FIX Fixes encoders for string dtypes (scikit-learn#15763)

Co-authored-by: Guillaume Lemaitre <[email protected]>
Co-authored-by: Olivier Grisel <[email protected]>

doc/whats_new/v0.24.rst

@@ -662,6 +662,10 @@ Changelog
   encoded as all zeros. :pr:`14982` by
   :user:`Kevin Winata <kwinata>`.
 
+- |Fix| Fix incorrect encoding when using unicode string dtypes in
+  :class:`preprocessing.OneHotEncoder` and
+  :class:`preprocessing.OrdinalEncoder`. :pr:`15763` by `Thomas Fan`_.
+
 :mod:`sklearn.svm`
 ..................
 

sklearn/preprocessing/_encoders.py

@@ -90,7 +90,7 @@ def _fit(self, X, handle_unknown='error', force_all_finite=True):
                 cats = _unique(Xi)
             else:
                 cats = np.array(self.categories[i], dtype=Xi.dtype)
-                if Xi.dtype != object:
+                if Xi.dtype.kind not in 'OU':
                     sorted_cats = np.sort(cats)
                     error_msg = ("Unsorted categories are not "
                                  "supported for numerical categories")

sklearn/preprocessing/tests/test_encoders.py

@@ -830,6 +830,33 @@ def test_encoders_has_categorical_tags(Encoder):
     assert 'categorical' in Encoder()._get_tags()['X_types']
 
 
+@pytest.mark.parametrize('input_dtype', ['O', 'U'])
+@pytest.mark.parametrize('category_dtype', ['O', 'U'])
+@pytest.mark.parametrize('array_type', ['list', 'array', 'dataframe'])
+def test_encoders_unicode_categories(input_dtype, category_dtype, array_type):
+    """Check that encoding work with string and object dtypes.
+    Non-regression test for:
+    https://github.com/scikit-learn/scikit-learn/issues/15616
+    https://github.com/scikit-learn/scikit-learn/issues/15726
+    """
+
+    X = np.array([['b'], ['a']], dtype=input_dtype)
+    categories = [np.array(['b', 'a'], dtype=category_dtype)]
+    ohe = OneHotEncoder(categories=categories, sparse=False).fit(X)
+
+    X_test = _convert_container([['a'], ['a'], ['b'], ['a']], array_type)
+    X_trans = ohe.transform(X_test)
+
+    expected = np.array([[0, 1], [0, 1], [1, 0], [0, 1]])
+    assert_allclose(X_trans, expected)
+
+    oe = OrdinalEncoder(categories=categories).fit(X)
+    X_trans = oe.transform(X_test)
+
+    expected = np.array([[1], [1], [0], [1]])
+    assert_array_equal(X_trans, expected)
+
+
 @pytest.mark.parametrize("missing_value", [np.nan, None])
 def test_ohe_missing_values_get_feature_names(missing_value):
     # encoder with missing values with object dtypes

sklearn/utils/_encode.py

@@ -173,7 +173,7 @@ def _encode(values, *, uniques, check_unknown=True):
     encoded : ndarray
         Encoded values
     """
-    if values.dtype == object:
+    if values.dtype.kind in 'OU':
         try:
             return _map_to_integer(values, uniques)
         except KeyError as e: