================================== FAILURES ===================================
______________________ test_singular_values[randomized] _______________________

svd_solver = 'randomized'

    @pytest.mark.parametrize("svd_solver", ["full", "auto", "randomized"])
    def test_singular_values(svd_solver):
        # Check that the IncrementalPCA output has the correct singular values
    
        rng = np.random.RandomState(0)
        n_samples = 1000
        n_features = 100
    
        X = datasets.make_low_rank_matrix(
            n_samples, n_features, tail_strength=0.0, effective_rank=10, random_state=rng
        )
        X = da.from_array(X, chunks=[200, -1])
    
        pca = PCA(n_components=10, svd_solver=svd_solver, random_state=rng).fit(X)
        ipca = IncrementalPCA(n_components=10, batch_size=100, svd_solver=svd_solver).fit(X)
        assert_array_almost_equal(pca.singular_values_, ipca.singular_values_, 2)
    
        # Compare to the Frobenius norm
        X_pca = pca.transform(X)
        X_ipca = ipca.transform(X)
        assert_array_almost_equal(
            np.sum(pca.singular_values_ ** 2.0), np.linalg.norm(X_pca, "fro") ** 2.0, 12
E        x: array(6.38)
E        y: array(6.39)

tests\test_incremental_pca.py:368: AssertionError