8000 Fix error when using Calibrated with Voting · scikit-learn/scikit-learn@34322ee · GitHub
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Commit 34322ee

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Clément Fauchereau
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Fix error when using Calibrated with Voting
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+42
-15
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3 files changed

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‎doc/whats_new/v1.0.rst

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Original file line numberDiff line numberDiff line change
@@ -135,6 +135,10 @@ 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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138+
- |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

Lines changed: 20 additions & 14 deletions
Original file line numberDiff line numberDiff line change
@@ -257,9 +257,10 @@ def fit(self, X, y, sample_weight=None):
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check_is_fitted(self.base_estimator, attributes=["classes_"])
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self.classes_ = self.base_estimator.classes_
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260-
pred_method = _get_prediction_method(base_estimator)
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pred_method, method_name = _get_prediction_method(base_estimator)
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n_classes = len(self.classes_)
262-
predictions = _compute_predictions(pred_method, X, n_classes)
262+
predictions = _compute_predictions(pred_method, method_name, X,
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n_classes)
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264265
calibrated_classifier = _fit_calibrator(
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base_estimator, predictions, y, self.classes_, self.method,
@@ -310,12 +311,13 @@ def fit(self, X, y, sample_weight=None):
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)
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else:
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this_estimator = clone(base_estimator)
313-
method_name = _get_prediction_method(this_estimator).__name__
314+
_, method_name = _get_prediction_method(this_estimator)
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pred_method = partial(
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cross_val_predict, estimator=this_estimator, X=X, y=y,
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cv=cv, method=method_name, n_jobs=self.n_jobs
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)
318-
predictions = _compute_predictions(pred_method, X, n_classes)
319+
predictions = _compute_predictions(pred_method, method_name, X,
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n_classes)
319321

320322
if sample_weight is not None and supports_sw:
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this_estimator.fit(X, y, sample_weight)
@@ -441,8 +443,9 @@ def _fit_classifier_calibrator_pair(estimator, X, y, train, test, supports_sw,
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estimator.fit(X_train, y_train)
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443445
n_classes = len(classes)
444-
pred_method = _get_prediction_method(estimator)
445-
predictions = _compute_predictions(pred_method, X_test, n_classes)
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pred_method, method_name = _get_prediction_method(estimator)
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predictions = _compute_predictions(pred_method, method_name, X_test,
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n_classes)
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447450
calibrated_classifier = _fit_calibrator(
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estimator, predictions, y_test, classes, method, sample_weight=sw_test
@@ -465,18 +468,21 @@ def _get_prediction_method(clf):
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-------
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prediction_method : callable
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The prediction method.
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method_name : str
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The name of the prediction method.
468473
"""
469474
if hasattr(clf, 'decision_function'):
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method = getattr(clf, 'decision_function')
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return method, 'decision_function'
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elif hasattr(clf, 'predict_proba'):
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method = getattr(clf, 'predict_proba')
479+
return method, 'predict_proba'
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else:
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raise RuntimeError("'base_estimator' has no 'decision_function' or "
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"'predict_proba' method.")
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return method
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478484

479-
def _compute_predictions(pred_method, X, n_classes):
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def _compute_predictions(pred_method, method_name, X, n_classes):
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"""Return predictions for `X` and reshape binary outputs to shape
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(n_samples, 1).
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@@ -485,6 +491,9 @@ def _compute_predictions(pred_method, X, n_classes):
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pred_method : callable
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Prediction method.
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method_name: str
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Name of the prediction method
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X : array-like or None
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Data used to obtain predictions.
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@@ -498,10 +507,6 @@ def _compute_predictions(pred_method, X, n_classes):
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(X.shape[0], 1).
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"""
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predictions = pred_method(X=X)
501-
if hasattr(pred_method, '__name__'):
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method_name = pred_method.__name__
503-
else:
504-
method_name = signature(pred_method).parameters['method'].default
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506511
if method_name == 'decision_function':
507512
if predictions.ndim == 1:
@@ -634,8 +639,9 @@ def predict_proba(self, X):
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The predicted probabilities. Can be exact zeros.
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"""
636641
n_classes = len(self.classes)
637-
pred_method = _get_prediction_method(self.base_estimator)
638-
predictions = _compute_predictions(pred_method, X, n_classes)
642+
pred_method, method_name = _get_prediction_method(self.base_estimator)
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predictions = _compute_predictions(pred_method, method_name, X,
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n_classes)
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640646
label_encoder = LabelEncoder().fit(self.classes)
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pos_class_indices = label_encoder.transform(

‎sklearn/tests/test_calibration.py

Lines changed: 18 additions & 1 deletion
Original file line numberDiff line numberDiff line change
@@ -20,7 +20,8 @@
2020
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
23-
from sklearn.ensemble import RandomForestClassifier, RandomForestRegressor
23+
from sklearn.ensemble import (RandomForestClassifier, RandomForestRegressor,
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VotingClassifier)
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from sklearn.svm import LinearSVC
2526
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):
607608
calibrators, calib_clf.calibrated_classifiers_[0].calibrators
608609
):
609610
assert clf1 is clf2
611+
612+
613+
def test_calibration_votingclassifier():
614+
# Check that `CalibratedClassifier` works with `VotingClassifier`.
615+
# The method `predict_proba` from `VotingClassifier` behaves
616+
# differently than in other classifiers.
617+
X, y = make_classification(n_samples=10, n_features=5,
618+
n_classes=2, random_state=7)
619+
vote = VotingClassifier(
620+
estimators=[('dummy'+str(i), DummyClassifier()) for i in range(3)],
621+
voting="soft"
622+
)
623+
624+
vote.fit(X, y)
625+
calib_clf = CalibratedClassifierCV(base_estimator=vote, cv="prefit")
626+
calib_clf.fit(X, y)

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