check_is_fitted validation for transform() and inverse_transform()

Author: olologin

sklearn/preprocessing/label.py

@@ -742,6 +742,8 @@ def transform(self, y):
             A matrix such that `y_indicator[i, j] = 1` iff `classes_[j]` is in
             `y[i]`, and 0 otherwise.
         """
+        check_is_fitted(self, 'classes_')
+
         class_to_index = dict(zip(self.classes_, range(len(self.classes_))))
         yt = self._transform(y, class_to_index)
 
@@ -788,6 +790,8 @@ def inverse_transform(self, yt):
             The set of labels for each sample such that `y[i]` consists of
             `classes_[j]` for each `yt[i, j] == 1`.
         """
+        check_is_fitted(self, 'classes_')
+
         if yt.shape[1] != len(self.classes_):
             raise ValueError('Expected indicator for {0} classes, but got {1}'
                              .format(len(self.classes_), yt.shape[1]))