --- /dev/null +++ b/tests/files_for_tests/law_school_dataset_20k/python_3_11/Metrics_law_school_LogisticRegression_50_Estimators_202409014_220132.csv @@ -0,0 +1,19 @@ +Metric,overall,male_priv,male_dis,race_priv,race_dis,male&race_priv,male&race_dis,Model_Name,Virny_Random_State,Model_Params +Statistical_Bias,0.13885050770015747,0.12866008788771274,0.15231641959517375,0.11307644203241121,0.28219333978923217,0.12620325356436243,0.2856353057004457,LogisticRegression,100,"{'C': 0.1, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'deprecated', 'n_jobs': None, 'penalty': 'l2', 'random_state': None, 'solver': 'lbfgs', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}" +Mean_Prediction,0.10452067758387495,0.09387206767499487,0.11859205496346649,0.07599895190606157,0.2631446598235752,0.0885215884645407,0.29020707554463304,LogisticRegression,100,"{'C': 0.1, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'deprecated', 'n_jobs': None, 'penalty': 'l2', 'random_state': None, 'solver': 'lbfgs', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}" +IQR,0.012301095217005956,0.011473328744497403,0.013394929484249399,0.009755741419230564,0.02645711649611641,0.011003116163415115,0.027365518778378423,LogisticRegression,100,"{'C': 0.1, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'deprecated', 'n_jobs': None, 'penalty': 'l2', 'random_state': None, 'solver': 'lbfgs', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}" +Overall_Uncertainty,0.3427925391226378,0.3232693942336032,0.3685909805831478,0.29307883438932947,0.6192760137119832,0.3161116384666409,0.6524526891604193,LogisticRegression,100,"{'C': 0.1, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'deprecated', 'n_jobs': None, 'penalty': 'l2', 'random_state': None, 'solver': 'lbfgs', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}" +Aleatoric_Uncertainty,0.34187640247836215,0.32240015679448136,0.3676128699892046,0.2923757999920252,0.6171747058960655,0.31529238636428736,0.6504121046508058,LogisticRegression,100,"{'C': 0.1, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'deprecated', 'n_jobs': None, 'penalty': 'l2', 'random_state': None, 'solver': 'lbfgs', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}" +Std,0.009203351829509116,0.008639388543288133,0.00994858902915827,0.007305676473074892,0.019757300262927203,0.008266836789368464,0.02007260214386879,LogisticRegression,100,"{'C': 0.1, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'deprecated', 'n_jobs': None, 'penalty': 'l2', 'random_state': None, 'solver': 'lbfgs', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}" +Epistemic_Uncertainty,0.0009161366442756447,0.0008692374391218172,0.0009781105939432044,0.0007030343973042918,0.0021013078159176635,0.0008192521023535626,0.00204058450961353,LogisticRegression,100,"{'C': 0.1, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'deprecated', 'n_jobs': None, 'penalty': 'l2', 'random_state': None, 'solver': 'lbfgs', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}" +Jitter,0.00885832025117741,0.00783645890788747,0.010208637026239083,0.0053438596085059135,0.028404043005214732,0.006967762561943823,0.030800247371675905,LogisticRegression,100,"{'C': 0.1, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'deprecated', 'n_jobs': None, 'penalty': 'l2', 'random_state': None, 'solver': 'lbfgs', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}" +Label_Stability,0.987423076923077,0.9890878378378378,0.9852232142857142,0.9923539421440726,0.9599999999999999,0.9902558746736293,0.9545454545454546,LogisticRegression,100,"{'C': 0.1, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'deprecated', 'n_jobs': None, 'penalty': 'l2', 'random_state': None, 'solver': 'lbfgs', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}" +TPR,0.9847103004291845,0.9897674418604652,0.9778200253485425,0.9944836040453571,0.9161290322580645,0.9911021814006888,0.8934426229508197,LogisticRegression,100,"{'C': 0.1, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'deprecated', 'n_jobs': None, 'penalty': 'l2', 'random_state': None, 'solver': 'lbfgs', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}" +TNR,0.24305555555555555,0.17889908256880735,0.308411214953271,0.11787072243346007,0.4378698224852071,0.16473988439306358,0.5581395348837209,LogisticRegression,100,"{'C': 0.1, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'deprecated', 'n_jobs': None, 'penalty': 'l2', 'random_state': None, 'solver': 'lbfgs', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}" +PPV,0.9182091045522761,0.9224100563502384,0.9124778237729154,0.9332758124820247,0.817658349328215,0.9227685729556387,0.8515625,LogisticRegression,100,"{'C': 0.1, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'deprecated', 'n_jobs': None, 'penalty': 'l2', 'random_state': None, 'solver': 'lbfgs', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}" +FNR,0.01528969957081545,0.010232558139534883,0.022179974651457542,0.0055163959546429666,0.08387096774193549,0.008897818599311137,0.10655737704918032,LogisticRegression,100,"{'C': 0.1, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'deprecated', 'n_jobs': None, 'penalty': 'l2', 'random_state': None, 'solver': 'lbfgs', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}" +FPR,0.7569444444444444,0.8211009174311926,0.6915887850467289,0.8821292775665399,0.5621301775147929,0.8352601156069365,0.4418604651162791,LogisticRegression,100,"{'C': 0.1, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'deprecated', 'n_jobs': None, 'penalty': 'l2', 'random_state': None, 'solver': 'lbfgs', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}" +Accuracy,0.9076923076923077,0.9151182432432432,0.8978794642857143,0.9290981281905842,0.7886435331230284,0.9164490861618799,0.806060606060606,LogisticRegression,100,"{'C': 0.1, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'deprecated', 'n_jobs': None, 'penalty': 'l2', 'random_state': None, 'solver': 'lbfgs', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}" +F1,0.9502976960911209,0.9549024007179717,0.9440195778525543,0.9629080118694362,0.8640973630831643,0.9557154719070025,0.872,LogisticRegression,100,"{'C': 0.1, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'deprecated', 'n_jobs': None, 'penalty': 'l2', 'random_state': None, 'solver': 'lbfgs', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}" +Selection-Rate,0.9610576923076923,0.9742398648648649,0.9436383928571429,0.9861032331253545,0.8217665615141956,0.977023498694517,0.7757575757575758,LogisticRegression,100,"{'C': 0.1, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'deprecated', 'n_jobs': None, 'penalty': 'l2', 'random_state': None, 'solver': 'lbfgs', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}" +Sample_Size,4160.0,2368.0,1792.0,3526.0,634.0,3830.0,330.0,LogisticRegression,100,"{'C': 0.1, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'deprecated', 'n_jobs': None, 'penalty': 'l2', 'random_state': None, 'solver': 'lbfgs', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}" From d6a6bd191c5ceb4bfab3d6602a570b5b84cb28be Mon Sep 17 00:00:00 2001 From: Denys Herasymuk Date: Sat, 14 Sep 2024 13:25:59 +0300 Subject: [PATCH 24/27] Removed duplication of requirements --- .github/workflows/build-virny.yml | 2 +- lib_base_packages.txt | 14 -------------- requirements.txt | 12 +++--------- setup.py | 15 ++++++++++++--- 4 files changed, 16 insertions(+), 27 deletions(-) delete mode 100644 lib_base_packages.txt diff --git a/.github/workflows/build-virny.yml b/.github/workflows/build-virny.yml index 817316b4..ff3bdb32 100644 --- a/.github/workflows/build-virny.yml +++ b/.github/workflows/build-virny.yml @@ -48,7 +48,7 @@ jobs: - name: Build Virny run: | source ~/.venv/bin/activate - pip install -e ".[dev,docs]" + pip install -e ".