========================================================================= FAILURES =========================================================================
_________________________________________________________ test_score[dataframe-50-50-30-5000000.0] _________________________________________________________

nrows = 5000000.0, ncols = 30, nclusters = 50, n_parts = 50, input_type = 'dataframe'
client = <Client: 'tcp://127.0.0.1:38493' processes=4 threads=4, memory=404.32 GB>

    @pytest.mark.mg
    @pytest.mark.parametrize("nrows", [unit_param(1e3), quality_param(1e5),
                                       stress_param(5e6)])
    @pytest.mark.parametrize("ncols", [10, 30])
    @pytest.mark.parametrize("nclusters", [unit_param(5), quality_param(10),
                                           stress_param(50)])
    @pytest.mark.parametrize("n_parts", [unit_param(None), quality_param(7),
                                         stress_param(50)])
    @pytest.mark.parametrize("input_type", ["dataframe", "array"])
    def test_score(nrows, ncols, nclusters, n_parts,
                   input_type, client):

        from cuml.dask.cluster import KMeans as cumlKMeans

        from cuml.dask.datasets import make_blobs

        X, y = make_blobs(n_samples=int(nrows),
                          n_features=ncols,
                          centers=nclusters,
                          n_parts=n_parts,
                          cluster_std=0.01,
                          shuffle=False,
                          random_state=10)

        if input_type == "dataframe":
            X_train = to_dask_cudf(X)
            y_train = to_dask_cudf(y)
            y = y_train
        elif input_type == "array":
            X_train, y_train = X, y

        cumlModel = cumlKMeans(init="k-means||",
                               n_clusters=nclusters,
                               random_state=10)

        cumlModel.fit(X_train)

        actual_score = cumlModel.score(X_train)

        local_model = cumlModel.get_combined_model()
        expected_score = local_model.score(X_train.compute())

>       assert actual_score == expected_score
E       assert array(-14973.28295898) == -14973.283203125
E         +array(-14973.28295898)
E         --14973.283203125

dask/test_kmeans.py:196: AssertionError
================================================================= short test summary info ==================================================================
FAILED dask/test_kmeans.py::test_score[dataframe-50-50-30-5000000.0] - assert array(-14973.28295898) == -14973.283203125
==================================================================== 1 failed in 13.74s ====================================================================

___________________________________________________________ test_score[array-50-50-10-5000000.0] ___________________________________________________________

nrows = 5000000.0, ncols = 10, nclusters = 50, n_parts = 50, input_type = 'array'
client = <Client: 'tcp://127.0.0.1:35383' processes=4 threads=4, memory=404.32 GB>

    @pytest.mark.mg
    @pytest.mark.parametrize("nrows", [unit_param(1e3), quality_param(1e5),
                                       stress_param(5e6)])
    @pytest.mark.parametrize("ncols", [10, 30])
    @pytest.mark.parametrize("nclusters", [unit_param(5), quality_param(10),
                                           stress_param(50)])
    @pytest.mark.parametrize("n_parts", [unit_param(None), quality_param(7),
                                         stress_param(50)])
    @pytest.mark.parametrize("input_type", ["dataframe", "array"])
    def test_score(nrows, ncols, nclusters, n_parts,
                   input_type, client):

        from cuml.dask.cluster import KMeans as cumlKMeans

        from cuml.dask.datasets import make_blobs

        X, y = make_blobs(n_samples=int(nrows),
                          n_features=ncols,
                          centers=nclusters,
                          n_parts=n_parts,
                          cluster_std=0.01,
                          shuffle=False,
                          random_state=10)

        if input_type == "dataframe":
            X_train = to_dask_cudf(X)
            y_train = to_dask_cudf(y)
            y = y_train
        elif input_type == "array":
            X_train, y_train = X, y

        cumlModel = cumlKMeans(init="k-means||",
                               n_clusters=nclusters,
                               random_state=10)

        cumlModel.fit(X_train)

        actual_score = cumlModel.score(X_train)

        local_model = cumlModel.get_combined_model()
        expected_score = local_model.score(X_train.compute())

>       assert actual_score == expected_score
E       assert array(-5003.67196655) == -5003.67236328125
E         +array(-5003.67196655)
E         --5003.67236328125

dask/test_kmeans.py:196: AssertionError
================================================================= short test summary info ==================================================================
FAILED dask/test_kmeans.py::test_score[array-50-50-10-5000000.0] - assert array(-5003.67196655) == -5003.67236328125
==================================================================== 1 failed in 11.54s ====================================================================