sklearn/linear_model/tests/test_logistic.py::test_max_iter <- ../../../miniconda/envs/testenv/lib/python3.6/site-packages/sklearn/linear_model/tests/test_logistic.py FAILED [ 14%]

...

=================================== FAILURES ===================================
________________________________ test_max_iter _________________________________
    @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)
X          = array([[ 5.1,  3.5,  1.4,  0.2],
       [ 4.9,  3. ,  1.4,  0.2],
       [ 4.7,  3.2,  1.3,  0.2],
       [ 4.6,  3.1,...  2.5,  5. ,  1.9],
       [ 6.5,  3. ,  5.2,  2. ],
       [ 6.2,  3.4,  5.4,  2.3],
       [ 5.9,  3. ,  5.1,  1.8]])
lr         = LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,
          intercept_scaling=1, max_iter=1...r', n_jobs=1,
          penalty='l2', random_state=0, solver='lbfgs', tol=1e-15,
          verbose=0, warm_start=False)
max_iter   = 1
multi_class = 'ovr'
solver     = 'lbfgs'
solvers    = ['newton-cg', 'liblinear', 'sag', 'saga', 'lbfgs']
y_bin      = array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0])
../../../miniconda/envs/testenv/lib/python3.6/site-packages/sklearn/linear_model/tests/test_logistic.py:1023: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../../../miniconda/envs/testenv/lib/python3.6/unittest/case.py:829: in assertEqual
    assertion_func(first, second, msg=msg)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
self = <unittest.case.TestCase testMethod=__init__>, first = 0, second = 1
msg = '0 != 1'
    def _baseAssertEqual(self, first, second, msg=None):
        """The default assertEqual implementation, not type specific."""
        if not first == second:
            standardMsg = '%s != %s' % _common_shorten_repr(first, second)
            msg = self._formatMessage(msg, standardMsg)
>           raise self.failureException(msg)
E           AssertionError: 0 != 1
first      = 0
msg        = '0 != 1'
second     = 1
self       = <unittest.case.TestCase testMethod=__init__>
standardMsg = '0 != 1'
../../../miniconda/envs/testenv/lib/python3.6/unittest/case.py:822: AssertionError