MAINT Remove redundancy in #9552 (#9573)

liamge · jnothman · commit 202b5321f179 · 2017-10-27T06:57:20.000+11:00
diff --git a/sklearn/preprocessing/tests/test_data.py b/sklearn/preprocessing/tests/test_data.py
@@ -13,6 +13,7 @@
 
 from sklearn.utils import gen_batches
 
+from sklearn.utils.testing import assert_raise_message
 from sklearn.utils.testing import assert_almost_equal
 from sklearn.utils.testing import clean_warning_registry
 from sklearn.utils.testing import assert_array_almost_equal
@@ -932,6 +933,10 @@ def test_quantile_transform_check_error():
     assert_raises_regex(ValueError, "'output_distribution' has to be either"
                         " 'normal' or 'uniform'. Got 'rnd' instead.",
                         transformer.inverse_transform, X_tran)
+    # check that an error is raised if input is scalar
+    assert_raise_message(ValueError,
+                         'Expected 2D array, got scalar array instead',
+                         transformer.transform, 10)
 
 
 def test_quantile_transform_sparse_ignore_zeros():
@@ -1157,14 +1162,16 @@ def test_quantile_transform_bounds():
     X = np.random.random((1000, 1))
     transformer = QuantileTransformer()
     transformer.fit(X)
-    assert_equal(transformer.transform(-10), transformer.transform(np.min(X)))
-    assert_equal(transformer.transform(10), transformer.transform(np.max(X)))
-    assert_equal(transformer.inverse_transform(-10),
+    assert_equal(transformer.transform([[-10]]),
+                 transformer.transform([[np.min(X)]]))
+    assert_equal(transformer.transform([[10]]),
+                 transformer.transform([[np.max(X)]]))
+    assert_equal(transformer.inverse_transform([[-10]]),
                  transformer.inverse_transform(
-                     np.min(transformer.references_)))
-    assert_equal(transformer.inverse_transform(10),
+                     [[np.min(transformer.references_)]]))
+    assert_equal(transformer.inverse_transform([[10]]),
                  transformer.inverse_transform(
-                     np.max(transformer.references_)))
+                     [[np.max(transformer.references_)]]))
 
 
 def test_quantile_transform_and_inverse():
diff --git a/sklearn/utils/tests/test_validation.py b/sklearn/utils/tests/test_validation.py
@@ -142,9 +142,13 @@ def test_check_array():
     # ensure_2d=False
     X_array = check_array([0, 1, 2], ensure_2d=False)
     assert_equal(X_array.ndim, 1)
-    # ensure_2d=True
+    # ensure_2d=True with 1d array
     assert_raise_message(ValueError, 'Expected 2D array, got 1D array instead',
                          check_array, [0, 1, 2], ensure_2d=True)
+    # ensure_2d=True with scalar array
+    assert_raise_message(ValueError,
+                         'Expected 2D array, got scalar array instead',
+                         check_array, 10, ensure_2d=True)
     # don't allow ndim > 3
     X_ndim = np.arange(8).reshape(2, 2, 2)
     assert_raises(ValueError, check_array, X_ndim)
diff --git a/sklearn/utils/validation.py b/sklearn/utils/validation.py
@@ -459,13 +459,20 @@ def check_array(array, accept_sparse=False, dtype="numeric", order=None,
         _ensure_no_complex_data(array)
 
         if ensure_2d:
+            # If input is scalar raise error
+            if array.ndim == 0:
+                raise ValueError(
+                    "Expected 2D array, got scalar array instead:\narray={}.\n"
+                    "Reshape your data either using array.reshape(-1, 1) if "
+                    "your data has a single feature or array.reshape(1, -1) "
+                    "if it contains a single sample.".format(array))
+            # If input is 1D raise error
             if array.ndim == 1:
                 raise ValueError(
                     "Expected 2D array, got 1D array instead:\narray={}.\n"
                     "Reshape your data either using array.reshape(-1, 1) if "
                     "your data has a single feature or array.reshape(1, -1) "
                     "if it contains a single sample.".format(array))
-            array = np.atleast_2d(array)
             # To ensure that array flags are maintained
             array = np.array(array, dtype=dtype, order=order, copy=copy)
 
