xhluca
diff --git a/‎sklearn/preprocessing/_encoders.py
Lines changed: 3 additions & 8 deletions b/‎sklearn/preprocessing/_encoders.py
Lines changed: 3 additions & 8 deletions
diff --git a/‎sklearn/preprocessing/tests/test_encoders.py
Lines changed: 0 additions & 23 deletions b/‎sklearn/preprocessing/tests/test_encoders.py
Lines changed: 0 additions & 23 deletions
@@ -21,8 +21,10 @@
 from .base import _transform_selected
 from .label import _encode, _encode_check_unknown
 
+
 range = six.moves.range
 
+
 __all__ = [
     'OneHotEncoder',
     'OrdinalEncoder'
@@ -381,12 +383,6 @@ def _handle_deprecations(self, X):
                     "The 'categorical_features' keyword is deprecated in "
                     "version 0.20 and will be removed in 0.22. You can "
                     "use the ColumnTransformer instead.", DeprecationWarning)
-                # Set categories_ to empty list if no categorical columns exist
-                n_features = X.shape[1]
-                sel = np.zeros(n_features, dtype=bool)
-                sel[np.asarray(self.categorical_features)] = True
-                if sum(sel) == 0:
-                    self.categories_ = []
                 self._legacy_mode = True
             self._categorical_features = self.categorical_features
         else:
@@ -595,7 +591,6 @@ def transform(self, X):
         X_out : sparse matrix if sparse=True else a 2-d array
             Transformed input.
         """
-        check_is_fitted(self, 'categories_')
         if self._legacy_mode:
             return _transform_selected(X, self._legacy_transform, self.dtype,
                                        self._categorical_features,
@@ -688,7 +683,7 @@ def get_feature_names(self, input_features=None):
         cats = self.categories_
         if input_features is None:
             input_features = ['x%d' % i for i in range(len(cats))]
-        elif len(input_features) != len(self.categories_):
+        elif(len(input_features) != len(self.categories_)):
             raise ValueError(
                 "input_features should have length equal to number of "
                 "features ({}), got {}".format(len(self.categories_),
 
@@ -7,7 +7,6 @@
 from scipy import sparse
 import pytest
 
-from sklearn.exceptions import NotFittedError
 from sklearn.utils.testing import assert_array_equal
 from sklearn.utils.testing import assert_equal
 from sklearn.utils.testing import assert_raises
@@ -251,28 +250,6 @@ def test_one_hot_encoder_handle_unknown():
     assert_raises(ValueError, oh.fit, X)
 
 
-def test_one_hot_encoder_not_fitted():
-    X = np.array([['a'], ['b']])
-    enc = OneHotEncoder(categories=['a', 'b'])
-    msg = ("This OneHotEncoder instance is not fitted yet. "
-           "Call 'fit' with appropriate arguments before using this method.")
-    with pytest.raises(NotFittedError, match=msg):
-        enc.transform(X)
-
-
-def test_one_hot_encoder_no_categorical_features():
-    X = np.array([[3, 2, 1], [0, 1, 1]], dtype='float64')
-
-    cat = [False, False, False]
-    enc = OneHotEncoder(categorical_features=cat)
-    with ignore_warnings(category=(DeprecationWarning, FutureWarning)):
-        X_tr = enc.fit_transform(X)
-    expected_features = np.array(list(), dtype='object')
-    assert_array_equal(X, X_tr)
-    assert_array_equal(enc.get_feature_names(), expected_features)
-    assert enc.categories_ == []
-
-
 @pytest.mark.parametrize("output_dtype", [np.int32, np.float32, np.float64])
 @pytest.mark.parametrize("input_dtype", [np.int32, np.float32, np.float64])
 def test_one_hot_encoder_dtype(input_dtype, output_dtype):