10BC0 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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