TST Use pytest.raises instead of legacy constructions (#12057)

rth · jorisvandenbossche · commit ec69171d523c · 2018-09-13T13:57:43.000+02:00
diff --git a/sklearn/ensemble/tests/test_voting_classifier.py b/sklearn/ensemble/tests/test_voting_classifier.py
@@ -203,18 +203,14 @@ def test_predict_proba_on_toy_problem():
     assert_almost_equal(t21, eclf_res[2][1], decimal=1)
     assert_almost_equal(t31, eclf_res[3][1], decimal=1)
 
-    try:
+    with pytest.raises(
+            AttributeError,
+            match="predict_proba is not available when voting='hard'"):
         eclf = VotingClassifier(estimators=[
                                 ('lr', clf1), ('rf', clf2), ('gnb', clf3)],
                                 voting='hard')
         eclf.fit(X, y).predict_proba(X)
 
-    except AttributeError:
-        pass
-    else:
-        raise AssertionError('AttributeError for voting == "hard"'
-                             ' and with predict_proba not raised')
-
 
 def test_multilabel():
     """Check if error is raised for multilabel classification."""
diff --git a/sklearn/model_selection/tests/test_split.py b/sklearn/model_selection/tests/test_split.py
@@ -482,13 +482,10 @@ def test_shuffle_kfold_stratifiedkfold_reproducibility():
                                                 cv.split(*data)):
                 # cv.split(...) returns an array of tuples, each tuple
                 # consisting of an array with train indices and test indices
-                try:
+                with pytest.raises(AssertionError,
+                                   message="The splits for data, are same even"
+                                           " when random state is not set"):
                     np.testing.assert_array_equal(test_a, test_b)
-                except AssertionError:
-                    pass
-                else:
-                    raise AssertionError("The splits for data, are same even "
-                                         "when random state is not set")
 
 
 def test_shuffle_stratifiedkfold():
