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gistfile1.txt
pynndescent> ============================= test session starts ==============================
pynndescent> platform linux -- Python 3.10.9, pytest-7.2.0, pluggy-1.0.0
pynndescent> rootdir: /build/pynndescent-0.5.8
pynndescent> collected 143 items / 4 deselected / 139 selected
pynndescent>
pynndescent> pynndescent/tests/test_distances.py ..................FFFFFFFFF......... [ 25%]
pynndescent> ......... [ 32%]
pynndescent> pynndescent/tests/test_pynndescent_.py ................................. [ 56%]
pynndescent> ........................ [ 73%]
pynndescent> pynndescent/tests/test_rank.py ..................................... [100%]
pynndescent>
pynndescent> =================================== FAILURES ===================================
pynndescent> _____________________ test_sparse_spatial_check[euclidean] _____________________
pynndescent>
pynndescent> sparse_spatial_data = <12x20 sparse matrix of type '<class 'numpy.float32'>'
pynndescent> with 61 stored elements in Compressed Sparse Row format>
pynndescent> metric = 'euclidean', decimal = 6
pynndescent>
pynndescent> @pytest.mark.parametrize(
pynndescent> "metric",
pynndescent> [
pynndescent> "euclidean",
pynndescent> "manhattan",
pynndescent> "chebyshev",
pynndescent> "minkowski",
pynndescent> "hamming",
pynndescent> "canberra",
pynndescent> "cosine",
pynndescent> "braycurtis",
pynndescent> "correlation",
pynndescent> ],
pynndescent> )
pynndescent> def test_sparse_spatial_check(sparse_spatial_data, metric, decimal=6):
pynndescent> if metric in spdist.sparse_named_distances:
pynndescent> > dist_matrix = pairwise_distances(
pynndescent> sparse_spatial_data.todense().astype(np.float32), metric=metric
pynndescent> )
pynndescent>
pynndescent> pynndescent/tests/test_distances.py:111:
pynndescent> _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
pynndescent> /nix/store/cn7gxxzfngznz7kkwcmcaa379sg8w8vb-python3.10-scikit-learn-1.2.0/lib/python3.10/site-packages/sklearn/metrics/pairwise.py:2039: in pairwise_distances
pynndescent> return _parallel_pairwise(X, Y, func, n_jobs, **kwds)
pynndescent> /nix/store/cn7gxxzfngznz7kkwcmcaa379sg8w8vb-python3.10-scikit-learn-1.2.0/lib/python3.10/site-packages/sklearn/metrics/pairwise.py:1579: in _parallel_pairwise
pynndescent> return func(X, Y, **kwds)
pynndescent> /nix/store/cn7gxxzfngznz7kkwcmcaa379sg8w8vb-python3.10-scikit-learn-1.2.0/lib/python3.10/site-packages/sklearn/metrics/pairwise.py:300: in euclidean_distances
pynndescent> X, Y = check_pairwise_arrays(X, Y)
pynndescent> /nix/store/cn7gxxzfngznz7kkwcmcaa379sg8w8vb-python3.10-scikit-learn-1.2.0/lib/python3.10/site-packages/sklearn/metrics/pairwise.py:146: in check_pairwise_arrays
pynndescent> X = Y = check_array(
pynndescent> _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
pynndescent>
pynndescent> array = matrix([[ 0. , -0.02546946, 0. , 0. , 0.45841283,
pynndescent> 0. , 0. , 0. ... , 0. ,
pynndescent> 0. , 0. , 0. , 0. , 0. ]],
pynndescent> dtype=float32)
pynndescent> accept_sparse = 'csr'
pynndescent>
pynndescent> def check_array(
pynndescent> array,
pynndescent> accept_sparse=False,
pynndescent> *,
pynndescent> accept_large_sparse=True,
pynndescent> dtype="numeric",
pynndescent> order=None,
pynndescent> copy=False,
pynndescent> force_all_finite=True,
pynndescent> ensure_2d=True,
pynndescent> allow_nd=False,
pynndescent> ensure_min_samples=1,
pynndescent> ensure_min_features=1,
pynndescent> estimator=None,
pynndescent> input_name="",
pynndescent> ):
pynndescent>
pynndescent> """Input validation on an array, list, sparse matrix or similar.
pynndescent>
pynndescent> By default, the input is checked to be a non-empty 2D array containing
pynndescent> only finite values. If the dtype of the array is object, attempt
pynndescent> converting to float, raising on failure.
pynndescent>
pynndescent> Parameters
pynndescent> ----------
pynndescent> array : object
pynndescent> Input object to check / convert.
pynndescent>
pynndescent> accept_sparse : str, bool or list/tuple of str, default=False
pynndescent> String[s] representing allowed sparse matrix formats, such as 'csc',
pynndescent> 'csr', etc. If the input is sparse but not in the allowed format,
pynndescent> it will be converted to the first listed format. True allows the input
pynndescent> to be any format. False means that a sparse matrix input will
pynndescent> raise an error.
pynndescent>
pynndescent> accept_large_sparse : bool, default=True
pynndescent> If a CSR, CSC, COO or BSR sparse matrix is supplied and accepted by
pynndescent> accept_sparse, accept_large_sparse=False will cause it to be accepted
pynndescent> only if its indices are stored with a 32-bit dtype.
pynndescent>
pynndescent> .. versionadded:: 0.20
pynndescent>
pynndescent> dtype : 'numeric', type, list of type or None, default='numeric'
pynndescent> Data type of result. If None, the dtype of the input is preserved.
pynndescent> If "numeric", dtype is preserved unless array.dtype is object.
pynndescent> If dtype is a list of types, conversion on the first type is only
pynndescent> performed if the dtype of the input is not in the list.
pynndescent>
pynndescent> order : {'F', 'C'} or None, default=None
pynndescent> Whether an array will be forced to be fortran or c-style.
pynndescent> When order is None (default), then if copy=False, nothing is ensured
pynndescent> about the memory layout of the output array; otherwise (copy=True)
pynndescent> the memory layout of the returned array is kept as close as possible
pynndescent> to the original array.
pynndescent>
pynndescent> copy : bool, default=False
pynndescent> Whether a forced copy will be triggered. If copy=False, a copy might
pynndescent> be triggered by a conversion.
pynndescent>
pynndescent> force_all_finite : bool or 'allow-nan', default=True
pynndescent> Whether to raise an error on np.inf, np.nan, pd.NA in array. The
pynndescent> possibilities are:
pynndescent>
pynndescent> - True: Force all values of array to be finite.
pynndescent> - False: accepts np.inf, np.nan, pd.NA in array.
pynndescent> - 'allow-nan': accepts only np.nan and pd.NA values in array. Values
pynndescent> cannot be infinite.
pynndescent>
pynndescent> .. versionadded:: 0.20
pynndescent> ``force_all_finite`` accepts the string ``'allow-nan'``.
pynndescent>
pynndescent> .. versionchanged:: 0.23
pynndescent> Accepts `pd.NA` and converts it into `np.nan`
pynndescent>
pynndescent> ensure_2d : bool, default=True
pynndescent> Whether to raise a value error if array is not 2D.
pynndescent>
pynndescent> allow_nd : bool, default=False
pynndescent> Whether to allow array.ndim > 2.
pynndescent>
pynndescent> ensure_min_samples : int, default=1
pynndescent> Make sure that the array has a minimum number of samples in its first
pynndescent> axis (rows for a 2D array). Setting to 0 disables this check.
pynndescent>
pynndescent> ensure_min_features : int, default=1
pynndescent> Make sure that the 2D array has some minimum number of features
pynndescent> (columns). The default value of 1 rejects empty datasets.
pynndescent> This check is only enforced when the input data has effectively 2
pynndescent> dimensions or is originally 1D and ``ensure_2d`` is True. Setting to 0
pynndescent> disables this check.
pynndescent>
pynndescent> estimator : str or estimator instance, default=None
pynndescent> If passed, include the name of the estimator in warning messages.
pynndescent>
pynndescent> input_name : str, default=""
pynndescent> The data name used to construct the error message. In particular
pynndescent> if `input_name` is "X" and the data has NaN values and
pynndescent> allow_nan is False, the error message will link to the imputer
pynndescent> documentation.
pynndescent>
pynndescent> .. versionadded:: 1.1.0
pynndescent>
pynndescent> Returns
pynndescent> -------
pynndescent> array_converted : object
pynndescent> The converted and validated array.
pynndescent> """
pynndescent> if isinstance(array, np.matrix):
pynndescent> > raise TypeError(
pynndescent> "np.matrix is not supported. Please convert to a numpy array with "
pynndescent> "np.asarray. For more information see: "
pynndescent> "https://numpy.org/doc/stable/reference/generated/numpy.matrix.html"
pynndescent> )
pynndescent> 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
pynndescent>
pynndescent> /nix/store/cn7gxxzfngznz7kkwcmcaa379sg8w8vb-python3.10-scikit-learn-1.2.0/lib/python3.10/site-packages/sklearn/utils/validation.py:737: TypeError
pynndescent> _____________________ test_sparse_spatial_check[manhattan] _____________________
pynndescent>
pynndescent> sparse_spatial_data = <12x20 sparse matrix of type '<class 'numpy.float32'>'
pynndescent> with 72 stored elements in Compressed Sparse Row format>
pynndescent> metric = 'manhattan', decimal = 6
pynndescent>
pynndescent> @pytest.mark.parametrize(
pynndescent> "metric",
pynndescent> [
pynndescent> "euclidean",
pynndescent> "manhattan",
pynndescent> "chebyshev",
pynndescent> "minkowski",
pynndescent> "hamming",
pynndescent> "canberra",
pynndescent> "cosine",
pynndescent> "braycurtis",
pynndescent> "correlation",
pynndescent> ],
pynndescent> )
pynndescent> def test_sparse_spatial_check(sparse_spatial_data, metric, decimal=6):
pynndescent> if metric in spdist.sparse_named_distances:
pynndescent> > dist_matrix = pairwise_distances(
pynndescent> sparse_spatial_data.todense().astype(np.float32), metric=metric
pynndescent> )
pynndescent>
pynndescent> pynndescent/tests/test_distances.py:111:
pynndescent> _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
pynndescent> /nix/store/cn7gxxzfngznz7kkwcmcaa379sg8w8vb-python3.10-scikit-learn-1.2.0/lib/python3.10/site-packages/sklearn/metrics/pairwise.py:2039: in pairwise_distances
pynndescent> return _parallel_pairwise(X, Y, func, n_jobs, **kwds)
pynndescent> /nix/store/cn7gxxzfngznz7kkwcmcaa379sg8w8vb-python3.10-scikit-learn-1.2.0/lib/python3.10/site-packages/sklearn/metrics/pairwise.py:1579: in _parallel_pairwise
pynndescent> return func(X, Y, **kwds)
pynndescent> /nix/store/cn7gxxzfngznz7kkwcmcaa379sg8w8vb-python3.10-scikit-learn-1.2.0/lib/python3.10/site-packages/sklearn/metrics/pairwise.py:948: in manhattan_distances
pynndescent> X, Y = check_pairwise_arrays(X, Y)
pynndescent> /nix/store/cn7gxxzfngznz7kkwcmcaa379sg8w8vb-python3.10-scikit-learn-1.2.0/lib/python3.10/site-packages/sklearn/metrics/pairwise.py:146: in check_pairwise_arrays
pynndescent> X = Y = check_array(
pynndescent> _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
pynndescent>
pynndescent> array = matrix([[-1.6336606 , -0.32145873, 0. , 0. , 0. ,
pynndescent> 1.3702277 , 0. , -0.711960... , 0. ,
pynndescent> 0. , 0. , 0. , 0. , 0. ]],
pynndescent> dtype=float32)
pynndescent> accept_sparse = 'csr'
pynndescent>
pynndescent> def check_array(
pynndescent> array,
pynndescent> accept_sparse=False,
pynndescent> *,
pynndescent> accept_large_sparse=True,
pynndescent> dtype="numeric",
pynndescent> order=None,
pynndescent> copy=False,
pynndescent> force_all_finite=True,
pynndescent> ensure_2d=True,
pynndescent> allow_nd=False,
pynndescent> ensure_min_samples=1,
pynndescent> ensure_min_features=1,
pynndescent> estimator=None,
pynndescent> input_name="",
pynndescent> ):
pynndescent>
pynndescent> """Input validation on an array, list, sparse matrix or similar.
pynndescent>
pynndescent> By default, the input is checked to be a non-empty 2D array containing
pynndescent> only finite values. If the dtype of the array is object, attempt
pynndescent> converting to float, raising on failure.
pynndescent>
pynndescent> Parameters
pynndescent> ----------
pynndescent> array : object
pynndescent> Input object to check / convert.
pynndescent>
pynndescent> accept_sparse : str, bool or list/tuple of str, default=False
pynndescent> String[s] representing allowed sparse matrix formats, such as 'csc',
pynndescent> 'csr', etc. If the input is sparse but not in the allowed format,
pynndescent> it will be converted to the first listed format. True allows the input
pynndescent> to be any format. False means that a sparse matrix input will
pynndescent> raise an error.
pynndescent>
pynndescent> accept_large_sparse : bool, default=True
pynndescent> If a CSR, CSC, COO or BSR sparse matrix is supplied and accepted by
pynndescent> accept_sparse, accept_large_sparse=False will cause it to be accepted
pynndescent> only if its indices are stored with a 32-bit dtype.
pynndescent>
pynndescent> .. versionadded:: 0.20
pynndescent>
pynndescent> dtype : 'numeric', type, list of type or None, default='numeric'
pynndescent> Data type of result. If None, the dtype of the input is preserved.
pynndescent> If "numeric", dtype is preserved unless array.dtype is object.
pynndescent> If dtype is a list of types, conversion on the first type is only
pynndescent> performed if the dtype of the input is not in the list.
pynndescent>
pynndescent> order : {'F', 'C'} or None, default=None
pynndescent> Whether an array will be forced to be fortran or c-style.
pynndescent> When order is None (default), then if copy=False, nothing is ensured
pynndescent> about the memory layout of the output array; otherwise (copy=True)
pynndescent> the memory layout of the returned array is kept as close as possible
pynndescent> to the original array.
pynndescent>
pynndescent> copy : bool, default=False
pynndescent> Whether a forced copy will be triggered. If copy=False, a copy might
pynndescent> be triggered by a conversion.
pynndescent>
pynndescent> force_all_finite : bool or 'allow-nan', default=True
pynndescent> Whether to raise an error on np.inf, np.nan, pd.NA in array. The
pynndescent> possibilities are:
pynndescent>
pynndescent> - True: Force all values of array to be finite.
pynndescent> - False: accepts np.inf, np.nan, pd.NA in array.
pynndescent> - 'allow-nan': accepts only np.nan and pd.NA values in array. Values
pynndescent> cannot be infinite.
pynndescent>
pynndescent> .. versionadded:: 0.20
pynndescent> ``force_all_finite`` accepts the string ``'allow-nan'``.
pynndescent>
pynndescent> .. versionchanged:: 0.23
pynndescent> Accepts `pd.NA` and converts it into `np.nan`
pynndescent>
pynndescent> ensure_2d : bool, default=True
pynndescent> Whether to raise a value error if array is not 2D.
pynndescent>
pynndescent> allow_nd : bool, default=False
pynndescent> Whether to allow array.ndim > 2.
pynndescent>
pynndescent> ensure_min_samples : int, default=1
pynndescent> Make sure that the array has a minimum number of samples in its first
pynndescent> axis (rows for a 2D array). Setting to 0 disables this check.
pynndescent>
pynndescent> ensure_min_features : int, default=1
pynndescent> Make sure that the 2D array has some minimum number of features
pynndescent> (columns). The default value of 1 rejects empty datasets.
pynndescent> This check is only enforced when the input data has effectively 2
pynndescent> dimensions or is originally 1D and ``ensure_2d`` is True. Setting to 0
pynndescent> disables this check.
pynndescent>
pynndescent> estimator : str or estimator instance, default=None
pynndescent> If passed, include the name of the estimator in warning messages.
pynndescent>
pynndescent> input_name : str, default=""
pynndescent> The data name used to construct the error message. In particular
pynndescent> if `input_name` is "X" and the data has NaN values and
pynndescent> allow_nan is False, the error message will link to the imputer
pynndescent> documentation.
pynndescent>
pynndescent> .. versionadded:: 1.1.0
pynndescent>
pynndescent> Returns
pynndescent> -------
pynndescent> array_converted : object
pynndescent> The converted and validated array.
pynndescent> """
pynndescent> if isinstance(array, np.matrix):
pynndescent> > raise TypeError(
pynndescent> "np.matrix is not supported. Please convert to a numpy array with "
pynndescent> "np.asarray. For more information see: "
pynndescent> "https://numpy.org/doc/stable/reference/generated/numpy.matrix.html"
pynndescent> )
pynndescent> 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
pynndescent>
pynndescent> /nix/store/cn7gxxzfngznz7kkwcmcaa379sg8w8vb-python3.10-scikit-learn-1.2.0/lib/python3.10/site-packages/sklearn/utils/validation.py:737: TypeError
pynndescent> _____________________ test_sparse_spatial_check[chebyshev] _____________________
pynndescent>
pynndescent> sparse_spatial_data = <12x20 sparse matrix of type '<class 'numpy.float32'>'
pynndescent> with 63 stored elements in Compressed Sparse Row format>
pynndescent> metric = 'chebyshev', decimal = 6
pynndescent>
pynndescent> @pytest.mark.parametrize(
pynndescent> "metric",
pynndescent> [
pynndescent> "euclidean",
pynndescent> "manhattan",
pynndescent> "chebyshev",
pynndescent> "minkowski",
pynndescent> "hamming",
pynndescent> "canberra",
pynndescent> "cosine",
pynndescent> "braycurtis",
pynndescent> "correlation",
pynndescent> ],
pynndescent> )
pynndescent> def test_sparse_spatial_check(sparse_spatial_data, metric, decimal=6):
pynndescent> if metric in spdist.sparse_named_distances:
pynndescent> > dist_matrix = pairwise_distances(
pynndescent> sparse_spatial_data.todense().astype(np.float32), metric=metric
pynndescent> )
pynndescent>
pynndescent> pynndescent/tests/test_distances.py:111:
pynndescent> _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
pynndescent> /nix/store/cn7gxxzfngznz7kkwcmcaa379sg8w8vb-python3.10-scikit-learn-1.2.0/lib/python3.10/site-packages/sklearn/metrics/pairwise.py:2027: in pairwise_distances
pynndescent> X, Y = check_pairwise_arrays(
pynndescent> /nix/store/cn7gxxzfngznz7kkwcmcaa379sg8w8vb-python3.10-scikit-learn-1.2.0/lib/python3.10/site-packages/sklearn/metrics/pairwise.py:146: in check_pairwise_arrays
pynndescent> X = Y = check_array(
pynndescent> _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
pynndescent>
pynndescent> array = matrix([[ 0. , 0. , 0. , 0.11813139, 0. ,
pynndescent> 0. , -0.21905616, 0.417503... , 0. ,
pynndescent> 0. , 0. , 0. , 0. , 0. ]],
pynndescent> dtype=float32)
pynndescent> accept_sparse = 'csr'
pynndescent>
pynndescent> def check_array(
pynndescent> array,
pynndescent> accept_sparse=False,
pynndescent> *,
pynndescent> accept_large_sparse=True,
pynndescent> dtype="numeric",
pynndescent> order=None,
pynndescent> copy=False,
pynndescent> force_all_finite=True,
pynndescent> ensure_2d=True,
pynndescent> allow_nd=False,
pynndescent> ensure_min_samples=1,
pynndescent> ensure_min_features=1,
pynndescent> estimator=None,
pynndescent> input_name="",
pynndescent> ):
pynndescent>
pynndescent> """Input validation on an array, list, sparse matrix or similar.
pynndescent>
pynndescent> By default, the input is checked to be a non-empty 2D array containing
pynndescent> only finite values. If the dtype of the array is object, attempt
pynndescent> converting to float, raising on failure.
pynndescent>
pynndescent> Parameters
pynndescent> ----------
pynndescent> array : object
pynndescent> Input object to check / convert.
pynndescent>
pynndescent> accept_sparse : str, bool or list/tuple of str, default=False
pynndescent> String[s] representing allowed sparse matrix formats, such as 'csc',
pynndescent> 'csr', etc. If the input is sparse but not in the allowed format,
pynndescent> it will be converted to the first listed format. True allows the input
pynndescent> to be any format. False means that a sparse matrix input will
pynndescent> raise an error.
pynndescent>
pynndescent> accept_large_sparse : bool, default=True
pynndescent> If a CSR, CSC, COO or BSR sparse matrix is supplied and accepted by
pynndescent> accept_sparse, accept_large_sparse=False will cause it to be accepted
pynndescent> only if its indices are stored with a 32-bit dtype.
