gistfile1.txt

umap-learn> ============================= test session starts ==============================
umap-learn> platform linux -- Python 3.10.9, pytest-7.2.0, pluggy-1.0.0
umap-learn> rootdir: /build/source
umap-learn> collecting ... 2023-01-28 22:09:52.880147: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  SSE3 SSE4.1 SSE4.2 AVX
umap-learn> To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.
umap-learn> collected 199 items / 4 deselected / 195 selected                              
umap-learn> 
umap-learn> umap/tests/test_aligned_umap.py ...                                      [  1%]
umap-learn> umap/tests/test_chunked_parallel_spatial_metric.py sssssssssssssssssssss [ 12%]
umap-learn> sssssssssssssssssssss                                                    [ 23%]
umap-learn> umap/tests/test_composite_models.py .s..                                 [ 25%]
umap-learn> umap/tests/test_densmap.py .s.                                           [ 26%]
umap-learn> umap/tests/test_parametric_umap.py ......                                [ 29%]
umap-learn> umap/tests/test_umap_metrics.py ..................FFFFFFFFFFFFFFFFF..... [ 50%]
umap-learn> .                                                                        [ 50%]
umap-learn> umap/tests/test_umap_nn.py ..sssssss..                                   [ 56%]
umap-learn> umap/tests/test_umap_on_iris.py ...........                              [ 62%]
umap-learn> umap/tests/test_umap_ops.py ..................F                          [ 71%]
umap-learn> umap/tests/test_umap_repeated_data.py .........                          [ 76%]
umap-learn> umap/tests/test_umap_trustworthiness.py ..........                       [ 81%]
umap-learn> umap/tests/test_umap_validation_params.py .............................. [ 96%]
umap-learn> ......                                                                   [100%]
umap-learn> 
umap-learn> =================================== FAILURES ===================================
umap-learn> ____________________________ test_sparse_euclidean _____________________________
umap-learn> 
umap-learn> sparse_spatial_data = <12x20 sparse matrix of type '<class 'numpy.float64'>'
umap-learn>         with 72 stored elements in Compressed Sparse Row format>
umap-learn> 
umap-learn>     def test_sparse_euclidean(sparse_spatial_data):
umap-learn> >       sparse_spatial_check("euclidean", sparse_spatial_data)
umap-learn> 
umap-learn> umap/tests/test_umap_metrics.py:245: 
umap-learn> _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
umap-learn> umap/tests/test_umap_metrics.py:125: in sparse_spatial_check
umap-learn>     dist_matrix = pairwise_distances(sparse_spatial_data.todense(), metric=metric)
umap-learn> /nix/store/5l733c5yjbmkd53d0nd35aln9jmpka8y-python3.10-scikit-learn-1.2.1/lib/python3.10/site-packages/sklearn/metrics/pairwise.py:2039: in pairwise_distances
umap-learn>     return _parallel_pairwise(X, Y, func, n_jobs, **kwds)
umap-learn> /nix/store/5l733c5yjbmkd53d0nd35aln9jmpka8y-python3.10-scikit-learn-1.2.1/lib/python3.10/site-packages/sklearn/metrics/pairwise.py:1579: in _parallel_pairwise
umap-learn>     return func(X, Y, **kwds)
umap-learn> /nix/store/5l733c5yjbmkd53d0nd35aln9jmpka8y-python3.10-scikit-learn-1.2.1/lib/python3.10/site-packages/sklearn/metrics/pairwise.py:300: in euclidean_distances
umap-learn>     X, Y = check_pairwise_arrays(X, Y)
umap-learn> /nix/store/5l733c5yjbmkd53d0nd35aln9jmpka8y-python3.10-scikit-learn-1.2.1/lib/python3.10/site-packages/sklearn/metrics/pairwise.py:146: in check_pairwise_arrays
umap-learn>     X = Y = check_array(
umap-learn> _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
umap-learn> 
umap-learn> array = matrix([[ 0.        ,  0.        ,  0.        ,  0.3154439 , -0.90423903,
umap-learn>           0.        , -1.2634011 , -0.742659...   ,  0.        ,  0.        ,  0.        ,
umap-learn>           0.        ,  0.        ,  0.        ,  0.        ,  0.        ]])
umap-learn> accept_sparse = 'csr'
umap-learn> 
umap-learn>     def check_array(
umap-learn>         array,
umap-learn>         accept_sparse=False,
umap-learn>         *,
umap-learn>         accept_large_sparse=True,
umap-learn>         dtype="numeric",
umap-learn>         order=None,
umap-learn>         copy=False,
umap-learn>         force_all_finite=True,
umap-learn>         ensure_2d=True,
umap-learn>         allow_nd=False,
umap-learn>         ensure_min_samples=1,
umap-learn>         ensure_min_features=1,
umap-learn>         estimator=None,
umap-learn>         input_name="",
umap-learn>     ):
umap-learn>     
umap-learn>         """Input validation on an array, list, sparse matrix or similar.
umap-learn>     
umap-learn>         By default, the input is checked to be a non-empty 2D array containing
umap-learn>         only finite values. If the dtype of the array is object, attempt
umap-learn>         converting to float, raising on failure.
umap-learn>     
umap-learn>         Parameters
umap-learn>         ----------
umap-learn>         array : object
umap-learn>             Input object to check / convert.
umap-learn>     
umap-learn>         accept_sparse : str, bool or list/tuple of str, default=False
umap-learn>             String[s] representing allowed sparse matrix formats, such as 'csc',
umap-learn>             'csr', etc. If the input is sparse but not in the allowed format,
umap-learn>             it will be converted to the first listed format. True allows the input
umap-learn>             to be any format. False means that a sparse matrix input will
umap-learn>             raise an error.
umap-learn>     
umap-learn>         accept_large_sparse : bool, default=True
umap-learn>             If a CSR, CSC, COO or BSR sparse matrix is supplied and accepted by
umap-learn>             accept_sparse, accept_large_sparse=False will cause it to be accepted
umap-learn>             only if its indices are stored with a 32-bit dtype.
umap-learn>     
umap-learn>             .. versionadded:: 0.20
umap-learn>     
umap-learn>         dtype : 'numeric', type, list of type or None, default='numeric'
umap-learn>             Data type of result. If None, the dtype of the input is preserved.
umap-learn>             If "numeric", dtype is preserved unless array.dtype is object.
umap-learn>             If dtype is a list of types, conversion on the first type is only
umap-learn>             performed if the dtype of the input is not in the list.
umap-learn>     
umap-learn>         order : {'F', 'C'} or None, default=None
umap-learn>             Whether an array will be forced to be fortran or c-style.
umap-learn>             When order is None (default), then if copy=False, nothing is ensured
umap-learn>             about the memory layout of the output array; otherwise (copy=True)
umap-learn>             the memory layout of the returned array is kept as close as possible
umap-learn>             to the original array.
umap-learn>     
umap-learn>         copy : bool, default=False
umap-learn>             Whether a forced copy will be triggered. If copy=False, a copy might
umap-learn>             be triggered by a conversion.
umap-learn>     
umap-learn>         force_all_finite : bool or 'allow-nan', default=True
umap-learn>             Whether to raise an error on np.inf, np.nan, pd.NA in array. The
umap-learn>             possibilities are:
umap-learn>     
umap-learn>             - True: Force all values of array to be finite.
umap-learn>             - False: accepts np.inf, np.nan, pd.NA in array.
umap-learn>             - 'allow-nan': accepts only np.nan and pd.NA values in array. Values
umap-learn>               cannot be infinite.
umap-learn>     
umap-learn>             .. versionadded:: 0.20
umap-learn>                ``force_all_finite`` accepts the string ``'allow-nan'``.
umap-learn>     
umap-learn>             .. versionchanged:: 0.23
umap-learn>                Accepts `pd.NA` and converts it into `np.nan`
umap-learn>     
umap-learn>         ensure_2d : bool, default=True
umap-learn>             Whether to raise a value error if array is not 2D.
umap-learn>     
umap-learn>         allow_nd : bool, default=False
umap-learn>             Whether to allow array.ndim > 2.
umap-learn>     
umap-learn>         ensure_min_samples : int, default=1
umap-learn>             Make sure that the array has a minimum number of samples in its first
umap-learn>             axis (rows for a 2D array). Setting to 0 disables this check.
umap-learn>     
umap-learn>         ensure_min_features : int, default=1
umap-learn>             Make sure that the 2D array has some minimum number of features
umap-learn>             (columns). The default value of 1 rejects empty datasets.
umap-learn>             This check is only enforced when the input data has effectively 2
umap-learn>             dimensions or is originally 1D and ``ensure_2d`` is True. Setting to 0
umap-learn>             disables this check.
umap-learn>     
umap-learn>         estimator : str or estimator instance, default=None
umap-learn>             If passed, include the name of the estimator in warning messages.
umap-learn>     
umap-learn>         input_name : str, default=""
umap-learn>             The data name used to construct the error message. In particular
umap-learn>             if `input_name` is "X" and the data has NaN values and
umap-learn>             allow_nan is False, the error message will link to the imputer
umap-learn>             documentation.
umap-learn>     
umap-learn>             .. versionadded:: 1.1.0
umap-learn>     
umap-learn>         Returns
umap-learn>         -------
umap-learn>         array_converted : object
umap-learn>             The converted and validated array.
umap-learn>         """
umap-learn>         if isinstance(array, np.matrix):
umap-learn> >           raise TypeError(
umap-learn>                 "np.matrix is not supported. Please convert to a numpy array with "
umap-learn>                 "np.asarray. For more information see: "
umap-learn>                 "https://numpy.org/doc/stable/reference/generated/numpy.matrix.html"
umap-learn>             )
umap-learn> E           TypeError: np.matrix is not supported. Please convert to a numpy array with np.asarray. For more information see: https://numpy.org/doc/stable/reference/generated/numpy.matrix.html
umap-learn> 
umap-learn> /nix/store/5l733c5yjbmkd53d0nd35aln9jmpka8y-python3.10-scikit-learn-1.2.1/lib/python3.10/site-packages/sklearn/utils/validation.py:737: TypeError
umap-learn> ____________________________ test_sparse_manhattan _____________________________
umap-learn> 
umap-learn> sparse_spatial_data = <12x20 sparse matrix of type '<class 'numpy.float64'>'
umap-learn>         with 72 stored elements in Compressed Sparse Row format>
umap-learn> 
umap-learn>     def test_sparse_manhattan(sparse_spatial_data):
umap-learn> >       sparse_spatial_check("manhattan", sparse_spatial_data)
umap-learn> 
umap-learn> umap/tests/test_umap_metrics.py:249: 
umap-learn> _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
umap-learn> umap/tests/test_umap_metrics.py:125: in sparse_spatial_check
umap-learn>     dist_matrix = pairwise_distances(sparse_spatial_data.todense(), metric=metric)
umap-learn> /nix/store/5l733c5yjbmkd53d0nd35aln9jmpka8y-python3.10-scikit-learn-1.2.1/lib/python3.10/site-packages/sklearn/metrics/pairwise.py:2039: in pairwise_distances
umap-learn>     return _parallel_pairwise(X, Y, func, n_jobs, **kwds)
umap-learn> /nix/store/5l733c5yjbmkd53d0nd35aln9jmpka8y-python3.10-scikit-learn-1.2.1/lib/python3.10/site-packages/sklearn/metrics/pairwise.py:1579: in _parallel_pairwise
umap-learn>     return func(X, Y, **kwds)
umap-learn> /nix/store/5l733c5yjbmkd53d0nd35aln9jmpka8y-python3.10-scikit-learn-1.2.1/lib/python3.10/site-packages/sklearn/metrics/pairwise.py:948: in manhattan_distances
umap-learn>     X, Y = check_pairwise_arrays(X, Y)
umap-learn> /nix/store/5l733c5yjbmkd53d0nd35aln9jmpka8y-python3.10-scikit-learn-1.2.1/lib/python3.10/site-packages/sklearn/metrics/pairwise.py:146: in check_pairwise_arrays
umap-learn>     X = Y = check_array(
umap-learn> _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
umap-learn> 
umap-learn> array = matrix([[ 0.        ,  0.        ,  0.        ,  0.3154439 , -0.90423903,
umap-learn>           0.        , -1.2634011 , -0.742659...   ,  0.        ,  0.        ,  0.        ,
umap-learn>           0.        ,  0.        ,  0.        ,  0.        ,  0.        ]])
umap-learn> accept_sparse = 'csr'
umap-learn> 
umap-learn>     def check_array(
umap-learn>         array,
umap-learn>         accept_sparse=False,
umap-learn>         *,
umap-learn>         accept_large_sparse=True,
umap-learn>         dtype="numeric",
umap-learn>         order=None,
umap-learn>         copy=False,
umap-learn>         force_all_finite=True,
umap-learn>         ensure_2d=True,
umap-learn>         allow_nd=False,
umap-learn>         ensure_min_samples=1,
umap-learn>         ensure_min_features=1,
umap-learn>         estimator=None,
umap-learn>         input_name="",
umap-learn>     ):
umap-learn>     
umap-learn>         """Input validation on an array, list, sparse matrix or similar.
umap-learn>     
umap-learn>         By default, the input is checked to be a non-empty 2D array containing
umap-learn>         only finite values. If the dtype of the array is object, attempt
umap-learn>         converting to float, raising on failure.
umap-learn>     
umap-learn>         Parameters
umap-learn>         ----------
umap-learn>         array : object
umap-learn>             Input object to check / convert.
umap-learn>     
umap-learn>         accept_sparse : str, bool or list/tuple of str, default=False
umap-learn>             String[s] representing allowed sparse matrix formats, such as 'csc',
umap-learn>             'csr', etc. If the input is sparse but not in the allowed format,
umap-learn>             it will be converted to the first listed format. True allows the input
umap-learn>             to be any format. False means that a sparse matrix input will
umap-learn>             raise an error.
umap-learn>     
umap-learn>         accept_large_sparse : bool, default=True
umap-learn>             If a CSR, CSC, COO or BSR sparse matrix is supplied and accepted by
umap-learn>             accept_sparse, accept_large_sparse=False will cause it to be accepted
umap-learn>             only if its indices are stored with a 32-bit dtype.
umap-learn>     
umap-learn>             .. versionadded:: 0.20
umap-learn>     
umap-learn>         dtype : 'numeric', type, list of type or None, default='numeric'
umap-learn>             Data type of result. If None, the dtype of the input is preserved.
umap-learn>             If "numeric", dtype is preserved unless array.dtype is object.
umap-learn>             If dtype is a list of types, conversion on the first type is only
umap-learn>             performed if the dtype of the input is not in the list.
umap-learn>     
umap-learn>         order : {'F', 'C'} or None, default=None
umap-learn>             Whether an array will be forced to be fortran or c-style.
umap-learn>             When order is None (default), then if copy=False, nothing is ensured
umap-learn>             about the memory layout of the output array; otherwise (copy=True)
umap-learn>             the memory layout of the returned array is kept as close as possible
umap-learn>             to the original array.
umap-learn>     
umap-learn>         copy : bool, default=False
umap-learn>             Whether a forced copy will be triggered. If copy=False, a copy might
umap-learn>             be triggered by a conversion.
umap-learn>     
umap-learn>         force_all_finite : bool or 'allow-nan', default=True
umap-learn>             Whether to raise an error on np.inf, np.nan, pd.NA in array. The
umap-learn>             possibilities are:
umap-learn>     
umap-learn>             - True: Force all values of array to be finite.
umap-learn>             - False: accepts np.inf, np.nan, pd.NA in array.
umap-learn>             - 'allow-nan': accepts only np.nan and pd.NA values in array. Values
umap-learn>               cannot be infinite.
umap-learn>     
umap-learn>             .. versionadded:: 0.20
umap-learn>                ``force_all_finite`` accepts the string ``'allow-nan'``.
umap-learn>     
umap-learn>             .. versionchanged:: 0.23
umap-learn>                Accepts `pd.NA` and converts it into `np.nan`
umap-learn>     
umap-learn>         ensure_2d : bool, default=True
umap-learn>             Whether to raise a value error if array is not 2D.
umap-learn>     
umap-learn>         allow_nd : bool, default=False
umap-learn>             Whether to allow array.ndim > 2.
umap-learn>     
umap-learn>         ensure_min_samples : int, default=1
umap-learn>             Make sure that the array has a minimum number of samples in its first
umap-learn>             axis (rows for a 2D array). Setting to 0 disables this check.
umap-learn>     
umap-learn>         ensure_min_features : int, default=1
umap-learn>             Make sure that the 2D array has some minimum number of features
umap-learn>             (columns). The default value of 1 rejects empty datasets.
umap-learn>             This check is only enforced when the input data has effectively 2
umap-learn>             dimensions or is originally 1D and ``ensure_2d`` is True. Setting to 0
umap-learn>             disables this check.
umap-learn>     
umap-learn>         estimator : str or estimator instance, default=None
umap-learn>             If passed, include the name of the estimator in warning messages.
umap-learn>     
umap-learn>         input_name : str, default=""
umap-learn>             The data name used to construct the error message. In particular
umap-learn>             if `input_name` is "X" and the data has NaN values and
umap-learn>             allow_nan is False, the error message will link to the imputer
umap-learn>             documentation.
umap-learn>     
umap-learn>             .. versionadded:: 1.1.0
umap-learn>     
umap-learn>         Returns
umap-learn>         -------
umap-learn>         array_converted : object
umap-learn>             The converted and validated array.
umap-learn>         """
umap-learn>         if isinstance(array, np.matrix):
umap-learn> >           raise TypeError(
umap-learn>                 "np.matrix is not supported. Please convert to a numpy array with "
umap-learn>                 "np.asarray. For more information see: "
umap-learn>                 "https://numpy.org/doc/stable/reference/generated/numpy.matrix.html"
umap-learn>             )
umap-learn> E           TypeError: np.matrix is not supported. Please convert to a numpy array with np.asarray. For more information see: https://numpy.org/doc/stable/reference/generated/numpy.matrix.html
umap-learn> 
umap-learn> /nix/store/5l733c5yjbmkd53d0nd35aln9jmpka8y-python3.10-scikit-learn-1.2.1/lib/python3.10/site-packages/sklearn/utils/validation.py:737: TypeError
umap-learn> ____________________________ test_sparse_chebyshev _____________________________
umap-learn> 
umap-learn> sparse_spatial_data = <12x20 sparse matrix of type '<class 'numpy.float64'>'
umap-learn>         with 72 stored elements in Compressed Sparse Row format>
umap-learn> 
umap-learn>     def test_sparse_chebyshev(sparse_spatial_data):
umap-learn> >       sparse_spatial_check("chebyshev", sparse_spatial_data)
umap-learn> 
umap-learn> umap/tests/test_umap_metrics.py:253: 
umap-learn> _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
umap-learn> umap/tests/test_umap_metrics.py:125: in sparse_spatial_check
umap-learn>     dist_matrix = pairwise_distances(sparse_spatial_data.todense(), metric=metric)
umap-learn> /nix/store/5l733c5yjbmkd53d0nd35aln9jmpka8y-python3.10-scikit-learn-1.2.1/lib/python3.10/site-packages/sklearn/metrics/pairwise.py:2027: in pairwise_distances
umap-learn>     X, Y = check_pairwise_arrays(
umap-learn> /nix/store/5l733c5yjbmkd53d0nd35aln9jmpka8y-python3.10-scikit-learn-1.2.1/lib/python3.10/site-packages/sklearn/metrics/pairwise.py:146: in check_pairwise_arrays
umap-learn>     X = Y = check_array(
umap-learn> _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
umap-learn> 
umap-learn> array = matrix([[ 0.        ,  0.        ,  0.        ,  0.3154439 , -0.90423903,
umap-learn>           0.        , -1.2634011 , -0.742659...   ,  0.        ,  0.        ,  0.        ,
umap-learn>           0.        ,  0.        ,  0.        ,  0.        ,  0.        ]])
umap-learn> accept_sparse = 'csr'
umap-learn> 
umap-learn>     def check_array(
umap-learn>         array,
umap-learn>         accept_sparse=False,
umap-learn>         *,
umap-learn>         accept_large_sparse=True,
umap-learn>         dtype="numeric",
umap-learn>         order=None,
umap-learn>         copy=False,
umap-learn>         force_all_finite=True,
umap-learn>         ensure_2d=True,
umap-learn>         allow_nd=False,
umap-learn>         ensure_min_samples=1,
umap-learn>         ensure_min_features=1,
umap-learn>         estimator=None,
umap-learn>         input_name="",
umap-learn>     ):
umap-learn>     
umap-learn>         """Input validation on an array, list, sparse matrix or similar.
