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* Added strategy option to use mdi as measure * Removed mdi strategy * Added classes for both unawareness strategies * Docstring cleanup * Deleted old unawareness class * Fixed correlation suppression
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src/aequitas/flow/methods/preprocessing/feature_importance_suppression.py
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,137 @@ | ||
| from typing import Optional | ||
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| import pandas as pd | ||
| from sklearn.ensemble import RandomForestClassifier | ||
| from sklearn.model_selection import train_test_split | ||
| from sklearn.metrics import roc_auc_score | ||
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| from ...utils import create_logger | ||
| from .preprocessing import PreProcessing | ||
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| class FeatureImportanceSuppression(PreProcessing): | ||
| def __init__( | ||
| self, | ||
| auc_threshold: Optional[int] = 0.5, | ||
| feature_importance_threshold: Optional[float] = 0.1, | ||
| n_estimators: Optional[int] = 10, | ||
| seed: int = 0, | ||
| ): | ||
| """Iterively removes the most important features with respect to the sensitive | ||
| attribute. | ||
| Parameters | ||
| ---------- | ||
| auc_threshold : int, optional | ||
| The value of AUC above which the removal of features continues. Defaults to | ||
| 0.5. | ||
| feature_importance_threshold : float, optional | ||
| The value of feature importance above which the most important feature needs | ||
| to have to be removed. Defaults to 0.1. | ||
| n_estimators : int, optional | ||
| The number of trees in the random forest. Defaults to 10. | ||
| seed : int, optional | ||
| The seed for the random forest. Defaults to 0. | ||
| """ | ||
| self.logger = create_logger( | ||
| "methods.preprocessing.FeatureImportanceSuppression" | ||
| ) | ||
| self.logger.info( | ||
| "Instantiating a FeatureImportanceSuppression preprocessing method." | ||
| ) | ||
| self.used_in_inference = True | ||
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| self.auc_threshold = auc_threshold | ||
| self.feature_importance_threshold = feature_importance_threshold | ||
| self.n_estimators = n_estimators | ||
| self.seed = seed | ||
|
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| def fit(self, X: pd.DataFrame, y: pd.Series, s: Optional[pd.Series]) -> None: | ||
| """Iteratively removes the most important features to predict the sensitive | ||
| attribute. | ||
| Parameters | ||
| ---------- | ||
| X : pandas.DataFrame | ||
| Feature matrix. | ||
| y : pandas.Series | ||
| Label vector. | ||
| s : pandas.Series | ||
| Protected attribute vector. | ||
| """ | ||
| super().fit(X, y, s) | ||
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| self.logger.info("Identifying features to remove.") | ||
|
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| rf = RandomForestClassifier( | ||
| n_estimators=self.n_estimators, random_state=self.seed | ||
| ) | ||
|
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| features = pd.concat([X, y], axis=1) | ||
| features = pd.get_dummies(features) | ||
| target = s.copy() | ||
|
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| features_train, features_val, target_train, target_val = train_test_split( | ||
| features, target | ||
| ) | ||
| self.remove_features = [] | ||
|
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| while features_train.shape[1] > 1: | ||
| rf.fit(features_train, target_train) | ||
| predictions = rf.predict_proba(features_val)[:, 1] | ||
| auc = roc_auc_score(target_val, predictions) | ||
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| if auc > self.auc_threshold: | ||
| scores = pd.Series( | ||
| rf.feature_importances_, index=features_train.columns | ||
| ) | ||
| feature = scores.sort_values(ascending=False).index[0] | ||
| if scores[feature] < self.feature_importance_threshold: | ||
| break | ||
|
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| i = feature.rfind("_") | ||
| if feature[:i] in X.columns: | ||
| eliminate = [ | ||
| col | ||
| for col in features_train.columns | ||
| if col.startswith(feature[:i]) | ||
| ] | ||
| self.remove_features.append(feature[:i]) | ||
| else: | ||
| eliminate = [feature] | ||
| self.remove_features.append(feature) | ||
|
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| features_train = features_train.drop(columns=eliminate) | ||
| features_val = features_val.drop(columns=eliminate) | ||
| else: | ||
| break | ||
|
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||
| def transform( | ||
| self, X: pd.DataFrame, y: pd.Series, s: Optional[pd.Series] = None | ||
| ) -> tuple[pd.DataFrame, pd.Series, pd.Series]: | ||
| """Removes the features which are related with the sensitive attribute the most | ||
| from the data. | ||
| Parameters | ||
| ---------- | ||
| X : pd.DataFrame | ||
| Feature matrix. | ||
| y : pd.Series | ||
| Label vector. | ||
| s : pd.Series, optional | ||
| Protected attribute vector. | ||
| Returns | ||
| ------- | ||
| tuple[pd.DataFrame, pd.Series, pd.Series] | ||
| The transformed input, X, y, and s. | ||
| """ | ||
| super().transform(X, y, s) | ||
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| self.logger.info( | ||
| f"Removing most correlated features with sensitive attribute: " | ||
| f"{self.remove_features}" | ||
| ) | ||
| X_transformed = X.drop(columns=self.remove_features) | ||
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| return X_transformed, y, s |