pynndescent>
pynndescent> .. versionadded:: 0.20
pynndescent>
pynndescent> dtype : 'numeric', type, list of type or None, default='numeric'
pynndescent> Data type of result. If None, the dtype of the input is preserved.
pynndescent> If "numeric", dtype is preserved unless array.dtype is object.
pynndescent> If dtype is a list of types, conversion on the first type is only
pynndescent> performed if the dtype of the input is not in the list.
pynndescent>
pynndescent> order : {'F', 'C'} or None, default=None
pynndescent> Whether an array will be forced to be fortran or c-style.
pynndescent> When order is None (default), then if copy=False, nothing is ensured
pynndescent> about the memory layout of the output array; otherwise (copy=True)
pynndescent> the memory layout of the returned array is kept as close as possible
pynndescent> to the original array.
pynndescent>
pynndescent> copy : bool, default=False
pynndescent> Whether a forced copy will be triggered. If copy=False, a copy might
pynndescent> be triggered by a conversion.
pynndescent>
pynndescent> force_all_finite : bool or 'allow-nan', default=True
pynndescent> Whether to raise an error on np.inf, np.nan, pd.NA in array. The
pynndescent> possibilities are:
pynndescent>
pynndescent> - True: Force all values of array to be finite.
pynndescent> - False: accepts np.inf, np.nan, pd.NA in array.
pynndescent> - 'allow-nan': accepts only np.nan and pd.NA values in array. Values
pynndescent> cannot be infinite.
pynndescent>
pynndescent> .. versionadded:: 0.20
pynndescent> ``force_all_finite`` accepts the string ``'allow-nan'``.
pynndescent>
pynndescent> .. versionchanged:: 0.23
pynndescent> Accepts `pd.NA` and converts it into `np.nan`
pynndescent>
pynndescent> ensure_2d : bool, default=True
pynndescent> Whether to raise a value error if array is not 2D.
pynndescent>
pynndescent> allow_nd : bool, default=False
pynndescent> Whether to allow array.ndim > 2.
pynndescent>
pynndescent> ensure_min_samples : int, default=1
pynndescent> Make sure that the array has a minimum number of samples in its first
pynndescent> axis (rows for a 2D array). Setting to 0 disables this check.
pynndescent>
pynndescent> ensure_min_features : int, default=1
pynndescent> Make sure that the 2D array has some minimum number of features
pynndescent> (columns). The default value of 1 rejects empty datasets.
pynndescent> This check is only enforced when the input data has effectively 2
pynndescent> dimensions or is originally 1D and ``ensure_2d`` is True. Setting to 0
pynndescent> disables this check.
pynndescent>
pynndescent> estimator : str or estimator instance, default=None
pynndescent> If passed, include the name of the estimator in warning messages.
pynndescent>
pynndescent> input_name : str, default=""
pynndescent> The data name used to construct the error message. In particular
pynndescent> if `input_name` is "X" and the data has NaN values and
pynndescent> allow_nan is False, the error message will link to the imputer
pynndescent> documentation.
pynndescent>
pynndescent> .. versionadded:: 1.1.0
pynndescent>
pynndescent> Returns
pynndescent> -------
pynndescent> array_converted : object
pynndescent> The converted and validated array.
pynndescent> """
pynndescent> if isinstance(array, np.matrix):
pynndescent> > raise TypeError(
pynndescent> "np.matrix is not supported. Please convert to a numpy array with "
pynndescent> "np.asarray. For more information see: "
pynndescent> "https://numpy.org/doc/stable/reference/generated/numpy.matrix.html"
pynndescent> )
pynndescent> 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
pynndescent>
pynndescent> /nix/store/cn7gxxzfngznz7kkwcmcaa379sg8w8vb-python3.10-scikit-learn-1.2.0/lib/python3.10/site-packages/sklearn/utils/validation.py:737: TypeError
pynndescent> _____________________ test_sparse_spatial_check[minkowski] _____________________
pynndescent>
pynndescent> sparse_spatial_data = <12x20 sparse matrix of type '<class 'numpy.float32'>'
pynndescent> with 82 stored elements in Compressed Sparse Row format>
pynndescent> metric = 'minkowski', decimal = 6
pynndescent>
pynndescent> @pytest.mark.parametrize(
pynndescent> "metric",
pynndescent> [
pynndescent> "euclidean",
pynndescent> "manhattan",
pynndescent> "chebyshev",
pynndescent> "minkowski",
pynndescent> "hamming",
pynndescent> "canberra",
pynndescent> "cosine",
pynndescent> "braycurtis",
pynndescent> "correlation",
pynndescent> ],
pynndescent> )
pynndescent> def test_sparse_spatial_check(sparse_spatial_data, metric, decimal=6):
pynndescent> if metric in spdist.sparse_named_distances:
pynndescent> > dist_matrix = pairwise_distances(
pynndescent> sparse_spatial_data.todense().astype(np.float32), metric=metric
pynndescent> )
pynndescent>
pynndescent> pynndescent/tests/test_distances.py:111:
pynndescent> _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
pynndescent> /nix/store/cn7gxxzfngznz7kkwcmcaa379sg8w8vb-python3.10-scikit-learn-1.2.0/lib/python3.10/site-packages/sklearn/metrics/pairwise.py:2027: in pairwise_distances
pynndescent> X, Y = check_pairwise_arrays(
pynndescent> /nix/store/cn7gxxzfngznz7kkwcmcaa379sg8w8vb-python3.10-scikit-learn-1.2.0/lib/python3.10/site-packages/sklearn/metrics/pairwise.py:146: in check_pairwise_arrays
pynndescent> X = Y = check_array(
pynndescent> _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
pynndescent>
pynndescent> array = matrix([[ 0.0000000e+00, 0.0000000e+00, -1.0411386e+00, 0.0000000e+00,
pynndescent> -5.6272256e-01, 0.0000000e+00, 0.0...0e+00, 0.0000000e+00,
pynndescent> 0.0000000e+00, 0.0000000e+00, 0.0000000e+00, 0.0000000e+00]],
pynndescent> dtype=float32)
pynndescent> accept_sparse = 'csr'
pynndescent>
pynndescent> def check_array(
pynndescent> array,
pynndescent> accept_sparse=False,
pynndescent> *,
pynndescent> accept_large_sparse=True,
pynndescent> dtype="numeric",
pynndescent> order=None,
pynndescent> copy=False,
pynndescent> force_all_finite=True,
pynndescent> ensure_2d=True,
pynndescent> allow_nd=False,
pynndescent> ensure_min_samples=1,
pynndescent> ensure_min_features=1,
pynndescent> estimator=None,
pynndescent> input_name="",
pynndescent> ):
pynndescent>
pynndescent> """Input validation on an array, list, sparse matrix or similar.
pynndescent>
pynndescent> By default, the input is checked to be a non-empty 2D array containing
pynndescent> only finite values. If the dtype of the array is object, attempt
pynndescent> converting to float, raising on failure.
pynndescent>
pynndescent> Parameters
pynndescent> ----------
pynndescent> array : object
pynndescent> Input object to check / convert.
