[MRG] Fail imputer early when number of features are not the same in fit and transform (scikit-learn#7374)

Author: MechCoder

sklearn/preprocessing/imputation.py

@@ -309,11 +309,17 @@ def transform(self, X):
  """
  if self.axis == 0:
  check_is_fitted(self, 'statistics_')
+ X = check_array(X, accept_sparse='csc', dtype=FLOAT_DTYPES,
+ force_all_finite=False, copy=self.copy)
+ statistics = self.statistics_
+ if X.shape[1] != statistics.shape[0]:
+ raise ValueError("X has %d features per sample, expected %d"
+ % (X.shape[1], self.statistics_.shape[0]))
 
  # Since two different arrays can be provided in fit(X) and
  # transform(X), the imputation data need to be recomputed
  # when the imputation is done per sample
- if self.axis == 1:
+ else:
  X = check_array(X, accept_sparse='csr', dtype=FLOAT_DTYPES,
  force_all_finite=False, copy=self.copy)
 
@@ -328,10 +334,6 @@ def transform(self, X):
  self.strategy,
  self.missing_values,
  self.axis)
- else:
- X = check_array(X, accept_sparse='csc', dtype=FLOAT_DTYPES,
- force_all_finite=False, copy=self.copy)
- statistics = self.statistics_
 
  # Delete the invalid rows/columns
  invalid_mask = np.isnan(statistics)