[MRG + 1] FIX bug where passing numpy array for weights raises error (Issue scikit-learn#7983) (scikit-learn#7989)

Author: vincentpham1991

doc/whats_new.rst

@@ -108,6 +108,10 @@ Bug fixes
      ``pandas.Series`` in their ``fit`` function. :issue:`7825` by
      `Kathleen Chen`_.
 
+   - Fix a bug where :class:`sklearn.ensemble.VotingClassifier` raises an error
+     when a numpy array is passed in for weights. :issue:`7983` by
+     :user:`Vincent Pham <vincentpham1991>`.
+
 .. _changes_0_18_1:
 
 Version 0.18.1
@@ -4830,3 +4834,5 @@ David Huard, Dave Morrill, Ed Schofield, Travis Oliphant, Pearu Peterson.
 .. _Ron Weiss: http://www.ee.columbia.edu/~ronw
 
 .. _Kathleen Chen: https://github.com/kchen17
+
+.. _Vincent Pham: https://github.com/vincentpham1991

sklearn/ensemble/tests/test_voting_classifier.py

@@ -258,3 +258,20 @@ def test_sample_weight():
         voting='soft')
     msg = ('Underlying estimator \'knn\' does not support sample weights.')
     assert_raise_message(ValueError, msg, eclf3.fit, X, y, sample_weight)
+
+
+def test_estimator_weights_format():
+    # Test estimator weights inputs as list and array
+    clf1 = LogisticRegression(random_state=123)
+    clf2 = RandomForestClassifier(random_state=123)
+    eclf1 = VotingClassifier(estimators=[
+                ('lr', clf1), ('rf', clf2)],
+                weights=[1, 2],
+                voting='soft')
+    eclf2 = VotingClassifier(estimators=[
+                ('lr', clf1), ('rf', clf2)],
+                weights=np.array((1, 2)),
+                voting='soft')
+    eclf1.fit(X, y)
+    eclf2.fit(X, y)
+    assert_array_equal(eclf1.predict_proba(X), eclf2.predict_proba(X))

sklearn/ensemble/voting_classifier.py

@@ -141,7 +141,8 @@ def fit(self, X, y, sample_weight=None):
                                  ' should be a list of (string, estimator)'
                                  ' tuples')
 
-        if self.weights and len(self.weights) != len(self.estimators):
+        if (self.weights is not None and
+                len(self.weights) != len(self.estimators)):
             raise ValueError('Number of classifiers and weights must be equal'
                              '; got %d weights, %d estimators'
                              % (len(self.weights), len(self.estimators)))