========================================================================= FAILURES =========================================================================
____________________________________________ test_knn_separate_index_search[braycurtis-50-1000-500000-ndarray] _____________________________________________

input_type = 'ndarray', nrows = 500000, n_feats = 1000, k = 50, metric = 'braycurtis'

    @pytest.mark.parametrize('input_type', ['dataframe', 'ndarray'])
    @pytest.mark.parametrize('nrows', [unit_param(500), quality_param(5000),
                             stress_param(500000)])
    @pytest.mark.parametrize('n_feats', [unit_param(3), quality_param(100),
                             stress_param(1000)])
    @pytest.mark.parametrize('k', [unit_param(3), quality_param(30),
                             stress_param(50)])
    @pytest.mark.parametrize("metric", valid_metrics())
    def test_knn_separate_index_search(input_type, nrows, n_feats, k, metric):
        X, _ = make_blobs(n_samples=nrows,
                          n_features=n_feats, random_state=0)

        X_index = X[:100]
        X_search = X[101:]

        p = 5  # Testing 5-norm of the minkowski metric only
        knn_sk = skKNN(metric=metric, p=p)  # Testing
        knn_sk.fit(X_index)
        D_sk, I_sk = knn_sk.kneighbors(X_search, k)

        X_orig = X_index

        if input_type == "dataframe":
            X_index = cudf.DataFrame(X_index)
            X_search = cudf.DataFrame(X_search)

        knn_cu = cuKNN(metric=metric, p=p)
        knn_cu.fit(X_index)
        D_cuml, I_cuml = knn_cu.kneighbors(X_search, k)

        if input_type == "dataframe":
            assert isinstance(D_cuml, cudf.DataFrame)
            assert isinstance(I_cuml, cudf.DataFrame)
            D_cuml_arr = D_cuml.as_gpu_matrix().copy_to_host()
            I_cuml_arr = I_cuml.as_gpu_matrix().copy_to_host()
        else:
            assert isinstance(D_cuml, np.ndarray)
            assert isinstance(I_cuml, np.ndarray)
            D_cuml_arr = D_cuml
            I_cuml_arr = I_cuml

        # Assert the cuml model was properly reverted
        np.testing.assert_allclose(knn_cu._X_m.to_output("numpy"), X_orig,
                                   atol=1e-3, rtol=1e-3)

        if metric == 'braycurtis':
            diff = D_cuml_arr - D_sk
            # Braycurtis has a few differences, but this is computed by FAISS.
            # So long as the indices all match below, the small discrepancy
            # should be okay.
>           assert len(diff[diff > 1e-2]) / X_search.shape[0] < 0.06
E           assert (8331690 / 499899) < 0.06
E            +  where 8331690 = len(array([0.31385932, 0.32012013, 0.32082888, ..., 0.32249511, 0.32323558,\n       0.32362939]))

test_nearest_neighbors.py:159: AssertionError
===================================================================== warnings summary =====================================================================
../../../../../opt/conda/envs/rapids/lib/python3.7/importlib/_bootstrap.py:219
../../../../../opt/conda/envs/rapids/lib/python3.7/importlib/_bootstrap.py:219
  /opt/conda/envs/rapids/lib/python3.7/importlib/_bootstrap.py:219: RuntimeWarning: numpy.ufunc size changed, may indicate binary incompatibility. Expected 192 from C header, got 216 from PyObject
    return f(*args, **kwds)

../../../../../opt/conda/envs/rapids/lib/python3.7/site-packages/sklearn/utils/deprecation.py:143
  /opt/conda/envs/rapids/lib/python3.7/site-packages/sklearn/utils/deprecation.py:143: FutureWarning: The sklearn.datasets.samples_generator module is  deprecated in version 0.22 and will be removed in version 0.24. The corresponding classes / functions should instead be imported from sklearn.datasets. Anything that cannot be imported from sklearn.datasets is now part of the private API.
    warnings.warn(message, FutureWarning)

-- Docs: https://docs.pytest.org/en/stable/warnings.html
================================================================= short test summary info ==================================================================
FAILED test_nearest_neighbors.py::test_knn_separate_index_search[braycurtis-50-1000-500000-ndarray] - assert (8331690 / 499899) < 0.06
======================================================== 1 failed, 3 warnings in 108.51s (0:01:48) =========================================================