Merge pull request #7282 from asnt/fix-convergence-warnings

lesteve · web-flow · commit bfbc0bc072bd · 2016-08-31T14:02:15.000+02:00
[MRG+2] Fix ConvergenceWarning's in tests
diff --git a/sklearn/decomposition/tests/test_dict_learning.py b/sklearn/decomposition/tests/test_dict_learning.py
@@ -1,4 +1,7 @@
 import numpy as np
+
+from sklearn.exceptions import ConvergenceWarning
+
 from sklearn.utils import check_array
 
 from sklearn.utils.testing import assert_array_almost_equal
@@ -66,9 +69,12 @@ def test_dict_learning_lassocd_readonly_data():
     n_components = 12
     with TempMemmap(X) as X_read_only:
         dico = DictionaryLearning(n_components, transform_algorithm='lasso_cd',
-                                  transform_alpha=0.001, random_state=0, n_jobs=-1)
-        code = dico.fit(X_read_only).transform(X_read_only)
-        assert_array_almost_equal(np.dot(code, dico.components_), X_read_only, decimal=2)
+                                  transform_alpha=0.001, random_state=0,
+                                  n_jobs=-1)
+        with ignore_warnings(category=ConvergenceWarning):
+            code = dico.fit(X_read_only).transform(X_read_only)
+        assert_array_almost_equal(np.dot(code, dico.components_), X_read_only,
+                                  decimal=2)
 
 
 def test_dict_learning_nonzero_coefs():
diff --git a/sklearn/neural_network/tests/test_mlp.py b/sklearn/neural_network/tests/test_mlp.py
@@ -14,6 +14,7 @@
 
 from sklearn.datasets import load_digits, load_boston
 from sklearn.datasets import make_regression, make_multilabel_classification
+from sklearn.exceptions import ConvergenceWarning
 from sklearn.externals.six.moves import cStringIO as StringIO
 from sklearn.metrics import roc_auc_score
 from sklearn.neural_network import MLPClassifier
@@ -22,7 +23,7 @@
 from sklearn.preprocessing import StandardScaler, MinMaxScaler
 from scipy.sparse import csr_matrix
 from sklearn.utils.testing import (assert_raises, assert_greater, assert_equal,
-                                   assert_false)
+                                   assert_false, ignore_warnings)
 
 
 np.seterr(all='warn')
@@ -60,7 +61,8 @@ def test_alpha():
 
     for alpha in alpha_values:
         mlp = MLPClassifier(hidden_layer_sizes=10, alpha=alpha, random_state=1)
-        mlp.fit(X, y)
+        with ignore_warnings(category=ConvergenceWarning):
+            mlp.fit(X, y)
         alpha_vectors.append(np.array([absolute_sum(mlp.coefs_[0]),
                                        absolute_sum(mlp.coefs_[1])]))
 
@@ -262,17 +264,18 @@ def test_lbfgs_regression():
 
 
 def test_learning_rate_warmstart():
-    # Tests that warm_start reuses past solution."""
+    # Test that warm_start reuses past solution."""
     X = [[3, 2], [1, 6], [5, 6], [-2, -4]]
     y = [1, 1, 1, 0]
     for learning_rate in ["invscaling", "constant"]:
         mlp = MLPClassifier(algorithm='sgd', hidden_layer_sizes=4,
                             learning_rate=learning_rate, max_iter=1,
                             power_t=0.25, warm_start=True)
-        mlp.fit(X, y)
-        prev_eta = mlp._optimizer.learning_rate
-        mlp.fit(X, y)
-        post_eta = mlp._optimizer.learning_rate
+        with ignore_warnings(category=ConvergenceWarning):
+            mlp.fit(X, y)
+            prev_eta = mlp._optimizer.learning_rate
+            mlp.fit(X, y)
+            post_eta = mlp._optimizer.learning_rate
 
         if learning_rate == 'constant':
             assert_equal(prev_eta, post_eta)
@@ -321,15 +324,16 @@ def test_partial_fit_classes_error():
 
 def test_partial_fit_classification():
     # Test partial_fit on classification.
-    # `partial_fit` should yield the same results as 'fit'for binary and
+    # `partial_fit` should yield the same results as 'fit' for binary and
     # multi-class classification.
     for X, y in classification_datasets:
         X = X
         y = y
         mlp = MLPClassifier(algorithm='sgd', max_iter=100, random_state=1,
                             tol=0, alpha=1e-5, learning_rate_init=0.2)
 