umap-learn>     
umap-learn>         By default, the input is checked to be a non-empty 2D array containing
umap-learn>         only finite values. If the dtype of the array is object, attempt
umap-learn>         converting to float, raising on failure.
umap-learn>     
umap-learn>         Parameters
umap-learn>         ----------
umap-learn>         array : object
umap-learn>             Input object to check / convert.
umap-learn>     
umap-learn>         accept_sparse : str, bool or list/tuple of str, default=False
umap-learn>             String[s] representing allowed sparse matrix formats, such as 'csc',
umap-learn>             'csr', etc. If the input is sparse but not in the allowed format,
umap-learn>             it will be converted to the first listed format. True allows the input
umap-learn>             to be any format. False means that a sparse matrix input will
umap-learn>             raise an error.
umap-learn>     
umap-learn>         accept_large_sparse : bool, default=True
umap-learn>             If a CSR, CSC, COO or BSR sparse matrix is supplied and accepted by
umap-learn>             accept_sparse, accept_large_sparse=False will cause it to be accepted
umap-learn>             only if its indices are stored with a 32-bit dtype.
umap-learn>     
umap-learn>             .. versionadded:: 0.20
umap-learn>     
umap-learn>         dtype : 'numeric', type, list of type or None, default='numeric'
umap-learn>             Data type of result. If None, the dtype of the input is preserved.
umap-learn>             If "numeric", dtype is preserved unless array.dtype is object.
umap-learn>             If dtype is a list of types, conversion on the first type is only
umap-learn>             performed if the dtype of the input is not in the list.
umap-learn>     
umap-learn>         order : {'F', 'C'} or None, default=None
umap-learn>             Whether an array will be forced to be fortran or c-style.
umap-learn>             When order is None (default), then if copy=False, nothing is ensured
umap-learn>             about the memory layout of the output array; otherwise (copy=True)
umap-learn>             the memory layout of the returned array is kept as close as possible
umap-learn>             to the original array.
umap-learn>     
umap-learn>         copy : bool, default=False
umap-learn>             Whether a forced copy will be triggered. If copy=False, a copy might
umap-learn>             be triggered by a conversion.
umap-learn>     
umap-learn>         force_all_finite : bool or 'allow-nan', default=True
umap-learn>             Whether to raise an error on np.inf, np.nan, pd.NA in array. The
umap-learn>             possibilities are:
umap-learn>     
umap-learn>             - True: Force all values of array to be finite.
umap-learn>             - False: accepts np.inf, np.nan, pd.NA in array.
umap-learn>             - 'allow-nan': accepts only np.nan and pd.NA values in array. Values
umap-learn>               cannot be infinite.
umap-learn>     
umap-learn>             .. versionadded:: 0.20
umap-learn>                ``force_all_finite`` accepts the string ``'allow-nan'``.
umap-learn>     
umap-learn>             .. versionchanged:: 0.23
umap-learn>                Accepts `pd.NA` and converts it into `np.nan`
umap-learn>     
umap-learn>         ensure_2d : bool, default=True
umap-learn>             Whether to raise a value error if array is not 2D.
umap-learn>     
umap-learn>         allow_nd : bool, default=False
umap-learn>             Whether to allow array.ndim > 2.
umap-learn>     
umap-learn>         ensure_min_samples : int, default=1
umap-learn>             Make sure that the array has a minimum number of samples in its first
umap-learn>             axis (rows for a 2D array). Setting to 0 disables this check.
umap-learn>     
umap-learn>         ensure_min_features : int, default=1
umap-learn>             Make sure that the 2D array has some minimum number of features
umap-learn>             (columns). The default value of 1 rejects empty datasets.
umap-learn>             This check is only enforced when the input data has effectively 2
umap-learn>             dimensions or is originally 1D and ``ensure_2d`` is True. Setting to 0
umap-learn>             disables this check.
umap-learn>     
umap-learn>         estimator : str or estimator instance, default=None
umap-learn>             If passed, include the name of the estimator in warning messages.
umap-learn>     
umap-learn>         input_name : str, default=""
umap-learn>             The data name used to construct the error message. In particular
umap-learn>             if `input_name` is "X" and the data has NaN values and
umap-learn>             allow_nan is False, the error message will link to the imputer
umap-learn>             documentation.
umap-learn>     
umap-learn>             .. versionadded:: 1.1.0
umap-learn>     
umap-learn>         Returns
umap-learn>         -------
umap-learn>         array_converted : object
umap-learn>             The converted and validated array.
umap-learn>         """
umap-learn>         if isinstance(array, np.matrix):
umap-learn> >           raise TypeError(
umap-learn>                 "np.matrix is not supported. Please convert to a numpy array with "
umap-learn>                 "np.asarray. For more information see: "
umap-learn>                 "https://numpy.org/doc/stable/reference/generated/numpy.matrix.html"
umap-learn>             )
umap-learn> E           TypeError: np.matrix is not supported. Please convert to a numpy array with np.asarray. For more information see: https://numpy.org/doc/stable/reference/generated/numpy.matrix.html
umap-learn> 
umap-learn> /nix/store/5l733c5yjbmkd53d0nd35aln9jmpka8y-python3.10-scikit-learn-1.2.1/lib/python3.10/site-packages/sklearn/utils/validation.py:737: TypeError
umap-learn> ____________________________ test_sparse_minkowski _____________________________
umap-learn> 
umap-learn> sparse_spatial_data = <12x20 sparse matrix of type '<class 'numpy.float64'>'
umap-learn>         with 72 stored elements in Compressed Sparse Row format>
umap-learn> 
umap-learn>     def test_sparse_minkowski(sparse_spatial_data):
umap-learn> >       sparse_spatial_check("minkowski", sparse_spatial_data)
umap-learn> 
umap-learn> umap/tests/test_umap_metrics.py:257: 
umap-learn> _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
umap-learn> umap/tests/test_umap_metrics.py:125: in sparse_spatial_check
umap-learn>     dist_matrix = pairwise_distances(sparse_spatial_data.todense(), metric=metric)
umap-learn> /nix/store/5l733c5yjbmkd53d0nd35aln9jmpka8y-python3.10-scikit-learn-1.2.1/lib/python3.10/site-packages/sklearn/metrics/pairwise.py:2027: in pairwise_distances
umap-learn>     X, Y = check_pairwise_arrays(
umap-learn> /nix/store/5l733c5yjbmkd53d0nd35aln9jmpka8y-python3.10-scikit-learn-1.2.1/lib/python3.10/site-packages/sklearn/metrics/pairwise.py:146: in check_pairwise_arrays
umap-learn>     X = Y = check_array(
umap-learn> _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
umap-learn> 
umap-learn> array = matrix([[ 0.        ,  0.        ,  0.        ,  0.3154439 , -0.90423903,
umap-learn>           0.        , -1.2634011 , -0.742659...   ,  0.        ,  0.        ,  0.        ,
umap-learn>           0.        ,  0.        ,  0.        ,  0.        ,  0.        ]])
umap-learn> accept_sparse = 'csr'
umap-learn> 
umap-learn>     def check_array(
umap-learn>         array,
umap-learn>         accept_sparse=False,
umap-learn>         *,
umap-learn>         accept_large_sparse=True,
umap-learn>         dtype="numeric",
umap-learn>         order=None,
umap-learn>         copy=False,
umap-learn>         force_all_finite=True,
umap-learn>         ensure_2d=True,
umap-learn>         allow_nd=False,
umap-learn>         ensure_min_samples=1,
umap-learn>         ensure_min_features=1,
umap-learn>         estimator=None,
umap-learn>         input_name="",
umap-learn>     ):
umap-learn>     
umap-learn>         """Input validation on an array, list, sparse matrix or similar.
umap-learn>     
umap-learn>         By default, the input is checked to be a non-empty 2D array containing
umap-learn>         only finite values. If the dtype of the array is object, attempt
umap-learn>         converting to float, raising on failure.
umap-learn>     
umap-learn>         Parameters
umap-learn>         ----------
umap-learn>         array : object
umap-learn>             Input object to check / convert.
umap-learn>     
umap-learn>         accept_sparse : str, bool or list/tuple of str, default=False
umap-learn>             String[s] representing allowed sparse matrix formats, such as 'csc',
umap-learn>             'csr', etc. If the input is sparse but not in the allowed format,
umap-learn>             it will be converted to the first listed format. True allows the input
umap-learn>             to be any format. False means that a sparse matrix input will
umap-learn>             raise an error.
umap-learn>     
umap-learn>         accept_large_sparse : bool, default=True
umap-learn>             If a CSR, CSC, COO or BSR sparse matrix is supplied and accepted by
umap-learn>             accept_sparse, accept_large_sparse=False will cause it to be accepted
umap-learn>             only if its indices are stored with a 32-bit dtype.
umap-learn>     
umap-learn>             .. versionadded:: 0.20
umap-learn>     
umap-learn>         dtype : 'numeric', type, list of type or None, default='numeric'
umap-learn>             Data type of result. If None, the dtype of the input is preserved.
umap-learn>             If "numeric", dtype is preserved unless array.dtype is object.
umap-learn>             If dtype is a list of types, conversion on the first type is only
umap-learn>             performed if the dtype of the input is not in the list.
umap-learn>     
umap-learn>         order : {'F', 'C'} or None, default=None
umap-learn>             Whether an array will be forced to be fortran or c-style.
umap-learn>             When order is None (default), then if copy=False, nothing is ensured
umap-learn>             about the memory layout of the output array; otherwise (copy=True)
umap-learn>             the memory layout of the returned array is kept as close as possible
umap-learn>             to the original array.
umap-learn>     
umap-learn>         copy : bool, default=False
umap-learn>             Whether a forced copy will be triggered. If copy=False, a copy might
umap-learn>             be triggered by a conversion.
umap-learn>     
umap-learn>         force_all_finite : bool or 'allow-nan', default=True
umap-learn>             Whether to raise an error on np.inf, np.nan, pd.NA in array. The
umap-learn>             possibilities are:
umap-learn>     
umap-learn>             - True: Force all values of array to be finite.
umap-learn>             - False: accepts np.inf, np.nan, pd.NA in array.
umap-learn>             - 'allow-nan': accepts only np.nan and pd.NA values in array. Values
umap-learn>               cannot be infinite.
umap-learn>     
umap-learn>             .. versionadded:: 0.20
umap-learn>                ``force_all_finite`` accepts the string ``'allow-nan'``.
umap-learn>     
umap-learn>             .. versionchanged:: 0.23
umap-learn>                Accepts `pd.NA` and converts it into `np.nan`
umap-learn>     
umap-learn>         ensure_2d : bool, default=True
umap-learn>             Whether to raise a value error if array is not 2D.
umap-learn>     
umap-learn>         allow_nd : bool, default=False
umap-learn>             Whether to allow array.ndim > 2.
umap-learn>     
umap-learn>         ensure_min_samples : int, default=1
umap-learn>             Make sure that the array has a minimum number of samples in its first
umap-learn>             axis (rows for a 2D array). Setting to 0 disables this check.
umap-learn>     
umap-learn>         ensure_min_features : int, default=1
umap-learn>             Make sure that the 2D array has some minimum number of features
umap-learn>             (columns). The default value of 1 rejects empty datasets.
umap-learn>             This check is only enforced when the input data has effectively 2
umap-learn>             dimensions or is originally 1D and ``ensure_2d`` is True. Setting to 0
umap-learn>             disables this check.
umap-learn>     
umap-learn>         estimator : str or estimator instance, default=None
umap-learn>             If passed, include the name of the estimator in warning messages.
umap-learn>     
umap-learn>         input_name : str, default=""
umap-learn>             The data name used to construct the error message. In particular
umap-learn>             if `input_name` is "X" and the data has NaN values and
umap-learn>             allow_nan is False, the error message will link to the imputer
umap-learn>             documentation.
umap-learn>     
umap-learn>             .. versionadded:: 1.1.0
umap-learn>     
umap-learn>         Returns
umap-learn>         -------
umap-learn>         array_converted : object
umap-learn>             The converted and validated array.
umap-learn>         """
umap-learn>         if isinstance(array, np.matrix):
umap-learn> >           raise TypeError(
umap-learn>                 "np.matrix is not supported. Please convert to a numpy array with "
umap-learn>                 "np.asarray. For more information see: "
umap-learn>                 "https://numpy.org/doc/stable/reference/generated/numpy.matrix.html"
umap-learn>             )
umap-learn> E           TypeError: np.matrix is not supported. Please convert to a numpy array with np.asarray. For more information see: https://numpy.org/doc/stable/reference/generated/numpy.matrix.html
umap-learn> 
umap-learn> /nix/store/5l733c5yjbmkd53d0nd35aln9jmpka8y-python3.10-scikit-learn-1.2.1/lib/python3.10/site-packages/sklearn/utils/validation.py:737: TypeError
umap-learn> _____________________________ test_sparse_hamming ______________________________
umap-learn> 
umap-learn> sparse_spatial_data = <12x20 sparse matrix of type '<class 'numpy.float64'>'
umap-learn>         with 72 stored elements in Compressed Sparse Row format>
umap-learn> 
umap-learn>     def test_sparse_hamming(sparse_spatial_data):
umap-learn> >       sparse_spatial_check("hamming", sparse_spatial_data)
umap-learn> 
umap-learn> umap/tests/test_umap_metrics.py:261: 
umap-learn> _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
umap-learn> umap/tests/test_umap_metrics.py:125: in sparse_spatial_check
umap-learn>     dist_matrix = pairwise_distances(sparse_spatial_data.todense(), metric=metric)
umap-learn> /nix/store/5l733c5yjbmkd53d0nd35aln9jmpka8y-python3.10-scikit-learn-1.2.1/lib/python3.10/site-packages/sklearn/metrics/pairwise.py:2027: in pairwise_distances
umap-learn>     X, Y = check_pairwise_arrays(
umap-learn> /nix/store/5l733c5yjbmkd53d0nd35aln9jmpka8y-python3.10-scikit-learn-1.2.1/lib/python3.10/site-packages/sklearn/metrics/pairwise.py:146: in check_pairwise_arrays
umap-learn>     X = Y = check_array(
umap-learn> _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
umap-learn> 
umap-learn> array = matrix([[ 0.        ,  0.        ,  0.        ,  0.3154439 , -0.90423903,
umap-learn>           0.        , -1.2634011 , -0.742659...   ,  0.        ,  0.        ,  0.        ,
umap-learn>           0.        ,  0.        ,  0.        ,  0.        ,  0.        ]])
umap-learn> accept_sparse = 'csr'
umap-learn> 
umap-learn>     def check_array(
umap-learn>         array,
umap-learn>         accept_sparse=False,
umap-learn>         *,
umap-learn>         accept_large_sparse=True,
umap-learn>         dtype="numeric",
umap-learn>         order=None,
umap-learn>         copy=False,
umap-learn>         force_all_finite=True,
umap-learn>         ensure_2d=True,
umap-learn>         allow_nd=False,
umap-learn>         ensure_min_samples=1,
umap-learn>         ensure_min_features=1,
umap-learn>         estimator=None,
umap-learn>         input_name="",
umap-learn>     ):
umap-learn>     
umap-learn>         """Input validation on an array, list, sparse matrix or similar.
umap-learn>     
umap-learn>         By default, the input is checked to be a non-empty 2D array containing
umap-learn>         only finite values. If the dtype of the array is object, attempt
umap-learn>         converting to float, raising on failure.
umap-learn>     
umap-learn>         Parameters
umap-learn>         ----------
umap-learn>         array : object
umap-learn>             Input object to check / convert.
umap-learn>     
umap-learn>         accept_sparse : str, bool or list/tuple of str, default=False
umap-learn>             String[s] representing allowed sparse matrix formats, such as 'csc',
umap-learn>             'csr', etc. If the input is sparse but not in the allowed format,
umap-learn>             it will be converted to the first listed format. True allows the input
umap-learn>             to be any format. False means that a sparse matrix input will
umap-learn>             raise an error.
umap-learn>     
umap-learn>         accept_large_sparse : bool, default=True
umap-learn>             If a CSR, CSC, COO or BSR sparse matrix is supplied and accepted by
umap-learn>             accept_sparse, accept_large_sparse=False will cause it to be accepted
umap-learn>             only if its indices are stored with a 32-bit dtype.
umap-learn>     
umap-learn>             .. versionadded:: 0.20
umap-learn>     
umap-learn>         dtype : 'numeric', type, list of type or None, default='numeric'
umap-learn>             Data type of result. If None, the dtype of the input is preserved.
umap-learn>             If "numeric", dtype is preserved unless array.dtype is object.
umap-learn>             If dtype is a list of types, conversion on the first type is only
umap-learn>             performed if the dtype of the input is not in the list.
umap-learn>     
umap-learn>         order : {'F', 'C'} or None, default=None
umap-learn>             Whether an array will be forced to be fortran or c-style.
umap-learn>             When order is None (default), then if copy=False, nothing is ensured
umap-learn>             about the memory layout of the output array; otherwise (copy=True)
umap-learn>             the memory layout of the returned array is kept as close as possible
umap-learn>             to the original array.
umap-learn>     
umap-learn>         copy : bool, default=False
umap-learn>             Whether a forced copy will be triggered. If copy=False, a copy might
umap-learn>             be triggered by a conversion.
umap-learn>     
umap-learn>         force_all_finite : bool or 'allow-nan', default=True
umap-learn>             Whether to raise an error on np.inf, np.nan, pd.NA in array. The
umap-learn>             possibilities are:
umap-learn>     
umap-learn>             - True: Force all values of array to be finite.
umap-learn>             - False: accepts np.inf, np.nan, pd.NA in array.
umap-learn>             - 'allow-nan': accepts only np.nan and pd.NA values in array. Values
umap-learn>               cannot be infinite.
umap-learn>     
umap-learn>             .. versionadded:: 0.20
umap-learn>                ``force_all_finite`` accepts the string ``'allow-nan'``.
umap-learn>     
umap-learn>             .. versionchanged:: 0.23
umap-learn>                Accepts `pd.NA` and converts it into `np.nan`
umap-learn>     
umap-learn>         ensure_2d : bool, default=True
umap-learn>             Whether to raise a value error if array is not 2D.
umap-learn>     
umap-learn>         allow_nd : bool, default=False
umap-learn>             Whether to allow array.ndim > 2.
umap-learn>     
umap-learn>         ensure_min_samples : int, default=1
umap-learn>             Make sure that the array has a minimum number of samples in its first
umap-learn>             axis (rows for a 2D array). Setting to 0 disables this check.
umap-learn>     
umap-learn>         ensure_min_features : int, default=1
umap-learn>             Make sure that the 2D array has some minimum number of features
umap-learn>             (columns). The default value of 1 rejects empty datasets.
umap-learn>             This check is only enforced when the input data has effectively 2
umap-learn>             dimensions or is originally 1D and ``ensure_2d`` is True. Setting to 0
umap-learn>             disables this check.
umap-learn>     
umap-learn>         estimator : str or estimator instance, default=None
umap-learn>             If passed, include the name of the estimator in warning messages.
umap-learn>     
umap-learn>         input_name : str, default=""
umap-learn>             The data name used to construct the error message. In particular
umap-learn>             if `input_name` is "X" and the data has NaN values and
umap-learn>             allow_nan is False, the error message will link to the imputer
umap-learn>             documentation.
umap-learn>     
umap-learn>             .. versionadded:: 1.1.0
umap-learn>     
umap-learn>         Returns
umap-learn>         -------
umap-learn>         array_converted : object
umap-learn>             The converted and validated array.
umap-learn>         """
umap-learn>         if isinstance(array, np.matrix):
umap-learn> >           raise TypeError(
umap-learn>                 "np.matrix is not supported. Please convert to a numpy array with "
umap-learn>                 "np.asarray. For more information see: "
umap-learn>                 "https://numpy.org/doc/stable/reference/generated/numpy.matrix.html"
umap-learn>             )
umap-learn> E           TypeError: np.matrix is not supported. Please convert to a numpy array with np.asarray. For more information see: https://numpy.org/doc/stable/reference/generated/numpy.matrix.html
umap-learn> 
umap-learn> /nix/store/5l733c5yjbmkd53d0nd35aln9jmpka8y-python3.10-scikit-learn-1.2.1/lib/python3.10/site-packages/sklearn/utils/validation.py:737: TypeError
umap-learn> _____________________________ test_sparse_canberra _____________________________
umap-learn> 
umap-learn> sparse_spatial_data = <12x20 sparse matrix of type '<class 'numpy.float64'>'
umap-learn>         with 72 stored elements in Compressed Sparse Row format>
umap-learn> 
umap-learn>     def test_sparse_canberra(sparse_spatial_data):
umap-learn> >       sparse_spatial_check("canberra", sparse_spatial_data)
umap-learn> 
umap-learn> umap/tests/test_umap_metrics.py:265: 
umap-learn> _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
umap-learn> umap/tests/test_umap_metrics.py:125: in sparse_spatial_check
umap-learn>     dist_matrix = pairwise_distances(sparse_spatial_data.todense(), metric=metric)
umap-learn> /nix/store/5l733c5yjbmkd53d0nd35aln9jmpka8y-python3.10-scikit-learn-1.2.1/lib/python3.10/site-packages/sklearn/metrics/pairwise.py:2027: in pairwise_distances
umap-learn>     X, Y = check_pairwise_arrays(
umap-learn> /nix/store/5l733c5yjbmkd53d0nd35aln9jmpka8y-python3.10-scikit-learn-1.2.1/lib/python3.10/site-packages/sklearn/metrics/pairwise.py:146: in check_pairwise_arrays
umap-learn>     X = Y = check_array(
umap-learn> _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
umap-learn> 
umap-learn> array = matrix([[ 0.        ,  0.        ,  0.        ,  0.3154439 , -0.90423903,
umap-learn>           0.        , -1.2634011 , -0.742659...   ,  0.        ,  0.        ,  0.        ,
umap-learn>           0.        ,  0.        ,  0.        ,  0.        ,  0.        ]])
umap-learn> accept_sparse = 'csr'
umap-learn> 
umap-learn>     def check_array(
umap-learn>         array,
umap-learn>         accept_sparse=False,
umap-learn>         *,
umap-learn>         accept_large_sparse=True,
umap-learn>         dtype="numeric",
umap-learn>         order=None,
umap-learn>         copy=False,
umap-learn>         force_all_finite=True,
umap-learn>         ensure_2d=True,
umap-learn>         allow_nd=False,
umap-learn>         ensure_min_samples=1,
umap-learn>         ensure_min_features=1,
umap-learn>         estimator=None,
umap-learn>         input_name="",
umap-learn>     ):
umap-learn>     
umap-learn>         """Input validation on an array, list, sparse matrix or similar.
umap-learn>     
umap-learn>         By default, the input is checked to be a non-empty 2D array containing
umap-learn>         only finite values. If the dtype of the array is object, attempt
umap-learn>         converting to float, raising on failure.
umap-learn>     
umap-learn>         Parameters
umap-learn>         ----------
umap-learn>         array : object
umap-learn>             Input object to check / convert.
umap-learn>     
umap-learn>         accept_sparse : str, bool or list/tuple of str, default=False
umap-learn>             String[s] representing allowed sparse matrix formats, such as 'csc',
umap-learn>             'csr', etc. If the input is sparse but not in the allowed format,
umap-learn>             it will be converted to the first listed format. True allows the input
umap-learn>             to be any format. False means that a sparse matrix input will
umap-learn>             raise an error.
umap-learn>     
umap-learn>         accept_large_sparse : bool, default=True
umap-learn>             If a CSR, CSC, COO or BSR sparse matrix is supplied and accepted by
umap-learn>             accept_sparse, accept_large_sparse=False will cause it to be accepted
umap-learn>             only if its indices are stored with a 32-bit dtype.
umap-learn>     
umap-learn>             .. versionadded:: 0.20
umap-learn>     
umap-learn>         dtype : 'numeric', type, list of type or None, default='numeric'
umap-learn>             Data type of result. If None, the dtype of the input is preserved.
umap-learn>             If "numeric", dtype is preserved unless array.dtype is object.
umap-learn>             If dtype is a list of types, conversion on the first type is only
umap-learn>             performed if the dtype of the input is not in the list.
umap-learn>     
umap-learn>         order : {'F', 'C'} or None, default=None
umap-learn>             Whether an array will be forced to be fortran or c-style.
umap-learn>             When order is None (default), then if copy=False, nothing is ensured
umap-learn>             about the memory layout of the output array; otherwise (copy=True)
umap-learn>             the memory layout of the returned array is kept as close as possible
umap-learn>             to the original array.
umap-learn>     
umap-learn>         copy : bool, default=False
umap-learn>             Whether a forced copy will be triggered. If copy=False, a copy might
umap-learn>             be triggered by a conversion.
umap-learn>     
umap-learn>         force_all_finite : bool or 'allow-nan', default=True
umap-learn>             Whether to raise an error on np.inf, np.nan, pd.NA in array. The
umap-learn>             possibilities are:
umap-learn>     
umap-learn>             - True: Force all values of array to be finite.
umap-learn>             - False: accepts np.inf, np.nan, pd.NA in array.
umap-learn>             - 'allow-nan': accepts only np.nan and pd.NA values in array. Values
umap-learn>               cannot be infinite.
umap-learn>     
umap-learn>             .. versionadded:: 0.20
umap-learn>                ``force_all_finite`` accepts the string ``'allow-nan'``.
umap-learn>     
umap-learn>             .. versionchanged:: 0.23
umap-learn>                Accepts `pd.NA` and converts it into `np.nan`
umap-learn>     
umap-learn>         ensure_2d : bool, default=True
umap-learn>             Whether to raise a value error if array is not 2D.
umap-learn>     
umap-learn>         allow_nd : bool, default=False
umap-learn>             Whether to allow array.ndim > 2.
umap-learn>     
umap-learn>         ensure_min_samples : int, default=1
umap-learn>             Make sure that the array has a minimum number of samples in its first
umap-learn>             axis (rows for a 2D array). Setting to 0 disables this check.
umap-learn>     
umap-learn>         ensure_min_features : int, default=1
umap-learn>             Make sure that the 2D array has some minimum number of features
umap-learn>             (columns). The default value of 1 rejects empty datasets.
umap-learn>             This check is only enforced when the input data has effectively 2
umap-learn>             dimensions or is originally 1D and ``ensure_2d`` is True. Setting to 0
umap-learn>             disables this check.
umap-learn>     
umap-learn>         estimator : str or estimator instance, default=None
umap-learn>             If passed, include the name of the estimator in warning messages.
umap-learn>     
umap-learn>         input_name : str, default=""
umap-learn>             The data name used to construct the error message. In particular
umap-learn>             if `input_name` is "X" and the data has NaN values and
umap-learn>             allow_nan is False, the error message will link to the imputer
umap-learn>             documentation.
umap-learn>     
umap-learn>             .. versionadded:: 1.1.0
umap-learn>     
umap-learn>         Returns
umap-learn>         -------
umap-learn>         array_converted : object
umap-learn>             The converted and validated array.
umap-learn>         """
umap-learn>         if isinstance(array, np.matrix):
umap-learn> >           raise TypeError(
umap-learn>                 "np.matrix is not supported. Please convert to a numpy array with "
umap-learn>                 "np.asarray. For more information see: "
umap-learn>                 "https://numpy.org/doc/stable/reference/generated/numpy.matrix.html"
umap-learn>             )
umap-learn> E           TypeError: np.matrix is not supported. Please convert to a numpy array with np.asarray. For more information see: https://numpy.org/doc/stable/reference/generated/numpy.matrix.html
umap-learn> 
umap-learn> /nix/store/5l733c5yjbmkd53d0nd35aln9jmpka8y-python3.10-scikit-learn-1.2.1/lib/python3.10/site-packages/sklearn/utils/validation.py:737: TypeError
umap-learn> ______________________________ test_sparse_cosine ______________________________
umap-learn> 
umap-learn> sparse_spatial_data = <12x20 sparse matrix of type '<class 'numpy.float64'>'
umap-learn>         with 72 stored elements in Compressed Sparse Row format>
umap-learn> 
umap-learn>     def test_sparse_cosine(sparse_spatial_data):
umap-learn> >       sparse_spatial_check("cosine", sparse_spatial_data)
umap-learn> 
umap-learn> umap/tests/test_umap_metrics.py:269: 
umap-learn> _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
umap-learn> umap/tests/test_umap_metrics.py:125: in sparse_spatial_check
umap-learn>     dist_matrix = pairwise_distances(sparse_spatial_data.todense(), metric=metric)
umap-learn> /nix/store/5l733c5yjbmkd53d0nd35aln9jmpka8y-python3.10-scikit-learn-1.2.1/lib/python3.10/site-packages/sklearn/metrics/pairwise.py:2039: in pairwise_distances
umap-learn>     return _parallel_pairwise(X, Y, func, n_jobs, **kwds)
umap-learn> /nix/store/5l733c5yjbmkd53d0nd35aln9jmpka8y-python3.10-scikit-learn-1.2.1/lib/python3.10/site-packages/sklearn/metrics/pairwise.py:1579: in _parallel_pairwise
umap-learn>     return func(X, Y, **kwds)
umap-learn> /nix/store/5l733c5yjbmkd53d0nd35aln9jmpka8y-python3.10-scikit-learn-1.2.1/lib/python3.10/site-packages/sklearn/metrics/pairwise.py:1000: in cosine_distances
umap-learn>     S = cosine_similarity(X, Y)
umap-learn> /nix/store/5l733c5yjbmkd53d0nd35aln9jmpka8y-python3.10-scikit-learn-1.2.1/lib/python3.10/site-packages/sklearn/metrics/pairwise.py:1393: in cosine_similarity
umap-learn>     X, Y = check_pairwise_arrays(X, Y)
umap-learn> /nix/store/5l733c5yjbmkd53d0nd35aln9jmpka8y-python3.10-scikit-learn-1.2.1/lib/python3.10/site-packages/sklearn/metrics/pairwise.py:146: in check_pairwise_arrays
umap-learn>     X = Y = check_array(
umap-learn> _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
umap-learn> 
umap-learn> array = matrix([[ 0.        ,  0.        ,  0.        ,  0.3154439 , -0.90423903,
umap-learn>           0.        , -1.2634011 , -0.742659...   ,  0.        ,  0.        ,  0.        ,
umap-learn>           0.        ,  0.        ,  0.        ,  0.        ,  0.        ]])
umap-learn> accept_sparse = 'csr'
umap-learn> 
umap-learn>     def check_array(
umap-learn>         array,
umap-learn>         accept_sparse=False,
umap-learn>         *,
umap-learn>         accept_large_sparse=True,
umap-learn>         dtype="numeric",
umap-learn>         order=None,
umap-learn>         copy=False,
umap-learn>         force_all_finite=True,
umap-learn>         ensure_2d=True,
umap-learn>         allow_nd=False,
umap-learn>         ensure_min_samples=1,
umap-learn>         ensure_min_features=1,
umap-learn>         estimator=None,
umap-learn>         input_name="",
umap-learn>     ):
umap-learn>     
umap-learn>         """Input validation on an array, list, sparse matrix or similar.
umap-learn>     
umap-learn>         By default, the input is checked to be a non-empty 2D array containing
umap-learn>         only finite values. If the dtype of the array is object, attempt
umap-learn>         converting to float, raising on failure.
umap-learn>     
umap-learn>         Parameters
umap-learn>         ----------
umap-learn>         array : object
umap-learn>             Input object to check / convert.
umap-learn>     
umap-learn>         accept_sparse : str, bool or list/tuple of str, default=False
umap-learn>             String[s] representing allowed sparse matrix formats, such as 'csc',
umap-learn>             'csr', etc. If the input is sparse but not in the allowed format,
umap-learn>             it will be converted to the first listed format. True allows the input
umap-learn>             to be any format. False means that a sparse matrix input will
umap-learn>             raise an error.
umap-learn>     
umap-learn>         accept_large_sparse : bool, default=True
umap-learn>             If a CSR, CSC, COO or BSR sparse matrix is supplied and accepted by
umap-learn>             accept_sparse, accept_large_sparse=False will cause it to be accepted
umap-learn>             only if its indices are stored with a 32-bit dtype.
umap-learn>     
umap-learn>             .. versionadded:: 0.20
umap-learn>     
umap-learn>         dtype : 'numeric', type, list of type or None, default='numeric'
umap-learn>             Data type of result. If None, the dtype of the input is preserved.
umap-learn>             If "numeric", dtype is preserved unless array.dtype is object.
umap-learn>             If dtype is a list of types, conversion on the first type is only
umap-learn>             performed if the dtype of the input is not in the list.
umap-learn>     
umap-learn>         order : {'F', 'C'} or None, default=None
umap-learn>             Whether an array will be forced to be fortran or c-style.
umap-learn>             When order is None (default), then if copy=False, nothing is ensured
umap-learn>             about the memory layout of the output array; otherwise (copy=True)
umap-learn>             the memory layout of the returned array is kept as close as possible
umap-learn>             to the original array.
umap-learn>     
umap-learn>         copy : bool, default=False
umap-learn>             Whether a forced copy will be triggered. If copy=False, a copy might
umap-learn>             be triggered by a conversion.
umap-learn>     
umap-learn>         force_all_finite : bool or 'allow-nan', default=True
umap-learn>             Whether to raise an error on np.inf, np.nan, pd.NA in array. The
umap-learn>             possibilities are:
umap-learn>     
umap-learn>             - True: Force all values of array to be finite.
umap-learn>             - False: accepts np.inf, np.nan, pd.NA in array.
umap-learn>             - 'allow-nan': accepts only np.nan and pd.NA values in array. Values
umap-learn>               cannot be infinite.
umap-learn>     
umap-learn>             .. versionadded:: 0.20
umap-learn>                ``force_all_finite`` accepts the string ``'allow-nan'``.
umap-learn>     
umap-learn>             .. versionchanged:: 0.23
umap-learn>                Accepts `pd.NA` and converts it into `np.nan`
umap-learn>     
umap-learn>         ensure_2d : bool, default=True
umap-learn>             Whether to raise a value error if array is not 2D.
umap-learn>     
umap-learn>         allow_nd : bool, default=False
umap-learn>             Whether to allow array.ndim > 2.
umap-learn>     
umap-learn>         ensure_min_samples : int, default=1
umap-learn>             Make sure that the array has a minimum number of samples in its first
umap-learn>             axis (rows for a 2D array). Setting to 0 disables this check.
umap-learn>     
umap-learn>         ensure_min_features : int, default=1
umap-learn>             Make sure that the 2D array has some minimum number of features
umap-learn>             (columns). The default value of 1 rejects empty datasets.
umap-learn>             This check is only enforced when the input data has effectively 2
umap-learn>             dimensions or is originally 1D and ``ensure_2d`` is True. Setting to 0
umap-learn>             disables this check.
umap-learn>     
umap-learn>         estimator : str or estimator instance, default=None
umap-learn>             If passed, include the name of the estimator in warning messages.
umap-learn>     
umap-learn>         input_name : str, default=""
umap-learn>             The data name used to construct the error message. In particular
umap-learn>             if `input_name` is "X" and the data has NaN values and
umap-learn>             allow_nan is False, the error message will link to the imputer
umap-learn>             documentation.
umap-learn>     
umap-learn>             .. versionadded:: 1.1.0
umap-learn>     
umap-learn>         Returns
umap-learn>         -------
umap-learn>         array_converted : object
umap-learn>             The converted and validated array.
umap-learn>         """
umap-learn>         if isinstance(array, np.matrix):
umap-learn> >           raise TypeError(
umap-learn>                 "np.matrix is not supported. Please convert to a numpy array with "
umap-learn>                 "np.asarray. For more information see: "
umap-learn>                 "https://numpy.org/doc/stable/reference/generated/numpy.matrix.html"
umap-learn>             )
umap-learn> E           TypeError: np.matrix is not supported. Please convert to a numpy array with np.asarray. For more information see: https://numpy.org/doc/stable/reference/generated/numpy.matrix.html
umap-learn> 
umap-learn> /nix/store/5l733c5yjbmkd53d0nd35aln9jmpka8y-python3.10-scikit-learn-1.2.1/lib/python3.10/site-packages/sklearn/utils/validation.py:737: TypeError
umap-learn> ___________________________ test_sparse_correlation ____________________________
umap-learn> 
umap-learn> sparse_spatial_data = <12x20 sparse matrix of type '<class 'numpy.float64'>'
umap-learn>         with 72 stored elements in Compressed Sparse Row format>
umap-learn> 
umap-learn>     def test_sparse_correlation(sparse_spatial_data):
umap-learn> >       sparse_spatial_check("correlation", sparse_spatial_data)
umap-learn> 
umap-learn> umap/tests/test_umap_metrics.py:273: 
umap-learn> _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
umap-learn> umap/tests/test_umap_metrics.py:125: in sparse_spatial_check
umap-learn>     dist_matrix = pairwise_distances(sparse_spatial_data.todense(), metric=metric)
umap-learn> /nix/store/5l733c5yjbmkd53d0nd35aln9jmpka8y-python3.10-scikit-learn-1.2.1/lib/python3.10/site-packages/sklearn/metrics/pairwise.py:2027: in pairwise_distances
umap-learn>     X, Y = check_pairwise_arrays(
umap-learn> /nix/store/5l733c5yjbmkd53d0nd35aln9jmpka8y-python3.10-scikit-learn-1.2.1/lib/python3.10/site-packages/sklearn/metrics/pairwise.py:146: in check_pairwise_arrays
umap-learn>     X = Y = check_array(
umap-learn> _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
umap-learn> 
umap-learn> array = matrix([[ 0.        ,  0.        ,  0.        ,  0.3154439 , -0.90423903,
umap-learn>           0.        , -1.2634011 , -0.742659...   ,  0.        ,  0.        ,  0.        ,
umap-learn>           0.        ,  0.        ,  0.        ,  0.        ,  0.        ]])
umap-learn> accept_sparse = 'csr'
umap-learn> 
umap-learn>     def check_array(
umap-learn>         array,
umap-learn>         accept_sparse=False,
umap-learn>         *,
umap-learn>         accept_large_sparse=True,
umap-learn>         dtype="numeric",
umap-learn>         order=None,
umap-learn>         copy=False,
umap-learn>         force_all_finite=True,
umap-learn>         ensure_2d=True,
umap-learn>         allow_nd=False,
umap-learn>         ensure_min_samples=1,
umap-learn>         ensure_min_features=1,
umap-learn>         estimator=None,
umap-learn>         input_name="",
umap-learn>     ):
umap-learn>     
umap-learn>         """Input validation on an array, list, sparse matrix or similar.
umap-learn>     
umap-learn>         By default, the input is checked to be a non-empty 2D array containing
umap-learn>         only finite values. If the dtype of the array is object, attempt
umap-learn>         converting to float, raising on failure.
umap-learn>     
umap-learn>         Parameters
umap-learn>         ----------
umap-learn>         array : object
umap-learn>             Input object to check / convert.
umap-learn>     
umap-learn>         accept_sparse : str, bool or list/tuple of str, default=False
umap-learn>             String[s] representing allowed sparse matrix formats, such as 'csc',
umap-learn>             'csr', etc. If the input is sparse but not in the allowed format,
umap-learn>             it will be converted to the first listed format. True allows the input
umap-learn>             to be any format. False means that a sparse matrix input will
umap-learn>             raise an error.
umap-learn>     
umap-learn>         accept_large_sparse : bool, default=True
umap-learn>             If a CSR, CSC, COO or BSR sparse matrix is supplied and accepted by
umap-learn>             accept_sparse, accept_large_sparse=False will cause it to be accepted
umap-learn>             only if its indices are stored with a 32-bit dtype.
umap-learn>     
umap-learn>             .. versionadded:: 0.20
umap-learn>     
umap-learn>         dtype : 'numeric', type, list of type or None, default='numeric'
umap-learn>             Data type of result. If None, the dtype of the input is preserved.
umap-learn>             If "numeric", dtype is preserved unless array.dtype is object.
umap-learn>             If dtype is a list of types, conversion on the first type is only
umap-learn>             performed if the dtype of the input is not in the list.
umap-learn>     
umap-learn>         order : {'F', 'C'} or None, default=None
umap-learn>             Whether an array will be forced to be fortran or c-style.
umap-learn>             When order is None (default), then if copy=False, nothing is ensured
umap-learn>             about the memory layout of the output array; otherwise (copy=True)
umap-learn>             the memory layout of the returned array is kept as close as possible
umap-learn>             to the original array.
umap-learn>     
umap-learn>         copy : bool, default=False
umap-learn>             Whether a forced copy will be triggered. If copy=False, a copy might
umap-learn>             be triggered by a conversion.
umap-learn>     
umap-learn>         force_all_finite : bool or 'allow-nan', default=True
umap-learn>             Whether to raise an error on np.inf, np.nan, pd.NA in array. The
umap-learn>             possibilities are:
umap-learn>     
umap-learn>             - True: Force all values of array to be finite.
umap-learn>             - False: accepts np.inf, np.nan, pd.NA in array.
umap-learn>             - 'allow-nan': accepts only np.nan and pd.NA values in array. Values
umap-learn>               cannot be infinite.
umap-learn>     
umap-learn>             .. versionadded:: 0.20
umap-learn>                ``force_all_finite`` accepts the string ``'allow-nan'``.
umap-learn>     
umap-learn>             .. versionchanged:: 0.23
umap-learn>                Accepts `pd.NA` and converts it into `np.nan`
umap-learn>     
umap-learn>         ensure_2d : bool, default=True
umap-learn>             Whether to raise a value error if array is not 2D.
umap-learn>     
umap-learn>         allow_nd : bool, default=False
umap-learn>             Whether to allow array.ndim > 2.
umap-learn>     
umap-learn>         ensure_min_samples : int, default=1
umap-learn>             Make sure that the array has a minimum number of samples in its first
umap-learn>             axis (rows for a 2D array). Setting to 0 disables this check.
umap-learn>     
umap-learn>         ensure_min_features : int, default=1
umap-learn>             Make sure that the 2D array has some minimum number of features
umap-learn>             (columns). The default value of 1 rejects empty datasets.
umap-learn>             This check is only enforced when the input data has effectively 2
umap-learn>             dimensions or is originally 1D and ``ensure_2d`` is True. Setting to 0
umap-learn>             disables this check.
umap-learn>     
umap-learn>         estimator : str or estimator instance, default=None
umap-learn>             If passed, include the name of the estimator in warning messages.
umap-learn>     
umap-learn>         input_name : str, default=""
umap-learn>             The data name used to construct the error message. In particular
umap-learn>             if `input_name` is "X" and the data has NaN values and
umap-learn>             allow_nan is False, the error message will link to the imputer
umap-learn>             documentation.
umap-learn>     
umap-learn>             .. versionadded:: 1.1.0
umap-learn>     
umap-learn>         Returns
umap-learn>         -------
umap-learn>         array_converted : object
umap-learn>             The converted and validated array.
umap-learn>         """
umap-learn>         if isinstance(array, np.matrix):
umap-learn> >           raise TypeError(
umap-learn>                 "np.matrix is not supported. Please convert to a numpy array with "
umap-learn>                 "np.asarray. For more information see: "
umap-learn>                 "https://numpy.org/doc/stable/reference/generated/numpy.matrix.html"
umap-learn>             )
umap-learn> E           TypeError: np.matrix is not supported. Please convert to a numpy array with np.asarray. For more information see: https://numpy.org/doc/stable/reference/generated/numpy.matrix.html
umap-learn> 
umap-learn> /nix/store/5l733c5yjbmkd53d0nd35aln9jmpka8y-python3.10-scikit-learn-1.2.1/lib/python3.10/site-packages/sklearn/utils/validation.py:737: TypeError
umap-learn> ____________________________ test_sparse_braycurtis ____________________________
umap-learn> 
umap-learn> sparse_spatial_data = <12x20 sparse matrix of type '<class 'numpy.float64'>'
umap-learn>         with 72 stored elements in Compressed Sparse Row format>
umap-learn> 
umap-learn>     def test_sparse_braycurtis(sparse_spatial_data):
umap-learn> >       sparse_spatial_check("braycurtis", sparse_spatial_data)
umap-learn> 
umap-learn> umap/tests/test_umap_metrics.py:277: 
umap-learn> _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
umap-learn> umap/tests/test_umap_metrics.py:125: in sparse_spatial_check
umap-learn>     dist_matrix = pairwise_distances(sparse_spatial_data.todense(), metric=metric)
umap-learn> /nix/store/5l733c5yjbmkd53d0nd35aln9jmpka8y-python3.10-scikit-learn-1.2.1/lib/python3.10/site-packages/sklearn/metrics/pairwise.py:2027: in pairwise_distances
umap-learn>     X, Y = check_pairwise_arrays(
umap-learn> /nix/store/5l733c5yjbmkd53d0nd35aln9jmpka8y-python3.10-scikit-learn-1.2.1/lib/python3.10/site-packages/sklearn/metrics/pairwise.py:146: in check_pairwise_arrays
umap-learn>     X = Y = check_array(
umap-learn> _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
umap-learn> 
umap-learn> array = matrix([[ 0.        ,  0.        ,  0.        ,  0.3154439 , -0.90423903,
umap-learn>           0.        , -1.2634011 , -0.742659...   ,  0.        ,  0.        ,  0.        ,
umap-learn>           0.        ,  0.        ,  0.        ,  0.        ,  0.        ]])
umap-learn> accept_sparse = 'csr'
umap-learn> 
umap-learn>     def check_array(
umap-learn>         array,
umap-learn>         accept_sparse=False,
umap-learn>         *,
umap-learn>         accept_large_sparse=True,
umap-learn>         dtype="numeric",
umap-learn>         order=None,
umap-learn>         copy=False,
umap-learn>         force_all_finite=True,
umap-learn>         ensure_2d=True,
umap-learn>         allow_nd=False,
umap-learn>         ensure_min_samples=1,
umap-learn>         ensure_min_features=1,
umap-learn>         estimator=None,
umap-learn>         input_name="",
umap-learn>     ):
umap-learn>     
umap-learn>         """Input validation on an array, list, sparse matrix or similar.
umap-learn>     
umap-learn>         By default, the input is checked to be a non-empty 2D array containing
umap-learn>         only finite values. If the dtype of the array is object, attempt
umap-learn>         converting to float, raising on failure.
umap-learn>     
umap-learn>         Parameters
umap-learn>         ----------
umap-learn>         array : object
umap-learn>             Input object to check / convert.
umap-learn>     
umap-learn>         accept_sparse : str, bool or list/tuple of str, default=False
umap-learn>             String[s] representing allowed sparse matrix formats, such as 'csc',
umap-learn>             'csr', etc. If the input is sparse but not in the allowed format,
umap-learn>             it will be converted to the first listed format. True allows the input
umap-learn>             to be any format. False means that a sparse matrix input will
umap-learn>             raise an error.
umap-learn>     
umap-learn>         accept_large_sparse : bool, default=True
umap-learn>             If a CSR, CSC, COO or BSR sparse matrix is supplied and accepted by
umap-learn>             accept_sparse, accept_large_sparse=False will cause it to be accepted
umap-learn>             only if its indices are stored with a 32-bit dtype.
umap-learn>     
umap-learn>             .. versionadded:: 0.20
umap-learn>     
umap-learn>         dtype : 'numeric', type, list of type or None, default='numeric'
umap-learn>             Data type of result. If None, the dtype of the input is preserved.
umap-learn>             If "numeric", dtype is preserved unless array.dtype is object.
umap-learn>             If dtype is a list of types, conversion on the first type is only
umap-learn>             performed if the dtype of the input is not in the list.
umap-learn>     
umap-learn>         order : {'F', 'C'} or None, default=None
umap-learn>             Whether an array will be forced to be fortran or c-style.
umap-learn>             When order is None (default), then if copy=False, nothing is ensured
umap-learn>             about the memory layout of the output array; otherwise (copy=True)
umap-learn>             the memory layout of the returned array is kept as close as possible
umap-learn>             to the original array.
umap-learn>     
umap-learn>         copy : bool, default=False
umap-learn>             Whether a forced copy will be triggered. If copy=False, a copy might
umap-learn>             be triggered by a conversion.
umap-learn>     
umap-learn>         force_all_finite : bool or 'allow-nan', default=True
umap-learn>             Whether to raise an error on np.inf, np.nan, pd.NA in array. The
umap-learn>             possibilities are:
umap-learn>     
umap-learn>             - True: Force all values of array to be finite.
umap-learn>             - False: accepts np.inf, np.nan, pd.NA in array.
umap-learn>             - 'allow-nan': accepts only np.nan and pd.NA values in array. Values
umap-learn>               cannot be infinite.
umap-learn>     
umap-learn>             .. versionadded:: 0.20
umap-learn>                ``force_all_finite`` accepts the string ``'allow-nan'``.
umap-learn>     
umap-learn>             .. versionchanged:: 0.23
umap-learn>                Accepts `pd.NA` and converts it into `np.nan`
umap-learn>     
umap-learn>         ensure_2d : bool, default=True
umap-learn>             Whether to raise a value error if array is not 2D.
umap-learn>     
umap-learn>         allow_nd : bool, default=False
umap-learn>             Whether to allow array.ndim > 2.
umap-learn>     
umap-learn>         ensure_min_samples : int, default=1
umap-learn>             Make sure that the array has a minimum number of samples in its first
umap-learn>             axis (rows for a 2D array). Setting to 0 disables this check.
umap-learn>     
umap-learn>         ensure_min_features : int, default=1
umap-learn>             Make sure that the 2D array has some minimum number of features
umap-learn>             (columns). The default value of 1 rejects empty datasets.
umap-learn>             This check is only enforced when the input data has effectively 2
umap-learn>             dimensions or is originally 1D and ``ensure_2d`` is True. Setting to 0
umap-learn>             disables this check.
umap-learn>     
umap-learn>         estimator : str or estimator instance, default=None
umap-learn>             If passed, include the name of the estimator in warning messages.
umap-learn>     
umap-learn>         input_name : str, default=""
umap-learn>             The data name used to construct the error message. In particular
umap-learn>             if `input_name` is "X" and the data has NaN values and
umap-learn>             allow_nan is False, the error message will link to the imputer
umap-learn>             documentation.
umap-learn>     
umap-learn>             .. versionadded:: 1.1.0
umap-learn>     
umap-learn>         Returns
umap-learn>         -------
umap-learn>         array_converted : object
umap-learn>             The converted and validated array.
umap-learn>         """
umap-learn>         if isinstance(array, np.matrix):
umap-learn> >           raise TypeError(
umap-learn>                 "np.matrix is not supported. Please convert to a numpy array with "
umap-learn>                 "np.asarray. For more information see: "
umap-learn>                 "https://numpy.org/doc/stable/reference/generated/numpy.matrix.html"
umap-learn>             )
umap-learn> E           TypeError: np.matrix is not supported. Please convert to a numpy array with np.asarray. For more information see: https://numpy.org/doc/stable/reference/generated/numpy.matrix.html
umap-learn> 
umap-learn> /nix/store/5l733c5yjbmkd53d0nd35aln9jmpka8y-python3.10-scikit-learn-1.2.1/lib/python3.10/site-packages/sklearn/utils/validation.py:737: TypeError
umap-learn> _____________________________ test_sparse_jaccard ______________________________
umap-learn> 
umap-learn> sparse_binary_data = <12x20 sparse matrix of type '<class 'numpy.bool_'>'
umap-learn>         with 72 stored elements in Compressed Sparse Row format>
umap-learn> 
umap-learn>     def test_sparse_jaccard(sparse_binary_data):
umap-learn> >       sparse_binary_check("jaccard", sparse_binary_data)
umap-learn> 
umap-learn> umap/tests/test_umap_metrics.py:286: 
umap-learn> _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
umap-learn> umap/tests/test_umap_metrics.py:144: in sparse_binary_check
umap-learn>     dist_matrix = pairwise_distances(sparse_binary_data.todense(), metric=metric)
umap-learn> /nix/store/5l733c5yjbmkd53d0nd35aln9jmpka8y-python3.10-scikit-learn-1.2.1/lib/python3.10/site-packages/sklearn/metrics/pairwise.py:2027: in pairwise_distances
umap-learn>     X, Y = check_pairwise_arrays(
umap-learn> /nix/store/5l733c5yjbmkd53d0nd35aln9jmpka8y-python3.10-scikit-learn-1.2.1/lib/python3.10/site-packages/sklearn/metrics/pairwise.py:146: in check_pairwise_arrays
umap-learn>     X = Y = check_array(
umap-learn> _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
umap-learn> 
umap-learn> array = matrix([[False, False, False,  True,  True, False,  True,  True, False,
umap-learn>           True,  True, False, False, False, Fa..., False, False, False,
umap-learn>          False, False, False, False, False, False, False, False, False,
umap-learn>          False, False]])
umap-learn> accept_sparse = 'csr'
umap-learn> 
umap-learn>     def check_array(
umap-learn>         array,
umap-learn>         accept_sparse=False,
umap-learn>         *,
umap-learn>         accept_large_sparse=True,
umap-learn>         dtype="numeric",
umap-learn>         order=None,
umap-learn>         copy=False,
umap-learn>         force_all_finite=True,
umap-learn>         ensure_2d=True,
umap-learn>         allow_nd=False,
umap-learn>         ensure_min_samples=1,
umap-learn>         ensure_min_features=1,
umap-learn>         estimator=None,
umap-learn>         input_name="",
umap-learn>     ):
umap-learn>     
umap-learn>         """Input validation on an array, list, sparse matrix or similar.
umap-learn>     
umap-learn>         By default, the input is checked to be a non-empty 2D array containing
umap-learn>         only finite values. If the dtype of the array is object, attempt
umap-learn>         converting to float, raising on failure.
umap-learn>     
umap-learn>         Parameters
umap-learn>         ----------
umap-learn>         array : object
umap-learn>             Input object to check / convert.
umap-learn>     
umap-learn>         accept_sparse : str, bool or list/tuple of str, default=False
umap-learn>             String[s] representing allowed sparse matrix formats, such as 'csc',
umap-learn>             'csr', etc. If the input is sparse but not in the allowed format,
umap-learn>             it will be converted to the first listed format. True allows the input
umap-learn>             to be any format. False means that a sparse matrix input will
umap-learn>             raise an error.
umap-learn>     
umap-learn>         accept_large_sparse : bool, default=True
umap-learn>             If a CSR, CSC, COO or BSR sparse matrix is supplied and accepted by
umap-learn>             accept_sparse, accept_large_sparse=False will cause it to be accepted
umap-learn>             only if its indices are stored with a 32-bit dtype.
umap-learn>     
umap-learn>             .. versionadded:: 0.20
umap-learn>     
umap-learn>         dtype : 'numeric', type, list of type or None, default='numeric'
umap-learn>             Data type of result. If None, the dtype of the input is preserved.
umap-learn>             If "numeric", dtype is preserved unless array.dtype is object.
umap-learn>             If dtype is a list of types, conversion on the first type is only
umap-learn>             performed if the dtype of the input is not in the list.
umap-learn>     
umap-learn>         order : {'F', 'C'} or None, default=None
umap-learn>             Whether an array will be forced to be fortran or c-style.
umap-learn>             When order is None (default), then if copy=False, nothing is ensured
umap-learn>             about the memory layout of the output array; otherwise (copy=True)
umap-learn>             the memory layout of the returned array is kept as close as possible
umap-learn>             to the original array.
umap-learn>     
umap-learn>         copy : bool, default=False
umap-learn>             Whether a forced copy will be triggered. If copy=False, a copy might
umap-learn>             be triggered by a conversion.
umap-learn>     
umap-learn>         force_all_finite : bool or 'allow-nan', default=True
umap-learn>             Whether to raise an error on np.inf, np.nan, pd.NA in array. The
umap-learn>             possibilities are:
umap-learn>     
umap-learn>             - True: Force all values of array to be finite.
umap-learn>             - False: accepts np.inf, np.nan, pd.NA in array.
umap-learn>             - 'allow-nan': accepts only np.nan and pd.NA values in array. Values
umap-learn>               cannot be infinite.
umap-learn>     
umap-learn>             .. versionadded:: 0.20
umap-learn>                ``force_all_finite`` accepts the string ``'allow-nan'``.
umap-learn>     
umap-learn>             .. versionchanged:: 0.23
umap-learn>                Accepts `pd.NA` and converts it into `np.nan`
umap-learn>     
umap-learn>         ensure_2d : bool, default=True
umap-learn>             Whether to raise a value error if array is not 2D.
umap-learn>     
umap-learn>         allow_nd : bool, default=False
umap-learn>             Whether to allow array.ndim > 2.
umap-learn>     
umap-learn>         ensure_min_samples : int, default=1
umap-learn>             Make sure that the array has a minimum number of samples in its first
umap-learn>             axis (rows for a 2D array). Setting to 0 disables this check.
umap-learn>     
umap-learn>         ensure_min_features : int, default=1
umap-learn>             Make sure that the 2D array has some minimum number of features
umap-learn>             (columns). The default value of 1 rejects empty datasets.
umap-learn>             This check is only enforced when the input data has effectively 2
umap-learn>             dimensions or is originally 1D and ``ensure_2d`` is True. Setting to 0
umap-learn>             disables this check.
umap-learn>     
umap-learn>         estimator : str or estimator instance, default=None
umap-learn>             If passed, include the name of the estimator in warning messages.
umap-learn>     
umap-learn>         input_name : str, default=""
umap-learn>             The data name used to construct the error message. In particular
umap-learn>             if `input_name` is "X" and the data has NaN values and
umap-learn>             allow_nan is False, the error message will link to the imputer
umap-learn>             documentation.
umap-learn>     
umap-learn>             .. versionadded:: 1.1.0
umap-learn>     
umap-learn>         Returns
umap-learn>         -------
umap-learn>         array_converted : object
umap-learn>             The converted and validated array.
umap-learn>         """
umap-learn>         if isinstance(array, np.matrix):
umap-learn> >           raise TypeError(
umap-learn>                 "np.matrix is not supported. Please convert to a numpy array with "
umap-learn>                 "np.asarray. For more information see: "
umap-learn>                 "https://numpy.org/doc/stable/reference/generated/numpy.matrix.html"
umap-learn>             )
umap-learn> E           TypeError: np.matrix is not supported. Please convert to a numpy array with np.asarray. For more information see: https://numpy.org/doc/stable/reference/generated/numpy.matrix.html
umap-learn> 
umap-learn> /nix/store/5l733c5yjbmkd53d0nd35aln9jmpka8y-python3.10-scikit-learn-1.2.1/lib/python3.10/site-packages/sklearn/utils/validation.py:737: TypeError
umap-learn> _____________________________ test_sparse_matching _____________________________
umap-learn> 
umap-learn> sparse_binary_data = <12x20 sparse matrix of type '<class 'numpy.bool_'>'
umap-learn>         with 72 stored elements in Compressed Sparse Row format>
umap-learn> 
umap-learn>     def test_sparse_matching(sparse_binary_data):
umap-learn> >       sparse_binary_check("matching", sparse_binary_data)
umap-learn> 
umap-learn> umap/tests/test_umap_metrics.py:290: 
umap-learn> _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
umap-learn> umap/tests/test_umap_metrics.py:144: in sparse_binary_check
umap-learn>     dist_matrix = pairwise_distances(sparse_binary_data.todense(), metric=metric)
umap-learn> /nix/store/5l733c5yjbmkd53d0nd35aln9jmpka8y-python3.10-scikit-learn-1.2.1/lib/python3.10/site-packages/sklearn/metrics/pairwise.py:2027: in pairwise_distances
umap-learn>     X, Y = check_pairwise_arrays(
umap-learn> /nix/store/5l733c5yjbmkd53d0nd35aln9jmpka8y-python3.10-scikit-learn-1.2.1/lib/python3.10/site-packages/sklearn/metrics/pairwise.py:146: in check_pairwise_arrays
umap-learn>     X = Y = check_array(
umap-learn> _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
umap-learn> 
umap-learn> array = matrix([[False, False, False,  True,  True, False,  True,  True, False,
umap-learn>           True,  True, False, False, False, Fa..., False, False, False,
umap-learn>          False, False, False, False, False, False, False, False, False,
umap-learn>          False, False]])
umap-learn> accept_sparse = 'csr'
umap-learn> 
umap-learn>     def check_array(
umap-learn>         array,
umap-learn>         accept_sparse=False,
umap-learn>         *,
umap-learn>         accept_large_sparse=True,
umap-learn>         dtype="numeric",
umap-learn>         order=None,
umap-learn>         copy=False,
umap-learn>         force_all_finite=True,
umap-learn>         ensure_2d=True,
umap-learn>         allow_nd=False,
umap-learn>         ensure_min_samples=1,
umap-learn>         ensure_min_features=1,
umap-learn>         estimator=None,
umap-learn>         input_name="",
umap-learn>     ):
umap-learn>     
umap-learn>         """Input validation on an array, list, sparse matrix or similar.
umap-learn>     
umap-learn>         By default, the input is checked to be a non-empty 2D array containing
umap-learn>         only finite values. If the dtype of the array is object, attempt
umap-learn>         converting to float, raising on failure.
umap-learn>     
umap-learn>         Parameters
umap-learn>         ----------
umap-learn>         array : object
umap-learn>             Input object to check / convert.
umap-learn>     
umap-learn>         accept_sparse : str, bool or list/tuple of str, default=False
umap-learn>             String[s] representing allowed sparse matrix formats, such as 'csc',
umap-learn>             'csr', etc. If the input is sparse but not in the allowed format,
umap-learn>             it will be converted to the first listed format. True allows the input
umap-learn>             to be any format. False means that a sparse matrix input will
umap-learn>             raise an error.
umap-learn>     
umap-learn>         accept_large_sparse : bool, default=True
umap-learn>             If a CSR, CSC, COO or BSR sparse matrix is supplied and accepted by
umap-learn>             accept_sparse, accept_large_sparse=False will cause it to be accepted
umap-learn>             only if its indices are stored with a 32-bit dtype.
umap-learn>     
umap-learn>             .. versionadded:: 0.20
umap-learn>     
umap-learn>         dtype : 'numeric', type, list of type or None, default='numeric'
umap-learn>             Data type of result. If None, the dtype of the input is preserved.
umap-learn>             If "numeric", dtype is preserved unless array.dtype is object.
umap-learn>             If dtype is a list of types, conversion on the first type is only
umap-learn>             performed if the dtype of the input is not in the list.
umap-learn>     
umap-learn>         order : {'F', 'C'} or None, default=None
umap-learn>             Whether an array will be forced to be fortran or c-style.
umap-learn>             When order is None (default), then if copy=False, nothing is ensured
umap-learn>             about the memory layout of the output array; otherwise (copy=True)
umap-learn>             the memory layout of the returned array is kept as close as possible
umap-learn>             to the original array.
umap-learn>     
umap-learn>         copy : bool, default=False
umap-learn>             Whether a forced copy will be triggered. If copy=False, a copy might
umap-learn>             be triggered by a conversion.
umap-learn>     
umap-learn>         force_all_finite : bool or 'allow-nan', default=True
umap-learn>             Whether to raise an error on np.inf, np.nan, pd.NA in array. The
umap-learn>             possibilities are:
umap-learn>     
umap-learn>             - True: Force all values of array to be finite.
umap-learn>             - False: accepts np.inf, np.nan, pd.NA in array.
umap-learn>             - 'allow-nan': accepts only np.nan and pd.NA values in array. Values
umap-learn>               cannot be infinite.
umap-learn>     
umap-learn>             .. versionadded:: 0.20
umap-learn>                ``force_all_finite`` accepts the string ``'allow-nan'``.
umap-learn>     
umap-learn>             .. versionchanged:: 0.23
umap-learn>                Accepts `pd.NA` and converts it into `np.nan`
umap-learn>     
umap-learn>         ensure_2d : bool, default=True
umap-learn>             Whether to raise a value error if array is not 2D.
umap-learn>     
umap-learn>         allow_nd : bool, default=False
umap-learn>             Whether to allow array.ndim > 2.
umap-learn>     
umap-learn>         ensure_min_samples : int, default=1
umap-learn>             Make sure that the array has a minimum number of samples in its first
umap-learn>             axis (rows for a 2D array). Setting to 0 disables this check.
umap-learn>     
umap-learn>         ensure_min_features : int, default=1
umap-learn>             Make sure that the 2D array has some minimum number of features
umap-learn>             (columns). The default value of 1 rejects empty datasets.
umap-learn>             This check is only enforced when the input data has effectively 2
umap-learn>             dimensions or is originally 1D and ``ensure_2d`` is True. Setting to 0
umap-learn>             disables this check.
umap-learn>     
umap-learn>         estimator : str or estimator instance, default=None
umap-learn>             If passed, include the name of the estimator in warning messages.
umap-learn>     
umap-learn>         input_name : str, default=""
umap-learn>             The data name used to construct the error message. In particular
umap-learn>             if `input_name` is "X" and the data has NaN values and
umap-learn>             allow_nan is False, the error message will link to the imputer
umap-learn>             documentation.
umap-learn>     
umap-learn>             .. versionadded:: 1.1.0
umap-learn>     
umap-learn>         Returns
umap-learn>         -------
umap-learn>         array_converted : object
umap-learn>             The converted and validated array.
umap-learn>         """
umap-learn>         if isinstance(array, np.matrix):
umap-learn> >           raise TypeError(
umap-learn>                 "np.matrix is not supported. Please convert to a numpy array with "
umap-learn>                 "np.asarray. For more information see: "
umap-learn>                 "https://numpy.org/doc/stable/reference/generated/numpy.matrix.html"
umap-learn>             )
umap-learn> E           TypeError: np.matrix is not supported. Please convert to a numpy array with np.asarray. For more information see: https://numpy.org/doc/stable/reference/generated/numpy.matrix.html
umap-learn> 
umap-learn> /nix/store/5l733c5yjbmkd53d0nd35aln9jmpka8y-python3.10-scikit-learn-1.2.1/lib/python3.10/site-packages/sklearn/utils/validation.py:737: TypeError
umap-learn> _______________________________ test_sparse_dice _______________________________
umap-learn> 
umap-learn> sparse_binary_data = <12x20 sparse matrix of type '<class 'numpy.bool_'>'
umap-learn>         with 72 stored elements in Compressed Sparse Row format>
umap-learn> 
umap-learn>     def test_sparse_dice(sparse_binary_data):
umap-learn> >       sparse_binary_check("dice", sparse_binary_data)
umap-learn> 
umap-learn> umap/tests/test_umap_metrics.py:294: 
umap-learn> _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
umap-learn> umap/tests/test_umap_metrics.py:144: in sparse_binary_check
umap-learn>     dist_matrix = pairwise_distances(sparse_binary_data.todense(), metric=metric)
umap-learn> /nix/store/5l733c5yjbmkd53d0nd35aln9jmpka8y-python3.10-scikit-learn-1.2.1/lib/python3.10/site-packages/sklearn/metrics/pairwise.py:2027: in pairwise_distances
umap-learn>     X, Y = check_pairwise_arrays(
umap-learn> /nix/store/5l733c5yjbmkd53d0nd35aln9jmpka8y-python3.10-scikit-learn-1.2.1/lib/python3.10/site-packages/sklearn/metrics/pairwise.py:146: in check_pairwise_arrays
umap-learn>     X = Y = check_array(
umap-learn> _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
umap-learn> 
umap-learn> array = matrix([[False, False, False,  True,  True, False,  True,  True, False,
umap-learn>           True,  True, False, False, False, Fa..., False, False, False,
umap-learn>          False, False, False, False, False, False, False, False, False,
umap-learn>          False, False]])
umap-learn> accept_sparse = 'csr'
umap-learn> 
umap-learn>     def check_array(
umap-learn>         array,
umap-learn>         accept_sparse=False,
umap-learn>         *,
umap-learn>         accept_large_sparse=True,
umap-learn>         dtype="numeric",
umap-learn>         order=None,
umap-learn>         copy=False,
umap-learn>         force_all_finite=True,
umap-learn>         ensure_2d=True,
umap-learn>         allow_nd=False,
umap-learn>         ensure_min_samples=1,
umap-learn>         ensure_min_features=1,
umap-learn>         estimator=None,
umap-learn>         input_name="",
umap-learn>     ):
umap-learn>     
umap-learn>         """Input validation on an array, list, sparse matrix or similar.
umap-learn>     
umap-learn>         By default, the input is checked to be a non-empty 2D array containing
umap-learn>         only finite values. If the dtype of the array is object, attempt
umap-learn>         converting to float, raising on failure.
umap-learn>     
umap-learn>         Parameters
umap-learn>         ----------
umap-learn>         array : object
umap-learn>             Input object to check / convert.
umap-learn>     
umap-learn>         accept_sparse : str, bool or list/tuple of str, default=False
umap-learn>             String[s] representing allowed sparse matrix formats, such as 'csc',
umap-learn>             'csr', etc. If the input is sparse but not in the allowed format,
umap-learn>             it will be converted to the first listed format. True allows the input
umap-learn>             to be any format. False means that a sparse matrix input will
umap-learn>             raise an error.
umap-learn>     
umap-learn>         accept_large_sparse : bool, default=True
umap-learn>             If a CSR, CSC, COO or BSR sparse matrix is supplied and accepted by
umap-learn>             accept_sparse, accept_large_sparse=False will cause it to be accepted
umap-learn>             only if its indices are stored with a 32-bit dtype.
umap-learn>     
umap-learn>             .. versionadded:: 0.20
umap-learn>     
umap-learn>         dtype : 'numeric', type, list of type or None, default='numeric'
umap-learn>             Data type of result. If None, the dtype of the input is preserved.
umap-learn>             If "numeric", dtype is preserved unless array.dtype is object.
umap-learn>             If dtype is a list of types, conversion on the first type is only
umap-learn>             performed if the dtype of the input is not in the list.
umap-learn>     
umap-learn>         order : {'F', 'C'} or None, default=None
umap-learn>             Whether an array will be forced to be fortran or c-style.
umap-learn>             When order is None (default), then if copy=False, nothing is ensured
umap-learn>             about the memory layout of the output array; otherwise (copy=True)
umap-learn>             the memory layout of the returned array is kept as close as possible
umap-learn>             to the original array.
umap-learn>     
umap-learn>         copy : bool, default=False
umap-learn>             Whether a forced copy will be triggered. If copy=False, a copy might
umap-learn>             be triggered by a conversion.
umap-learn>     
umap-learn>         force_all_finite : bool or 'allow-nan', default=True
umap-learn>             Whether to raise an error on np.inf, np.nan, pd.NA in array. The
umap-learn>             possibilities are:
umap-learn>     
umap-learn>             - True: Force all values of array to be finite.
umap-learn>             - False: accepts np.inf, np.nan, pd.NA in array.
umap-learn>             - 'allow-nan': accepts only np.nan and pd.NA values in array. Values
umap-learn>               cannot be infinite.
umap-learn>     
umap-learn>             .. versionadded:: 0.20
umap-learn>                ``force_all_finite`` accepts the string ``'allow-nan'``.
umap-learn>     
umap-learn>             .. versionchanged:: 0.23
umap-learn>                Accepts `pd.NA` and converts it into `np.nan`
umap-learn>     
umap-learn>         ensure_2d : bool, default=True
umap-learn>             Whether to raise a value error if array is not 2D.
umap-learn>     
umap-learn>         allow_nd : bool, default=False
umap-learn>             Whether to allow array.ndim > 2.
umap-learn>     
umap-learn>         ensure_min_samples : int, default=1
umap-learn>             Make sure that the array has a minimum number of samples in its first
umap-learn>             axis (rows for a 2D array). Setting to 0 disables this check.
umap-learn>     
umap-learn>         ensure_min_features : int, default=1
umap-learn>             Make sure that the 2D array has some minimum number of features
umap-learn>             (columns). The default value of 1 rejects empty datasets.
umap-learn>             This check is only enforced when the input data has effectively 2
umap-learn>             dimensions or is originally 1D and ``ensure_2d`` is True. Setting to 0
umap-learn>             disables this check.
umap-learn>     
umap-learn>         estimator : str or estimator instance, default=None
umap-learn>             If passed, include the name of the estimator in warning messages.
umap-learn>     
umap-learn>         input_name : str, default=""
umap-learn>             The data name used to construct the error message. In particular
umap-learn>             if `input_name` is "X" and the data has NaN values and
umap-learn>             allow_nan is False, the error message will link to the imputer
umap-learn>             documentation.
umap-learn>     
umap-learn>             .. versionadded:: 1.1.0
umap-learn>     
umap-learn>         Returns
umap-learn>         -------
umap-learn>         array_converted : object
umap-learn>             The converted and validated array.
umap-learn>         """
umap-learn>         if isinstance(array, np.matrix):
umap-learn> >           raise TypeError(
umap-learn>                 "np.matrix is not supported. Please convert to a numpy array with "
umap-learn>                 "np.asarray. For more information see: "
umap-learn>                 "https://numpy.org/doc/stable/reference/generated/numpy.matrix.html"
umap-learn>             )
umap-learn> E           TypeError: np.matrix is not supported. Please convert to a numpy array with np.asarray. For more information see: https://numpy.org/doc/stable/reference/generated/numpy.matrix.html
umap-learn> 
umap-learn> /nix/store/5l733c5yjbmkd53d0nd35aln9jmpka8y-python3.10-scikit-learn-1.2.1/lib/python3.10/site-packages/sklearn/utils/validation.py:737: TypeError
umap-learn> ____________________________ test_sparse_kulsinski _____________________________
umap-learn> 
umap-learn> sparse_binary_data = <12x20 sparse matrix of type '<class 'numpy.bool_'>'
umap-learn>         with 72 stored elements in Compressed Sparse Row format>
umap-learn> 
umap-learn>     def test_sparse_kulsinski(sparse_binary_data):
umap-learn> >       sparse_binary_check("kulsinski", sparse_binary_data)
umap-learn> 
umap-learn> umap/tests/test_umap_metrics.py:298: 
umap-learn> _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
umap-learn> umap/tests/test_umap_metrics.py:144: in sparse_binary_check
umap-learn>     dist_matrix = pairwise_distances(sparse_binary_data.todense(), metric=metric)
umap-learn> /nix/store/5l733c5yjbmkd53d0nd35aln9jmpka8y-python3.10-scikit-learn-1.2.1/lib/python3.10/site-packages/sklearn/metrics/pairwise.py:2027: in pairwise_distances
umap-learn>     X, Y = check_pairwise_arrays(
umap-learn> /nix/store/5l733c5yjbmkd53d0nd35aln9jmpka8y-python3.10-scikit-learn-1.2.1/lib/python3.10/site-packages/sklearn/metrics/pairwise.py:146: in check_pairwise_arrays
umap-learn>     X = Y = check_array(
umap-learn> _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
umap-learn> 
umap-learn> array = matrix([[False, False, False,  True,  True, False,  True,  True, False,
umap-learn>           True,  True, False, False, False, Fa..., False, False, False,
umap-learn>          False, False, False, False, False, False, False, False, False,
umap-learn>          False, False]])
umap-learn> accept_sparse = 'csr'
umap-learn> 
umap-learn>     def check_array(
umap-learn>         array,
umap-learn>         accept_sparse=False,
umap-learn>         *,
umap-learn>         accept_large_sparse=True,
umap-learn>         dtype="numeric",
umap-learn>         order=None,
umap-learn>         copy=False,
umap-learn>         force_all_finite=True,
umap-learn>         ensure_2d=True,
umap-learn>         allow_nd=False,
umap-learn>         ensure_min_samples=1,
umap-learn>         ensure_min_features=1,
umap-learn>         estimator=None,
umap-learn>         input_name="",
umap-learn>     ):
umap-learn>     
umap-learn>         """Input validation on an array, list, sparse matrix or similar.
umap-learn>     
umap-learn>         By default, the input is checked to be a non-empty 2D array containing
umap-learn>         only finite values. If the dtype of the array is object, attempt
umap-learn>         converting to float, raising on failure.
umap-learn>     
umap-learn>         Parameters
umap-learn>         ----------
umap-learn>         array : object
umap-learn>             Input object to check / convert.
umap-learn>     
umap-learn>         accept_sparse : str, bool or list/tuple of str, default=False
umap-learn>             String[s] representing allowed sparse matrix formats, such as 'csc',
umap-learn>             'csr', etc. If the input is sparse but not in the allowed format,
umap-learn>             it will be converted to the first listed format. True allows the input
umap-learn>             to be any format. False means that a sparse matrix input will
umap-learn>             raise an error.
umap-learn>     
umap-learn>         accept_large_sparse : bool, default=True
umap-learn>             If a CSR, CSC, COO or BSR sparse matrix is supplied and accepted by
umap-learn>             accept_sparse, accept_large_sparse=False will cause it to be accepted
umap-learn>             only if its indices are stored with a 32-bit dtype.
umap-learn>     
umap-learn>             .. versionadded:: 0.20
umap-learn>     
umap-learn>         dtype : 'numeric', type, list of type or None, default='numeric'
umap-learn>             Data type of result. If None, the dtype of the input is preserved.
umap-learn>             If "numeric", dtype is preserved unless array.dtype is object.
umap-learn>             If dtype is a list of types, conversion on the first type is only
umap-learn>             performed if the dtype of the input is not in the list.
umap-learn>     
umap-learn>         order : {'F', 'C'} or None, default=None
umap-learn>             Whether an array will be forced to be fortran or c-style.
umap-learn>             When order is None (default), then if copy=False, nothing is ensured
umap-learn>             about the memory layout of the output array; otherwise (copy=True)
umap-learn>             the memory layout of the returned array is kept as close as possible
umap-learn>             to the original array.
umap-learn>     
umap-learn>         copy : bool, default=False
umap-learn>             Whether a forced copy will be triggered. If copy=False, a copy might
umap-learn>             be triggered by a conversion.
umap-learn>     
umap-learn>         force_all_finite : bool or 'allow-nan', default=True
umap-learn>             Whether to raise an error on np.inf, np.nan, pd.NA in array. The
umap-learn>             possibilities are:
umap-learn>     
umap-learn>             - True: Force all values of array to be finite.
umap-learn>             - False: accepts np.inf, np.nan, pd.NA in array.
umap-learn>             - 'allow-nan': accepts only np.nan and pd.NA values in array. Values
umap-learn>               cannot be infinite.
umap-learn>     
umap-learn>             .. versionadded:: 0.20
umap-learn>                ``force_all_finite`` accepts the string ``'allow-nan'``.
umap-learn>     
umap-learn>             .. versionchanged:: 0.23
umap-learn>                Accepts `pd.NA` and converts it into `np.nan`
umap-learn>     
umap-learn>         ensure_2d : bool, default=True
umap-learn>             Whether to raise a value error if array is not 2D.
umap-learn>     
umap-learn>         allow_nd : bool, default=False
umap-learn>             Whether to allow array.ndim > 2.
umap-learn>     
umap-learn>         ensure_min_samples : int, default=1
umap-learn>             Make sure that the array has a minimum number of samples in its first
umap-learn>             axis (rows for a 2D array). Setting to 0 disables this check.
umap-learn>     
umap-learn>         ensure_min_features : int, default=1
umap-learn>             Make sure that the 2D array has some minimum number of features
umap-learn>             (columns). The default value of 1 rejects empty datasets.
umap-learn>             This check is only enforced when the input data has effectively 2
umap-learn>             dimensions or is originally 1D and ``ensure_2d`` is True. Setting to 0
umap-learn>             disables this check.
umap-learn>     
umap-learn>         estimator : str or estimator instance, default=None
umap-learn>             If passed, include the name of the estimator in warning messages.
umap-learn>     
umap-learn>         input_name : str, default=""
umap-learn>             The data name used to construct the error message. In particular
umap-learn>             if `input_name` is "X" and the data has NaN values and
umap-learn>             allow_nan is False, the error message will link to the imputer
umap-learn>             documentation.
umap-learn>     
umap-learn>             .. versionadded:: 1.1.0
umap-learn>     
umap-learn>         Returns
umap-learn>         -------
umap-learn>         array_converted : object
umap-learn>             The converted and validated array.
umap-learn>         """
umap-learn>         if isinstance(array, np.matrix):
umap-learn> >           raise TypeError(
umap-learn>                 "np.matrix is not supported. Please convert to a numpy array with "
umap-learn>                 "np.asarray. For more information see: "
umap-learn>                 "https://numpy.org/doc/stable/reference/generated/numpy.matrix.html"
umap-learn>             )
umap-learn> E           TypeError: np.matrix is not supported. Please convert to a numpy array with np.asarray. For more information see: https://numpy.org/doc/stable/reference/generated/numpy.matrix.html
umap-learn> 
umap-learn> /nix/store/5l733c5yjbmkd53d0nd35aln9jmpka8y-python3.10-scikit-learn-1.2.1/lib/python3.10/site-packages/sklearn/utils/validation.py:737: TypeError
umap-learn> __________________________ test_sparse_rogerstanimoto __________________________
umap-learn> 
umap-learn> sparse_binary_data = <12x20 sparse matrix of type '<class 'numpy.bool_'>'
umap-learn>         with 72 stored elements in Compressed Sparse Row format>
umap-learn> 
umap-learn>     def test_sparse_rogerstanimoto(sparse_binary_data):
umap-learn> >       sparse_binary_check("rogerstanimoto", sparse_binary_data)
umap-learn> 
umap-learn> umap/tests/test_umap_metrics.py:302: 
umap-learn> _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
umap-learn> umap/tests/test_umap_metrics.py:144: in sparse_binary_check
umap-learn>     dist_matrix = pairwise_distances(sparse_binary_data.todense(), metric=metric)
umap-learn> /nix/store/5l733c5yjbmkd53d0nd35aln9jmpka8y-python3.10-scikit-learn-1.2.1/lib/python3.10/site-packages/sklearn/metrics/pairwise.py:2027: in pairwise_distances
umap-learn>     X, Y = check_pairwise_arrays(
umap-learn> /nix/store/5l733c5yjbmkd53d0nd35aln9jmpka8y-python3.10-scikit-learn-1.2.1/lib/python3.10/site-packages/sklearn/metrics/pairwise.py:146: in check_pairwise_arrays
umap-learn>     X = Y = check_array(
umap-learn> _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
umap-learn> 
umap-learn> array = matrix([[False, False, False,  True,  True, False,  True,  True, False,
umap-learn>           True,  True, False, False, False, Fa..., False, False, False,
umap-learn>          False, False, False, False, False, False, False, False, False,
umap-learn>          False, False]])
umap-learn> accept_sparse = 'csr'
umap-learn> 
umap-learn>     def check_array(
umap-learn>         array,
umap-learn>         accept_sparse=False,
umap-learn>         *,
umap-learn>         accept_large_sparse=True,
umap-learn>         dtype="numeric",
umap-learn>         order=None,
umap-learn>         copy=False,
umap-learn>         force_all_finite=True,
umap-learn>         ensure_2d=True,
umap-learn>         allow_nd=False,
umap-learn>         ensure_min_samples=1,
umap-learn>         ensure_min_features=1,
umap-learn>         estimator=None,
umap-learn>         input_name="",
umap-learn>     ):
umap-learn>     
umap-learn>         """Input validation on an array, list, sparse matrix or similar.
umap-learn>     
umap-learn>         By default, the input is checked to be a non-empty 2D array containing
umap-learn>         only finite values. If the dtype of the array is object, attempt
umap-learn>         converting to float, raising on failure.
umap-learn>     
umap-learn>         Parameters
umap-learn>         ----------
umap-learn>         array : object
umap-learn>             Input object to check / convert.
umap-learn>     
umap-learn>         accept_sparse : str, bool or list/tuple of str, default=False
umap-learn>             String[s] representing allowed sparse matrix formats, such as 'csc',
umap-learn>             'csr', etc. If the input is sparse but not in the allowed format,
umap-learn>             it will be converted to the first listed format. True allows the input
umap-learn>             to be any format. False means that a sparse matrix input will
umap-learn>             raise an error.
umap-learn>     
umap-learn>         accept_large_sparse : bool, default=True
umap-learn>             If a CSR, CSC, COO or BSR sparse matrix is supplied and accepted by
umap-learn>             accept_sparse, accept_large_sparse=False will cause it to be accepted
umap-learn>             only if its indices are stored with a 32-bit dtype.
umap-learn>     
umap-learn>             .. versionadded:: 0.20
umap-learn>     
umap-learn>         dtype : 'numeric', type, list of type or None, default='numeric'
umap-learn>             Data type of result. If None, the dtype of the input is preserved.
umap-learn>             If "numeric", dtype is preserved unless array.dtype is object.
umap-learn>             If dtype is a list of types, conversion on the first type is only
umap-learn>             performed if the dtype of the input is not in the list.
umap-learn>     
umap-learn>         order : {'F', 'C'} or None, default=None
umap-learn>             Whether an array will be forced to be fortran or c-style.
umap-learn>             When order is None (default), then if copy=False, nothing is ensured
umap-learn>             about the memory layout of the output array; otherwise (copy=True)
umap-learn>             the memory layout of the returned array is kept as close as possible
umap-learn>             to the original array.
umap-learn>     
umap-learn>         copy : bool, default=False
umap-learn>             Whether a forced copy will be triggered. If copy=False, a copy might
umap-learn>             be triggered by a conversion.
umap-learn>     
umap-learn>         force_all_finite : bool or 'allow-nan', default=True
umap-learn>             Whether to raise an error on np.inf, np.nan, pd.NA in array. The
umap-learn>             possibilities are:
umap-learn>     
umap-learn>             - True: Force all values of array to be finite.
umap-learn>             - False: accepts np.inf, np.nan, pd.NA in array.
umap-learn>             - 'allow-nan': accepts only np.nan and pd.NA values in array. Values
umap-learn>               cannot be infinite.
umap-learn>     
umap-learn>             .. versionadded:: 0.20
umap-learn>                ``force_all_finite`` accepts the string ``'allow-nan'``.
umap-learn>     
umap-learn>             .. versionchanged:: 0.23
umap-learn>                Accepts `pd.NA` and converts it into `np.nan`
umap-learn>     
umap-learn>         ensure_2d : bool, default=True
umap-learn>             Whether to raise a value error if array is not 2D.
umap-learn>     
umap-learn>         allow_nd : bool, default=False
umap-learn>             Whether to allow array.ndim > 2.
umap-learn>     
umap-learn>         ensure_min_samples : int, default=1
umap-learn>             Make sure that the array has a minimum number of samples in its first
umap-learn>             axis (rows for a 2D array). Setting to 0 disables this check.
umap-learn>     
umap-learn>         ensure_min_features : int, default=1
umap-learn>             Make sure that the 2D array has some minimum number of features
umap-learn>             (columns). The default value of 1 rejects empty datasets.
umap-learn>             This check is only enforced when the input data has effectively 2
umap-learn>             dimensions or is originally 1D and ``ensure_2d`` is True. Setting to 0
umap-learn>             disables this check.
umap-learn>     
umap-learn>         estimator : str or estimator instance, default=None
umap-learn>             If passed, include the name of the estimator in warning messages.
umap-learn>     
umap-learn>         input_name : str, default=""
umap-learn>             The data name used to construct the error message. In particular
umap-learn>             if `input_name` is "X" and the data has NaN values and
umap-learn>             allow_nan is False, the error message will link to the imputer
umap-learn>             documentation.
umap-learn>     
umap-learn>             .. versionadded:: 1.1.0
umap-learn>     
umap-learn>         Returns
umap-learn>         -------
umap-learn>         array_converted : object
umap-learn>             The converted and validated array.
umap-learn>         """
umap-learn>         if isinstance(array, np.matrix):
umap-learn> >           raise TypeError(
umap-learn>                 "np.matrix is not supported. Please convert to a numpy array with "
umap-learn>                 "np.asarray. For more information see: "
umap-learn>                 "https://numpy.org/doc/stable/reference/generated/numpy.matrix.html"
umap-learn>             )
umap-learn> E           TypeError: np.matrix is not supported. Please convert to a numpy array with np.asarray. For more information see: https://numpy.org/doc/stable/reference/generated/numpy.matrix.html
umap-learn> 
umap-learn> /nix/store/5l733c5yjbmkd53d0nd35aln9jmpka8y-python3.10-scikit-learn-1.2.1/lib/python3.10/site-packages/sklearn/utils/validation.py:737: TypeError
umap-learn> ____________________________ test_sparse_russellrao ____________________________
umap-learn> 
umap-learn> sparse_binary_data = <12x20 sparse matrix of type '<class 'numpy.bool_'>'
umap-learn>         with 72 stored elements in Compressed Sparse Row format>
umap-learn> 
umap-learn>     def test_sparse_russellrao(sparse_binary_data):
umap-learn> >       sparse_binary_check("russellrao", sparse_binary_data)
umap-learn> 
umap-learn> umap/tests/test_umap_metrics.py:306: 
umap-learn> _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
umap-learn> umap/tests/test_umap_metrics.py:144: in sparse_binary_check
umap-learn>     dist_matrix = pairwise_distances(sparse_binary_data.todense(), metric=metric)
umap-learn> /nix/store/5l733c5yjbmkd53d0nd35aln9jmpka8y-python3.10-scikit-learn-1.2.1/lib/python3.10/site-packages/sklearn/metrics/pairwise.py:2027: in pairwise_distances
umap-learn>     X, Y = check_pairwise_arrays(
umap-learn> /nix/store/5l733c5yjbmkd53d0nd35aln9jmpka8y-python3.10-scikit-learn-1.2.1/lib/python3.10/site-packages/sklearn/metrics/pairwise.py:146: in check_pairwise_arrays
umap-learn>     X = Y = check_array(
umap-learn> _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
umap-learn> 
umap-learn> array = matrix([[False, False, False,  True,  True, False,  True,  True, False,
umap-learn>           True,  True, False, False, False, Fa..., False, False, False,
umap-learn>          False, False, False, False, False, False, False, False, False,
umap-learn>          False, False]])
umap-learn> accept_sparse = 'csr'
umap-learn> 
umap-learn>     def check_array(
umap-learn>         array,
umap-learn>         accept_sparse=False,
umap-learn>         *,
umap-learn>         accept_large_sparse=True,
umap-learn>         dtype="numeric",
umap-learn>         order=None,
umap-learn>         copy=False,
umap-learn>         force_all_finite=True,
umap-learn>         ensure_2d=True,
umap-learn>         allow_nd=False,
umap-learn>         ensure_min_samples=1,
umap-learn>         ensure_min_features=1,
umap-learn>         estimator=None,
umap-learn>         input_name="",
umap-learn>     ):
umap-learn>     
umap-learn>         """Input validation on an array, list, sparse matrix or similar.
umap-learn>     
umap-learn>         By default, the input is checked to be a non-empty 2D array containing
umap-learn>         only finite values. If the dtype of the array is object, attempt
umap-learn>         converting to float, raising on failure.
umap-learn>     
umap-learn>         Parameters
umap-learn>         ----------
umap-learn>         array : object
umap-learn>             Input object to check / convert.
umap-learn>     
umap-learn>         accept_sparse : str, bool or list/tuple of str, default=False
umap-learn>             String[s] representing allowed sparse matrix formats, such as 'csc',
umap-learn>             'csr', etc. If the input is sparse but not in the allowed format,
umap-learn>             it will be converted to the first listed format. True allows the input
umap-learn>             to be any format. False means that a sparse matrix input will
umap-learn>             raise an error.
umap-learn>     
umap-learn>         accept_large_sparse : bool, default=True
umap-learn>             If a CSR, CSC, COO or BSR sparse matrix is supplied and accepted by
umap-learn>             accept_sparse, accept_large_sparse=False will cause it to be accepted
umap-learn>             only if its indices are stored with a 32-bit dtype.
umap-learn>     
umap-learn>             .. versionadded:: 0.20
umap-learn>     
umap-learn>         dtype : 'numeric', type, list of type or None, default='numeric'
umap-learn>             Data type of result. If None, the dtype of the input is preserved.
umap-learn>             If "numeric", dtype is preserved unless array.dtype is object.
umap-learn>             If dtype is a list of types, conversion on the first type is only
umap-learn>             performed if the dtype of the input is not in the list.
umap-learn>     
umap-learn>         order : {'F', 'C'} or None, default=None
umap-learn>             Whether an array will be forced to be fortran or c-style.
umap-learn>             When order is None (default), then if copy=False, nothing is ensured
umap-learn>             about the memory layout of the output array; otherwise (copy=True)
umap-learn>             the memory layout of the returned array is kept as close as possible
umap-learn>             to the original array.
umap-learn>     
umap-learn>         copy : bool, default=False
umap-learn>             Whether a forced copy will be triggered. If copy=False, a copy might
umap-learn>             be triggered by a conversion.
umap-learn>     
umap-learn>         force_all_finite : bool or 'allow-nan', default=True
umap-learn>             Whether to raise an error on np.inf, np.nan, pd.NA in array. The
umap-learn>             possibilities are:
umap-learn>     
umap-learn>             - True: Force all values of array to be finite.
umap-learn>             - False: accepts np.inf, np.nan, pd.NA in array.
umap-learn>             - 'allow-nan': accepts only np.nan and pd.NA values in array. Values
umap-learn>               cannot be infinite.
umap-learn>     
umap-learn>             .. versionadded:: 0.20
umap-learn>                ``force_all_finite`` accepts the string ``'allow-nan'``.
umap-learn>     
umap-learn>             .. versionchanged:: 0.23
umap-learn>                Accepts `pd.NA` and converts it into `np.nan`
umap-learn>     
umap-learn>         ensure_2d : bool, default=True
umap-learn>             Whether to raise a value error if array is not 2D.
umap-learn>     
umap-learn>         allow_nd : bool, default=False
umap-learn>             Whether to allow array.ndim > 2.
umap-learn>     
umap-learn>         ensure_min_samples : int, default=1
umap-learn>             Make sure that the array has a minimum number of samples in its first
umap-learn>             axis (rows for a 2D array). Setting to 0 disables this check.
umap-learn>     
umap-learn>         ensure_min_features : int, default=1
umap-learn>             Make sure that the 2D array has some minimum number of features
umap-learn>             (columns). The default value of 1 rejects empty datasets.
umap-learn>             This check is only enforced when the input data has effectively 2
umap-learn>             dimensions or is originally 1D and ``ensure_2d`` is True. Setting to 0
umap-learn>             disables this check.
umap-learn>     
umap-learn>         estimator : str or estimator instance, default=None
umap-learn>             If passed, include the name of the estimator in warning messages.
umap-learn>     
umap-learn>         input_name : str, default=""
umap-learn>             The data name used to construct the error message. In particular
umap-learn>             if `input_name` is "X" and the data has NaN values and
umap-learn>             allow_nan is False, the error message will link to the imputer
umap-learn>             documentation.
umap-learn>     
umap-learn>             .. versionadded:: 1.1.0
umap-learn>     
umap-learn>         Returns
umap-learn>         -------
umap-learn>         array_converted : object
umap-learn>             The converted and validated array.
umap-learn>         """
umap-learn>         if isinstance(array, np.matrix):
umap-learn> >           raise TypeError(
umap-learn>                 "np.matrix is not supported. Please convert to a numpy array with "
umap-learn>                 "np.asarray. For more information see: "
umap-learn>                 "https://numpy.org/doc/stable/reference/generated/numpy.matrix.html"
umap-learn>             )
umap-learn> E           TypeError: np.matrix is not supported. Please convert to a numpy array with np.asarray. For more information see: https://numpy.org/doc/stable/reference/generated/numpy.matrix.html
umap-learn> 
umap-learn> /nix/store/5l733c5yjbmkd53d0nd35aln9jmpka8y-python3.10-scikit-learn-1.2.1/lib/python3.10/site-packages/sklearn/utils/validation.py:737: TypeError
umap-learn> __________________________ test_sparse_sokalmichener ___________________________
umap-learn> 
umap-learn> sparse_binary_data = <12x20 sparse matrix of type '<class 'numpy.bool_'>'
umap-learn>         with 72 stored elements in Compressed Sparse Row format>
umap-learn> 
umap-learn>     def test_sparse_sokalmichener(sparse_binary_data):
umap-learn> >       sparse_binary_check("sokalmichener", sparse_binary_data)
umap-learn> 
umap-learn> umap/tests/test_umap_metrics.py:310: 
umap-learn> _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
umap-learn> umap/tests/test_umap_metrics.py:144: in sparse_binary_check
umap-learn>     dist_matrix = pairwise_distances(sparse_binary_data.todense(), metric=metric)
umap-learn> /nix/store/5l733c5yjbmkd53d0nd35aln9jmpka8y-python3.10-scikit-learn-1.2.1/lib/python3.10/site-packages/sklearn/metrics/pairwise.py:2027: in pairwise_distances
umap-learn>     X, Y = check_pairwise_arrays(
umap-learn> /nix/store/5l733c5yjbmkd53d0nd35aln9jmpka8y-python3.10-scikit-learn-1.2.1/lib/python3.10/site-packages/sklearn/metrics/pairwise.py:146: in check_pairwise_arrays
umap-learn>     X = Y = check_array(
umap-learn> _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
umap-learn> 
umap-learn> array = matrix([[False, False, False,  True,  True, False,  True,  True, False,
umap-learn>           True,  True, False, False, False, Fa..., False, False, False,
umap-learn>          False, False, False, False, False, False, False, False, False,
umap-learn>          False, False]])
umap-learn> accept_sparse = 'csr'
umap-learn> 
umap-learn>     def check_array(
umap-learn>         array,
umap-learn>         accept_sparse=False,
umap-learn>         *,
umap-learn>         accept_large_sparse=True,
umap-learn>         dtype="numeric",
umap-learn>         order=None,
umap-learn>         copy=False,
umap-learn>         force_all_finite=True,
umap-learn>         ensure_2d=True,
umap-learn>         allow_nd=False,
umap-learn>         ensure_min_samples=1,
umap-learn>         ensure_min_features=1,
umap-learn>         estimator=None,
umap-learn>         input_name="",
umap-learn>     ):
umap-learn>     
umap-learn>         """Input validation on an array, list, sparse matrix or similar.
umap-learn>     
umap-learn>         By default, the input is checked to be a non-empty 2D array containing
umap-learn>         only finite values. If the dtype of the array is object, attempt
umap-learn>         converting to float, raising on failure.
umap-learn>     
umap-learn>         Parameters
umap-learn>         ----------
umap-learn>         array : object
umap-learn>             Input object to check / convert.
umap-learn>     
umap-learn>         accept_sparse : str, bool or list/tuple of str, default=False
umap-learn>             String[s] representing allowed sparse matrix formats, such as 'csc',
umap-learn>             'csr', etc. If the input is sparse but not in the allowed format,
umap-learn>             it will be converted to the first listed format. True allows the input
umap-learn>             to be any format. False means that a sparse matrix input will
umap-learn>             raise an error.
umap-learn>     
umap-learn>         accept_large_sparse : bool, default=True
umap-learn>             If a CSR, CSC, COO or BSR sparse matrix is supplied and accepted by
umap-learn>             accept_sparse, accept_large_sparse=False will cause it to be accepted
umap-learn>             only if its indices are stored with a 32-bit dtype.
umap-learn>     
umap-learn>             .. versionadded:: 0.20
umap-learn>     
umap-learn>         dtype : 'numeric', type, list of type or None, default='numeric'
umap-learn>             Data type of result. If None, the dtype of the input is preserved.
umap-learn>             If "numeric", dtype is preserved unless array.dtype is object.
umap-learn>             If dtype is a list of types, conversion on the first type is only
umap-learn>             performed if the dtype of the input is not in the list.
umap-learn>     
umap-learn>         order : {'F', 'C'} or None, default=None
umap-learn>             Whether an array will be forced to be fortran or c-style.
umap-learn>             When order is None (default), then if copy=False, nothing is ensured
umap-learn>             about the memory layout of the output array; otherwise (copy=True)
umap-learn>             the memory layout of the returned array is kept as close as possible
umap-learn>             to the original array.
umap-learn>     
umap-learn>         copy : bool, default=False
umap-learn>             Whether a forced copy will be triggered. If copy=False, a copy might
umap-learn>             be triggered by a conversion.
umap-learn>     
umap-learn>         force_all_finite : bool or 'allow-nan', default=True
umap-learn>             Whether to raise an error on np.inf, np.nan, pd.NA in array. The
umap-learn>             possibilities are:
umap-learn>     
umap-learn>             - True: Force all values of array to be finite.
umap-learn>             - False: accepts np.inf, np.nan, pd.NA in array.
umap-learn>             - 'allow-nan': accepts only np.nan and pd.NA values in array. Values
umap-learn>               cannot be infinite.
umap-learn>     
umap-learn>             .. versionadded:: 0.20
umap-learn>                ``force_all_finite`` accepts the string ``'allow-nan'``.
umap-learn>     
umap-learn>             .. versionchanged:: 0.23
umap-learn>                Accepts `pd.NA` and converts it into `np.nan`
umap-learn>     
umap-learn>         ensure_2d : bool, default=True
umap-learn>             Whether to raise a value error if array is not 2D.
umap-learn>     
umap-learn>         allow_nd : bool, default=False
umap-learn>             Whether to allow array.ndim > 2.
umap-learn>     
umap-learn>         ensure_min_samples : int, default=1
umap-learn>             Make sure that the array has a minimum number of samples in its first
umap-learn>             axis (rows for a 2D array). Setting to 0 disables this check.
umap-learn>     
umap-learn>         ensure_min_features : int, default=1
umap-learn>             Make sure that the 2D array has some minimum number of features
umap-learn>             (columns). The default value of 1 rejects empty datasets.
umap-learn>             This check is only enforced when the input data has effectively 2
umap-learn>             dimensions or is originally 1D and ``ensure_2d`` is True. Setting to 0
umap-learn>             disables this check.
umap-learn>     
umap-learn>         estimator : str or estimator instance, default=None
umap-learn>             If passed, include the name of the estimator in warning messages.
umap-learn>     
umap-learn>         input_name : str, default=""
umap-learn>             The data name used to construct the error message. In particular
umap-learn>             if `input_name` is "X" and the data has NaN values and
umap-learn>             allow_nan is False, the error message will link to the imputer
umap-learn>             documentation.
umap-learn>     
umap-learn>             .. versionadded:: 1.1.0
umap-learn>     
umap-learn>         Returns
umap-learn>         -------
umap-learn>         array_converted : object
umap-learn>             The converted and validated array.
umap-learn>         """
umap-learn>         if isinstance(array, np.matrix):
umap-learn> >           raise TypeError(
umap-learn>                 "np.matrix is not supported. Please convert to a numpy array with "
umap-learn>                 "np.asarray. For more information see: "
umap-learn>                 "https://numpy.org/doc/stable/reference/generated/numpy.matrix.html"
umap-learn>             )
umap-learn> E           TypeError: np.matrix is not supported. Please convert to a numpy array with np.asarray. For more information see: https://numpy.org/doc/stable/reference/generated/numpy.matrix.html
umap-learn> 
umap-learn> /nix/store/5l733c5yjbmkd53d0nd35aln9jmpka8y-python3.10-scikit-learn-1.2.1/lib/python3.10/site-packages/sklearn/utils/validation.py:737: TypeError
umap-learn> ___________________________ test_sparse_sokalsneath ____________________________
umap-learn> 
umap-learn> sparse_binary_data = <12x20 sparse matrix of type '<class 'numpy.bool_'>'
umap-learn>         with 72 stored elements in Compressed Sparse Row format>
umap-learn> 
umap-learn>     def test_sparse_sokalsneath(sparse_binary_data):
umap-learn> >       sparse_binary_check("sokalsneath", sparse_binary_data)
umap-learn> 
umap-learn> umap/tests/test_umap_metrics.py:314: 
umap-learn> _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
umap-learn> umap/tests/test_umap_metrics.py:144: in sparse_binary_check
umap-learn>     dist_matrix = pairwise_distances(sparse_binary_data.todense(), metric=metric)
umap-learn> /nix/store/5l733c5yjbmkd53d0nd35aln9jmpka8y-python3.10-scikit-learn-1.2.1/lib/python3.10/site-packages/sklearn/metrics/pairwise.py:2027: in pairwise_distances
umap-learn>     X, Y = check_pairwise_arrays(
umap-learn> /nix/store/5l733c5yjbmkd53d0nd35aln9jmpka8y-python3.10-scikit-learn-1.2.1/lib/python3.10/site-packages/sklearn/metrics/pairwise.py:146: in check_pairwise_arrays
umap-learn>     X = Y = check_array(
umap-learn> _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
umap-learn> 
umap-learn> array = matrix([[False, False, False,  True,  True, False,  True,  True, False,
umap-learn>           True,  True, False, False, False, Fa..., False, False, False,
umap-learn>          False, False, False, False, False, False, False, False, False,
umap-learn>          False, False]])
umap-learn> accept_sparse = 'csr'
umap-learn> 
umap-learn>     def check_array(
umap-learn>         array,
umap-learn>         accept_sparse=False,
umap-learn>         *,
umap-learn>         accept_large_sparse=True,
umap-learn>         dtype="numeric",
umap-learn>         order=None,
umap-learn>         copy=False,
umap-learn>         force_all_finite=True,
umap-learn>         ensure_2d=True,
umap-learn>         allow_nd=False,
umap-learn>         ensure_min_samples=1,
umap-learn>         ensure_min_features=1,
umap-learn>         estimator=None,
umap-learn>         input_name="",
umap-learn>     ):
umap-learn>     
umap-learn>         """Input validation on an array, list, sparse matrix or similar.
umap-learn>     
umap-learn>         By default, the input is checked to be a non-empty 2D array containing
umap-learn>         only finite values. If the dtype of the array is object, attempt
umap-learn>         converting to float, raising on failure.
umap-learn>     
umap-learn>         Parameters
umap-learn>         ----------
umap-learn>         array : object
umap-learn>             Input object to check / convert.
umap-learn>     
umap-learn>         accept_sparse : str, bool or list/tuple of str, default=False
umap-learn>             String[s] representing allowed sparse matrix formats, such as 'csc',
umap-learn>             'csr', etc. If the input is sparse but not in the allowed format,
umap-learn>             it will be converted to the first listed format. True allows the input
umap-learn>             to be any format. False means that a sparse matrix input will
umap-learn>             raise an error.
umap-learn>     
umap-learn>         accept_large_sparse : bool, default=True
umap-learn>             If a CSR, CSC, COO or BSR sparse matrix is supplied and accepted by
umap-learn>             accept_sparse, accept_large_sparse=False will cause it to be accepted
umap-learn>             only if its indices are stored with a 32-bit dtype.
umap-learn>     
umap-learn>             .. versionadded:: 0.20
umap-learn>     
umap-learn>         dtype : 'numeric', type, list of type or None, default='numeric'
umap-learn>             Data type of result. If None, the dtype of the input is preserved.
umap-learn>             If "numeric", dtype is preserved unless array.dtype is object.
umap-learn>             If dtype is a list of types, conversion on the first type is only
umap-learn>             performed if the dtype of the input is not in the list.
umap-learn>     
umap-learn>         order : {'F', 'C'} or None, default=None
umap-learn>             Whether an array will be forced to be fortran or c-style.
umap-learn>             When order is None (default), then if copy=False, nothing is ensured
umap-learn>             about the memory layout of the output array; otherwise (copy=True)
umap-learn>             the memory layout of the returned array is kept as close as possible
umap-learn>             to the original array.
umap-learn>     
umap-learn>         copy : bool, default=False
umap-learn>             Whether a forced copy will be triggered. If copy=False, a copy might
umap-learn>             be triggered by a conversion.
umap-learn>     
umap-learn>         force_all_finite : bool or 'allow-nan', default=True
umap-learn>             Whether to raise an error on np.inf, np.nan, pd.NA in array. The
umap-learn>             possibilities are:
umap-learn>     
umap-learn>             - True: Force all values of array to be finite.
umap-learn>             - False: accepts np.inf, np.nan, pd.NA in array.
umap-learn>             - 'allow-nan': accepts only np.nan and pd.NA values in array. Values
umap-learn>               cannot be infinite.
umap-learn>     
umap-learn>             .. versionadded:: 0.20
umap-learn>                ``force_all_finite`` accepts the string ``'allow-nan'``.
umap-learn>     
umap-learn>             .. versionchanged:: 0.23
umap-learn>                Accepts `pd.NA` and converts it into `np.nan`
umap-learn>     
umap-learn>         ensure_2d : bool, default=True
umap-learn>             Whether to raise a value error if array is not 2D.
umap-learn>     
umap-learn>         allow_nd : bool, default=False
umap-learn>             Whether to allow array.ndim > 2.
umap-learn>     
umap-learn>         ensure_min_samples : int, default=1
umap-learn>             Make sure that the array has a minimum number of samples in its first
umap-learn>             axis (rows for a 2D array). Setting to 0 disables this check.
umap-learn>     
umap-learn>         ensure_min_features : int, default=1
umap-learn>             Make sure that the 2D array has some minimum number of features
umap-learn>             (columns). The default value of 1 rejects empty datasets.
umap-learn>             This check is only enforced when the input data has effectively 2
umap-learn>             dimensions or is originally 1D and ``ensure_2d`` is True. Setting to 0
umap-learn>             disables this check.
umap-learn>     
umap-learn>         estimator : str or estimator instance, default=None
umap-learn>             If passed, include the name of the estimator in warning messages.
umap-learn>     
umap-learn>         input_name : str, default=""
umap-learn>             The data name used to construct the error message. In particular
umap-learn>             if `input_name` is "X" and the data has NaN values and
umap-learn>             allow_nan is False, the error message will link to the imputer
umap-learn>             documentation.
umap-learn>     
umap-learn>             .. versionadded:: 1.1.0
umap-learn>     
umap-learn>         Returns
umap-learn>         -------
umap-learn>         array_converted : object
umap-learn>             The converted and validated array.
umap-learn>         """
umap-learn>         if isinstance(array, np.matrix):
umap-learn> >           raise TypeError(
umap-learn>                 "np.matrix is not supported. Please convert to a numpy array with "
umap-learn>                 "np.asarray. For more information see: "
umap-learn>                 "https://numpy.org/doc/stable/reference/generated/numpy.matrix.html"
umap-learn>             )
umap-learn> E           TypeError: np.matrix is not supported. Please convert to a numpy array with np.asarray. For more information see: https://numpy.org/doc/stable/reference/generated/numpy.matrix.html
umap-learn> 
umap-learn> /nix/store/5l733c5yjbmkd53d0nd35aln9jmpka8y-python3.10-scikit-learn-1.2.1/lib/python3.10/site-packages/sklearn/utils/validation.py:737: TypeError
umap-learn> ________________________ test_component_layout_options _________________________
umap-learn> 
umap-learn> nn_data = array([[0.77634894, 0.52528136, 0.97117066, 0.27605402, 0.74789432],
umap-learn>        [0.07109226, 0.74870202, 0.57328734, 0.440... 0.        , 0.        , 0.        , 0.        ],
umap-learn>        [0.        , 0.        , 0.        , 0.        , 0.        ]])
umap-learn> 
umap-learn>     def test_component_layout_options(nn_data):
umap-learn>         dmat = pairwise_distances(nn_data[:1000])
umap-learn>         n_components = 5
umap-learn>         component_labels = np.repeat(np.arange(5), dmat.shape[0] // 5)
umap-learn> >       single = component_layout(
umap-learn>             dmat,
umap-learn>             n_components,
umap-learn>             component_labels,
umap-learn>             2,
umap-learn>             np.random,
umap-learn>             metric="precomputed",
umap-learn>             metric_kwds={"linkage": "single"},
umap-learn>         )
umap-learn> 
umap-learn> umap/tests/test_umap_ops.py:290: 
umap-learn> _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
umap-learn> umap/spectral.py:137: in component_layout
umap-learn>     ).fit_transform(affinity_matrix)
umap-learn> /nix/store/5l733c5yjbmkd53d0nd35aln9jmpka8y-python3.10-scikit-learn-1.2.1/lib/python3.10/site-packages/sklearn/manifold/_spectral_embedding.py:713: in fit_transform
umap-learn>     self.fit(X)
umap-learn> /nix/store/5l733c5yjbmkd53d0nd35aln9jmpka8y-python3.10-scikit-learn-1.2.1/lib/python3.10/site-packages/sklearn/manifold/_spectral_embedding.py:675: in fit
umap-learn>     self._validate_params()
umap-learn> /nix/store/5l733c5yjbmkd53d0nd35aln9jmpka8y-python3.10-scikit-learn-1.2.1/lib/python3.10/site-packages/sklearn/base.py:581: in _validate_params
umap-learn>     validate_parameter_constraints(
umap-learn> _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
umap-learn> 
umap-learn> parameter_constraints = {'affinity': [<sklearn.utils._param_validation.StrOptions object at 0x7ffbe1bedd50>, <built-in function callable>], 'e... object at 0x7ffbe1beda20>], 'gamma': [<sklearn.utils._param_validation.Interval object at 0x7ffbe1bedc60>, None], ...}
umap-learn> params = {'affinity': 'precomputed', 'eigen_solver': None, 'eigen_tol': 'auto', 'gamma': None, ...}
umap-learn> caller_name = 'SpectralEmbedding'
umap-learn> 
umap-learn>     def validate_parameter_constraints(parameter_constraints, params, caller_name):
umap-learn>         """Validate types and values of given parameters.
umap-learn>     
umap-learn>         Parameters
umap-learn>         ----------
umap-learn>         parameter_constraints : dict or {"no_validation"}
umap-learn>             If "no_validation", validation is skipped for this parameter.
umap-learn>     
umap-learn>             If a dict, it must be a dictionary `param_name: list of constraints`.
umap-learn>             A parameter is valid if it satisfies one of the constraints from the list.
umap-learn>             Constraints can be:
umap-learn>             - an Interval object, representing a continuous or discrete range of numbers
umap-learn>             - the string "array-like"
umap-learn>             - the string "sparse matrix"
umap-learn>             - the string "random_state"
umap-learn>             - callable
umap-learn>             - None, meaning that None is a valid value for the parameter
umap-learn>             - any type, meaning that any instance of this type is valid
umap-learn>             - an Options object, representing a set of elements of a given type
umap-learn>             - a StrOptions object, representing a set of strings
umap-learn>             - the string "boolean"
umap-learn>             - the string "verbose"
umap-learn>             - the string "cv_object"
umap-learn>             - the string "missing_values"
umap-learn>             - a HasMethods object, representing method(s) an object must have
umap-learn>             - a Hidden object, representing a constraint not meant to be exposed to the user
umap-learn>     
umap-learn>         params : dict
umap-learn>             A dictionary `param_name: param_value`. The parameters to validate against the
umap-learn>             constraints.
umap-learn>     
umap-learn>         caller_name : str
umap-learn>             The name of the estimator or function or method that called this function.
umap-learn>         """
umap-learn>         for param_name, param_val in params.items():
umap-learn>             # We allow parameters to not have a constraint so that third party estimators
umap-learn>             # can inherit from sklearn estimators without having to necessarily use the
umap-learn>             # validation tools.
umap-learn>             if param_name not in parameter_constraints:
umap-learn>                 continue
umap-learn>     
umap-learn>             constraints = parameter_constraints[param_name]
umap-learn>     
umap-learn>             if constraints == "no_validation":
umap-learn>                 continue
umap-learn>     
umap-learn>             constraints = [make_constraint(constraint) for constraint in constraints]
umap-learn>     
umap-learn>             for constraint in constraints:
umap-learn>                 if constraint.is_satisfied_by(param_val):
umap-learn>                     # this constraint is satisfied, no need to check further.
umap-learn>                     break
umap-learn>             else:
umap-learn>                 # No constraint is satisfied, raise with an informative message.
umap-learn>     
umap-learn>                 # Ignore constraints that we don't want to expose in the error message,
umap-learn>                 # i.e. options that are for internal purpose or not officially supported.
umap-learn>                 constraints = [
umap-learn>                     constraint for constraint in constraints if not constraint.hidden
umap-learn>                 ]
umap-learn>     
umap-learn>                 if len(constraints) == 1:
umap-learn>                     constraints_str = f"{constraints[0]}"
umap-learn>                 else:
umap-learn>                     constraints_str = (
umap-learn>                         f"{', '.join([str(c) for c in constraints[:-1]])} or"
umap-learn>                         f" {constraints[-1]}"
umap-learn>                     )
umap-learn>     
umap-learn> >               raise InvalidParameterError(
umap-learn>                     f"The {param_name!r} parameter of {caller_name} must be"
umap-learn>                     f" {constraints_str}. Got {param_val!r} instead."
umap-learn>                 )
umap-learn> E               sklearn.utils._param_validation.InvalidParameterError: The 'random_state' parameter of SpectralEmbedding must be an int in the range [0, 4294967295], an instance of 'numpy.random.mtrand.RandomState' or None. Got <module 'numpy.random' from '/nix/store/jirzlk538n9bbbwp54rd9wcsb8nf3rf0-python3.10-numpy-1.23.5/lib/python3.10/site-packages/numpy/random/__init__.py'> instead.
umap-learn> 
umap-learn> /nix/store/5l733c5yjbmkd53d0nd35aln9jmpka8y-python3.10-scikit-learn-1.2.1/lib/python3.10/site-packages/sklearn/utils/_param_validation.py:97: InvalidParameterError
umap-learn> =============================== warnings summary ===============================
umap-learn> umap/tests/test_chunked_parallel_spatial_metric.py:234
umap-learn>   /build/source/umap/tests/test_chunked_parallel_spatial_metric.py:234: PytestUnknownMarkWarning: Unknown pytest.mark.benchmark - is this a typo?  You can register custom marks to avoid this warning - for details, see https://docs.pytest.org/en/stable/how-to/mark.html
umap-learn>     @pytest.mark.benchmark(
umap-learn> 
umap-learn> umap/tests/test_chunked_parallel_spatial_metric.py:258
umap-learn>   /build/source/umap/tests/test_chunked_parallel_spatial_metric.py:258: PytestUnknownMarkWarning: Unknown pytest.mark.benchmark - is this a typo?  You can register custom marks to avoid this warning - for details, see https://docs.pytest.org/en/stable/how-to/mark.html
umap-learn>     @pytest.mark.benchmark(
umap-learn> 
umap-learn> umap/tests/test_chunked_parallel_spatial_metric.py:288
umap-learn>   /build/source/umap/tests/test_chunked_parallel_spatial_metric.py:288: PytestUnknownMarkWarning: Unknown pytest.mark.benchmark - is this a typo?  You can register custom marks to avoid this warning - for details, see https://docs.pytest.org/en/stable/how-to/mark.html
umap-learn>     @pytest.mark.benchmark(
umap-learn> 
umap-learn> umap/tests/test_chunked_parallel_spatial_metric.py:312
umap-learn>   /build/source/umap/tests/test_chunked_parallel_spatial_metric.py:312: PytestUnknownMarkWarning: Unknown pytest.mark.benchmark - is this a typo?  You can register custom marks to avoid this warning - for details, see https://docs.pytest.org/en/stable/how-to/mark.html
umap-learn>     @pytest.mark.benchmark(
umap-learn> 
umap-learn> umap/plot.py:20
umap-learn>   /build/source/umap/plot.py:20: UserWarning: The umap.plot package requires extra plotting libraries to be installed.
umap-learn>       You can install these via pip using
umap-learn>   
umap-learn>       pip install umap-learn[plot]
umap-learn>   
umap-learn>       or via conda using
umap-learn>   
umap-learn>        conda install pandas matplotlib datashader bokeh holoviews colorcet scikit-image
umap-learn>       
umap-learn>     warn(
umap-learn> 
umap-learn> umap/tests/test_aligned_umap.py::test_local_clustering
umap-learn> umap/tests/test_aligned_umap.py::test_local_clustering
umap-learn> umap/tests/test_umap_on_iris.py::test_umap_clusterability_on_supervised_iris
umap-learn> umap/tests/test_umap_ops.py::test_blobs_cluster
umap-learn> umap/tests/test_umap_ops.py::test_multi_component_layout
umap-learn> umap/tests/test_umap_ops.py::test_multi_component_layout_precomputed
umap-learn>   /nix/store/5l733c5yjbmkd53d0nd35aln9jmpka8y-python3.10-scikit-learn-1.2.1/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning
umap-learn>     warnings.warn(
umap-learn> 
umap-learn> umap/tests/test_aligned_umap.py::test_aligned_update
umap-learn>   /build/source/umap/aligned_umap.py:188: NumbaTypeSafetyWarning: unsafe cast from int64 to int32. Precision may be lost.
umap-learn>     if i in relation_dict:
umap-learn> 
umap-learn> umap/tests/test_parametric_umap.py::test_create_model
umap-learn> umap/tests/test_parametric_umap.py::test_global_loss
umap-learn> umap/tests/test_parametric_umap.py::test_inverse_transform
umap-learn> umap/tests/test_parametric_umap.py::test_nonparametric
umap-learn> umap/tests/test_parametric_umap.py::test_custom_encoder_decoder
umap-learn> umap/tests/test_parametric_umap.py::test_validation
umap-learn>   /build/source/umap/parametric_umap.py:148: UserWarning: tensorflow_probability not installed or incompatible to current                 tensorflow installation. Setting global_correlation_loss_weight to zero.
umap-learn>     warn(
umap-learn> 
umap-learn> umap/tests/test_parametric_umap.py::test_custom_encoder_decoder
umap-learn> umap/tests/test_parametric_umap.py::test_validation
umap-learn>   /build/source/umap/parametric_umap.py:375: UserWarning: Data should be scaled to the range 0-1 for cross-entropy reconstruction loss.
umap-learn>     warn(
umap-learn> 
umap-learn> umap/tests/test_umap_metrics.py::test_hellinger
umap-learn>   /build/source/umap/tests/test_umap_metrics.py:405: RuntimeWarning: invalid value encountered in sqrt
umap-learn>     dist_matrix = np.sqrt(dist_matrix)
umap-learn> 
umap-learn> umap/tests/test_umap_on_iris.py::test_precomputed_transform_on_iris
umap-learn> umap/tests/test_umap_on_iris.py::test_precomputed_sparse_transform_on_iris
umap-learn> umap/tests/test_umap_ops.py::test_multi_component_layout_precomputed
umap-learn> umap/tests/test_umap_ops.py::test_disconnected_data_precomputed[True-1]
umap-learn> umap/tests/test_umap_ops.py::test_disconnected_data_precomputed[False-1]
umap-learn> umap/tests/test_umap_trustworthiness.py::test_sparse_precomputed_metric_umap_trustworthiness
umap-learn> umap/tests/test_umap_validation_params.py::test_umap_update_bad_params
umap-learn>   /build/source/umap/umap_.py:1780: UserWarning: using precomputed metric; inverse_transform will be unavailable
umap-learn>     warn("using precomputed metric; inverse_transform will be unavailable")
umap-learn> 
umap-learn> umap/tests/test_umap_on_iris.py::test_precomputed_transform_on_iris
umap-learn> umap/tests/test_umap_on_iris.py::test_precomputed_sparse_transform_on_iris
umap-learn>   /build/source/umap/umap_.py:2830: UserWarning: Transforming new data with precomputed metric. We are assuming the input data is a matrix of distances from the new points to the points in the training set. If the input matrix is sparse, it should contain distances from the new points to their nearest neighbours or approximate nearest neighbours in the training set.
umap-learn>     warn(
umap-learn> 
umap-learn> umap/tests/test_umap_on_iris.py::test_precomputed_sparse_transform_on_iris
umap-learn> umap/tests/test_umap_ops.py::test_disconnected_data_precomputed[True-1]
umap-learn> umap/tests/test_umap_trustworthiness.py::test_sparse_precomputed_metric_umap_trustworthiness
umap-learn>   /build/source/umap/umap_.py:2378: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.
umap-learn>   Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations
umap-learn>     (X.shape[0], self.n_neighbors), dtype=np.int
umap-learn> 
umap-learn> umap/tests/test_umap_on_iris.py::test_precomputed_sparse_transform_on_iris
umap-learn> umap/tests/test_umap_ops.py::test_disconnected_data_precomputed[True-1]
umap-learn> umap/tests/test_umap_trustworthiness.py::test_sparse_precomputed_metric_umap_trustworthiness
umap-learn>   /build/source/umap/umap_.py:2380: DeprecationWarning: `np.float` is a deprecated alias for the builtin `float`. To silence this warning, use `float` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.float64` here.
umap-learn>   Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations
umap-learn>     self._knn_dists = np.zeros(self._knn_indices.shape, dtype=np.float)
umap-learn> 
umap-learn> umap/tests/test_umap_ops.py::test_multi_component_layout
umap-learn> umap/tests/test_umap_ops.py::test_multi_component_layout_precomputed
umap-learn>   /nix/store/5l733c5yjbmkd53d0nd35aln9jmpka8y-python3.10-scikit-learn-1.2.1/lib/python3.10/site-packages/sklearn/manifold/_spectral_embedding.py:274: UserWarning: Graph is not fully connected, spectral embedding may not work as expected.
umap-learn>     warnings.warn(
umap-learn> 
umap-learn> umap/tests/test_umap_ops.py::test_disconnected_data[True-jaccard-1]
umap-learn> umap/tests/test_umap_ops.py::test_disconnected_data[True-jaccard-5]
umap-learn> umap/tests/test_umap_ops.py::test_disconnected_data[True-hellinger-1]
umap-learn> umap/tests/test_umap_ops.py::test_disconnected_data[True-hellinger-5]
umap-learn> umap/tests/test_umap_ops.py::test_disconnected_data[False-jaccard-1]
umap-learn> umap/tests/test_umap_ops.py::test_disconnected_data[False-jaccard-5]
umap-learn> umap/tests/test_umap_ops.py::test_disconnected_data[False-hellinger-1]
umap-learn> umap/tests/test_umap_ops.py::test_disconnected_data[False-hellinger-5]
umap-learn>   /nix/store/z5pkmmsdg3bmb35pmsv4rjca1qi7dbnf-python3.10-pytest-7.2.0/lib/python3.10/site-packages/_pytest/python.py:195: PytestRemovedIn8Warning: Passing None has been deprecated.
umap-learn>   See https://docs.pytest.org/en/latest/how-to/capture-warnings.html#additional-use-cases-of-warnings-in-tests for alternatives in common use cases.
umap-learn>     result = testfunction(**testargs)
umap-learn> 
umap-learn> umap/tests/test_umap_ops.py::test_disconnected_data_precomputed[True-1]
umap-learn>   /build/source/umap/umap_.py:125: UserWarning: A few of your vertices were disconnected from the manifold.  This shouldn't cause problems.
umap-learn>   Disconnection_distance = 1 has removed 3 edges.
umap-learn>   It has only fully disconnected 1 vertices.
umap-learn>   Use umap.utils.disconnected_vertices() to identify them.
umap-learn>     warn(
umap-learn> 
umap-learn> umap/tests/test_umap_ops.py::test_disconnected_data_precomputed[False-1]
umap-learn>   /build/source/umap/umap_.py:125: UserWarning: A few of your vertices were disconnected from the manifold.  This shouldn't cause problems.
umap-learn>   Disconnection_distance = 1 has removed 22 edges.
umap-learn>   It has only fully disconnected 1 vertices.
umap-learn>   Use umap.utils.disconnected_vertices() to identify them.
umap-learn>     warn(
umap-learn> 
umap-learn> umap/tests/test_umap_repeated_data.py::test_repeated_points_large_sparse_spatial
umap-learn> umap/tests/test_umap_repeated_data.py::test_repeated_points_small_sparse_spatial
umap-learn> umap/tests/test_umap_repeated_data.py::test_repeated_points_large_sparse_binary
umap-learn> umap/tests/test_umap_repeated_data.py::test_repeated_points_small_sparse_binary
umap-learn>   /nix/store/jirzlk538n9bbbwp54rd9wcsb8nf3rf0-python3.10-numpy-1.23.5/lib/python3.10/site-packages/numpy/lib/arraysetops.py:272: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray.
umap-learn>     ar = np.asanyarray(ar)
umap-learn> 
umap-learn> umap/tests/test_umap_repeated_data.py::test_repeated_points_large_n
umap-learn> umap/tests/test_umap_validation_params.py::test_umap_too_many_neighbors_warns
umap-learn> umap/tests/test_umap_validation_params.py::test_umap_inverse_transform_fails_expectedly
umap-learn>   /build/source/umap/umap_.py:2344: UserWarning: n_neighbors is larger than the dataset size; truncating to X.shape[0] - 1
umap-learn>     warn(
umap-learn> 
umap-learn> umap/tests/test_umap_validation_params.py::test_umap_inverse_transform_fails_expectedly
umap-learn>   /build/source/umap/umap_.py:1802: UserWarning: gradient function is not yet implemented for dice distance metric; inverse_transform will be unavailable
umap-learn>     warn(
umap-learn> 
umap-learn> -- Docs: https://docs.pytest.org/en/stable/how-to/capture-warnings.html
umap-learn> =========================== short test summary info ============================
umap-learn> FAILED umap/tests/test_umap_metrics.py::test_sparse_euclidean - TypeError: np.matrix is not supported. Please convert to a numpy array with...
umap-learn> FAILED umap/tests/test_umap_metrics.py::test_sparse_manhattan - TypeError: np.matrix is not supported. Please convert to a numpy array with...
umap-learn> FAILED umap/tests/test_umap_metrics.py::test_sparse_chebyshev - TypeError: np.matrix is not supported. Please convert to a numpy array with...
umap-learn> FAILED umap/tests/test_umap_metrics.py::test_sparse_minkowski - TypeError: np.matrix is not supported. Please convert to a numpy array with...
umap-learn> FAILED umap/tests/test_umap_metrics.py::test_sparse_hamming - TypeError: np.matrix is not supported. Please convert to a numpy array with...
umap-learn> FAILED umap/tests/test_umap_metrics.py::test_sparse_canberra - TypeError: np.matrix is not supported. Please convert to a numpy array with...
umap-learn> FAILED umap/tests/test_umap_metrics.py::test_sparse_cosine - TypeError: np.matrix is not supported. Please convert to a numpy array with...
umap-learn> FAILED umap/tests/test_umap_metrics.py::test_sparse_correlation - TypeError: np.matrix is not supported. Please convert to a numpy array with...
umap-learn> FAILED umap/tests/test_umap_metrics.py::test_sparse_braycurtis - TypeError: np.matrix is not supported. Please convert to a numpy array with...
umap-learn> FAILED umap/tests/test_umap_metrics.py::test_sparse_jaccard - TypeError: np.matrix is not supported. Please convert to a numpy array with...
umap-learn> FAILED umap/tests/test_umap_metrics.py::test_sparse_matching - TypeError: np.matrix is not supported. Please convert to a numpy array with...
umap-learn> FAILED umap/tests/test_umap_metrics.py::test_sparse_dice - TypeError: np.matrix is not supported. Please convert to a numpy array with...
umap-learn> FAILED umap/tests/test_umap_metrics.py::test_sparse_kulsinski - TypeError: np.matrix is not supported. Please convert to a numpy array with...
umap-learn> FAILED umap/tests/test_umap_metrics.py::test_sparse_rogerstanimoto - TypeError: np.matrix is not supported. Please convert to a numpy array with...
umap-learn> FAILED umap/tests/test_umap_metrics.py::test_sparse_russellrao - TypeError: np.matrix is not supported. Please convert to a numpy array with...
umap-learn> FAILED umap/tests/test_umap_metrics.py::test_sparse_sokalmichener - TypeError: np.matrix is not supported. Please convert to a numpy array with...
umap-learn> FAILED umap/tests/test_umap_metrics.py::test_sparse_sokalsneath - TypeError: np.matrix is not supported. Please convert to a numpy array with...
umap-learn> FAILED umap/tests/test_umap_ops.py::test_component_layout_options - sklearn.utils._param_validation.InvalidParameterError: The 'random_state' p...
umap-learn> = 18 failed, 126 passed, 51 skipped, 4 deselected, 56 warnings in 575.50s (0:09:35) =