Address @jnothman's comments.

mblondel · mblondel · commit e11873f62ad6 · 2013-06-29T19:18:12.000+09:00
diff --git a/sklearn/preprocessing.py b/sklearn/preprocessing.py
@@ -39,6 +39,7 @@
            'LabelEncoder',
            'MinMaxScaler',
            'Normalizer',
+           'OneHotEncoder',
            'StandardScaler',
            'binarize',
            'normalize',
@@ -635,7 +636,7 @@ def transform(self, X, y=None, copy=None):
         return binarize(X, threshold=self.threshold, copy=copy)
 
 
-def _transform_selected(X, transform, selected):
+def _transform_selected(X, transform, selected="all"):
     """Apply a transform function to portion of selected features
 
     Parameters
@@ -653,10 +654,10 @@ def _transform_selected(X, transform, selected):
     -------
     X : array or sparse matrix, shape=(n_samples, n_features_new)
     """
-    if len(selected) == 0:
-        return X
-    elif selected == "all":
+    if selected == "all":
         return transform(X)
+    elif len(selected) == 0:
+        return X
     else:
         X = atleast2d_or_csc(X)
         n_features = X.shape[1]
diff --git a/sklearn/tests/test_preprocessing.py b/sklearn/tests/test_preprocessing.py
@@ -629,6 +629,7 @@ def test_transform_selected():
     Xexpected = [[1, 1, 1], [0, 1, 1]]
     _check_transform_selected(X, Xexpected, [0, 1, 2])
     _check_transform_selected(X, Xexpected, [True, True, True])
+    _check_transform_selected(X, Xexpected, "all")
 
     _check_transform_selected(X, X, [])
     _check_transform_selected(X, X, [False, False, False])
@@ -656,6 +657,7 @@ def _check_one_hot(X, X2, cat, n_features):
     assert_array_equal(toarray(A), toarray(C))
     assert_array_equal(toarray(B), toarray(D))
 
+
 def test_one_hot_encoder_categorical_features():
     X = np.array([[3, 2, 1], [0, 1, 1]])
     X2 = np.array([[1, 1, 1]])
