Fixed the ghost code

alex-greff · alex-greff · commit 08b60a9e08d2 · 2021-03-21T17:40:11.000-04:00
diff --git a/sklearn/preprocessing/_data.py b/sklearn/preprocessing/_data.py
@@ -22,8 +22,7 @@
 from ..utils import check_array
 from ..utils.deprecation import deprecated
 from ..utils.extmath import row_norms
-from ..utils.extmath import (_incremental_mean_and_var,
-                             _incremental_weighted_mean_and_var)
+from ..utils.extmath import _incremental_mean_and_var
 from ..utils.sparsefuncs_fast import (inplace_csr_row_normalize_l1,
                                       inplace_csr_row_normalize_l2)
 from ..utils.sparsefuncs import (inplace_column_scale,
@@ -837,17 +836,11 @@ def partial_fit(self, X, y=None, sample_weight=None):
                 self.mean_ = None
                 self.var_ = None
                 self.n_samples_seen_ += X.shape[0] - np.isnan(X).sum(axis=0)
-
-            elif sample_weight is not None:
-                self.mean_, self.var_, self.n_samples_seen_ = \
-                    _incremental_weighted_mean_and_var(X, sample_weight,
-                                                       self.mean_,
-                                                       self.var_,
-                                                       self.n_samples_seen_)
             else:
                 self.mean_, self.var_, self.n_samples_seen_ = \
                     _incremental_mean_and_var(X, self.mean_, self.var_,
-                                              self.n_samples_seen_)
+                                              self.n_samples_seen_,
+                                              sample_weight=sample_weight)
 
         # for backward-compatibility, reduce n_samples_seen_ to an integer
         # if the number of samples is the same for each feature (i.e. no
