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[MRG+1] Pass sample_weight as kwargs in VotingClassifier #9493

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Merged
merged 3 commits into from
Aug 5, 2017
Merged

[MRG+1] Pass sample_weight as kwargs in VotingClassifier #9493

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39d2e33
Pass sample_weight as kwarg in VotingClassifier
jschendel Aug 4, 2017
3b17b16
Test to verify that VotingClassifier passes sample_weight as kwargs
jschendel Aug 4, 2017
5309384
Fix Python 2 SyntaxError in Test
jschendel Aug 5, 2017
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15 changes: 15 additions & 0 deletions sklearn/ensemble/tests/test_voting_classifier.py
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Original file line number Diff line number Diff line change
Expand Up @@ -17,6 +17,7 @@
from sklearn.svm import SVC
from sklearn.multiclass import OneVsRestClassifier
from sklearn.neighbors import KNeighborsClassifier
from sklearn.base import BaseEstimator, ClassifierMixin


# Load the iris dataset and randomly permute it
Expand Down Expand Up @@ -274,6 +275,20 @@ def test_sample_weight():
assert_raise_message(ValueError, msg, eclf3.fit, X, y, sample_weight)


def test_sample_weight_kwargs():
"""Check that VotingClassifier passes sample_weight as kwargs"""
class MockClassifier(BaseEstimator, ClassifierMixin):
"""Mock Classifier to check that sample_weight is received as kwargs"""
def fit(self, X, y, *args, **sample_weight):
assert_true('sample_weight' in sample_weight)

clf = MockClassifier()
eclf = VotingClassifier(estimators=[('mock', clf)], voting='soft')

# Should not raise an error.
eclf.fit(X, y, sample_weight=np.ones((len(y),)))


def test_set_params():
"""set_params should be able to set estimators"""
clf1 = LogisticRegression(random_state=123, C=1.0)
Expand Down
6 changes: 3 additions & 3 deletions sklearn/ensemble/voting_classifier.py
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Original file line number Diff line number Diff line change
Expand Up @@ -23,10 +23,10 @@
from ..utils.metaestimators import _BaseComposition


def _parallel_fit_estimator(estimator, X, y, sample_weight):
def _parallel_fit_estimator(estimator, X, y, sample_weight=None):
"""Private function used to fit an estimator within a job."""
if sample_weight is not None:
estimator.fit(X, y, sample_weight)
estimator.fit(X, y, sample_weight=sample_weight)
else:
estimator.fit(X, y)
return estimator
Expand Down Expand Up @@ -185,7 +185,7 @@ def fit(self, X, y, sample_weight=None):

self.estimators_ = Parallel(n_jobs=self.n_jobs)(
delayed(_parallel_fit_estimator)(clone(clf), X, transformed_y,
sample_weight)
sample_weight=sample_weight)
for clf in clfs if clf is not None)

return self
Expand Down