[test,docs]" pip install requests-toolbelt==1.0.0 # We should delete the git project from the build cache to avoid conflicts diff --git a/lib_base_packages.txt b/lib_base_packages.txt deleted file mode 100644 index a4d213c7..00000000 --- a/lib_base_packages.txt +++ /dev/null @@ -1,14 +0,0 @@ -setuptools>=74.1.0 -aif360[Reductions]>=0.6.1 -matplotlib>=3.6.2 -pandas>=1.5.2 -altair>=4.2.0 -scikit-learn>=1.2.0 -tqdm>=4.64.1 -gradio>=4.10.0 -seaborn>=0.12.1 -folktables>=0.0.11 -munch>=2.5.0 -PyYAML>=6.0 -requests-toolbelt>=1.0.0 -colorama>=0.4.6 diff --git a/requirements.txt b/requirements.txt index 1a35fdff..dad008ec 100644 --- a/requirements.txt +++ b/requirements.txt @@ -1,7 +1,5 @@ -wheel>=0.43.0 -aif360>=0.6.1 -numpy>=1.23.5 -fairlearn>=0.9.0 +setuptools>=74.1.0 +aif360[Reductions]>=0.6.1 matplotlib>=3.6.2 pandas>=1.5.2 altair>=4.2.0 @@ -10,11 +8,7 @@ tqdm>=4.64.1 gradio>=4.10.0 seaborn>=0.12.1 folktables>=0.0.11 -xgboost>=1.7.2 munch>=2.5.0 PyYAML>=6.0 -python-dotenv>=1.0.0 -pytest>=7.2.2 -pymongo==4.3.3 requests-toolbelt>=1.0.0 -colorama>=0.4.6 +colorama>=0.4.6 \ No newline at end of file diff --git a/setup.py b/setup.py index 04403492..5b0b73c4 100644 --- a/setup.py +++ b/setup.py @@ -28,11 +28,11 @@ with open(os.path.join(HERE, NAME, "__version__.py")) as f: exec(f.read(), about) -with pathlib.Path('lib_base_packages.txt').open() as lib_base_packages_txt: +with pathlib.Path('requirements.txt').open() as requirements: base_packages = [ str(requirement) for requirement - in pkg_resources.parse_requirements(lib_base_packages_txt) + in pkg_resources.parse_requirements(requirements) ] # This call to setup() does all the work @@ -54,7 +54,6 @@ "Topic :: Scientific/Engineering", "Programming Language :: Python", "Programming Language :: Python :: 3", - "Programming Language :: Python :: 3.8", "Programming Language :: Python :: 3.9", "Programming Language :: Python :: 3.10", "Programming Language :: Python :: 3.11", @@ -67,9 +66,18 @@ include_package_data=True, install_requires=base_packages, extras_require={ + "test": base_packages + + [ + "pytest~=7.2.1", + ], "dev": base_packages + [ "pytest~=7.2.1", + "aif360>=0.6.1", + "fairlearn>=0.9.0", + "xgboost>=1.7.2", + "python-dotenv>=1.0.0", + "pymongo==4.3.3", ], "docs": [ "scikit-learn", @@ -90,4 +98,5 @@ "spacy", ], }, + python_requires='>=3.9', ) From 107e32262e39b977a3530773019887de94f01517 Mon Sep 17 00:00:00 2001 From: Denys Herasymuk Date: Sat, 14 Sep 2024 14:28:44 +0300 Subject: [PATCH 25/27] Added a tutorial on how to use PyTorch Tabular models together with Virny --- .gitignore | 3 + docs/examples/.pages | 1 + .../Multiple_Models_Interface_Use_Case.ipynb | 2 +- .../Multiple_Models_Interface_Use_Case.md | 2 +- ...iple_Models_Interface_With_DB_Writer.ipynb | 2 +- ...ultiple_Models_Interface_With_DB_Writer.md | 2 +- ...Models_Interface_With_Error_Analysis.ipynb | 2 +- ...le_Models_Interface_With_Error_Analysis.md | 2 +- ...le_Models_Interface_With_Inprocessor.ipynb | 2 +- ...tiple_Models_Interface_With_Inprocessor.md | 2 +- ...ls_Interface_With_Multiple_Test_Sets.ipynb | 2 +- ...odels_Interface_With_Multiple_Test_Sets.md | 2 +- ..._Models_Interface_With_Postprocessor.ipynb | 2 +- ...ple_Models_Interface_With_Postprocessor.md | 2 +- ...odels_Interface_With_PyTorch_Tabular.ipynb | 1182 +++++++++++++++++ ...e_Models_Interface_With_PyTorch_Tabular.md | 660 +++++++++ ...ls_Interface_With_PyTorch_Tabular_39_0.png | Bin 0 -> 38997 bytes ...ls_Interface_With_PyTorch_Tabular_40_0.png | Bin 0 -> 41224 bytes docs/examples/experiment_config.yaml | 10 +- ...ssifier_10_Estimators_20240914__112149.csv | 19 + setup.py | 1 + 21 files changed, 1882 insertions(+), 18 deletions(-) create mode 100644 docs/examples/Multiple_Models_Interface_With_PyTorch_Tabular.ipynb create mode 100644 docs/examples/Multiple_Models_Interface_With_PyTorch_Tabular.md create mode 100644 docs/examples/Multiple_Models_Interface_With_PyTorch_Tabular_files/Multiple_Models_Interface_With_PyTorch_Tabular_39_0.png create mode 100644 docs/examples/Multiple_Models_Interface_With_PyTorch_Tabular_files/Multiple_Models_Interface_With_PyTorch_Tabular_40_0.png create mode 100644 docs/examples/results/diabetes_Metrics_20240914__112147/Metrics_diabetes_GANDALFClassifier_10_Estimators_20240914__112149.csv diff --git a/.gitignore b/.gitignore index fb0d19da..18822c52 100644 --- a/.gitignore +++ b/.gitignore @@ -9,6 +9,9 @@ notebooks .ipynb_checkpoints docs/examples/test.py tests/results +.pt_tmp +lightning_logs +saved_models # Remove big files from GitHub repo virny/datasets/2018 diff --git a/docs/examples/.pages b/docs/examples/.pages index 72a1eaad..a312fe70 100644 --- a/docs/examples/.pages +++ b/docs/examples/.pages @@ -6,3 +6,4 @@ nav: - Multiple_Models_Interface_With_Multiple_Test_Sets.md - Multiple_Models_Interface_With_Inprocessor.md - Multiple_Models_Interface_With_Postprocessor.md + - Multiple_Models_Interface_With_PyTorch_Tabular.md diff --git a/docs/examples/Multiple_Models_Interface_Use_Case.ipynb b/docs/examples/Multiple_Models_Interface_Use_Case.ipynb index 59fff6aa..a6a2eeb3 100644 --- a/docs/examples/Multiple_Models_Interface_Use_Case.ipynb +++ b/docs/examples/Multiple_Models_Interface_Use_Case.ipynb @@ -409,7 +409,7 @@ "outputs": [], "source": [ "column_transformer = ColumnTransformer(transformers=[\n", - " ('categorical_features', OneHotEncoder(handle_unknown='ignore', sparse=False), data_loader.categorical_columns),\n", + " ('categorical_features', OneHotEncoder(handle_unknown='ignore', sparse_output=False), data_loader.categorical_columns),\n", " ('numerical_features', StandardScaler(), data_loader.numerical_columns),\n", "])" ], diff --git a/docs/examples/Multiple_Models_Interface_Use_Case.md b/docs/examples/Multiple_Models_Interface_Use_Case.md index e8aaf36e..e6207a13 100644 --- a/docs/examples/Multiple_Models_Interface_Use_Case.md +++ b/docs/examples/Multiple_Models_Interface_Use_Case.md @@ -267,7 +267,7 @@ data_loader.X_data[data_loader.X_data.columns[:5]].head() ```python column_transformer = ColumnTransformer(transformers=[ - ('categorical_features', OneHotEncoder(handle_unknown='ignore', sparse=False), data_loader.categorical_columns), + ('categorical_features', OneHotEncoder(handle_unknown='ignore', sparse_output=False), data_loader.categorical_columns), ('numerical_features', StandardScaler(), data_loader.numerical_columns), ]) ``` diff --git a/docs/examples/Multiple_Models_Interface_With_DB_Writer.ipynb b/docs/examples/Multiple_Models_Interface_With_DB_Writer.ipynb index a1833630..c0237ce9 100644 --- a/docs/examples/Multiple_Models_Interface_With_DB_Writer.ipynb +++ b/docs/examples/Multiple_Models_Interface_With_DB_Writer.ipynb @@ -288,7 +288,7 @@ "outputs": [], "source": [ "column_transformer = ColumnTransformer(transformers=[\n", - " ('categorical_features', OneHotEncoder(handle_unknown='ignore', sparse=False), data_loader.categorical_columns),\n", + " ('categorical_features', OneHotEncoder(handle_unknown='ignore', sparse_output=False), data_loader.categorical_columns),\n", " ('numerical_features', StandardScaler(), data_loader.numerical_columns),\n", "])" ], diff --git a/docs/examples/Multiple_Models_Interface_With_DB_Writer.md b/docs/examples/Multiple_Models_Interface_With_DB_Writer.md index dcc47316..23d433cc 100644 --- a/docs/examples/Multiple_Models_Interface_With_DB_Writer.md +++ b/docs/examples/Multiple_Models_Interface_With_DB_Writer.md @@ -183,7 +183,7 @@ data_loader.X_data[data_loader.X_data.columns[:5]].head() ```python column_transformer = ColumnTransformer(transformers=[ - ('categorical_features', OneHotEncoder(handle_unknown='ignore', sparse=False), data_loader.categorical_columns), + ('categorical_features', OneHotEncoder(handle_unknown='ignore', sparse_output=False), data_loader.categorical_columns), ('numerical_features', StandardScaler(), data_loader.numerical_columns), ]) ``` diff --git a/docs/examples/Multiple_Models_Interface_With_Error_Analysis.ipynb b/docs/examples/Multiple_Models_Interface_With_Error_Analysis.ipynb index 739f0c00..13659c88 100644 --- a/docs/examples/Multiple_Models_Interface_With_Error_Analysis.ipynb +++ b/docs/examples/Multiple_Models_Interface_With_Error_Analysis.ipynb @@ -318,7 +318,7 @@ "outputs": [], "source": [ "column_transformer = ColumnTransformer(transformers=[\n", - " ('categorical_features', OneHotEncoder(handle_unknown='ignore', sparse=False), data_loader.categorical_columns),\n", + " ('categorical_features', OneHotEncoder(handle_unknown='ignore', sparse_output=False), data_loader.categorical_columns),\n", " ('numerical_features', StandardScaler(), data_loader.numerical_columns),\n", "])" ], diff --git