fix missing krylov option

rubywerman · rubywerman · commit c0da745e19c8 · 2020-07-10T11:30:01.000-05:00
diff --git a/sklearn/linear_model/tests/test_logistic.py b/sklearn/linear_model/tests/test_logistic.py
@@ -195,7 +195,8 @@ def test_predict_iris():
         assert np.mean(pred == target) > .95
 
 
-@pytest.mark.parametrize('solver', ['lbfgs', 'newton-cg', 'sag', 'saga'])
+@pytest.mark.parametrize('solver', ['lbfgs', 'newton-cg', 'sag', 'saga',
+                                    'trust-ncg', 'trust-krylov'])
 def test_multinomial_validation(solver):
     lr = LogisticRegression(C=-1, solver=solver, multi_class='multinomial')
     assert_raises(ValueError, lr.fit, [[0, 1], [1, 0]], [0, 1])
@@ -247,7 +248,8 @@ def test_check_solver_option(LR):
     assert_raise_message(ValueError, msg, lr.fit, X, y)
 
 
-@pytest.mark.parametrize('solver', ['lbfgs', 'newton-cg', 'sag', 'saga'])
+@pytest.mark.parametrize('solver', ['lbfgs', 'newton-cg', 'sag', 'saga',
+                                    'trust-ncg', 'trust-krylov'])
 def test_multinomial_binary(solver):
     # Test multinomial LR on a binary problem.
     target = (iris.target > 0).astype(np.intp)
@@ -1233,7 +1235,8 @@ def test_n_iter(solver):
     assert clf.n_iter_.shape == (1, n_cv_fold, n_Cs)
 
 
-@pytest.mark.parametrize('solver', ('newton-cg', 'sag', 'saga', 'lbfgs'))
+@pytest.mark.parametrize('solver', ('newton-cg', 'sag', 'saga', 'lbfgs',
+                                    'trust-ncg', 'trust-krylov'))
 @pytest.mark.parametrize('warm_start', (True, False))
 @pytest.mark.parametrize('fit_intercept', (True, False))
 @pytest.mark.parametrize('multi_class', ['ovr', 'multinomial'])
@@ -1694,7 +1697,7 @@ def test_logistic_regression_path_coefs_multinomial():
                                                Cs=3, tol=1e-3)],
                          ids=lambda x: x.__class__.__name__)
 @pytest.mark.parametrize('solver', ['liblinear', 'lbfgs', 'newton-cg', 'sag',
-                                    'saga'])
+                                    'saga', 'trust-ncg', 'trust-krylov'])
 def test_logistic_regression_multi_class_auto(est, solver):
     # check multi_class='auto' => multi_class='ovr' iff binary y or liblinear
 
@@ -1737,7 +1740,8 @@ def fit(X, y, **kw):
                                    solver=solver).coef_)
 
 
-@pytest.mark.parametrize('solver', ('lbfgs', 'newton-cg', 'sag', 'saga'))
+@pytest.mark.parametrize('solver', ('lbfgs', 'newton-cg', 'sag', 'saga',
+                                    'trust-ncg', 'trust-krylov'))
 def test_penalty_none(solver):
     # - Make sure warning is raised if penalty='none' and C is set to a
     #   non-default value.
