Make cosmetic adjustments, Fix random.py header, use message with ass…

…ert_warns

sklearn/dummy.py

@@ -1,6 +1,6 @@
 # Author: Mathieu Blondel <[email protected]>
 #         Arnaud Joly <[email protected]>
-#         Maheshakya Wijewardena<[email protected]>
+#         Maheshakya Wijewardena <[email protected]>
 # License: BSD 3 clause
 from __future__ import division
 
@@ -100,7 +100,7 @@ def fit(self, X, y, sample_weight=None):
 
         if self.strategy == "uniform" and sp.issparse(y):
             y = y.toarray()
-            warnings.warn('A Local copy of the target data has been converted '
+            warnings.warn('A local copy of the target data has been converted '
                           'to a numpy array. Predicting on sparse target data '
                           'with the uniform strategy would not save memory '
                           'and would be slower.',

sklearn/tests/test_dummy.py

@@ -10,7 +10,7 @@
 from sklearn.utils.testing import assert_almost_equal
 from sklearn.utils.testing import assert_raises
 from sklearn.utils.testing import assert_true
-from sklearn.utils.testing import assert_warns
+from sklearn.utils.testing import assert_warns_message
 
 from sklearn.dummy import DummyClassifier, DummyRegressor
 
@@ -430,7 +430,9 @@ def test_uniform_strategy_sparse_target_warning():
                                 [1, 1]]))
 
     clf = DummyClassifier(strategy="uniform", random_state=0)
-    assert_warns(UserWarning, clf.fit, X, y)
+    assert_warns_message(UserWarning,
+                         "the uniform strategy would not save memory",
+                         clf.fit, X, y)
 
     X = [[0]] * 500
     y_pred = clf.predict(X)

sklearn/utils/random.py

@@ -1,5 +1,6 @@
-# This file contains a backport of np.random.choice from numpy 1.7
-# The function can be removed when we bump the requirements to >=1.7
+# Author: Hamzeh Alsalhi <[email protected]>
+#
+# License: BSD 3 clause
 from __future__ import division
 import numpy as np
 import scipy.sparse as sp
@@ -13,6 +14,8 @@
 __all__ = ['sample_without_replacement', 'choice']
 
 
+# This is a backport of np.random.choice from numpy 1.7
+# The function can be removed when we bump the requirements to >=1.7
 def choice(a, size=None, replace=True, p=None, random_state=None):
     """
     choice(a, size=None, replace=True, p=None)

sklearn/utils/tests/test_multiclass.py

@@ -347,12 +347,15 @@ def test_class_distribution():
                   [1, 3, 0, 1]])
 
     classes, n_classes, class_prior = class_distribution(y)
-    classes_expected = [np.array([1, 2, 4]), np.array([0, 2, 3]),
-                        np.array([0]), np.array([1])]
+    classes_expected = [[1, 2, 4],
+                        [0, 2, 3],
+                        [0],
+                        [1]]
     n_classes_expected = [3, 3, 1, 1]
-    class_prior_expected = [np.array([3/6, 2/6, 1/6]),
-                            np.array([1/3, 1/3, 1/3]),
-                            np.array([1.0]), np.array([1.0])]
+    class_prior_expected = [[3/6, 2/6, 1/6],
+                            [1/3, 1/3, 1/3],
+                            [1.0],
+                            [1.0]]
 
     for k in range(y.shape[1]):
         assert_array_almost_equal(classes[k], classes_expected[k])
@@ -369,12 +372,15 @@ def test_class_distribution_sparse():
                                 [1, 3, 0, 1]]))
 
     classes, n_classes, class_prior = class_distribution(y)
-    classes_expected = [np.array([1, 2, 4]), np.array([0, 2, 3]),
-                        np.array([0]), np.array([1])]
+    classes_expected = [[1, 2, 4],
+                        [0, 2, 3],
+                        [0],
+                        [1]]
     n_classes_expected = [3, 3, 1, 1]
-    class_prior_expected = [np.array([3/6, 2/6, 1/6]),
-                            np.array([1/3, 1/3, 1/3]),
-                            np.array([1.0]), np.array([1.0])]
+    class_prior_expected = [[3/6, 2/6, 1/6],
+                            [1/3, 1/3, 1/3],
+                            [1.0],
+                            [1.0]]
 
     for k in range(y.shape[1]):
         assert_array_almost_equal(classes[k], classes_expected[k])
@@ -385,9 +391,10 @@ def test_class_distribution_sparse():
     (classes,
      n_classes,
      class_prior) = class_distribution(y, [1.0, 2.0, 1.0, 2.0, 1.0, 2.0])
-    class_prior_expected = [np.array([4/9, 3/9, 2/9]),
-                            np.array([2/9, 4/9, 3/9]),
-                            np.array([1.0]), np.array([1.0])]
+    class_prior_expected = [[4/9, 3/9, 2/9],
+                            [2/9, 4/9, 3/9],
+                            [1.0],
+                            [1.0]]
 
     for k in range(y.shape[1]):
         assert_array_almost_equal(classes[k], classes_expected[k])

sklearn/utils/tests/test_random.py

@@ -118,7 +118,7 @@ def test_random_choice_csc(n_samples=10000, random_state=24):
         assert_array_almost_equal(class_probabilites[k], p, decimal=1)
 
     # Implicit class probabilities
-    classes = [[0, 1],  [1, 2]]  # array like support
+    classes = [[0, 1],  [1, 2]]  # test for array-like support
     class_probabilites = [np.array([0.5, 0.5]), np.array([0, 1/2, 1/2])]
 
     got = random_choice_csc(n_samples=n_samples,