a/docs/examples/Multiple_Models_Interface_With_Error_Analysis.md b/docs/examples/Multiple_Models_Interface_With_Error_Analysis.md index 402aab08..e2b4ba16 100644 --- a/docs/examples/Multiple_Models_Interface_With_Error_Analysis.md +++ b/docs/examples/Multiple_Models_Interface_With_Error_Analysis.md @@ -189,7 +189,7 @@ data_loader.X_data[data_loader.X_data.columns[:5]].head() ```python column_transformer = ColumnTransformer(transformers=[ - ('categorical_features', OneHotEncoder(handle_unknown='ignore', sparse=False), data_loader.categorical_columns), + ('categorical_features', OneHotEncoder(handle_unknown='ignore', sparse_output=False), data_loader.categorical_columns), ('numerical_features', StandardScaler(), data_loader.numerical_columns), ]) ``` diff --git a/docs/examples/Multiple_Models_Interface_With_Inprocessor.ipynb b/docs/examples/Multiple_Models_Interface_With_Inprocessor.ipynb index 5248516d..ae8bb666 100644 --- a/docs/examples/Multiple_Models_Interface_With_Inprocessor.ipynb +++ b/docs/examples/Multiple_Models_Interface_With_Inprocessor.ipynb @@ -291,7 +291,7 @@ "outputs": [], "source": [ "column_transformer = ColumnTransformer(transformers=[\n", - " ('categorical_features', OneHotEncoder(handle_unknown='ignore', sparse=False), data_loader.categorical_columns),\n", + " ('categorical_features', OneHotEncoder(handle_unknown='ignore', sparse_output=False), data_loader.categorical_columns),\n", " ('numerical_features', StandardScaler(), data_loader.numerical_columns),\n", "])" ], diff --git a/docs/examples/Multiple_Models_Interface_With_Inprocessor.md b/docs/examples/Multiple_Models_Interface_With_Inprocessor.md index eeb2228f..ff826706 100644 --- a/docs/examples/Multiple_Models_Interface_With_Inprocessor.md +++ b/docs/examples/Multiple_Models_Interface_With_Inprocessor.md @@ -176,7 +176,7 @@ data_loader.X_data[data_loader.X_data.columns[:5]].head() ```python column_transformer = ColumnTransformer(transformers=[ - ('categorical_features', OneHotEncoder(handle_unknown='ignore', sparse=False), data_loader.categorical_columns), + ('categorical_features', OneHotEncoder(handle_unknown='ignore', sparse_output=False), data_loader.categorical_columns), ('numerical_features', StandardScaler(), data_loader.numerical_columns), ]) ``` diff --git a/docs/examples/Multiple_Models_Interface_With_Multiple_Test_Sets.ipynb b/docs/examples/Multiple_Models_Interface_With_Multiple_Test_Sets.ipynb index 687a9f73..0dcf2482 100644 --- a/docs/examples/Multiple_Models_Interface_With_Multiple_Test_Sets.ipynb +++ b/docs/examples/Multiple_Models_Interface_With_Multiple_Test_Sets.ipynb @@ -266,7 +266,7 @@ "outputs": [], "source": [ "column_transformer = ColumnTransformer(transformers=[\n", - " ('categorical_features', OneHotEncoder(handle_unknown='ignore', sparse=False), data_loader.categorical_columns),\n", + " ('categorical_features', OneHotEncoder(handle_unknown='ignore', sparse_output=False), data_loader.categorical_columns),\n", " ('numerical_features', StandardScaler(), data_loader.numerical_columns),\n", "])" ], diff --git a/docs/examples/Multiple_Models_Interface_With_Multiple_Test_Sets.md b/docs/examples/Multiple_Models_Interface_With_Multiple_Test_Sets.md index 4f76ec00..2961f160 100644 --- a/docs/examples/Multiple_Models_Interface_With_Multiple_Test_Sets.md +++ b/docs/examples/Multiple_Models_Interface_With_Multiple_Test_Sets.md @@ -175,7 +175,7 @@ data_loader.X_data[data_loader.X_data.columns[:5]].head() ```python column_transformer = ColumnTransformer(transformers=[ - ('categorical_features', OneHotEncoder(handle_unknown='ignore', sparse=False), data_loader.categorical_columns), + ('categorical_features', OneHotEncoder(handle_unknown='ignore', sparse_output=False), data_loader.categorical_columns), ('numerical_features', StandardScaler(), data_loader.numerical_columns), ]) ``` diff --git a/docs/examples/Multiple_Models_Interface_With_Postprocessor.ipynb b/docs/examples/Multiple_Models_Interface_With_Postprocessor.ipynb index 0838ce55..55f1a28c 100644 --- a/docs/examples/Multiple_Models_Interface_With_Postprocessor.ipynb +++ b/docs/examples/Multiple_Models_Interface_With_Postprocessor.ipynb @@ -334,7 +334,7 @@ "outputs": [], "source": [ "column_transformer = ColumnTransformer(transformers=[\n", - " ('categorical_features', OneHotEncoder(handle_unknown='ignore', sparse=False), data_loader.categorical_columns),\n", + " ('categorical_features', OneHotEncoder(handle_unknown='ignore', sparse_output=False), data_loader.categorical_columns),\n", " ('numerical_features', StandardScaler(), data_loader.numerical_columns),\n", "])" ], diff --git a/docs/examples/Multiple_Models_Interface_With_Postprocessor.md b/docs/examples/Multiple_Models_Interface_With_Postprocessor.md index 691e41f7..65825a45 100644 --- a/docs/examples/Multiple_Models_Interface_With_Postprocessor.md +++ b/docs/examples/Multiple_Models_Interface_With_Postprocessor.md @@ -208,7 +208,7 @@ data_loader.X_data[data_loader.X_data.columns[:5]].head() ```python column_transformer = ColumnTransformer(transformers=[ - ('categorical_features', OneHotEncoder(handle_unknown='ignore', sparse=False), data_loader.categorical_columns), + ('categorical_features', OneHotEncoder(handle_unknown='ignore', sparse_output=False), data_loader.categorical_columns), ('numerical_features', StandardScaler(), data_loader.numerical_columns), ]) ``` diff --git a/docs/examples/Multiple_Models_Interface_With_PyTorch_Tabular.ipynb b/docs/examples/Multiple_Models_Interface_With_PyTorch_Tabular.ipynb new file mode 100644 index 00000000..2e65decc --- /dev/null +++ b/docs/examples/Multiple_Models_Interface_With_PyTorch_Tabular.ipynb @@ -0,0 +1,1182 @@ +{ + "cells": [ + { + "cell_type": "code", + "id": "248cbed8", + "metadata": { + "ExecuteTime": { + "end_time": "2024-09-14T11:23:53.324534Z", + "start_time": "2024-09-14T11:23:53.018754Z" + } + }, + "source": [ + "%matplotlib inline\n", + "%load_ext autoreload\n", + "%autoreload 2" + ], + "outputs": [], + "execution_count": 1 + }, + { + "cell_type": "code", + "id": "7ec6cd08", + "metadata": { + "ExecuteTime": { + "end_time": "2024-09-14T11:23:53.339545Z", + "start_time": "2024-09-14T11:23:53.332357Z" + } + }, + "source": [ + "import os\n", + "import warnings\n", + "warnings.filterwarnings('ignore')\n", + "os.environ[\"PYTHONWARNINGS\"] = \"ignore\"" + ], + "outputs": [], + "execution_count": 2 + }, + { + "cell_type": "code", + "id": "b8cb69f2", + "metadata": { + "ExecuteTime": { + "end_time": "2024-09-14T11:23:53.450214Z", + "start_time": "2024-09-14T11:23:53.442005Z" + } + }, + "source": [ + "cur_folder_name = os.getcwd().split('/')[-1]\n", + "if cur_folder_name != \"Virny\":\n", + " os.chdir(\"../..\")\n", + "\n", + "print('Current location: ', os.getcwd())" + ], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current location: /Users/denys_herasymuk/UCU/4course_2term/Bachelor_Thesis/Code/Virny\n" + ] + } + ], + "execution_count": 3 + }, + { + "cell_type": "markdown", + "id": "a578f2ab", + "metadata": {}, + "source": "# Multiple Models Interface With PyTorch Tabular" + }, + { + "cell_type": "markdown", + "id": "2251a923", + "metadata": {}, + "source": [ + "In this example, we are going to conduct a performance profiling for 1 deep learning model from PyTorch Tabular. For that, we will use `compute_metrics_with_config` interface that can compute metrics for multiple models. Thus, we will need to do the next steps:\n", + "\n", + "* Initialize input variables\n", + "\n", + "* Compute subgroup metrics\n", + "\n", + "* Perform disparity metrics composition using the Metric Composer\n", + "\n", + "* Create static visualizations using the Metric Visualizer" + ] + }, + { + "cell_type": "markdown", + "id": "606df34d", + "metadata": {}, + "source": [ + "## Import dependencies" + ] + }, + { + "cell_type": "code", + "id": "7a9241de", + "metadata": { + "ExecuteTime": { + "end_time": "2024-09-14T11:23:55.246335Z", + "start_time": "2024-09-14T11:23:53.517179Z" + } + }, + "source": [ + "import os\n", + "from datetime import datetime, timezone\n", + "\n", + "from sklearn.compose import ColumnTransformer\n", + "from sklearn.preprocessing import OneHotEncoder\n", + "from sklearn.preprocessing import StandardScaler\n", + "\n", + "from virny.datasets import DiabetesDataset2019\n", + "from virny.utils.custom_initializers import create_config_obj, read_model_metric_dfs\n", + "from virny.user_interfaces.multiple_models_api import compute_metrics_with_config\n", + "from virny.preprocessing.basic_preprocessing import preprocess_dataset\n", + "from virny.custom_classes.metrics_visualizer import MetricsVisualizer\n", + "from virny.custom_classes.metrics_composer import MetricsComposer" + ], + "outputs": [], + "execution_count": 4 + }, + { + "cell_type": "markdown", + "id": "75699f5f", + "metadata": {}, + "source": [ + "## Initialize Input Variables" + ] + }, + { + "cell_type": "markdown", + "id": "e86f6556", + "metadata": {}, + "source": [ + "Based on the library flow, we need to create 3 input objects for a user interface:\n", + "\n", + "* A **config yaml** that is a file with configuration parameters for different user interfaces for metric computation.