Added comments to localize where readonly mode is broken

sklearn/utils/estimator_checks.py

@@ -397,9 +397,10 @@ def check_dtype_object(name, Estimator):
 def check_transformer_general(name, Transformer, readonly=False):
     X, y = _make_blobs_with_mode(n_samples=30, centers=[[0, 0, 0], [1, 1, 1]],
                                  random_state=0, n_features=2, cluster_std=0.1,
-                                 readonly=readonly)
-    X = StandardScaler().fit_transform(X)
-    X -= X.min()
+                                 positive=True, readonly=readonly)
+    # TODO: removed ScandardScaler as this breaks readonly mode
+    # X = StandardScaler().fit_transform(X)
+    # X -= X.min()
     _check_transformer(name, Transformer, X, y)
     _check_transformer(name, Transformer, X.tolist(), y.tolist())
 
@@ -708,6 +709,7 @@ def check_estimators_partial_fit_n_features(name, Alg):
 
 def check_clustering(name, Alg, readonly=False):
     X, y = _make_blobs_with_mode(n_samples=50, random_state=1, readonly=readonly)
+    # TODO: this breaks read only mode
     X, y = shuffle(X, y, random_state=7)
     X = StandardScaler().fit_transform(X)
     n_samples, n_features = X.shape
@@ -1038,7 +1040,7 @@ def check_regressors_int(name, Regressor):
 
 
 def check_regressors_train_readonly(name, Regressors):
-    check_regressors_train(name, Regressors, readonly=False)
+    check_regressors_train(name, Regressors, readonly=True)
 
 
 def check_regressors_train(name, Regressor, readonly=False):

sklearn/utils/validation.py

@@ -363,8 +363,10 @@ def check_array(array, accept_sparse=None, dtype="numeric", order=None,
     else:
         if ensure_2d:
             array = np.atleast_2d(array)
-
-        array = np.array(array, dtype=dtype, order=order, copy=copy)
+        if not copy:
+            array = np.asarray(array, dtype=dtype, order=order)
+        else:
+            array = np.array(array, dtype=dtype, order=order, copy=copy)
         # make sure we actually converted to numeric:
         if dtype_numeric and array.dtype.kind == "O":
             array = array.astype(np.float64)