8000 Fix error when using Calibrated with Voting · scikit-learn/scikit-learn@bc43de1 · GitHub
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Clément Fauchereau
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Fix error when using Calibrated with Voting
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‎doc/whats_new/v1.0.rst‎

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@@ -117,6 +117,10 @@ Changelog
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:class:`calibration.CalibratedClassifierCV` can now properly be used on
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prefitted pipelines. :pr:`19641` by :user:`Alek Lefebvre <AlekLefebvre>`.
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- |Fix| Fixed an error when using a ::class:`ensemble.VotingClassifier`
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as `base_estimator` in ::class:`calibration.CalibratedClassifierCV`.
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:pr:`20087` by :user:`Clément Fauchereau <clement-f>`.
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:mod:`sklearn.cluster`
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......................
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‎sklearn/calibration.py‎

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@@ -508,7 +508,8 @@ def _compute_predictions(pred_method, X, n_classes):
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if method_name == 'decision_function':
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if predictions.ndim == 1:
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predictions = predictions[:, np.newaxis]
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elif method_name == 'predict_proba':
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elif method_name == 'predict_proba' or method_name == '_predict_proba':
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# The `_predict_proba` option is needed for `VotingClassifier`
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if n_classes == 2:
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predictions = predictions[:, 1:]
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else: # pragma: no cover

‎sklearn/tests/test_calibration.py‎

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@@ -20,7 +20,8 @@
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from sklearn.preprocessing import LabelEncoder
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from sklearn.model_selection import KFold, cross_val_predict
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from sklearn.naive_bayes import MultinomialNB
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from sklearn.ensemble import RandomForestClassifier, RandomForestRegressor
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from sklearn.ensemble import (RandomForestClassifier, RandomForestRegressor,
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VotingClassifier)
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from sklearn.svm import LinearSVC
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from sklearn.isotonic import IsotonicRegression
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from sklearn.feature_extraction import DictVectorizer
@@ -607,3 +608,19 @@ def test_calibrated_classifier_cv_deprecation(data):
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calibrators, calib_clf.calibrated_classifiers_[0].calibrators
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):
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assert clf1 is clf2
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def test_calibration_votingclassifier():
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# Check that `CalibratedClassifier` works with `VotingClassifier`.
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# The method `predict_proba` from `VotingClassifier` behaves
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# differently than in other classifiers.
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X, y = make_classification(n_samples=10, n_features=5,
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n_classes=2, random_state=7)
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vote = VotingClassifier(
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estimators=[(f"dummy{i}", DummyClassifier()) for i in range(3)],
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voting="soft"
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)
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vote.fit(X, y)
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calib_clf = CalibratedClassifierCV(base_estimator=vote, cv="prefit")
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calib_clf.fit(X, y)
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