MAINT Use _check_sample_weight in GaussianNB (#15480)

Marie Douriez · rth · commit 6b6fa5d5ee38 · 2019-11-06T17:04:14.000+01:00
diff --git a/sklearn/naive_bayes.py b/sklearn/naive_bayes.py
@@ -21,18 +21,17 @@
 
 
 import numpy as np
-from scipy.sparse import issparse
 
 from .base import BaseEstimator, ClassifierMixin
 from .preprocessing import binarize
 from .preprocessing import LabelBinarizer
 from .preprocessing import label_binarize
-from .utils import check_X_y, check_array, check_consistent_length
+from .utils import check_X_y, check_array, deprecated
 from .utils.extmath import safe_sparse_dot
 from .utils.fixes import logsumexp
 from .utils.multiclass import _check_partial_fit_first_call
 from .utils.validation import check_is_fitted, check_non_negative, column_or_1d
-from .utils import deprecated
+from .utils.validation import _check_sample_weight
 
 __all__ = ['BernoulliNB', 'GaussianNB', 'MultinomialNB', 'ComplementNB',
            'CategoricalNB']
@@ -360,8 +359,7 @@ def _partial_fit(self, X, y, classes=None, _refit=False,
         """
         X, y = check_X_y(X, y)
         if sample_weight is not None:
-            sample_weight = check_array(sample_weight, ensure_2d=False)
-            check_consistent_length(y, sample_weight)
+            sample_weight = _check_sample_weight(sample_weight, X)
 
         # If the ratio of data variance between dimensions is too small, it
         # will cause numerical errors. To address this, we artificially