pynndescent>
pynndescent> accept_sparse : str, bool or list/tuple of str, default=False
pynndescent> String[s] representing allowed sparse matrix formats, such as 'csc',
pynndescent> 'csr', etc. If the input is sparse but not in the allowed format,
pynndescent> it will be converted to the first listed format. True allows the input
pynndescent> to be any format. False means that a sparse matrix input will
pynndescent> raise an error.
pynndescent>
pynndescent> accept_large_sparse : bool, default=True
pynndescent> If a CSR, CSC, COO or BSR sparse matrix is supplied and accepted by
pynndescent> accept_sparse, accept_large_sparse=False will cause it to be accepted
pynndescent> only if its indices are stored with a 32-bit dtype.
pynndescent>
pynndescent> .. versionadded:: 0.20
pynndescent>
pynndescent> dtype : 'numeric', type, list of type or None, default='numeric'
pynndescent> Data type of result. If None, the dtype of the input is preserved.
pynndescent> If "numeric", dtype is preserved unless array.dtype is object.
pynndescent> If dtype is a list of types, conversion on the first type is only
pynndescent> performed if the dtype of the input is not in the list.
pynndescent>
pynndescent> order : {'F', 'C'} or None, default=None
pynndescent> Whether an array will be forced to be fortran or c-style.
pynndescent> When order is None (default), then if copy=False, nothing is ensured
pynndescent> about the memory layout of the output array; otherwise (copy=True)
pynndescent> the memory layout of the returned array is kept as close as possible
pynndescent> to the original array.
pynndescent>
pynndescent> copy : bool, default=False
pynndescent> Whether a forced copy will be triggered. If copy=False, a copy might
pynndescent> be triggered by a conversion.
pynndescent>
pynndescent> force_all_finite : bool or 'allow-nan', default=True
pynndescent> Whether to raise an error on np.inf, np.nan, pd.NA in array. The
pynndescent> possibilities are:
pynndescent>
pynndescent> - True: Force all values of array to be finite.
pynndescent> - False: accepts np.inf, np.nan, pd.NA in array.
pynndescent> - 'allow-nan': accepts only np.nan and pd.NA values in array. Values
pynndescent> cannot be infinite.
pynndescent>
pynndescent> .. versionadded:: 0.20
pynndescent> ``force_all_finite`` accepts the string ``'allow-nan'``.
pynndescent>
pynndescent> .. versionchanged:: 0.23
pynndescent> Accepts `pd.NA` and converts it into `np.nan`
pynndescent>
pynndescent> ensure_2d : bool, default=True
pynndescent> Whether to raise a value error if array is not 2D.
pynndescent>
pynndescent> allow_nd : bool, default=False
pynndescent> Whether to allow array.ndim > 2.
pynndescent>
pynndescent> ensure_min_samples : int, default=1
pynndescent> Make sure that the array has a minimum number of samples in its first
pynndescent> axis (rows for a 2D array). Setting to 0 disables this check.
pynndescent>
pynndescent> ensure_min_features : int, default=1
pynndescent> Make sure that the 2D array has some minimum number of features
pynndescent> (columns). The default value of 1 rejects empty datasets.
pynndescent> This check is only enforced when the input data has effectively 2
pynndescent> dimensions or is originally 1D and ``ensure_2d`` is True. Setting to 0
pynndescent> disables this check.
pynndescent>
pynndescent> estimator : str or estimator instance, default=None
pynndescent> If passed, include the name of the estimator in warning messages.
pynndescent>
pynndescent> input_name : str, default=""
pynndescent> The data name used to construct the error message. In particular
pynndescent> if `input_name` is "X" and the data has NaN values and
pynndescent> allow_nan is False, the error message will link to the imputer
pynndescent> documentation.
pynndescent>
pynndescent> .. versionadded:: 1.1.0
pynndescent>
pynndescent> Returns
pynndescent> -------
pynndescent> array_converted : object
pynndescent> The converted and validated array.
pynndescent> """
pynndescent> if isinstance(array, np.matrix):
pynndescent> > raise TypeError(
pynndescent> "np.matrix is not supported. Please convert to a numpy array with "
pynndescent> "np.asarray. For more information see: "
pynndescent> "https://numpy.org/doc/stable/reference/generated/numpy.matrix.html"
pynndescent> )
pynndescent> 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
pynndescent>
pynndescent> /nix/store/cn7gxxzfngznz7kkwcmcaa379sg8w8vb-python3.10-scikit-learn-1.2.0/lib/python3.10/site-packages/sklearn/utils/validation.py:737: TypeError
pynndescent> ______________________ test_sparse_spatial_check[hamming] ______________________
pynndescent>
pynndescent> sparse_spatial_data = <12x20 sparse matrix of type '<class 'numpy.float32'>'
pynndescent> with 62 stored elements in Compressed Sparse Row format>
pynndescent> metric = 'hamming', decimal = 6
pynndescent>
pynndescent> @pytest.mark.parametrize(
pynndescent> "metric",
pynndescent> [
pynndescent> "euclidean",
pynndescent> "manhattan",
pynndescent> "chebyshev",
pynndescent> "minkowski",
pynndescent> "hamming",
pynndescent> "canberra",
pynndescent> "cosine",
pynndescent> "braycurtis",
pynndescent> "correlation",
pynndescent> ],
pynndescent> )
pynndescent> def test_sparse_spatial_check(sparse_spatial_data, metric, decimal=6):
pynndescent> if metric in spdist.sparse_named_distances:
pynndescent> > dist_matrix = pairwise_distances(
pynndescent> sparse_spatial_data.todense().astype(np.float32), metric=metric
pynndescent> )
pynndescent>
pynndescent> pynndescent/tests/test_distances.py:111:
pynndescent> _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
pynndescent> /nix/store/cn7gxxzfngznz7kkwcmcaa379sg8w8vb-python3.10-scikit-learn-1.2.0/lib/python3.10/site-packages/sklearn/metrics/pairwise.py:2027: in pairwise_distances
pynndescent> X, Y = check_pairwise_arrays(
pynndescent> /nix/store/cn7gxxzfngznz7kkwcmcaa379sg8w8vb-python3.10-scikit-learn-1.2.0/lib/python3.10/site-packages/sklearn/metrics/pairwise.py:146: in check_pairwise_arrays
pynndescent> X = Y = check_array(
pynndescent> _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
pynndescent>
pynndescent> array = matrix([[ 0. , 0. , 0. , 0. , 0. ,
pynndescent> 0. , 0. , 0. ... , 0. ,
pynndescent> 0. , 0. , 0. , 0. , 0. ]],
pynndescent> dtype=float32)
pynndescent> accept_sparse = 'csr'
pynndescent>
pynndescent> def check_array(
pynndescent> array,
pynndescent> accept_sparse=False,
pynndescent> *,
pynndescent> accept_large_sparse=True,
pynndescent> dtype="numeric",
pynndescent> order=None,
pynndescent> copy=False,
pynndescent> force_all_finite=True,
pynndescent> ensure_2d=True,
pynndescent> allow_nd=False,
pynndescent> ensure_min_samples=1,
pynndescent> ensure_min_features=1,
pynndescent> estimator=None,
pynndescent> input_name="",
pynndescent> ):
pynndescent>
pynndescent> """Input validation on an array, list, sparse matrix or similar.
