=================================== FAILURES ===================================\
______________________ test_pca_fit[dataframe-data_info1] ______________________\
\
data_info = [9000000.0, 5000, 30], input_type = 'dataframe'\
client = <Client: 'tcp://127.0.0.1:45801' processes=8 threads=8, memory=540.94 GB>\
\
    @pytest.mark.mg\
    @pytest.mark.parametrize("data_info", [unit_param([1000, 20, 30]),\
                             stress_param([9e6, 5000, 30])])\
    @pytest.mark.parametrize("input_type", ["dataframe", "array"])\
    def test_pca_fit(data_info, input_type, client):\
\
        nrows, ncols, n_parts = data_info\
        from cuml.dask.decomposition import TruncatedSVD as daskTPCA\
        from sklearn.decomposition import TruncatedSVD\
\
        from cuml.dask.datasets import make_blobs\
\
        X, _ = make_blobs(n_samples=nrows,\
                          n_features=ncols,\
                          centers=1,\
                          n_parts=n_parts,\
                          cluster_std=0.5,\
>                         random_state=10, dtype=np.float32)\
\
test_tsvd.py:43:\
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _\
../../common/memory_utils.py:56: in cupy_rmm_wrapper\
    return func(*args, **kwargs)\
../../dask/datasets/blobs.py:118: in make_blobs\
    dtype)\
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _\
\
rs = <cupy.random.generator.RandomState object at 0x7f9a8c528610>, centers = 1\
center_box = (-10, 10), n_samples = 9000000.0, n_features = 5000\
dtype = <class 'numpy.float32'>\
\
    def _get_centers(rs, centers, center_box, n_samples, n_features, dtype):\
        if isinstance(n_samples, numbers.Integral):\
            # Set n_centers by looking at centers arg\
            if centers is None:\
                centers = 3\
\
            if isinstance(centers, numbers.Integral):\
                n_centers = centers\
                centers = rs.uniform(center_box[0], center_box[1],\
                                     size=(n_centers, n_features),\
                                     dtype=dtype)\
\
            else:\
                if n_features != centers.shape[1]:\
                    raise ValueError("Expected `n_features` to be equal to"\
                                     " the length of axis 1 of centers array")\
                n_centers = centers.shape[0]\
\
        else:\
            # Set n_centers by looking at [n_samples] arg\
>           n_centers = len(n_samples)\
E           TypeError: object of type 'float' has no len()\
\
../../datasets/blobs.py:46: TypeError\
________________________ test_pca_fit[array-data_info1] ________________________\
\
data_info = [9000000.0, 5000, 30], input_type = 'array'\
client = <Client: 'tcp://127.0.0.1:45801' processes=8 threads=8, memory=540.94 GB>\
\
    @pytest.mark.mg\
    @pytest.mark.parametrize("data_info", [unit_param([1000, 20, 30]),\
                             stress_param([9e6, 5000, 30])])\
    @pytest.mark.parametrize("input_type", ["dataframe", "array"])\
    def test_pca_fit(data_info, input_type, client):\
\
        nrows, ncols, n_parts = data_info\
        from cuml.dask.decomposition import TruncatedSVD as daskTPCA\
        from sklearn.decomposition import TruncatedSVD\
\
        from cuml.dask.datasets import make_blobs\
\
        X, _ = make_blobs(n_samples=nrows,\
                          n_features=ncols,\
                          centers=1,\
                          n_parts=n_parts,\
                          cluster_std=0.5,\
>                         random_state=10, dtype=np.float32)\
\
test_tsvd.py:43:\
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _\
../../common/memory_utils.py:56: in cupy_rmm_wrapper\
    return func(*args, **kwargs)\
../../dask/datasets/blobs.py:118: in make_blobs\
    dtype)\
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _\
\
rs = <cupy.random.generator.RandomState object at 0x7f9a8c4f88d0>, centers = 1\
center_box = (-10, 10), n_samples = 9000000.0, n_features = 5000\
dtype = <class 'numpy.float32'>\
\
    def _get_centers(rs, centers, center_box, n_samples, n_features, dtype):\
        if isinstance(n_samples, numbers.Integral):\
            # Set n_centers by looking at centers arg\
            if centers is None:\
                centers = 3\
\
            if isinstance(centers, numbers.Integral):\
                n_centers = centers\
                centers = rs.uniform(center_box[0], center_box[1],\
                                     size=(n_centers, n_features),\
                                     dtype=dtype)\
\
            else:\
                if n_features != centers.shape[1]:\
                    raise ValueError("Expected `n_features` to be equal to"\
                                     " the length of axis 1 of centers array")\
                n_centers = centers.shape[0]\
\
        else:\
            # Set n_centers by looking at [n_samples] arg\
>           n_centers = len(n_samples)\
E           TypeError: object of type 'float' has no len()\
\
../../datasets/blobs.py:46: TypeError\
___________________ test_pca_fit_transform_fp32[data_info1] ____________________\
\
data_info = [9000000.0, 5000, 46]\
client = <Client: 'tcp://127.0.0.1:45801' processes=8 threads=8, memory=540.94 GB>\
\
    @pytest.mark.mg\
    @pytest.mark.parametrize("data_info", [unit_param([1000, 20, 46]),\
                             stress_param([9e6, 5000, 46])])\