\n", + "\n", + "* A **dataset class** that is a wrapper above the user’s raw dataset that includes its descriptive attributes like a target column, numerical columns, categorical columns, etc. This class must be inherited from the BaseDataset class, which was created for user convenience.\n", + "\n", + "* Finally, a **models config** that is a Python dictionary, where keys are model names and values are initialized models for analysis. This dictionary helps conduct audits for different analysis modes and analyze different types of models." + ] + }, + { + "cell_type": "code", + "source": [ + "DATASET_SPLIT_SEED = 42\n", + "MODELS_TUNING_SEED = 42\n", + "TEST_SET_FRACTION = 0.2" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2024-09-14T11:23:55.267600Z", + "start_time": "2024-09-14T11:23:55.250906Z" + } + }, + "id": "76d98eaabfcfc9c0", + "outputs": [], + "execution_count": 5 + }, + { + "cell_type": "markdown", + "source": [ + "### Create a config object" + ], + "metadata": { + "collapsed": false + }, + "id": "855fb160c6220866" + }, + { + "cell_type": "markdown", + "source": [ + "`compute_metrics_with_config` interface requires that your **yaml file** includes the following parameters:\n", + "\n", + "* **dataset_name**: str, a name of your dataset; it will be used to name files with metrics.\n", + "\n", + "* **bootstrap_fraction**: float, the fraction from a train set in the range [0.0 - 1.0] to fit models in bootstrap (usually more than 0.5).\n", + "\n", + "* **random_state**: int, a seed to control the randomness of the whole model evaluation pipeline.\n", + "\n", + "* **n_estimators**: int, the number of estimators for bootstrap to compute subgroup stability metrics.\n", + "\n", + "* **computation_mode**: str, 'default' or 'error_analysis'. Name of the computation mode. When a default computation mode measures metrics for sex_priv and sex_dis, an `error_analysis` mode measures metrics for (sex_priv, sex_priv_correct, sex_priv_incorrect) and (sex_dis, sex_dis_correct, sex_dis_incorrect). Therefore, a user can analyze how a model is certain about its incorrect predictions.\n", + "\n", + "* **sensitive_attributes_dct**: dict, a dictionary where keys are sensitive attribute names (including intersectional attributes), and values are disadvantaged values for these attributes. Intersectional attributes must include '&' between sensitive attributes. You do not need to specify disadvantaged values for intersectional groups since they will be derived from disadvantaged values in sensitive_attributes_dct for each separate sensitive attribute in this intersectional pair.\n", + "\n", + "Note that disadvantaged value in a sensitive attribute dictionary must be **the same as in the original dataset**. For example, when distinct values of the _sex_ column in the original dataset are 'F' and 'M', and after pre-processing they became 0 and 1 respectively, you still need to set a disadvantaged value as 'F' or 'M' in the sensitive attribute dictionary." + ], + "metadata": { + "collapsed": false + }, + "id": "1137cf9bc7be6964" + }, + { + "cell_type": "code", + "source": [ + "ROOT_DIR = os.path.join('docs', 'examples')\n", + "config_yaml_path = os.path.join(ROOT_DIR, 'experiment_config.yaml')\n", + "config_yaml_content = \"\"\"\n", + "random_state: 42\n", + "dataset_name: diabetes\n", + "bootstrap_fraction: 0.8\n", + "n_estimators: 10 # Better to input the higher number of estimators than 100; this is only for this use case example\n", + "sensitive_attributes_dct: {'Gender': 'Female'}\n", + "\"\"\"\n", + "\n", + "with open(config_yaml_path, 'w', encoding='utf-8') as f:\n", + " f.write(config_yaml_content)" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2024-09-14T11:23:55.289243Z", + "start_time": "2024-09-14T11:23:55.271318Z" + } + }, + "id": "efc95fa248b9f135", + "outputs": [], + "execution_count": 6 + }, + { + "cell_type": "code", + "source": [ + "config = create_config_obj(config_yaml_path=config_yaml_path)\n", + "SAVE_RESULTS_DIR_PATH = os.path.join(ROOT_DIR, 'results', f'{config.dataset_name}_Metrics_{datetime.now(timezone.utc).strftime(\"%Y%m%d__%H%M%S\")}')" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2024-09-14T11:23:55.311267Z", + "start_time": "2024-09-14T11:23:55.293090Z" + } + }, + "id": "f3a59ca9319a774d", + "outputs": [], + "execution_count": 7 + }, + { + "cell_type": "markdown", + "id": "74f57422", + "metadata": {}, + "source": [ + "### Preprocess the dataset and create a BaseFlowDataset class" + ] + }, + { + "cell_type": "markdown", + "id": "eed149cd", + "metadata": {}, + "source": [ + "Based on the BaseDataset class, your **dataset class** should include the following attributes:\n", + "\n", + "* **Obligatory attributes**: dataset, target, features, numerical_columns, categorical_columns\n", + "\n", + "* **Optional attributes**: X_data, y_data, columns_with_nulls\n", + "\n", + "For more details, please refer to the library documentation." + ] + }, + { + "cell_type": "code", + "id": "6c55c6a0", + "metadata": { + "ExecuteTime": { + "end_time": "2024-09-14T11:23:55.361275Z", + "start_time": "2024-09-14T11:23:55.326501Z" + } + }, + "source": [ + "data_loader = DiabetesDataset2019(with_nulls=False)\n", + "data_loader.X_data[data_loader.X_data.columns[:5]].head()" + ], + "outputs": [ + { + "data": { + "text/plain": [ + " BMI Sleep SoundSleep Pregnancies Age\n", + "0 39.0 8 6 0.0 50-59\n", + "1 28.0 8 6 0.0 50-59\n", + "2 24.0 6 6 0.0 40-49\n", + "3 23.0 8 6 0.0 50-59\n", + "4 27.0 8 8 0.0 40-49" + ], + "text/html": [ + "
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" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "execution_count": 8 + }, + { + "cell_type": "code", + "source": [ + "column_transformer = ColumnTransformer(transformers=[\n", + " ('categorical_features', OneHotEncoder(handle_unknown='ignore', sparse_output=False), data_loader.categorical_columns),\n", + " ('numerical_features', StandardScaler(), data_loader.numerical_columns),\n", + "])" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2024-09-14T11:23:55.402471Z", + "start_time": "2024-09-14T11:23:55.383278Z" + } + }, + "id": "8ee9e8a8c10245bf", + "outputs": [], + "execution_count": 9 + }, + { + "cell_type": "code", + "source": [ + "base_flow_dataset = preprocess_dataset(data_loader=data_loader,\n", + " column_transformer=column_transformer,\n", + " sensitive_attributes_dct=config.sensitive_attributes_dct,\n", + " test_set_fraction=TEST_SET_FRACTION,\n", + " dataset_split_seed=DATASET_SPLIT_SEED)" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2024-09-14T11:23:55.449199Z", + "start_time": "2024-09-14T11:23:55.420230Z" + } + }, + "id": "6dba3327ebe01279", + "outputs": [], + "execution_count": 10 + }, + { + "cell_type": "markdown", + "source": [ + "### Create a models config for metrics computation" + ], + "metadata": { + "collapsed": false + }, + "id": "c32119a0992e331c" + }, + { + "cell_type": "markdown", + "source": [ + "**models_config** is a Python dictionary, where keys are model names and values are initialized models for analysis" + ], + "metadata": { + "collapsed": false + }, + "id": "b37d875602beb206" + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2024-09-14T11:23:56.751272Z", + "start_time": "2024-09-14T11:23:55.493912Z" + } + }, + "cell_type": "code", + "source": [ + "from pytorch_tabular.models import GANDALFConfig\n", + "from pytorch_tabular import TabularModel\n", + "from pytorch_tabular.config import (\n", + " DataConfig,\n", + " OptimizerConfig,\n", + " TrainerConfig,\n", + ")\n", + "\n", + "data_config = DataConfig(\n", + " target=[\n", + " data_loader.target\n", + " ], # target should always be a list. Multi-targets are only supported for regression. Multi-Task Classification is not implemented\n", + " continuous_cols=[col for col in base_flow_dataset.X_train_val.columns if col.startswith('numerical_')],\n", + " categorical_cols=[col for col in base_flow_dataset.X_train_val.columns if col.startswith('categorical_')],\n", + ")\n", + "trainer_config = TrainerConfig(\n", + " batch_size=512,\n", + " max_epochs=10,\n", + " load_best=False,\n", + " trainer_kwargs=dict(enable_model_summary=False, # Turning off model summary\n", + " log_every_n_steps=None,\n", + " enable_progress_bar=False),\n", + ")\n", + "optimizer_config = OptimizerConfig()\n", + "model_config = GANDALFConfig(\n", + " task=\"classification\",\n", + " gflu_stages=6,\n", + " gflu_feature_init_sparsity=0.3,\n", + " gflu_dropout=0.0,\n", + " learning_rate=1e-3,\n", + ")" + ], + "id": "abf7aea027176d4", + "outputs": [], + "execution_count": 11 + }, + { + "cell_type": "code", + "source": [ + "models_config = {\n", + " 'GANDALFClassifier': TabularModel(\n", + " data_config=data_config,\n", + " model_config=model_config,\n", + " optimizer_config=optimizer_config,\n", + " trainer_config=trainer_config,\n", + " verbose=False,\n", + " suppress_lightning_logger=True,\n", + " ),\n", + "}" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2024-09-14T11:23:56.797689Z", + "start_time": "2024-09-14T11:23:56.756955Z" + } + }, + "id": "8c6061673bb72efa", + "outputs": [], + "execution_count": 12 + }, + { + "cell_type": "markdown", + "id": "f445b64a", + "metadata": {}, + "source": [ + "## Subgroup Metric Computation" + ] + }, + { + "cell_type": "markdown", + "id": "c3530f06", + "metadata": {}, + "source": [ + "After that we need to input the _BaseFlowDataset_ object, models config, and config yaml to a metric computation interface and execute it. The interface uses subgroup analyzers to compute different sets of metrics for each privileged and disadvantaged group. As for now, our library supports **Subgroup Variance Analyzer** and **Subgroup Error Analyzer**, but it is easily extensible to any other analyzers. When the variance and error analyzers complete metric computation, their metrics are combined, returned in a matrix format, and stored in a file if defined." + ] + }, + { + "cell_type": "code", + "id": "197eadaa", + "metadata": { + "ExecuteTime": { + "end_time": "2024-09-14T11:24:11.380323Z", + "start_time": "2024-09-14T11:23:56.818250Z" + } + }, + "source": [ + "metrics_dct = compute_metrics_with_config(base_flow_dataset, config, models_config, SAVE_RESULTS_DIR_PATH, notebook_logs_stdout=True)" + ], + "outputs": [ + { + "data": { + "text/plain": [ + "Analyze multiple models: 0%| | 0/1 [00:00\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
MetricoverallGender_privGender_disModel_Name
0Statistical_Bias0.2955970.3218310.248779GANDALFClassifier
1Mean_Prediction0.7387740.7528240.713700GANDALFClassifier
2Std0.0861630.0841640.089730GANDALFClassifier
3Aleatoric_Uncertainty0.6905770.6903980.690896GANDALFClassifier
4IQR0.1057060.1056390.105825GANDALFClassifier
5Overall_Uncertainty0.7227700.7205650.726706GANDALFClassifier
6Epistemic_Uncertainty0.0321930.0301670.035810GANDALFClassifier
7Jitter0.1048500.1001920.113162GANDALFClassifier
8Label_Stability0.8519340.8603450.836923GANDALFClassifier
9TPR0.3265310.2121210.562500GANDALFClassifier
10TNR0.9696970.9638550.979592GANDALFClassifier
11PPV0.8000000.7000000.900000GANDALFClassifier
12FNR0.6734690.7878790.437500GANDALFClassifier
13FPR0.0303030.0361450.020408GANDALFClassifier
14Accuracy0.7955800.7500000.876923GANDALFClassifier
15F10.4637680.3255810.692308GANDALFClassifier
16Selection-Rate0.1104970.0862070.153846GANDALFClassifier
17Sample_Size181.000000116.00000065.000000GANDALFClassifier
\n", + "" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "execution_count": 14 + }, + { + "cell_type": "markdown", + "id": "a7ff67e9", + "metadata": {}, + "source": [ + "## Disparity Metric Composition" + ] + }, + { + "cell_type": "markdown", + "id": "274c97e2", + "metadata": {}, + "source": [ + "To compose disparity metrics, the Metric Composer should be applied. **Metric Composer** is responsible for the second stage of the model audit. Currently, it computes our custom error disparity, stability disparity, and uncertainty disparity metrics, but extending it for new disparity metrics is very simple. We noticed that more and more disparity metrics have appeared during the last decade, but most of them are based on the same group specific metrics. Hence, such a separation of group specific and disparity metrics computation allows us to experiment with different combinations of group specific metrics and avoid group metrics recomputation for a new set of disparity metrics." + ] + }, + { + "cell_type": "code", + "id": "f94a20dc", + "metadata": { + "ExecuteTime": { + "end_time": "2024-09-14T11:24:11.507617Z", + "start_time": "2024-09-14T11:24:11.473712Z" + } + }, + "source": [ + "models_metrics_dct = read_model_metric_dfs(SAVE_RESULTS_DIR_PATH, model_names=list(models_config.keys()))" + ], + "outputs": [], + "execution_count": 15 + }, + { + "cell_type": "code", + "id": "b04d06cf", + "metadata": { + "ExecuteTime": { + "end_time": "2024-09-14T11:24:11.557060Z", + "start_time": "2024-09-14T11:24:11.523723Z" + } + }, + "source": [ + "metrics_composer = MetricsComposer(models_metrics_dct, config.sensitive_attributes_dct)" + ], + "outputs": [], + "execution_count": 16 + }, + { + "cell_type": "markdown", + "id": "e1a23ece", + "metadata": {}, + "source": [ + "Compute composed metrics" + ] + }, + { + "cell_type": "code", + "id": "be6ace22", + "metadata": { + "ExecuteTime": { + "end_time": "2024-09-14T11:24:11.624771Z", + "start_time": "2024-09-14T11:24:11.590344Z" + } + }, + "source": [ + "models_composed_metrics_df = metrics_composer.compose_metrics()" + ], + "outputs": [], + "execution_count": 17 + }, + { + "cell_type": "markdown", + "id": "deb45226", + "metadata": {}, + "source": [ + "## Metric Visualization" + ] + }, + { + "cell_type": "markdown", + "id": "2f5d4cdb", + "metadata": {}, + "source": [ + "**Metric Visualizer** allows us to build static visualizations for the computed metrics. It unifies different preprocessing methods for the computed metrics and creates various data formats required for visualizations. Hence, users can simply call methods of the MetricsVisualizer class and get custom plots for diverse metric analysis." + ] + }, + { + "cell_type": "code", + "id": "435b9d98", + "metadata": { + "ExecuteTime": { + "end_time": "2024-09-14T11:24:11.683705Z", + "start_time": "2024-09-14T11:24:11.648651Z" + } + }, + "source": [ + "visualizer = MetricsVisualizer(models_metrics_dct, models_composed_metrics_df, config.dataset_name,\n", + " model_names=list(models_config.keys()),\n", + " sensitive_attributes_dct=config.sensitive_attributes_dct)" + ], + "outputs": [], + "execution_count": 18 + }, + { + "cell_type": "code", + "id": "5efb1bf2", + "metadata": { + "ExecuteTime": { + "end_time": "2024-09-14T11:24:11.762657Z", + "start_time": "2024-09-14T11:24:11.708320Z" + } + }, + "source": [ + "visualizer.create_overall_metrics_bar_char(\n", + " metric_names=['Accuracy', 'F1', 'TPR', 'TNR', 'PPV', 'Selection-Rate'],\n", + " plot_title=\"Accuracy Metrics\"\n", + ")" + ], + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "
\n", + "" + ], + "text/plain": [ + "alt.Chart(...)" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], + "execution_count": 19 + }, + { + "cell_type": "code", + "id": "0eb8528e", + "metadata": { + "ExecuteTime": { + "end_time": "2024-09-14T11:24:11.836789Z", + "start_time": "2024-09-14T11:24:11.787552Z" + } + }, + "source": [ + "visualizer.create_overall_metrics_bar_char(\n", + " metric_names=['Aleatoric_Uncertainty', 'Overall_Uncertainty', 'Label_Stability', 'Std', 'IQR', 'Jitter'],\n", + " plot_title=\"Stability and Uncertainty Metrics\"\n", + ")" + ], + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "
\n", + "" + ], + "text/plain": [ + "alt.Chart(...)" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + } + ], + "execution_count": 20 + }, + { + "cell_type": "code", + "id": "df024aed", + "metadata": { + "ExecuteTime": { + "end_time": "2024-09-14T11:24:12.074578Z", + "start_time": "2024-09-14T11:24:11.860815Z" + } + }, + "source": [ + "visualizer.create_overall_metric_heatmap(\n", + " model_names=list(models_metrics_dct.keys()),\n", + " metrics_lst=visualizer.all_accuracy_metrics + visualizer.all_stability_metrics,\n", + " tolerance=0.005,\n", + ")" + ], + "outputs": [ + { + "data": { + "text/plain": [ + "
" + ], + "image/png": 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I+vnMsNrzxHbu3Kn09PQj1hUREaGWLVtq1qxZ+uKLL5ScnKxWrVpp4MCBSklJUceOHRUUFCRJ6tWrl+bPn68ff/xR55xzToMB2eHDh/X4448rKChI5513npKTkxUSEqKsrCxffZJkt9vl8XhUUlKi1atX6/nnn5ck/fjjj5KkCy+8UC+99JLefvttvfnmmyovL1fr1q11//3368orr6zzmQMHDtTAgQOP418DAADgxBGQAQAAHKedO3dq1KhRuv3223XnnXcqMDBQ27dvV15eniZOnKjOnTsrKipKNptNBoNBFRUVuvfeezVx4kRFRUWpf//+deZbt26dqqqqFBUVpRtuuEE//PCDpkyZor/97W9KSEiQy+WSxWJRUlKSpJoVWYMHDz5ifREREb4zy5588kl16dJFBoNBVqvV16c21Ordu7cSExO1cOFC3XrrrTKbzb6zxmrDstLSUt8KN0lKSEhQTEyM9u7dK0nq16+fzGazHnvsMS1fvlwOh0MbNmxQ27ZtNWrUKL3yyitauXKlevfurYEDB6p79+7yeDwNvo0TAACgMRz9JFYAAAA/VLtdcPny5Q22157fdejQIVVWVspgMKhdu3aSpLS0NPXq1UtpaWmKj49XXFyc0tLS9NRTTyktLU0vvPCC9uzZI0lyOp2SpB07dkiq2WqZkZGhIUOG6Ntvv9Xnn38u6efzvxITExUeHl5ny2JD7Ha7kpKSVFZWpoCAAAUEBNQJx6Sf31LZsmVL/fa3v9WmTZv04osvSpJMJpPvc71er6ZNmyaj0aibb75ZUs12z7i4OO3cuVOFhYVKSUnRf/7zH3Xv3l2ZmZnavXu3hg4dqkcffVR33nmnNmzYoN69e0uqCeYiIyMJxwAAQJPCCjIAAOCX8vLyjngo/bx58/TOO+/o008/1RtvvKG2bdvWaQ8LC1OLFi20e/duFRcXKzAwUImJiZKk7du3a8CAAb4VWlJN8NW2bVsNGzZML730kr7//nu1aNHC16e4uFhSzVslJWnUqFH6/PPP9cEHH6hPnz6+8C0uLk5xcXHKyso66lsna99kWVJSotzcXHXp0uWIz8FisejGG2/U119/ralTp6qkpER9+vRRdHS08vPzNXPmTC1btkxXXnmlzjnnHN+4Vq1a+Q7Yj4iIUN++fdWzZ896Qdz/+uW5agAAAE0FK8gAAMBZ55fh1P/Ky8vT0KFDNWDAAH377bd12jwejySpXbt2io+PV0FBgR577DGtW7euTrskpaenKycnR4WFhZJqth3a7Xbf6i6DwVDnP0nq27evTCaTli1bJqkmnCovL9fOnTsVGRnpC8I6dOig8ePHa8eOHZo+fbrvMyMjI5WYmKj9+/erqKjoiPdtMpmUnJwsr9erffv21am7oTFms1mvvPKKrrrqKs2ZM0d/+ctfdM899+gPf/iDNm3apDvvvFOTJ0/2bbuUpIceekgzZ870nYsm6VfDMQAAgKaKgAwAAJwVnE6nDhw4oIqKigZXKdWGR5s3b5bb7ZYkvfXWW8rOzpZUN/xKSEiQ0WhURESE9u/fr//7v//T3r17ZTQa5XK5JEnt27dXfn6+Dh06JKnm7YuxsbG+7ZK/VLvSKy0tTXFxcdq0aZOkn0O0DRs2yGaz+cImt9utnj17SpI+/fRTbdiwQVLNyrCUlBQ5HA7l5OTUua/a+WrFxsbKbrdr7969vrdJNqR2TFBQkB5//HF99NFHeuihh/Twww/ro48+0pIlSzRhwgTfdssjrVoDAABozthiCQAAmq3ly5dr3rx5Wr58uQoLCxUREaE2bdpo6NChuvjii2W3231vgPR4PDKZTNqzZ48CAwM1YsQILV68WHPmzNEf/vAHST+HRUlJSbJarYqLi9PNN9+su+66S48//rheeOEF2Ww2STUBmcPhUF5eniQpOjpaiYmJWrt2rUpKShQaGlqv3ujoaEVFRSkrK0tOp1MWi0Uej0f5+flyuVy6++67tWvXLuXk5MjhcCgyMlIFBQV6+eWX9ec//1mxsbG+rZw5OTnq2rWrr2aHw6HDhw+rqqpKaWlpiomJUUxMjLZt26aysjIFBQX5nsXRJCcnKzk5ucE2tkcCAICzFQEZAABoVmpDng8//FCvvvqqwsLCNGLECKWkpCgnJ0dLlizRpEmTlJ2drfvvv98X6tQePF9aWqry8nL95je/UV5enmbNmqVRo0YpPT1dHo9HBoNBsbGxvkPoBw8erBtvvFGvv/66/vznP+upp56SVLMaTJIOHDggqeZg/JSUFC1btkz79++vF5DVnhkWGBgok8mkvLw8JScn64cfflBAQIDsdrsqKiqUkZGhMWPGqFWrVgoMDNQLL7ygJUuW6MILL9SYMWOUkpKiwMBAffXVVwoNDdXu3buVk5Ojffv2aevWrYqLi9Njjz2m+Ph4JSYmqqqqyrc6joALAACgYQRkAACgWTEYDFq0aJH+8pe/aPDgwbrrrruUnJyswMBASdLvf/97Pfroo/rPf/6j9PR0XX755b5QraqqSlu3blVZWZl69+6t8vJy3XPPPXrxxRf17LPP+rYP2u12paam6vvvv1dlZaUefvhh5eTkaM6cOUpOTtadd96phIQERUREaPfu3SorK/O9OVKSsrOz1b59+wbrN5lMioyMVHV1tSRp9+7dKiws1IQJE3T33XerurpaAQEBkmq2Zt5www1avXq1Zs6cqb59+yotLU2hoaH65JNPNG/ePN+Wz7CwMCUlJSkjI0OhoaGy2+2aNm0aoRgAAMAxICADAADNSlFRkaZMmaKkpCT9/e9/V1BQkK+t9oysP/3pT4qLi1Pfvn0l/bxyKiAgQBs3blRUVJQ8Ho+GDBmiDh06aP78+Ro3bpx69Ojhm6t2m+G2bdvUtWtX/e1vf1NlZaVeeuklRUdH65prrlF6erov4KoNyAwGg7Zu3arhw4f7zgerrSsnJ0f79+9Xq1at1KpVK0lSWVmZJKlt27YymUy+LZy1zj33XN1222166aWX9OKLL+rxxx9X//795XQ61blzZ3Xs2FFt27ZtcEsn4RgAAMCxISADAADNyu7du7V9+3bdfffd9c7VMhgM8nq9CgoK0m233eZbVVaroKBAVVVV6tSpk6qqqhQYGKixY8fqiSee0LRp0/Too4/6VoHVBmSbN29W165dFRoaqokTJ6q8vFyPP/64wsPD1bVrV82ZM0cFBQVKSUlRQkKCIiMj67zJ8pf//+GHH7Rnzx5df/31kqTy8nLt2rVLYWFh6tatW4P3GxQUpJtuukldu3ZV586dZbFY9Le//e0UP1UAAAD/xmuIAABAs1L7lsjzzjuvwfbaMKo2HKt9Y6UkrVu3TocOHVJqaqrCwsIkSZdffrmGDx+uL7/8UkuWLPH1TUhIUEhIiNatWydJqq6uVnp6uv70pz8pLS1NTz31lNatWye32+07qD8uLs53dpnD4VBpaalyc3O1Zs0aPffcc3r00Ud1/vnn6ze/+Y2kmu2W3377rWJiYnzBXEPsdrsuvPBCRUdHn9AzAwAAwNGxggwAADQrtYftV1RUSFKdFWR79uzRjz/+qM2bN2v79u3atWuXbrjhBo0bN05SzQoyqSY8Kysr03fffaecnBxt375dkvTxxx+rZ8+e6tSpk2JiYhQfH6/NmzdLku98sk6dOukf//iHJkyYoB9++EHBwcE6ePCgJCkqKkqpqalatGiRnn32WeXm5mrnzp3Ky8tTQECAfvOb3+jBBx/0baMMDAzUokWLFBwcfCYeHQAAAI6AgAwAADQrtVsfc3NzJf38dsjt27frscce06ZNm9ShQwcFBQUpJydHmzZt8vUrKSmRJL300kuaOnWqTCaToqOjlZqaqnPPPVffffed3n77bT355JOKiopScnKyvv/+e0mS2fzz16Zu3brp6aef1u233+7bJul0OmW1WhUSEiKDwaCFCxcqPT1dF110kbp3767OnTsrLi6u3v0QjgEAADQ+AjIAANCstGzZUlLN4fm/lJqaqn/84x8ymUyKiYnRoUOHdMsttyg7O1tSzRbJVatWyWAw6J577lFaWpri4+MVFRUlu92u8vJyjR49WrNnz9b111+v9u3bq2XLllq6dKkKCgoUGRnp+yy3263zzz9fDz74oF577TWFh4fL6XTKYrHo/vvv1+TJk30rzgAAAND0EZABAIBmJTw8XKGhoVq+fLmkn7c+Wq1Wpaam+volJSUpKSlJWVlZqq6uVmBgoLZu3aqwsDCNGTOmTuAlSWFhYbrlllv03HPP6W9/+5tefvllxcfHS5I2btyo/v37+7Zz1m7zHDdunK677rp68wAAAKB54adNAADQrJjNZo0aNUpbt27VkiVLfAGZ1+ut08/hcKikpEQHDhxQbm6uysrKVFVVpW7duslqtdbp6/F4JEnXX3+9BgwYoM2bNysnJ0eXXHKJpk2bph49ekj6+QUAtVglBgAAcHZgBRkAAGh2br75Zi1btkx/+ctfZDAYNHDgQBkMBnm9XjkcDmVlZenVV1/VunXrdPnllys5OVmff/658vLyNGLECNntdt/ZZdLPQZfJZNLUqVPrfFZD54YBAADg7GLw/u/PrQAAAM3AV199paefflp5eXnq2rWrMjIyFBUVpZ07d+rrr79WVVWVxo4dq2uuuUZ2u13ffPONPvzwQ11//fXKyMho7PIBAADQhBCQAQCAZisnJ0cff/yx1qxZo/3796uwsFDBwcHq27evhg8frl69etXbTgkAAAD8LwIyAADQ7BUXF6uyslIxMTGcCwYAAIDjRkAGAACatdo3SwIAAAAnip9YAQBAs0Y4BgAAgJNFQAYAAAAAAAC/RkAGAAAAAAAAv0ZABgAAAAAAAL9GQAYAAAAAAAC/RkAGAAAAAAAAv0ZABgAAAAAAAL9GQAYAAOAHBg0apEGDBjV2GQAAAE0SARkAAAAAAAD8GgEZAAAAAAAA/BoBGQAAAAAAAPwaARkAAAAAAAD8GgEZAAAAAAAA/BoBGQA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+ }, + "metadata": {}, + "output_type": "display_data" + } + ], + "execution_count": 21 + }, + { + "cell_type": "code", + "id": "2326c129", + "metadata": { + "ExecuteTime": { + "end_time": "2024-09-14T11:24:12.278986Z", + "start_time": "2024-09-14T11:24:12.125253Z" + } + }, + "source": [ + "visualizer.create_disparity_metric_heatmap(\n", + " model_names=list(models_metrics_dct.keys()),\n", + " metrics_lst=[\n", + " # Error disparity metrics\n", + " 'Equalized_Odds_TPR',\n", + " 'Equalized_Odds_FPR',\n", + " 'Disparate_Impact',\n", + " # Stability disparity metrics\n", + " 'Label_Stability_Difference',\n", + " 'Aleatoric_Uncertainty_Difference',\n", + " 'Std_Ratio',\n", + " ],\n", + " groups_lst=config.sensitive_attributes_dct.keys(),\n", + " tolerance=0.005,\n", + ")" + ], + "outputs": [ + { + "data": { + "text/plain": [ + "
" + ], + "image/png": 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" + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "execution_count": 22 + }, + { + "cell_type": "code", + "source": [], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2024-09-14T11:24:12.303721Z", + "start_time": "2024-09-14T11:24:12.301931Z" + } + }, + "id": "a6ebe5c2fae387cb", + "outputs": [], + "execution_count": null + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.10" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/docs/examples/Multiple_Models_Interface_With_PyTorch_Tabular.md b/docs/examples/Multiple_Models_Interface_With_PyTorch_Tabular.md new file mode 100644 index 00000000..05d0cc10 --- /dev/null +++ b/docs/examples/Multiple_Models_Interface_With_PyTorch_Tabular.md @@ -0,0 +1,660 @@ +# Multiple Models Interface With PyTorch Tabular + +In this example, we are going to conduct a performance profiling for 1 deep learning model from PyTorch Tabular. For that, we will use `compute_metrics_with_config` interface that can compute metrics for multiple models. Thus, we will need to do the next steps: + +* Initialize input variables + +* Compute subgroup metrics + +* Perform disparity metrics composition using the Metric Composer + +* Create static visualizations using the Metric Visualizer + +## Import dependencies + + +```python +import os +from datetime import datetime, timezone + +from sklearn.compose import ColumnTransformer +from sklearn.preprocessing import OneHotEncoder +from sklearn.preprocessing