pynndescent>
pynndescent> By default, the input is checked to be a non-empty 2D array containing
pynndescent> only finite values. If the dtype of the array is object, attempt
pynndescent> converting to float, raising on failure.
pynndescent>
pynndescent> Parameters
pynndescent> ----------
pynndescent> array : object
pynndescent> Input object to check / convert.
pynndescent>
pynndescent> accept_sparse : str, bool or list/tuple of str, default=False
pynndescent> String[s] representing allowed sparse matrix formats, such as 'csc',
pynndescent> 'csr', etc. If the input is sparse but not in the allowed format,
pynndescent> it will be converted to the first listed format. True allows the input
pynndescent> to be any format. False means that a sparse matrix input will
pynndescent> raise an error.
pynndescent>
pynndescent> accept_large_sparse : bool, default=True
pynndescent> If a CSR, CSC, COO or BSR sparse matrix is supplied and accepted by
pynndescent> accept_sparse, accept_large_sparse=False will cause it to be accepted
pynndescent> only if its indices are stored with a 32-bit dtype.
pynndescent>
pynndescent> .. versionadded:: 0.20
pynndescent>
pynndescent> dtype : 'numeric', type, list of type or None, default='numeric'
pynndescent> Data type of result. If None, the dtype of the input is preserved.
pynndescent> If "numeric", dtype is preserved unless array.dtype is object.
pynndescent> If dtype is a list of types, conversion on the first type is only
pynndescent> performed if the dtype of the input is not in the list.
pynndescent>
pynndescent> order : {'F', 'C'} or None, default=None
pynndescent> Whether an array will be forced to be fortran or c-style.
pynndescent> When order is None (default), then if copy=False, nothing is ensured
pynndescent> about the memory layout of the output array; otherwise (copy=True)
pynndescent> the memory layout of the returned array is kept as close as possible
pynndescent> to the original array.
pynndescent>
pynndescent> copy : bool, default=False
pynndescent> Whether a forced copy will be triggered. If copy=False, a copy might
pynndescent> be triggered by a conversion.
pynndescent>
pynndescent> force_all_finite : bool or 'allow-nan', default=True
pynndescent> Whether to raise an error on np.inf, np.nan, pd.NA in array. The
pynndescent> possibilities are:
pynndescent>
pynndescent> - True: Force all values of array to be finite.
pynndescent> - False: accepts np.inf, np.nan, pd.NA in array.
pynndescent> - 'allow-nan': accepts only np.nan and pd.NA values in array. Values
pynndescent> cannot be infinite.
pynndescent>
pynndescent> .. versionadded:: 0.20
pynndescent> ``force_all_finite`` accepts the string ``'allow-nan'``.
pynndescent>
pynndescent> .. versionchanged:: 0.23
pynndescent> Accepts `pd.NA` and converts it into `np.nan`
pynndescent>
pynndescent> ensure_2d : bool, default=True
pynndescent> Whether to raise a value error if array is not 2D.
pynndescent>
pynndescent> allow_nd : bool, default=False
pynndescent> Whether to allow array.ndim > 2.
pynndescent>
pynndescent> ensure_min_samples : int, default=1
pynndescent> Make sure that the array has a minimum number of samples in its first
pynndescent> axis (rows for a 2D array). Setting to 0 disables this check.
pynndescent>
pynndescent> ensure_min_features : int, default=1
pynndescent> Make sure that the 2D array has some minimum number of features
pynndescent> (columns). The default value of 1 rejects empty datasets.
pynndescent> This check is only enforced when the input data has effectively 2
pynndescent> dimensions or is originally 1D and ``ensure_2d`` is True. Setting to 0
pynndescent> disables this check.
pynndescent>
pynndescent> estimator : str or estimator instance, default=None
pynndescent> If passed, include the name of the estimator in warning messages.
pynndescent>
pynndescent> input_name : str, default=""
pynndescent> The data name used to construct the error message. In particular
pynndescent> if `input_name` is "X" and the data has NaN values and
pynndescent> allow_nan is False, the error message will link to the imputer
pynndescent> documentation.
pynndescent>
pynndescent> .. versionadded:: 1.1.0
pynndescent>
pynndescent> Returns
pynndescent> -------
pynndescent> array_converted : object
pynndescent> The converted and validated array.
pynndescent> """
pynndescent> if isinstance(array, np.matrix):
pynndescent> > raise TypeError(
pynndescent> "np.matrix is not supported. Please convert to a numpy array with "
pynndescent> "np.asarray. For more information see: "
pynndescent> "https://numpy.org/doc/stable/reference/generated/numpy.matrix.html"
pynndescent> )
pynndescent> 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
pynndescent>
pynndescent> /nix/store/cn7gxxzfngznz7kkwcmcaa379sg8w8vb-python3.10-scikit-learn-1.2.0/lib/python3.10/site-packages/sklearn/utils/validation.py:737: TypeError
pynndescent> _____________________ test_sparse_spatial_check[canberra] ______________________
pynndescent>
pynndescent> sparse_spatial_data = <12x20 sparse matrix of type '<class 'numpy.float32'>'
pynndescent> with 81 stored elements in Compressed Sparse Row format>
pynndescent> metric = 'canberra', decimal = 6
pynndescent>
pynndescent> @pytest.mark.parametrize(
pynndescent> "metric",
pynndescent> [
pynndescent> "euclidean",
pynndescent> "manhattan",
pynndescent> "chebyshev",
pynndescent> "minkowski",
pynndescent> "hamming",
pynndescent> "canberra",
pynndescent> "cosine",
pynndescent> "braycurtis",
pynndescent> "correlation",
pynndescent> ],
pynndescent> )
pynndescent> def test_sparse_spatial_check(sparse_spatial_data, metric, decimal=6):
pynndescent> if metric in spdist.sparse_named_distances:
pynndescent> > dist_matrix = pairwise_distances(
pynndescent> sparse_spatial_data.todense().astype(np.float32), metric=metric
pynndescent> )
pynndescent>
pynndescent> pynndescent/tests/test_distances.py:111:
pynndescent> _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
pynndescent> /nix/store/cn7gxxzfngznz7kkwcmcaa379sg8w8vb-python3.10-scikit-learn-1.2.0/lib/python3.10/site-packages/sklearn/metrics/pairwise.py:2027: in pairwise_distances
pynndescent> X, Y = check_pairwise_arrays(
pynndescent> /nix/store/cn7gxxzfngznz7kkwcmcaa379sg8w8vb-python3.10-scikit-learn-1.2.0/lib/python3.10/site-packages/sklearn/metrics/pairwise.py:146: in check_pairwise_arrays
pynndescent> X = Y = check_array(
pynndescent> _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
pynndescent>
pynndescent> array = matrix([[ 0. , 0. , 0. , -0.14346407, 0. ,
pynndescent> 0.75328195, 0.39030677, -2.304208... , 0. ,
pynndescent> 0. , 0. , 0. , 0. , 0. ]],
pynndescent> dtype=float32)
pynndescent> accept_sparse = 'csr'
pynndescent>
pynndescent> def check_array(
pynndescent> array,
pynndescent> accept_sparse=False,
pynndescent> *,
pynndescent> accept_large_sparse=True,
pynndescent> dtype="numeric",
pynndescent> order=None,
pynndescent> copy=False,
pynndescent> force_all_finite=True,
pynndescent> ensure_2d=True,
pynndescent> allow_nd=False,
pynndescent> ensure_min_samples=1,
pynndescent> ensure_min_features=1,
pynndescent> estimator=None,
pynndescent> input_name="",
pynndescent> ):
pynndescent>
pynndescent> """Input validation on an array, list, sparse matrix or similar.
