minor changes

olologin · olologin · commit 04a54372e67b · 2016-10-19T02:46:01.000+03:00
diff --git a/sklearn/tree/_criterion.pyx b/sklearn/tree/_criterion.pyx
@@ -279,7 +279,7 @@ cdef class ClassificationCriterion(Criterion):
     def __reduce__(self):
         return (type(self),
                 (self.n_outputs,
-                 sizet_ptr_to_ndarray(self.n_classes, self.n_outputs).copy()),
+                 sizet_ptr_to_ndarray(self.n_classes, self.n_outputs)),
                 self.__getstate__())
 
     cdef void init(self, DOUBLE_t* y, SIZE_t y_stride,
diff --git a/sklearn/tree/_tree.pyx b/sklearn/tree/_tree.pyx
@@ -548,7 +548,7 @@ cdef class Tree:
     property n_classes:
         def __get__(self):
             # it's small; copy for memory safety
-            return sizet_ptr_to_ndarray(self.n_classes, self.n_outputs).copy()
+            return sizet_ptr_to_ndarray(self.n_classes, self.n_outputs)
 
     property children_left:
         def __get__(self):
@@ -615,7 +615,7 @@ cdef class Tree:
     def __reduce__(self):
         """Reduce re-implementation, for pickling."""
         return (Tree, (self.n_features,
-                       sizet_ptr_to_ndarray(self.n_classes, self.n_outputs).copy(),
+                       sizet_ptr_to_ndarray(self.n_classes, self.n_outputs),
                        self.n_outputs), self.__getstate__())
 
     def __getstate__(self):
diff --git a/sklearn/tree/_utils.pyx b/sklearn/tree/_utils.pyx
@@ -62,13 +62,10 @@ cdef inline UINT32_t our_rand_r(UINT32_t* seed) nogil:
 
 
 cdef inline np.ndarray sizet_ptr_to_ndarray(SIZE_t* data, SIZE_t size):
-    """Encapsulate data into a 1D numpy array of intp's. Make sure memory allocated in data is
-       You should ensure that the provided data pointer is not freed while the returned array
-       is in existence.
-    """
+    """Encapsulate data into a 1D numpy array of intp's."""
     cdef np.npy_intp shape[1]
     shape[0] = <np.npy_intp> size
-    return np.PyArray_SimpleNewFromData(1, shape, np.NPY_INTP, data)
+    return np.PyArray_SimpleNewFromData(1, shape, np.NPY_INTP, data).copy()
 
 
 cdef inline SIZE_t rand_int(SIZE_t low, SIZE_t high,
diff --git a/sklearn/tree/tests/test_tree.py b/sklearn/tree/tests/test_tree.py
@@ -1,8 +1,8 @@
 """
 Testing for the tree module (sklearn.tree).
 """
+import copy
 import pickle
-from copy import copy
 from functools import partial
 from itertools import product
 import struct
@@ -1600,6 +1600,7 @@ def test_no_sparse_y_support():
     for name in ALL_TREES:
         yield (check_no_sparse_y_support, name)
 
+
 def test_mae():
     # check MAE criterion produces correct results
     # on small toy dataset
@@ -1613,21 +1614,26 @@ def test_mae():
     assert_array_equal(dt_mae.tree_.impurity, [7.0/2.3, 3.0/0.7, 4.0/1.6])
     assert_array_equal(dt_mae.tree_.value.flat, [4.0, 6.0, 4.0])
 
+
 def test_criterion_copy():
-    # Let's check whether copy of our criterion has same type and properties as original
+    # Let's check whether copy of our criterion has the same type
+    # and properties as original
     n_outputs = 3
     n_classes = np.arange(3)
-    for _, typename in CRITERIA_CLF.items():
-        criteria = typename(n_outputs, n_classes)
-        typename_, (n_outputs_, n_classes_), _ = copy(criteria).__reduce__()
-        assert_equal(typename, typename_)
-        assert_equal(n_outputs, n_outputs_)
-        assert_array_equal(n_classes, n_classes_)
-
     n_samples = 100
-    for _, typename in CRITERIA_REG.items():
-        criteria = typename(n_outputs, n_samples)
-        typename_, (n_outputs_, n_samples_), _ = copy(criteria).__reduce__()
-        assert_equal(typename, typename_)
-        assert_equal(n_outputs, n_outputs_)
-        assert_equal(n_samples, n_samples_)
+    for copy_func in [copy.copy, copy.deepcopy]:
+        for _, typename in CRITERIA_CLF.items():
+            criteria = typename(n_outputs, n_classes)
+            result = copy_func(criteria).__reduce__()
+            typename_, (n_outputs_, n_classes_), _ = result
+            assert_equal(typename, typename_)
+            assert_equal(n_outputs, n_outputs_)
+            assert_array_equal(n_classes, n_classes_)
+
+        for _, typename in CRITERIA_REG.items():
+            criteria = typename(n_outputs, n_samples)
+            result = copy_func(criteria).__reduce__()
+            typename_, (n_outputs_, n_samples_), _ = result
+            assert_equal(typename, typename_)
+            assert_equal(n_outputs, n_outputs_)
+            assert_equal(n_samples, n_samples_)