import StandardScaler + +from virny.datasets import DiabetesDataset2019 +from virny.utils.custom_initializers import create_config_obj, read_model_metric_dfs +from virny.user_interfaces.multiple_models_api import compute_metrics_with_config +from virny.preprocessing.basic_preprocessing import preprocess_dataset +from virny.custom_classes.metrics_visualizer import MetricsVisualizer +from virny.custom_classes.metrics_composer import MetricsComposer +``` + +## Initialize Input Variables + +Based on the library flow, we need to create 3 input objects for a user interface: + +* A **config yaml** that is a file with configuration parameters for different user interfaces for metric computation. + +* A **dataset class** that is a wrapper above the user’s raw dataset that includes its descriptive attributes like a target column, numerical columns, categorical columns, etc. This class must be inherited from the BaseDataset class, which was created for user convenience. + +* Finally, a **models config** that is a Python dictionary, where keys are model names and values are initialized models for analysis. This dictionary helps conduct audits for different analysis modes and analyze different types of models. + + +```python +DATASET_SPLIT_SEED = 42 +MODELS_TUNING_SEED = 42 +TEST_SET_FRACTION = 0.2 +``` + +### Create a config object + +`compute_metrics_with_config` interface requires that your **yaml file** includes the following parameters: + +* **dataset_name**: str, a name of your dataset; it will be used to name files with metrics. + +* **bootstrap_fraction**: float, the fraction from a train set in the range [0.0 - 1.0] to fit models in bootstrap (usually more than 0.5). + +* **random_state**: int, a seed to control the randomness of the whole model evaluation pipeline. + +* **n_estimators**: int, the number of estimators for bootstrap to compute subgroup stability metrics. + +* **computation_mode**: str, 'default' or 'error_analysis'. Name of the computation mode. When a default computation mode measures metrics for sex_priv and sex_dis, an `error_analysis` mode measures metrics for (sex_priv, sex_priv_correct, sex_priv_incorrect) and (sex_dis, sex_dis_correct, sex_dis_incorrect). Therefore, a user can analyze how a model is certain about its incorrect predictions. + +* **sensitive_attributes_dct**: dict, a dictionary where keys are sensitive attribute names (including intersectional attributes), and values are disadvantaged values for these attributes. Intersectional attributes must include '&' between sensitive attributes. You do not need to specify disadvantaged values for intersectional groups since they will be derived from disadvantaged values in sensitive_attributes_dct for each separate sensitive attribute in this intersectional pair. + +Note that disadvantaged value in a sensitive attribute dictionary must be **the same as in the original dataset**. For example, when distinct values of the _sex_ column in the original dataset are 'F' and 'M', and after pre-processing they became 0 and 1 respectively, you still need to set a disadvantaged value as 'F' or 'M' in the sensitive attribute dictionary. + + +```python +ROOT_DIR = os.path.join('docs', 'examples') +config_yaml_path = os.path.join(ROOT_DIR, 'experiment_config.yaml') +config_yaml_content = """ +random_state: 42 +dataset_name: diabetes +bootstrap_fraction: 0.8 +n_estimators: 10 # Better to input the higher number of estimators than 100; this is only for this use case example +sensitive_attributes_dct: {'Gender': 'Female'} +""" + +with open(config_yaml_path, 'w', encoding='utf-8') as f: + f.write(config_yaml_content) +``` + + +```python +config = create_config_obj(config_yaml_path=config_yaml_path) +SAVE_RESULTS_DIR_PATH = os.path.join(ROOT_DIR, 'results', f'{config.dataset_name}_Metrics_{datetime.now(timezone.utc).strftime("%Y%m%d__%H%M%S")}') +``` + +### Preprocess the dataset and create a BaseFlowDataset class + +Based on the BaseDataset class, your **dataset class** should include the following attributes: + +* **Obligatory attributes**: dataset, target, features, numerical_columns, categorical_columns + +* **Optional attributes**: X_data, y_data, columns_with_nulls + +For more details, please refer to the library documentation. + + +```python +data_loader = DiabetesDataset2019(with_nulls=False) +data_loader.X_data[data_loader.X_data.columns[:5]].head() +``` + + + + +
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
BMISleepSoundSleepPregnanciesAge
039.0860.050-59
128.0860.050-59
224.0660.040-49
323.0860.050-59
427.0880.040-49
+
+ + + + +```python +column_transformer = ColumnTransformer(transformers=[ + ('categorical_features', OneHotEncoder(handle_unknown='ignore', sparse_output=False), data_loader.categorical_columns), + ('numerical_features', StandardScaler(), data_loader.numerical_columns), +]) +``` + + +```python +base_flow_dataset = preprocess_dataset(data_loader=data_loader, + column_transformer=column_transformer, + sensitive_attributes_dct=config.sensitive_attributes_dct, + test_set_fraction=TEST_SET_FRACTION, + dataset_split_seed=DATASET_SPLIT_SEED) +``` + +### Create a models config for metrics computation + +**models_config** is a Python dictionary, where keys are model names and values are initialized models for analysis + + +```python +from pytorch_tabular.models import GANDALFConfig +from pytorch_tabular import TabularModel +from pytorch_tabular.config import ( + DataConfig, + OptimizerConfig, + TrainerConfig, +) + +data_config = DataConfig( + target=[ + data_loader.target + ], # target should always be a list. Multi-targets are only supported for regression. Multi-Task Classification is not implemented + continuous_cols=[col for col in base_flow_dataset.X_train_val.columns if col.startswith('numerical_')], + categorical_cols=[col for col in base_flow_dataset.X_train_val.columns if col.startswith('categorical_')], +) +trainer_config = TrainerConfig( + batch_size=512, + max_epochs=10, + load_best=False, + trainer_kwargs=dict(enable_model_summary=False, # Turning off model summary + log_every_n_steps=None, + enable_progress_bar=False), +) +optimizer_config = OptimizerConfig() +model_config = GANDALFConfig( + task="classification", + gflu_stages=6, + gflu_feature_init_sparsity=0.3, + gflu_dropout=0.0, + learning_rate=1e-3, +) +``` + + +```python +models_config = { + 'GANDALFClassifier': TabularModel( + data_config=data_config, + model_config=model_config, + optimizer_config=optimizer_config, + trainer_config=trainer_config, + verbose=False, + suppress_lightning_logger=True, + ), +} +``` + +## Subgroup Metric Computation + +After that we need to input the _BaseFlowDataset_ object, models config, and config yaml to a metric computation interface and execute it. The interface uses subgroup analyzers to compute different sets of metrics for each privileged and disadvantaged group. As for now, our library supports **Subgroup Variance Analyzer** and **Subgroup Error Analyzer**, but it is easily extensible to any other analyzers. When the variance and error analyzers complete metric computation, their metrics are combined, returned in a matrix format, and stored in a file if defined. + + +```python +metrics_dct = compute_metrics_with_config(base_flow_dataset, config, models_config, SAVE_RESULTS_DIR_PATH, notebook_logs_stdout=True) +``` + + + Analyze multiple models: 0%| | 0/1 [00:00 + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
MetricoverallGender_privGender_disModel_Name
0Statistical_Bias0.2955970.3218310.248779GANDALFClassifier
1Mean_Prediction0.7387740.7528240.713700GANDALFClassifier
2Std0.0861630.0841640.089730GANDALFClassifier
3Aleatoric_Uncertainty0.6905770.6903980.690896GANDALFClassifier
4IQR0.1057060.1056390.105825GANDALFClassifier
5Overall_Uncertainty0.7227700.7205650.726706GANDALFClassifier
6Epistemic_Uncertainty0.0321930.0301670.035810GANDALFClassifier
7Jitter0.1048500.1001920.113162GANDALFClassifier
8Label_Stability0.8519340.8603450.836923GANDALFClassifier
9TPR0.3265310.2121210.562500GANDALFClassifier
10TNR0.9696970.9638550.979592GANDALFClassifier
11PPV0.8000000.7000000.900000GANDALFClassifier
12FNR0.6734690.7878790.437500GANDALFClassifier
13FPR0.0303030.0361450.020408GANDALFClassifier
