(py37) joel@MacBook-Pro:~/repos/scikit-learn(master*)$ git checkout upstream/pr/12760 sklearn/metrics/cluster/tests/test_unsupervised.py
(py37) joel@MacBook-Pro:~/repos/scikit-learn(master*)$ pytest sklearn/metrics/cluster/tests/test_unsupervised.py
============================================================================================= test session starts ==============================================================================================
platform darwin -- Python 3.7.2, pytest-4.1.1, py-1.7.0, pluggy-0.8.1
hypothesis profile 'default' -> database=DirectoryBasedExampleDatabase('/Users/joel/repos/scikit-learn/.hypothesis/examples')
rootdir: /Users/joel/repos/scikit-learn, inifile: setup.cfg
plugins: cov-2.6.1, hypothesis-4.6.0
collected 9 items

sklearn/metrics/cluster/tests/test_unsupervised.py ........F                                                                                                                                             [100%]

=================================================================================================== FAILURES ===================================================================================================
__________________________________________________________________________________________ test_davies_bouldin_score ___________________________________________________________________________________________

    def test_davies_bouldin_score():
        assert_raises_on_only_one_label(davies_bouldin_score)
        assert_raises_on_all_points_same_cluster(davies_bouldin_score)

        # Assert the value is 0. when all samples are equals
        assert davies_bouldin_score(np.ones((10, 2)),
                                    [0] * 5 + [1] * 5) == pytest.approx(0.0)

        # Assert the value is 0. when all the mean cluster are equal
        assert davies_bouldin_score([[-1, -1], [1, 1]] * 10,
                                    [0] * 10 + [1] * 10) == pytest.approx(0.0)

        # General case (with non numpy arrays)
        X = ([[0, 0], [1, 1]] * 5 + [[3, 3], [4, 4]] * 5 +
             [[0, 4], [1, 3]] * 5 + [[3, 1], [4, 0]] * 5)
        labels = [0] * 10 + [1] * 10 + [2] * 10 + [3] * 10
        pytest.approx(davies_bouldin_score(X, labels), 2 * np.sqrt(0.5) / 3)

        # Ensure divide by zero warning is not raised in general case
        with pytest.warns(None) as record:
            davies_bouldin_score(X, labels)
        div_zero_warnings = [
            warning for warning in record
            if "divide by zero encountered" in warning.message.args[0]
        ]
>       assert len(div_zero_warnings) == 0
E       assert 1 == 0
E        +  where 1 = len([<warnings.WarningMessage object at 0x11e0a7c18>])

sklearn/metrics/cluster/tests/test_unsupervised.py:244: AssertionError
================================================================================ 1 failed, 8 passed, 2 warnings in 2.60 seconds ================================================================================
(py37) joel@MacBook-Pro:~/repos/scikit-learn(master*)$