pynndescent>
pynndescent> By default, the input is checked to be a non-empty 2D array containing
pynndescent> only finite values. If the dtype of the array is object, attempt
pynndescent> converting to float, raising on failure.
pynndescent>
pynndescent> Parameters
pynndescent> ----------
pynndescent> array : object
pynndescent> Input object to check / convert.
pynndescent>
pynndescent> accept_sparse : str, bool or list/tuple of str, default=False
pynndescent> String[s] representing allowed sparse matrix formats, such as 'csc',
pynndescent> 'csr', etc. If the input is sparse but not in the allowed format,
pynndescent> it will be converted to the first listed format. True allows the input
pynndescent> to be any format. False means that a sparse matrix input will
pynndescent> raise an error.
pynndescent>
pynndescent> accept_large_sparse : bool, default=True
pynndescent> If a CSR, CSC, COO or BSR sparse matrix is supplied and accepted by
pynndescent> accept_sparse, accept_large_sparse=False will cause it to be accepted
pynndescent> only if its indices are stored with a 32-bit dtype.
pynndescent>
pynndescent> .. versionadded:: 0.20
pynndescent>
pynndescent> dtype : 'numeric', type, list of type or None, default='numeric'
pynndescent> Data type of result. If None, the dtype of the input is preserved.
pynndescent> If "numeric", dtype is preserved unless array.dtype is object.
pynndescent> If dtype is a list of types, conversion on the first type is only
pynndescent> performed if the dtype of the input is not in the list.
pynndescent>
pynndescent> order : {'F', 'C'} or None, default=None
pynndescent> Whether an array will be forced to be fortran or c-style.
pynndescent> When order is None (default), then if copy=False, nothing is ensured
pynndescent> about the memory layout of the output array; otherwise (copy=True)
pynndescent> the memory layout of the returned array is kept as close as possible
pynndescent> to the original array.
pynndescent>
pynndescent> copy : bool, default=False
pynndescent> Whether a forced copy will be triggered. If copy=False, a copy might
pynndescent> be triggered by a conversion.
pynndescent>
pynndescent> force_all_finite : bool or 'allow-nan', default=True
pynndescent> Whether to raise an error on np.inf, np.nan, pd.NA in array. The
pynndescent> possibilities are:
pynndescent>
pynndescent> - True: Force all values of array to be finite.
pynndescent> - False: accepts np.inf, np.nan, pd.NA in array.
pynndescent> - 'allow-nan': accepts only np.nan and pd.NA values in array. Values
pynndescent> cannot be infinite.
pynndescent>
pynndescent> .. versionadded:: 0.20
pynndescent> ``force_all_finite`` accepts the string ``'allow-nan'``.
pynndescent>
pynndescent> .. versionchanged:: 0.23
pynndescent> Accepts `pd.NA` and converts it into `np.nan`
pynndescent>
pynndescent> ensure_2d : bool, default=True
pynndescent> Whether to raise a value error if array is not 2D.
pynndescent>
pynndescent> allow_nd : bool, default=False
pynndescent> Whether to allow array.ndim > 2.
pynndescent>
pynndescent> ensure_min_samples : int, default=1
pynndescent> Make sure that the array has a minimum number of samples in its first
pynndescent> axis (rows for a 2D array). Setting to 0 disables this check.
pynndescent>
pynndescent> ensure_min_features : int, default=1
pynndescent> Make sure that the 2D array has some minimum number of features
pynndescent> (columns). The default value of 1 rejects empty datasets.
pynndescent> This check is only enforced when the input data has effectively 2
pynndescent> dimensions or is originally 1D and ``ensure_2d`` is True. Setting to 0
pynndescent> disables this check.
pynndescent>
pynndescent> estimator : str or estimator instance, default=None
pynndescent> If passed, include the name of the estimator in warning messages.
pynndescent>
pynndescent> input_name : str, default=""
pynndescent> The data name used to construct the error message. In particular
pynndescent> if `input_name` is "X" and the data has NaN values and
pynndescent> allow_nan is False, the error message will link to the imputer
pynndescent> documentation.
pynndescent>
pynndescent> .. versionadded:: 1.1.0
pynndescent>
pynndescent> Returns
pynndescent> -------
pynndescent> array_converted : object
pynndescent> The converted and validated array.
pynndescent> """
pynndescent> if isinstance(array, np.matrix):
pynndescent> > raise TypeError(
pynndescent> "np.matrix is not supported. Please convert to a numpy array with "
pynndescent> "np.asarray. For more information see: "
pynndescent> "https://numpy.org/doc/stable/reference/generated/numpy.matrix.html"
pynndescent> )
pynndescent> 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
pynndescent>
pynndescent> /nix/store/cn7gxxzfngznz7kkwcmcaa379sg8w8vb-python3.10-scikit-learn-1.2.0/lib/python3.10/site-packages/sklearn/utils/validation.py:737: TypeError
pynndescent> ______________________ test_sparse_spatial_check[cosine] _______________________
pynndescent>
pynndescent> sparse_spatial_data = <12x20 sparse matrix of type '<class 'numpy.float32'>'
pynndescent> with 69 stored elements in Compressed Sparse Row format>
pynndescent> metric = 'cosine', decimal = 6
pynndescent>
pynndescent> @pytest.mark.parametrize(
pynndescent> "metric",
pynndescent> [
pynndescent> "euclidean",
pynndescent> "manhattan",
pynndescent> "chebyshev",
pynndescent> "minkowski",
pynndescent> "hamming",
pynndescent> "canberra",
pynndescent> "cosine",
pynndescent> "braycurtis",
pynndescent> "correlation",
pynndescent> ],
pynndescent> )
pynndescent> def test_sparse_spatial_check(sparse_spatial_data, metric, decimal=6):
pynndescent> if metric in spdist.sparse_named_distances:
pynndescent> > dist_matrix = pairwise_distances(
pynndescent> sparse_spatial_data.todense().astype(np.float32), metric=metric
pynndescent> )
pynndescent>
pynndescent> pynndescent/tests/test_distances.py:111:
pynndescent> _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
pynndescent> /nix/store/cn7gxxzfngznz7kkwcmcaa379sg8w8vb-python3.10-scikit-learn-1.2.0/lib/python3.10/site-packages/sklearn/metrics/pairwise.py:2039: in pairwise_distances
pynndescent> return _parallel_pairwise(X, Y, func, n_jobs, **kwds)
pynndescent> /nix/store/cn7gxxzfngznz7kkwcmcaa379sg8w8vb-python3.10-scikit-learn-1.2.0/lib/python3.10/site-packages/sklearn/metrics/pairwise.py:1579: in _parallel_pairwise
pynndescent> return func(X, Y, **kwds)
