global_random_seed = 34

    def test_minibatch_sensible_reassign(global_random_seed):
        # check that identical initial clusters are reassigned
        # also a regression test for when there are more desired reassignments than
        # samples.
        zeroed_X, true_labels = make_blobs(
            n_samples=100, centers=5, random_state=global_random_seed
        )
        zeroed_X[::2, :] = 0
    
        km = MiniBatchKMeans(
            n_clusters=20, batch_size=10, random_state=global_random_seed, init="random"
        ).fit(zeroed_X)
        # there should not be too many exact zero cluster centers
>       assert km.cluster_centers_.any(axis=1).sum() > 10
E       AssertionError: assert 10 > 10
E        +  where 10 = <built-in method sum of numpy.ndarray object at 0x7f00eaaf0c90>()
E        +    where <built-in method sum of numpy.ndarray object at 0x7f00eaaf0c90> = array([ True, False,  True,  True, False, False, False,  True,  True,\n        True, False, False,  True, False, False, False, False,  True,\n        True,  True]).sum
E        +      where array([ True, False,  True,  True, False, False, False,  True,  True,\n        True, False, False,  True, False, False, False, False,  True,\n        True,  True]) = <built-in method any of numpy.ndarray object at 0x7f00eaae8390>(axis=1)
E        +        where <built-in method any of numpy.ndarray object at 0x7f00eaae8390> = array([[ -0.08411287,  -1.35341192],\n       [  0.        ,   0.        ],\n       [-10.04085907,  10.04083876],\n       ...     ],\n       [ -9.1038364 ,   6.86739558],\n       [ -9.66539459,   5.01011406],\n       [ -6.894048  ,  -1.75933572]]).any
E        +          where array([[ -0.08411287,  -1.35341192],\n       [  0.        ,   0.        ],\n       [-10.04085907,  10.04083876],\n       ...     ],\n       [ -9.1038364 ,   6.86739558],\n       [ -9.66539459,   5.01011406],\n       [ -6.894048  ,  -1.75933572]]) = MiniBatchKMeans(batch_size=10, init='random', n_clusters=20, random_state=34).cluster_centers_

global_random_seed = 34
km         = MiniBatchKMeans(batch_size=10, init='random', n_clusters=20, random_state=34)
true_labels = array([3, 0, 2, 4, 1, 0, 2, 3, 0, 1, 4, 2, 2, 0, 2, 4, 3, 4, 2, 3, 3, 4,
       2, 2, 1, 4, 3, 4, 3, 1, 1, 1, 4, 1, 4,...3,
       4, 1, 0, 3, 3, 4, 3, 2, 2, 2, 0, 2, 2, 4, 4, 0, 4, 2, 3, 0, 2, 1,
       3, 0, 1, 2, 0, 3, 1, 0, 0, 4, 2, 4])
zeroed_X   = array([[  0.        ,   0.        ],
       [ -9.93451852,   5.17769196],
       [  0.        ,   0.        ],
       ...     ],
       [ -5.62221677,   0.01380172],
       [  0.        ,   0.        ],
       [ -6.95512025,  -1.26162786]])

../1/s/sklearn/cluster/tests/test_k_means.py:466: AssertionError