    def test_pca_fit_transform_fp32(data_info, client):\
\
        nrows, ncols, n_parts = data_info\
        from cuml.dask.decomposition import TruncatedSVD as daskTPCA\
        from cuml.dask.datasets import make_blobs\
\
        X_cudf, _ = make_blobs(n_samples=nrows,\
                               n_features=ncols,\
                               centers=1,\
                               n_parts=n_parts,\
                               cluster_std=1.5,\
>                              random_state=10, dtype=np.float32)\
\
test_tsvd.py:87:\
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _\
../../common/memory_utils.py:56: in cupy_rmm_wrapper\
    return func(*args, **kwargs)\
../../dask/datasets/blobs.py:118: in make_blobs\
    dtype)\
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _\
\
rs = <cupy.random.generator.RandomState object at 0x7f9a8c452e50>, centers = 1\
center_box = (-10, 10), n_samples = 9000000.0, n_features = 5000\
dtype = <class 'numpy.float32'>\
\
    def _get_centers(rs, centers, center_box, n_samples, n_features, dtype):\
        if isinstance(n_samples, numbers.Integral):\
            # Set n_centers by looking at centers arg\
            if centers is None:\
                centers = 3\
\
            if isinstance(centers, numbers.Integral):\
                n_centers = centers\
                centers = rs.uniform(center_box[0], center_box[1],\
                                     size=(n_centers, n_features),\
                                     dtype=dtype)\
\
            else:\
                if n_features != centers.shape[1]:\
                    raise ValueError("Expected `n_features` to be equal to"\
                                     " the length of axis 1 of centers array")\
                n_centers = centers.shape[0]\
\
        else:\
            # Set n_centers by looking at [n_samples] arg\
>           n_centers = len(n_samples)\
E           TypeError: object of type 'float' has no len()\
\
../../datasets/blobs.py:46: TypeError\
___________________ test_pca_fit_transform_fp64[data_info1] ____________________\
\
data_info = [9000000.0, 5000, 33]\
client = <Client: 'tcp://127.0.0.1:45801' processes=8 threads=8, memory=540.94 GB>\
\
    @pytest.mark.mg\
    @pytest.mark.parametrize("data_info", [unit_param([1000, 20, 33]),\
                             stress_param([9e6, 5000, 33])])\
    def test_pca_fit_transform_fp64(data_info, client):\
\
        nrows, ncols, n_parts = data_info\
\
        from cuml.dask.decomposition import TruncatedSVD as daskTPCA\
        from cuml.dask.datasets import make_blobs\
\
        X_cudf, _ = make_blobs(n_samples=nrows,\
                               n_features=ncols,\
                               centers=1,\
                               n_parts=n_parts,\
                               cluster_std=1.5,\
>                              random_state=10, dtype=np.float64)\
\
test_tsvd.py:108:\
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _\
../../common/memory_utils.py:56: in cupy_rmm_wrapper\
    return func(*args, **kwargs)\
../../dask/datasets/blobs.py:118: in make_blobs\
    dtype)\
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _\
\
rs = <cupy.random.generator.RandomState object at 0x7f9a8c47ea50>, centers = 1\
center_box = (-10, 10), n_samples = 9000000.0, n_features = 5000\
dtype = <class 'numpy.float64'>\
\
    def _get_centers(rs, centers, center_box, n_samples, n_features, dtype):\
        if isinstance(n_samples, numbers.Integral):\
            # Set n_centers by looking at centers arg\
            if centers is None:\
                centers = 3\
\
            if isinstance(centers, numbers.Integral):\
                n_centers = centers\
                centers = rs.uniform(center_box[0], center_box[1],\
                                     size=(n_centers, n_features),\
                                     dtype=dtype)\
\
            else:\
                if n_features != centers.shape[1]:\
                    raise ValueError("Expected `n_features` to be equal to"\
                                     " the length of axis 1 of centers array")\
                n_centers = centers.shape[0]\
\
        else:\
            # Set n_centers by looking at [n_samples] arg\
>           n_centers = len(n_samples)\
E           TypeError: object of type 'float' has no len()\
\
../../datasets/blobs.py:46: TypeError\
=============================== warnings summary ===============================\
cuml/test/dask/test_tfidf.py: 12 warnings\
  /opt/conda/envs/rapids/lib/python3.7/site-packages/sklearn/feature_extraction/text.py:1447: RuntimeWarning: divide by zero encountered in true_divide\
    idf = np.log(n_samples / df) + 1\
\
cuml/test/dask/test_tsvd.py::test_pca_fit[dataframe-data_info1]\
  /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)\
\
-- Docs: https://docs.pytest.org/en/stable/warnings.html\
=========================== short test summary info ============================\
FAILED test_tsvd.py::test_pca_fit[dataframe-data_info1] - TypeError: object o...\
FAILED test_tsvd.py::test_pca_fit[array-data_info1] - TypeError: object of ty...\
FAILED test_tsvd.py::test_pca_fit_transform_fp32[data_info1] - TypeError: obj...\
FAILED test_tsvd.py::test_pca_fit_transform_fp64[data_info1] - TypeError: obj...\
= 4 failed, 206 passed, 4 skipped, 2 xfailed, 13 warnings in 153.21s (0:02:33) =}