-        mlp.fit(X, y)
+        with ignore_warnings(category=ConvergenceWarning):
+            mlp.fit(X, y)
         pred1 = mlp.predict(X)
         mlp = MLPClassifier(algorithm='sgd', random_state=1, alpha=1e-5,
                             learning_rate_init=0.2)
@@ -405,7 +409,8 @@ def test_predict_proba_binary():
     y = y_digits_binary[:50]
 
     clf = MLPClassifier(hidden_layer_sizes=5)
-    clf.fit(X, y)
+    with ignore_warnings(category=ConvergenceWarning):
+        clf.fit(X, y)
     y_proba = clf.predict_proba(X)
     y_log_proba = clf.predict_log_proba(X)
 
@@ -427,7 +432,8 @@ def test_predict_proba_multi():
     y = y_digits_multi[:10]
 
     clf = MLPClassifier(hidden_layer_sizes=5)
-    clf.fit(X, y)
+    with ignore_warnings(category=ConvergenceWarning):
+        clf.fit(X, y)
     y_proba = clf.predict_proba(X)
     y_log_proba = clf.predict_log_proba(X)
 
@@ -447,10 +453,11 @@ def test_sparse_matrices():
     y = y_digits_binary[:50]
     X_sparse = csr_matrix(X)
     mlp = MLPClassifier(random_state=1, hidden_layer_sizes=15)
-    mlp.fit(X, y)
-    pred1 = mlp.decision_function(X)
-    mlp.fit(X_sparse, y)
-    pred2 = mlp.decision_function(X_sparse)
+    with ignore_warnings(category=ConvergenceWarning):
+        mlp.fit(X, y)
+        pred1 = mlp.decision_function(X)
+        mlp.fit(X_sparse, y)
+        pred2 = mlp.decision_function(X_sparse)
     assert_almost_equal(pred1, pred2)
     pred1 = mlp.predict(X)
     pred2 = mlp.predict(X_sparse)
@@ -476,7 +483,8 @@ def test_verbose_sgd():
     old_stdout = sys.stdout
     sys.stdout = output = StringIO()
 
-    clf.fit(X, y)
+    with ignore_warnings(category=ConvergenceWarning):
+        clf.fit(X, y)
     clf.partial_fit(X, y)
 
     sys.stdout = old_stdout
diff --git a/sklearn/svm/tests/test_sparse.py b/sklearn/svm/tests/test_sparse.py
@@ -10,7 +10,8 @@
 from sklearn.svm.tests import test_svm
 from sklearn.exceptions import ConvergenceWarning
 from sklearn.utils.extmath import safe_sparse_dot
-from sklearn.utils.testing import assert_warns, assert_raise_message
+from sklearn.utils.testing import (assert_warns, assert_raise_message,
+                                   ignore_warnings)
 
 # test sample 1
 X = np.array([[-2, -1], [-1, -1], [-1, -2], [1, 1], [1, 2], [2, 1]])
@@ -334,7 +335,9 @@ def test_timeout():
 
 def test_consistent_proba():
     a = svm.SVC(probability=True, max_iter=1, random_state=0)
-    proba_1 = a.fit(X, Y).predict_proba(X)
+    with ignore_warnings(category=ConvergenceWarning):
+        proba_1 = a.fit(X, Y).predict_proba(X)
     a = svm.SVC(probability=True, max_iter=1, random_state=0)
-    proba_2 = a.fit(X, Y).predict_proba(X)
+    with ignore_warnings(category=ConvergenceWarning):
+        proba_2 = a.fit(X, Y).predict_proba(X)
     assert_array_almost_equal(proba_1, proba_2)
diff --git a/sklearn/tests/test_cross_validation.py b/sklearn/tests/test_cross_validation.py
@@ -7,6 +7,7 @@
 from scipy.sparse import csr_matrix
 from scipy import stats
 
+from sklearn.exceptions import ConvergenceWarning
 from sklearn.utils.testing import assert_true
 from sklearn.utils.testing import assert_false
 from sklearn.utils.testing import assert_equal
@@ -1167,14 +1168,16 @@ def test_cross_val_predict_input_types():
     assert_equal(predictions.shape, (10,))
 
     # test with multioutput y
-    predictions = cval.cross_val_predict(clf, X_sparse, X)
+    with ignore_warnings(category=ConvergenceWarning):
+        predictions = cval.cross_val_predict(clf, X_sparse, X)
     assert_equal(predictions.shape, (10, 2))
 
     predictions = cval.cross_val_predict(clf, X_sparse, y)
     assert_array_equal(predictions.shape, (10,))
 
     # test with multioutput y
-    predictions = cval.cross_val_predict(clf, X_sparse, X)
+    with ignore_warnings(category=ConvergenceWarning):
+        predictions = cval.cross_val_predict(clf, X_sparse, X)
     assert_array_equal(predictions.shape, (10, 2))
 
     # test with X and y as list
