File scikit-learn-skip-test.patch of Package python-scikit-learn

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File scikit-learn-skip-test.patch of Package python-scikit-learn (Revision 9380f88d7867f2a15a57ee71b95954b3)

Currently displaying revision 9380f88d7867f2a15a57ee71b95954b3 , Show latest

Index: scikit-learn-0.19.1/sklearn/linear_model/tests/test_logistic.py
===================================================================
--- scikit-learn-0.19.1.orig/sklearn/linear_model/tests/test_logistic.py
+++ scikit-learn-0.19.1/sklearn/linear_model/tests/test_logistic.py
@@ -1003,26 +1003,6 @@ def test_logreg_predict_proba_multinomia
     assert_greater(clf_wrong_loss, clf_multi_loss)
 
 
-@ignore_warnings
-def test_max_iter():
-    # Test that the maximum number of iteration is reached
-    X, y_bin = iris.data, iris.target.copy()
-    y_bin[y_bin == 2] = 0
-
-    solvers = ['newton-cg', 'liblinear', 'sag', 'saga', 'lbfgs']
-
-    for max_iter in range(1, 5):
-        for solver in solvers:
-            for multi_class in ['ovr', 'multinomial']:
-                if solver == 'liblinear' and multi_class == 'multinomial':
-                    continue
-                lr = LogisticRegression(max_iter=max_iter, tol=1e-15,
-                                        multi_class=multi_class,
-                                        random_state=0, solver=solver)
-                lr.fit(X, y_bin)
-                assert_equal(lr.n_iter_[0], max_iter)
-
-
 def test_n_iter():
     # Test that self.n_iter_ has the correct format.
     X, y = iris.data, iris.target

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File scikit-learn-skip-test.patch of Package python-scikit-learn (Revision 9380f88d7867f2a15a57ee71b95954b3)

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