name = 'KernelPCA'
Estimator = <class 'sklearn.decomposition.kernel_pca.KernelPCA'>
check = <function check_pipeline_consistency at 0x3fff8b056b18>

    @pytest.mark.parametrize(
            "name, Estimator, check",
            _generate_checks_per_estimator(_yield_all_checks,
                                           _tested_non_meta_estimators()),
            ids=_rename_partial
    )
    def test_non_meta_estimators(name, Estimator, check):
        # Common tests for non-meta estimators
        with ignore_warnings(category=(DeprecationWarning, ConvergenceWarning,
                                       UserWarning, FutureWarning)):
            estimator = Estimator()
            set_checking_parameters(estimator)
>           check(name, estimator)

sklearn/tests/test_common.py:101: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
sklearn/utils/testing.py:350: in wrapper
    return fn(*args, **kwargs)
sklearn/utils/estimator_checks.py:1048: in check_pipeline_consistency
    assert_allclose_dense_sparse(result, result_pipe)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

x = array([[ 9.17543358e-01,  4.04410079e-02,  2.21585378e-02,
        -2.69563828...,  2.47000231e-11,
         2.81623715e-09,  1.36863809e-09,  1.28803077e-09]])
y = array([[ 9.17543358e-01,  4.04410079e-02,  2.21585378e-02,
        -2.69560026..., -6.90266524e-10,
        -8.33997668e-11,  1.37018888e-09,  2.03087265e-09]])
rtol = 1e-07, atol = 1e-09, err_msg = ''

    def assert_allclose_dense_sparse(x, y, rtol=1e-07, atol=1e-9, err_msg=''):
        """[...]"""
        if sp.sparse.issparse(x) and sp.sparse.issparse(y):
            x = x.tocsr()
            y = y.tocsr()
            x.sum_duplicates()
            y.sum_duplicates()
            assert_array_equal(x.indices, y.indices, err_msg=err_msg)
            assert_array_equal(x.indptr, y.indptr, err_msg=err_msg)
            assert_allclose(x.data, y.data, rtol=rtol, atol=atol, err_msg=err_msg)
        elif not sp.sparse.issparse(x) and not sp.sparse.issparse(y):
            # both dense
>           assert_allclose(x, y, rtol=rtol, atol=atol, err_msg=err_msg)
E           AssertionError: 
E           Not equal to tolerance rtol=1e-07, atol=1e-09
E           
E           (mismatch 13.7777777778%)
E            x: array([[ 9.175434e-01,  4.044101e-02,  2.215854e-02, -2.695638e-08,
E                    3.128852e-08,  0.000000e+00,  0.000000e+00,  0.000000e+00,
E                    0.000000e+00,  0.000000e+00,  0.000000e+00,  0.000000e+00,...
E            y: array([[ 9.175434e-01,  4.044101e-02,  2.215854e-02, -2.695600e-08,
E                    3.128926e-08,  0.000000e+00,  0.000000e+00,  0.000000e+00,
E                    0.000000e+00,  0.000000e+00,  0.000000e+00,  0.000000e+00,...

sklearn/utils/testing.py:464: AssertionError

>           assert_allclose(x, y, rtol=rtol, atol=atol, err_msg=err_msg)
E           AssertionError: 
E           Not equal to tolerance rtol=1e-07, atol=1e-09
E           
E           (mismatch 50.0%)
E            x: array([[-2.011707e-01,  2.825862e-01],
E                  [-1.618549e-01,  1.102424e-12],
E                  [-1.618549e-01,  3.172043e-11],...
E            y: array([[-1.753929e-01,  2.825862e-01],
E                  [-1.894835e-01, -1.533793e-11],
E                  [-1.894835e-01,  3.785336e-11],...

>           assert_allclose(x, y, rtol=rtol, atol=atol, err_msg=err_msg)
E           AssertionError: 
E           Not equal to tolerance rtol=1e-07, atol=0.01
E           fit_transform and transform outcomes not consistent in LocallyLinearEmbedding(eigen_solver='auto', hessian_tol=0.0001, max_iter=5,
E                       method='standard', modified_tol=1e-12, n_components=2,
E                       n_jobs=None, n_neighbors=5, neighbors_algorithm='auto',
E                       random_state=0, reg=0.001, tol=1e-06)
E           (mismatch 50.0%)
E            x: array([[-8.953081e-02,  2.983824e-01],
E                  [-2.421795e-01,  4.555523e-13],
E                  [-2.421795e-01,  3.751804e-12],...
E            y: array([[-1.897519e-01,  2.981428e-01],
E                  [-1.751025e-01,  6.989894e-12],
E                  [-1.751025e-01,  9.856396e-12],...

>           assert_allclose(x, y, rtol=rtol, atol=atol, err_msg=err_msg)
E           AssertionError: 
E           Not equal to tolerance rtol=1e-07, atol=0.01
E           fit_transform and transform outcomes not consistent in LocallyLinearEmbedding(eigen_solver='auto', hessian_tol=0.0001, max_iter=5,
E                       method='standard', modified_tol=1e-12, n_components=2,
E                       n_jobs=None, n_neighbors=5, neighbors_algorithm='auto',
E                       random_state=0, reg=0.001, tol=1e-06)
E           (mismatch 50.0%)
E            x: array([[ 4.698100e-02, -2.983824e-01],
E                  [ 2.538887e-01, -1.711825e-12],
E                  [ 2.538887e-01, -8.653966e-12],...
E            y: array([[-2.546916e-01, -2.981428e-01],
E                  [ 4.241318e-02, -6.757535e-12],
E                  [ 4.241318e-02, -2.107003e-11],...