xhluca
diff --git a/‎sklearn/feature_selection/mutual_info_.py
Lines changed: 6 additions & 8 deletions b/‎sklearn/feature_selection/mutual_info_.py
Lines changed: 6 additions & 8 deletions
diff --git a/‎sklearn/feature_selection/tests/test_mutual_info.py
Lines changed: 5 additions & 13 deletions b/‎sklearn/feature_selection/tests/test_mutual_info.py
Lines changed: 5 additions & 13 deletions
@@ -10,7 +10,7 @@
 from ..preprocessing import scale
 from ..utils import check_random_state
 from ..utils.fixes import _astype_copy_false
-from ..utils.validation import check_array, check_X_y
+from ..utils.validation import check_X_y
 from ..utils.multiclass import check_classification_targets
 
 
@@ -247,16 +247,14 @@ def _estimate_mi(X, y, discrete_features='auto', discrete_target=False,
     X, y = check_X_y(X, y, accept_sparse='csc', y_numeric=not discrete_target)
     n_samples, n_features = X.shape
 
-    if isinstance(discrete_features, (str, bool)):
-        if isinstance(discrete_features, str):
-            if discrete_features == 'auto':
-                discrete_features = issparse(X)
-            else:
-                raise ValueError("Invalid string value for discrete_features.")
+    if discrete_features == 'auto':
+        discrete_features = issparse(X)
+
+    if isinstance(discrete_features, bool):
         discrete_mask = np.empty(n_features, dtype=bool)
         discrete_mask.fill(discrete_features)
     else:
-        discrete_features = check_array(discrete_features, ensure_2d=False)
+        discrete_features = np.asarray(discrete_features)
         if discrete_features.dtype != 'bool':
             discrete_mask = np.zeros(n_features, dtype=bool)
             discrete_mask[discrete_features] = True
 
@@ -183,26 +183,18 @@ def test_mutual_info_options():
     X_csr = csr_matrix(X)
 
     for mutual_info in (mutual_info_regression, mutual_info_classif):
-        assert_raises(ValueError, mutual_info, X_csr, y,
+        assert_raises(ValueError, mutual_info_regression, X_csr, y,
                       discrete_features=False)
-        assert_raises(ValueError, mutual_info, X, y,
-                      discrete_features='manual')
-        assert_raises(ValueError, mutual_info, X_csr, y,
-                      discrete_features=[True, False, True])
-        assert_raises(IndexError, mutual_info, X, y,
-                      discrete_features=[True, False, True, False])
-        assert_raises(IndexError, mutual_info, X, y, discrete_features=[1, 4])
 
         mi_1 = mutual_info(X, y, discrete_features='auto', random_state=0)
         mi_2 = mutual_info(X, y, discrete_features=False, random_state=0)
-        mi_3 = mutual_info(X_csr, y, discrete_features='auto', random_state=0)
-        mi_4 = mutual_info(X_csr, y, discrete_features=True, random_state=0)
-        mi_5 = mutual_info(X, y, discrete_features=[True, False, True],
+
+        mi_3 = mutual_info(X_csr, y, discrete_features='auto',
+                           random_state=0)
+        mi_4 = mutual_info(X_csr, y, discrete_features=True,
                            random_state=0)
-        mi_6 = mutual_info(X, y, discrete_features=[0, 2], random_state=0)
 
         assert_array_equal(mi_1, mi_2)
         assert_array_equal(mi_3, mi_4)
-        assert_array_equal(mi_5, mi_6)
 
     assert not np.allclose(mi_1, mi_3)