14Accuracy0.7955800.7500000.876923GANDALFClassifier
15F10.4637680.3255810.692308GANDALFClassifier
16Selection-Rate0.1104970.0862070.153846GANDALFClassifier
17Sample_Size181.000000116.00000065.000000GANDALFClassifier
+ + + + +## Disparity Metric Composition + +To compose disparity metrics, the Metric Composer should be applied. **Metric Composer** is responsible for the second stage of the model audit. Currently, it computes our custom error disparity, stability disparity, and uncertainty disparity metrics, but extending it for new disparity metrics is very simple. We noticed that more and more disparity metrics have appeared during the last decade, but most of them are based on the same group specific metrics. Hence, such a separation of group specific and disparity metrics computation allows us to experiment with different combinations of group specific metrics and avoid group metrics recomputation for a new set of disparity metrics. + + +```python +models_metrics_dct = read_model_metric_dfs(SAVE_RESULTS_DIR_PATH, model_names=list(models_config.keys())) +``` + + +```python +metrics_composer = MetricsComposer(models_metrics_dct, config.sensitive_attributes_dct) +``` + +Compute composed metrics + + +```python +models_composed_metrics_df = metrics_composer.compose_metrics() +``` + +## Metric Visualization + +**Metric Visualizer** allows us to build static visualizations for the computed metrics. It unifies different preprocessing methods for the computed metrics and creates various data formats required for visualizations. Hence, users can simply call methods of the MetricsVisualizer class and get custom plots for diverse metric analysis. + + +```python +visualizer = MetricsVisualizer(models_metrics_dct, models_composed_metrics_df, config.dataset_name, + model_names=list(models_config.keys()), + sensitive_attributes_dct=config.sensitive_attributes_dct) +``` + + +```python +visualizer.create_overall_metrics_bar_char( + metric_names=['Accuracy', 'F1', 'TPR', 'TNR', 'PPV', 'Selection-Rate'], + plot_title="Accuracy Metrics" +) +``` + + + + + +
+ + + + + +```python +visualizer.create_overall_metrics_bar_char( + metric_names=['Aleatoric_Uncertainty', 'Overall_Uncertainty', 'Label_Stability', 'Std', 'IQR', 'Jitter'], + plot_title="Stability and Uncertainty Metrics" +) +``` + + + + + +
+ + + + + +```python +visualizer.create_overall_metric_heatmap( + model_names=list(models_metrics_dct.keys()), + metrics_lst=visualizer.all_accuracy_metrics + visualizer.all_stability_metrics, + tolerance=0.005, +) +``` + + + +![png](Multiple_Models_Interface_With_PyTorch_Tabular_files/Multiple_Models_Interface_With_PyTorch_Tabular_39_0.png) + + + + +```python +visualizer.create_disparity_metric_heatmap( + model_names=list(models_metrics_dct.keys()), + metrics_lst=[ + # Error disparity metrics + 'Equalized_Odds_TPR', + 'Equalized_Odds_FPR', + 'Disparate_Impact', + # Stability disparity metrics + 'Label_Stability_Difference', + 'Aleatoric_Uncertainty_Difference', + 'Std_Ratio', + ], + groups_lst=config.sensitive_attributes_dct.keys(), + tolerance=0.005, +) +``` + + + +![png](Multiple_Models_Interface_With_PyTorch_Tabular_files/Multiple_Models_Interface_With_PyTorch_Tabular_40_0.png) diff --git a/docs/examples/Multiple_Models_Interface_With_PyTorch_Tabular_files/Multiple_Models_Interface_With_PyTorch_Tabular_39_0.png b/docs/examples/Multiple_Models_Interface_With_PyTorch_Tabular_files/Multiple_Models_Interface_With_PyTorch_Tabular_39_0.png new file mode 100644 index 0000000000000000000000000000000000000000..3a466295d8646b90e08dd64b20e53dd451430b80 GIT binary patch literal 38997 zcmeFZcU+X&wk?XrvS|rIxh9W3YvZzST z6i5ci6hVOkDMG!u=)Lbf=iEN$y!+0(_mBG?zuw(!sG`1at-0nLbBr;+=c>wbv^0z~ zR8&;7m|HinR8+s!QBm!d`JEbm^62?QDEvp65D|8}Lz zq*+o?MSQ~CxT$!P>FN&Vqoo zw#LiI?JdMQ5}wd-<=r?U%tf<1&-@e3t0Q&F%D8+V-!IHc$6m!gUiLAFi1;xu<`wVn z+d>jw^Om8`?IF^`HNe+M+RH$LzrLkvd-FToEBYGg`Ct6qL+bk#LtpuKQBlR5{>=nF zQ4G&4bzil=*gm3vUOLCou2_#XxOUCmui7C~VGk9RQfPIfiZQ&-;%j%dUK)d-nFVQ< zP)f`xTiM=N8d$RjGMeOUTeg4^7;w=XVK-qzN4d~)sj^@*SN zyD~NLNl))ktZM@3Jr^t1gsi%_wtfcthIY<$HH!{sNiKXC6B>dAwV-MGYcU=8VwBSw z8XD+WSr__gN?eIr+4?2ev9E!h@!KosN(SvBn`wP(va_OFkaV*Fzrm0cxibIM%;L99~Bkj;av=H2T@O+J^OuYd#x&> z`pzqYRTz7r{jeb}6o)(GSA8nKm#?@#JCI3~=ggTz<&VOq4Rqd1_2*~~9SR?`>&eo| zUm+87iDBA?YS*qk!m6v&v$EzSB%D%ubMflw)29QS;voc@#$NfLmEtzc)rRe`D=r&~ zX1{*znn`iLsZh69;CUV%T=ku{vc*b#euCK3?H-;%WpFUlysjcM|64Jn6Fqt_y;mH#It9z&( z=AYn2k=T}e^5E06aV$j-*6c1XHwl))-{Zg>mf3t)ZkP+Z+Ldpvl9HCj%flo8{P}YU zuV3#nGc!#to)mIl&^Pd$Ng;n`+a8JL6&KeT_Fjt}@}+oZ>lVuU?K`aH&R^W@T(-x^4j(lV8q46`^wyNckkXE8WC|0W7U}&c>KKXcr*n5 zuYEi5${f~141A*xYHDh@xw$b| zr7I%FUk|cKd;M~ehNRi{<;W{I+*kM`6rx3JJ4A=Ql7y|h>*n2f&z`-0_U21fUEL`5 zdJ_R|F0Na!=p`j30l~q!qOGrsC9KJj=)9E$WYdUMeGLBQ7qoQdskjrSh=-Tgw78$}-dGM6XudaJ z4rd|~O(=D>M#ljnK)o^iJgi&t__&GpuTlTXk4;vE4RaW{LsEbaPH_1X+miJI13x=swGEYeIx@nt`sAt<~-FB)@~6BTZGT8%xq7Rv#x%$ zw^Fb~x6q2i|MT0qIUCFp%cBbzxI6NwKbsJ|tzu)Ltny<6k<>8bG8-s5)Hc_fFMQ`C z)gVM0ih}K}MPKW_0!v&n1nuldwV%Y=fOA+}T+X*|w@LH;+MTINI~fIoy>lm_qoX4< zIvW4|`#p?IRlU=2r8my%QF%dukfye__2N(k&Edn^5&QSJ#(J<8AKr(qH1`-`o&$Ryn|x9r8HY#ip3BXmni?AHkiKBSR_FVPEs~2}vL_6Z;pAC%XX%tq zMp?T+2A0S9K)Sb@Y>Ldg&4;rp66NINv}y?D?)%`Fbmq(%xDb8~36hKD z>JaQ$JG-1X?^PX!z?*@rmvuQqFxA!7@X3AU9{DK33U!%?@kgMRUERqe=C23K+^v_! 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diff --git a/README.md b/README.md index aea30093..22d2884e 100644 --- a/README.md +++ b/README.md @@ -92,6 +92,7 @@ In contrast to existing fairness software libraries and model card generating fr * Metric static and interactive visualizations * Data loaders with subsampling for popular fair-ML benchmark datasets * User-friendly parameters input via config yaml files +* Integration with PyTorch Tabular Check out [our documentation](https://dataresponsibly.github.io/Virny/) for a comprehensive overview. diff --git a/docs/introduction/welcome_to_virny.md b/docs/introduction/welcome_to_virny.md index 9821f074..cd044f1c 100644 --- a/docs/introduction/welcome_to_virny.md +++ b/docs/introduction/welcome_to_virny.md @@ -62,6 +62,7 @@ In contrast to existing fairness software libraries and model card generating fr * Metric static and interactive visualizations * Data loaders with subsampling for popular fair-ML benchmark datasets * User-friendly parameters input via config yaml files +* Integration with PyTorch Tabular Check out [our documentation](https://dataresponsibly.github.io/Virny/) for a comprehensive overview. diff --git a/docs/release_notes/0.6.0.md b/docs/release_notes/0.6.0.md index 7dd7fdfa..69919c4b 100644 --- a/docs/release_notes/0.6.0.md +++ b/docs/release_notes/0.6.0.md @@ -9,6 +9,11 @@ * Now Virny supports Python 3.9, 3.10, 3.11, and 3.12! 🎉🥳 +## 🔥 Integration with PyTorch Tabular + +* Now Virny supports profiling of the tabular deep learning models from PyTorch Tabular + + ## ⚙️ Fitted Bootstrap Exporting * Added the `return_fitted_bootstrap` flag to metric computation interfaces to return a fitted bootstrap, which users can save to a pickle file later and reuse for future experiments From 2a9a5259c7f2c1d6d33402f8e4cb64c5c8a924f5 Mon Sep 17 00:00:00 2001 From: Denys Herasymuk Date: Sat, 14 Sep 2024 14:56:32 +0300 Subject: [PATCH 27/27] Added a tutorial on how to use PyTorch Tabular models together with Virny --- virny/custom_classes/metrics_visualizer.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/virny/custom_classes/metrics_visualizer.py b/virny/custom_classes/metrics_visualizer.py index 75a531c0..d63ec175 100644 --- a/virny/custom_classes/metrics_visualizer.py +++ b/virny/custom_classes/metrics_visualizer.py @@ -52,7 +52,7 @@ def __init__(self, models_metrics_dct: dict, models_composed_metrics_df: pd.Data for model_name in model_names: columns_to_group = [col for col in models_metrics_dct[model_name].columns if col not in ('Model_Seed', 'Run_Number')] - models_average_metrics_dct[model_name] = models_metrics_dct[model_name][columns_to_group].groupby(['Metric', 'Model_Name']).mean().reset_index() + models_average_metrics_dct[model_name] = models_metrics_dct[model_name][columns_to_group].groupby(['Metric', 'Model_Name']).mean(numeric_only=True).reset_index() # Create one average metrics df with all model_dfs models_average_metrics_df = pd.DataFrame()