sklearn/cluster/tests/test_k_means.py:390: AssertionError
_____________ test_kmeans_verbose[0-elkan] _____________

algorithm = 'elkan', tol = 0
capsys = <_pytest.capture.CaptureFixture object at 0x7fe92c5295e0>

    @pytest.mark.parametrize("algorithm", ["lloyd", "elkan"])
    @pytest.mark.parametrize("tol", [1e-2, 0])
    def test_kmeans_verbose(algorithm, tol, capsys):
        # Check verbose mode of KMeans for better coverage.
        X = np.random.RandomState(0).normal(size=(5000, 10))
    
        KMeans(
            algorithm=algorithm,
            n_clusters=n_clusters,
            random_state=42,
            init="random",
            n_init=1,
            tol=tol,
            verbose=1,
        ).fit(X)
    
        captured = capsys.readouterr()
    
        assert re.search(r"Initialization complete", captured.out)
        assert re.search(r"Iteration [0-9]+, inertia", captured.out)
    
        if tol == 0:
>           assert re.search(r"strict convergence", captured.out)
E           AssertionError: assert None
E            +  where None = <function search at 0x7fe9c8b85160>('strict convergence', 'Initialization complete\nIteration 0, inertia 64759.062154957086\nIteration 1, inertia 46751.170526691916\nIteration ... 297, inertia 45634.20523994775\nIteration 298, inertia 45040.45016538214\nIteration 299, inertia 47499.921554245804\n')
E            +    where <function search at 0x7fe9c8b85160> = re.search
E            +    and   'Initialization complete\nIteration 0, inertia 64759.062154957086\nIteration 1, inertia 46751.170526691916\nIteration ... 297, inertia 45634.20523994775\nIteration 298, inertia 45040.45016538214\nIteration 299, inertia 47499.921554245804\n' = CaptureResult(out='Initialization complete\nIteration 0, inertia 64759.062154957086\nIteration 1, inertia 46751.170526...rtia 45634.20523994775\nIteration 298, inertia 45040.45016538214\nIteration 299, inertia 47499.921554245804\n', err='').out

sklearn/cluster/tests/test_k_means.py:390: AssertionError
__________________ test_warm_start[1] __________________

seed = 1

    @pytest.mark.filterwarnings("ignore:.*did not converge.*")
    @pytest.mark.parametrize("seed", (0, 1, 2))
    def test_warm_start(seed):
        random_state = seed
        rng = np.random.RandomState(random_state)
        n_samples, n_features, n_components = 500, 2, 2
        X = rng.rand(n_samples, n_features)
    
        # Assert the warm_start give the same result for the same number of iter
        g = GaussianMixture(
            n_components=n_components,
            n_init=1,
            max_iter=2,
            reg_covar=0,
            random_state=random_state,
            warm_start=False,
        )
        h = GaussianMixture(
            n_components=n_components,
            n_init=1,
            max_iter=1,
            reg_covar=0,
            random_state=random_state,
            warm_start=True,
        )
    
        g.fit(X)
        score1 = h.fit(X).score(X)
        score2 = h.fit(X).score(X)
    
>       assert_almost_equal(g.weights_, h.weights_)
E       AssertionError: 
E       Arrays are not almost equal to 7 decimals
E       
E       Mismatched elements: 2 / 2 (100%)
E       Max absolute difference: 0.01287114
E       Max relative difference: 0.0258317
E        x: array([0.5111401, 0.4888599])
E        y: array([0.498269, 0.501731])

sklearn/mixture/tests/test_gaussian_mixture.py:841: AssertionError
= 15 failed, 24121 passed, 3119 skipped, 79 xfailed, 43 xpassed, 2437 warnings in 511.97s (0:08:31) =

= 24136 passed, 3119 skipped, 79 xfailed, 43 xpassed, 2434 warnings in 524.07s (0:08:44) =