Merge branch 'main' into python-2.12.X

.github/workflows/ci.yml

@@ -14,7 +14,7 @@ jobs:
     strategy:
       fail-fast: false
       matrix:
-        python: [3.8, 3.9, "3.10", 3.11, 3.12]
+        python: [3.9, "3.10", 3.11, 3.12]
         os: [ubuntu-latest, macos-13]
 
     uses: ./.github/workflows/build-virny.yml
@@ -27,7 +27,7 @@ jobs:
     strategy:
       fail-fast: false
       matrix:
-        python: [3.8, 3.9, "3.10", 3.11, 3.12]
+        python: [3.9, "3.10", 3.11, 3.12]
         os: [ubuntu-latest, macos-13]
 
     uses: ./.github/workflows/unit-tests.yml

README.md

@@ -53,7 +53,7 @@ For quickstart, look at [use case examples](https://dataresponsibly.github.io/Vi
 
 ## 🛠 Installation
 
-Virny supports **Python 3.8-3.12** and can be installed with `pip`:
+Virny supports **Python 3.9-3.12** and can be installed with `pip`:
 
 ```bash
 pip install virny
@@ -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).
+**Virny** is free and open-source software licensed under the [3-clause BSD license](https://github.com/DataResponsibly/Virny/blob/main/LICENSE).

docs/introduction/welcome_to_virny.md

@@ -23,7 +23,7 @@ For quickstart, look at [use case examples](https://dataresponsibly.github.io/Vi
 
 ## 🛠 Installation
 
-Virny supports **Python 3.8 and 3.9** and can be installed with `pip`:
+Virny supports **Python 3.9-3.12** and can be installed with `pip`:
 
 ```bash
 pip install virny

docs/release_notes/0.6.0.md

@@ -6,7 +6,7 @@
 
 ## 🚀 New Python Versions Support
 
-* Now Virny supports Python 3.8, 3.9, 3.10, 3.11, and 3.12! 🎉🥳
+* Now Virny supports Python 3.9, 3.10, 3.11, and 3.12! 🎉🥳
 
 
 ## ⚙️ Fitted Bootstrap Exporting

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'])

virny/analyzers/batch_overall_variance_analyzer.py

@@ -1,4 +1,3 @@
-import numpy as np
 import pandas as pd
 
 from virny.analyzers.abstract_overall_variance_analyzer import AbstractOverallVarianceAnalyzer
@@ -62,11 +61,11 @@ 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: np.ndarray, y_train: np.ndarray):
+    def _fit_model(self, classifier, X_train: pd.DataFrame, y_train: pd.DataFrame):
         """
         Fit a classifier that is an instance of self.base_model
         """
-        return classifier.fit(X_train, y_train)
+        return classifier.fit(X_train, y_train.values.ravel())
 
     def _batch_predict(self, classifier, X_test: pd.DataFrame):
         """

virny/utils/stability_utils.py

@@ -70,7 +70,7 @@ def count_prediction_metrics(y_true, uq_results, with_predict_proba: bool = True
         # Convert predict proba results of each model to correspondent labels.
         # Here we use int(x<0.5) since we use predict_prob()[:, 0] to make predictions.
         # Hence, if a value is, for example, 0.3 --> label == 1, 0.6 -- > label == 0
-        uq_labels = results.map(lambda x: int(x<0.5))
+        uq_labels = (results < 0.5).astype(int)
 
     # Compute metrics for prediction labels
     for metric in METRICS_FOR_LABELS: