scikit-learn · vighneshbirodkar · Sep 22, 2015 · Sep 23, 2015 · Sep 24, 2015 · Sep 24, 2015
diff --git a/sklearn/ensemble/tests/test_bagging.py b/sklearn/ensemble/tests/test_bagging.py
@@ -114,15 +114,15 @@ def fit(self, X, y):
             for f in ['predict', 'predict_proba', 'predict_log_proba', 'decision_function']:
                 # Trained on sparse format
                 sparse_classifier = BaggingClassifier(
-                    base_estimator=CustomSVC(),
+                    base_estimator=CustomSVC(decision_function_shape='ovr'),
                     random_state=1,
                     **params
                 ).fit(X_train_sparse, y_train)
                 sparse_results = getattr(sparse_classifier, f)(X_test_sparse)
 
                 # Trained on dense format
                 dense_classifier = BaggingClassifier(
-                    base_estimator=CustomSVC(),
+                    base_estimator=CustomSVC(decision_function_shape='ovr'),
                     random_state=1,
                     **params
                 ).fit(X_train, y_train)
@@ -439,7 +439,7 @@ def test_parallel_classification():
     assert_array_almost_equal(y1, y3)
 
     # decision_function
-    ensemble = BaggingClassifier(SVC(),
+    ensemble = BaggingClassifier(SVC(decision_function_shape='ovr'),
                                  n_jobs=3,
                                  random_state=0).fit(X_train, y_train)
 
@@ -449,7 +449,7 @@ def test_parallel_classification():
     decisions2 = ensemble.decision_function(X_test)
     assert_array_almost_equal(decisions1, decisions2)
 
-    ensemble = BaggingClassifier(SVC(),
+    ensemble = BaggingClassifier(SVC(decision_function_shape='ovr'),
                                  n_jobs=1,
                                  random_state=0).fit(X_train, y_train)
 

diff --git a/sklearn/linear_model/tests/test_least_angle.py b/sklearn/linear_model/tests/test_least_angle.py
@@ -333,7 +333,7 @@ def objective_function(coef):
     lars_coef_ = lars.coef_
     lars_obj = objective_function(lars_coef_)
 
-    coord_descent = linear_model.Lasso(alpha=alpha, tol=1e-10, normalize=False)
+    coord_descent = linear_model.Lasso(alpha=alpha, tol=1e-4, normalize=False)
     cd_coef_ = coord_descent.fit(X, y).coef_
     cd_obj = objective_function(cd_coef_)
 
@@ -360,6 +360,7 @@ def test_lars_n_nonzero_coefs(verbose=False):
     assert_equal(len(lars.alphas_), 7)
 
 
+@ignore_warnings
 def test_multitarget():
     # Assure that estimators receiving multidimensional y do the right thing
     X = diabetes.data

diff --git a/sklearn/linear_model/tests/test_sgd.py b/sklearn/linear_model/tests/test_sgd.py
@@ -14,6 +14,7 @@
 from sklearn.utils.testing import assert_false, assert_true
 from sklearn.utils.testing import assert_equal
 from sklearn.utils.testing import assert_raises_regexp
+from sklearn.utils.testing import ignore_warnings
 
 from sklearn import linear_model, datasets, metrics
 from sklearn.base import clone
@@ -1012,6 +1013,7 @@ def test_elasticnet_convergence(self):
                 assert_almost_equal(cd.coef_, sgd.coef_, decimal=2,
                                     err_msg=err_msg)
 
+    @ignore_warnings
     def test_partial_fit(self):
         third = X.shape[0] // 3
         clf = self.factory(alpha=0.01)

diff --git a/sklearn/svm/tests/test_sparse.py b/sklearn/svm/tests/test_sparse.py
@@ -81,8 +81,10 @@ def test_svc():
     kernels = ["linear", "poly", "rbf", "sigmoid"]
     for dataset in datasets:
         for kernel in kernels:
-            clf = svm.SVC(kernel=kernel, probability=True, random_state=0)
-            sp_clf = svm.SVC(kernel=kernel, probability=True, random_state=0)
+            clf = svm.SVC(kernel=kernel, probability=True, random_state=0,
+                          decision_function_shape='ovo')
+            sp_clf = svm.SVC(kernel=kernel, probability=True, random_state=0,
+                             decision_function_shape='ovo')
             check_svm_model_equal(clf, sp_clf, *dataset)
 
 
@@ -148,7 +150,8 @@ def test_sparse_decision_function():
     #returns the same as the one in libsvm
 
     # multi class:
-    clf = svm.SVC(kernel='linear', C=0.1).fit(iris.data, iris.target)
+    svc = svm.SVC(kernel='linear', C=0.1, decision_function_shape='ovo')
+    clf = svc.fit(iris.data, iris.target)
 
     dec = safe_sparse_dot(iris.data, clf.coef_.T) + clf.intercept_
 

diff --git a/sklearn/svm/tests/test_svm.py b/sklearn/svm/tests/test_svm.py
@@ -347,13 +347,14 @@ def test_decision_function_shape():
     assert_equal(dec.shape, (len(X_train), 10))
 
     # check deprecation warning
-    clf.decision_function_shape = None
+    clf = svm.SVC(kernel='linear', C=0.1).fit(X_train, y_train)
     msg = "change the shape of the decision function"
     dec = assert_warns_message(ChangedBehaviorWarning, msg,
                                clf.decision_function, X_train)
     assert_equal(dec.shape, (len(X_train), 10))
 
 
+@ignore_warnings
 def test_svr_decision_function():
     # Test SVR's decision_function
     # Sanity check, test that decision_function implemented in python

diff --git a/sklearn/tests/test_calibration.py b/sklearn/tests/test_calibration.py
@@ -9,6 +9,7 @@
                                    assert_greater_equal,
                                    assert_array_equal,
                                    assert_raises,
+                                   ignore_warnings,
                                    assert_warns_message)
 from sklearn.datasets import make_classification, make_blobs
 from sklearn.naive_bayes import MultinomialNB
@@ -23,6 +24,7 @@
 from sklearn.calibration import calibration_curve
 
 
+@ignore_warnings
 def test_calibration():
     """Test calibration objects with isotonic and sigmoid"""
     n_samples = 100

diff --git a/sklearn/tests/test_common.py b/sklearn/tests/test_common.py
@@ -21,6 +21,7 @@
 
 import sklearn
 from sklearn.cluster.bicluster import BiclusterMixin
+from sklearn.decomposition import ProjectedGradientNMF
 
 from sklearn.linear_model.base import LinearClassifierMixin
 from sklearn.utils.estimator_checks import (
@@ -66,8 +67,12 @@ def test_non_meta_estimators():
         if name.startswith("_"):
             continue
         for check in _yield_all_checks(name, Estimator):
-            yield check, name, Estimator
-
+            if issubclass(Estimator, ProjectedGradientNMF):
+                # The ProjectedGradientNMF class is deprecated
+                with ignore_warnings():
+                    yield check, name, Estimator
+            else:
+                yield check, name, Estimator
 
 def test_configure():
     # Smoke test the 'configure' step of setup, this tests all the
@@ -180,14 +185,24 @@ def test_non_transformer_estimators_n_iter():
 def test_transformer_n_iter():
     transformers = all_estimators(type_filter='transformer')
     for name, Estimator in transformers:
-        estimator = Estimator()
+        if issubclass(Estimator, ProjectedGradientNMF):
+            # The ProjectedGradientNMF class is deprecated
+            with ignore_warnings():
+                estimator = Estimator()
+        else:
+            estimator = Estimator()
         # Dependent on external solvers and hence accessing the iter
         # param is non-trivial.
         external_solver = ['Isomap', 'KernelPCA', 'LocallyLinearEmbedding',
                            'RandomizedLasso', 'LogisticRegressionCV']
 
         if hasattr(estimator, "max_iter") and name not in external_solver:
-            yield check_transformer_n_iter, name, estimator
+            if isinstance(estimator, ProjectedGradientNMF):
+                # The ProjectedGradientNMF class is deprecated
+                with ignore_warnings():
+                    yield check_transformer_n_iter, name, estimator
+            else:
+                yield check_transformer_n_iter, name, estimator
 
 
 def test_get_params_invariance():
@@ -198,4 +213,9 @@ def test_get_params_invariance():
     estimators = all_estimators(include_meta_estimators=False, include_other=True)
     for name, Estimator in estimators:
         if hasattr(Estimator, 'get_params'):
-            yield check_get_params_invariance, name, Estimator
+            # The ProjectedGradientNMF class is deprecated
+            if issubclass(Estimator, ProjectedGradientNMF):
+                with ignore_warnings():
+                    yield check_get_params_invariance, name, Estimator
+            else:
+                yield check_get_params_invariance, name, Estimator
diff --git a/sklearn/tests/test_pipeline.py b/sklearn/tests/test_pipeline.py
@@ -214,7 +214,7 @@ def test_pipeline_methods_preprocessing_svm():
     n_classes = len(np.unique(y))
     scaler = StandardScaler()
     pca = RandomizedPCA(n_components=2, whiten=True)
-    clf = SVC(probability=True, random_state=0)
+    clf = SVC(probability=True, random_state=0, decision_function_shape='ovr')
 
     for preprocessing in [scaler, pca]:
         pipe = Pipeline([('preprocess', preprocessing), ('svc', clf)])