pynndescent> /nix/store/cn7gxxzfngznz7kkwcmcaa379sg8w8vb-python3.10-scikit-learn-1.2.0/lib/python3.10/site-packages/sklearn/metrics/pairwise.py:1000: in cosine_distances
pynndescent> S = cosine_similarity(X, Y)
pynndescent> /nix/store/cn7gxxzfngznz7kkwcmcaa379sg8w8vb-python3.10-scikit-learn-1.2.0/lib/python3.10/site-packages/sklearn/metrics/pairwise.py:1393: in cosine_similarity
pynndescent> X, Y = check_pairwise_arrays(X, Y)
pynndescent> /nix/store/cn7gxxzfngznz7kkwcmcaa379sg8w8vb-python3.10-scikit-learn-1.2.0/lib/python3.10/site-packages/sklearn/metrics/pairwise.py:146: in check_pairwise_arrays
pynndescent> X = Y = check_array(
pynndescent> _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
pynndescent>
pynndescent> array = matrix([[ 0.7063732 , 0. , 0. , 0. , 0. ,
pynndescent> 0. , 0. , 0. ... , 0. ,
pynndescent> 0. , 0. , 0. , 0. , 0. ]],
pynndescent> dtype=float32)
pynndescent> accept_sparse = 'csr'
pynndescent>
pynndescent> def check_array(
pynndescent> array,
pynndescent> accept_sparse=False,
pynndescent> *,
pynndescent> accept_large_sparse=True,
pynndescent> dtype="numeric",
pynndescent> order=None,
pynndescent> copy=False,
pynndescent> force_all_finite=True,
pynndescent> ensure_2d=True,
pynndescent> allow_nd=False,
pynndescent> ensure_min_samples=1,
pynndescent> ensure_min_features=1,
pynndescent> estimator=None,
pynndescent> input_name="",
pynndescent> ):
pynndescent>
pynndescent> """Input validation on an array, list, sparse matrix or similar.
pynndescent>
pynndescent> By default, the input is checked to be a non-empty 2D array containing
pynndescent> only finite values. If the dtype of the array is object, attempt
pynndescent> converting to float, raising on failure.
pynndescent>
pynndescent> Parameters
pynndescent> ----------
pynndescent> array : object
pynndescent> Input object to check / convert.
pynndescent>
pynndescent> accept_sparse : str, bool or list/tuple of str, default=False
pynndescent> String[s] representing allowed sparse matrix formats, such as 'csc',
pynndescent> 'csr', etc. If the input is sparse but not in the allowed format,
pynndescent> it will be converted to the first listed format. True allows the input
pynndescent> to be any format. False means that a sparse matrix input will
pynndescent> raise an error.
pynndescent>
pynndescent> accept_large_sparse : bool, default=True
pynndescent> If a CSR, CSC, COO or BSR sparse matrix is supplied and accepted by
pynndescent> accept_sparse, accept_large_sparse=False will cause it to be accepted
pynndescent> only if its indices are stored with a 32-bit dtype.
pynndescent>
pynndescent> .. versionadded:: 0.20
pynndescent>
pynndescent> dtype : 'numeric', type, list of type or None, default='numeric'
pynndescent> Data type of result. If None, the dtype of the input is preserved.
pynndescent> If "numeric", dtype is preserved unless array.dtype is object.
pynndescent> If dtype is a list of types, conversion on the first type is only
pynndescent> performed if the dtype of the input is not in the list.
pynndescent>
pynndescent> order : {'F', 'C'} or None, default=None
pynndescent> Whether an array will be forced to be fortran or c-style.
pynndescent> When order is None (default), then if copy=False, nothing is ensured
pynndescent> about the memory layout of the output array; otherwise (copy=True)
pynndescent> the memory layout of the returned array is kept as close as possible
pynndescent> to the original array.
pynndescent>
pynndescent> copy : bool, default=False
pynndescent> Whether a forced copy will be triggered. If copy=False, a copy might
pynndescent> be triggered by a conversion.
pynndescent>
pynndescent> force_all_finite : bool or 'allow-nan', default=True
pynndescent> Whether to raise an error on np.inf, np.nan, pd.NA in array. The
pynndescent> possibilities are:
pynndescent>
pynndescent> - True: Force all values of array to be finite.
pynndescent> - False: accepts np.inf, np.nan, pd.NA in array.
pynndescent> - 'allow-nan': accepts only np.nan and pd.NA values in array. Values
pynndescent> cannot be infinite.
pynndescent>
pynndescent> .. versionadded:: 0.20
pynndescent> ``force_all_finite`` accepts the string ``'allow-nan'``.
pynndescent>
pynndescent> .. versionchanged:: 0.23
pynndescent> Accepts `pd.NA` and converts it into `np.nan`
pynndescent>
pynndescent> ensure_2d : bool, default=True
pynndescent> Whether to raise a value error if array is not 2D.
pynndescent>
pynndescent> allow_nd : bool, default=False
pynndescent> Whether to allow array.ndim > 2.
pynndescent>
pynndescent> ensure_min_samples : int, default=1
pynndescent> Make sure that the array has a minimum number of samples in its first
pynndescent> axis (rows for a 2D array). Setting to 0 disables this check.
pynndescent>
pynndescent> ensure_min_features : int, default=1
pynndescent> Make sure that the 2D array has some minimum number of features
pynndescent> (columns). The default value of 1 rejects empty datasets.
pynndescent> This check is only enforced when the input data has effectively 2
pynndescent> dimensions or is originally 1D and ``ensure_2d`` is True. Setting to 0
pynndescent> disables this check.
pynndescent>
pynndescent> estimator : str or estimator instance, default=None
pynndescent> If passed, include the name of the estimator in warning messages.
pynndescent>
pynndescent> input_name : str, default=""
pynndescent> The data name used to construct the error message. In particular
pynndescent> if `input_name` is "X" and the data has NaN values and
pynndescent> allow_nan is False, the error message will link to the imputer
pynndescent> documentation.
pynndescent>
pynndescent> .. versionadded:: 1.1.0
pynndescent>
pynndescent> Returns
pynndescent> -------
pynndescent> array_converted : object
pynndescent> The converted and validated array.
pynndescent> """
pynndescent> if isinstance(array, np.matrix):
pynndescent> > raise TypeError(
pynndescent> "np.matrix is not supported. Please convert to a numpy array with "
pynndescent> "np.asarray. For more information see: "
pynndescent> "https://numpy.org/doc/stable/reference/generated/numpy.matrix.html"
pynndescent> )
pynndescent> 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
pynndescent>
pynndescent> /nix/store/cn7gxxzfngznz7kkwcmcaa379sg8w8vb-python3.10-scikit-learn-1.2.0/lib/python3.10/site-packages/sklearn/utils/validation.py:737: TypeError
pynndescent> ____________________ test_sparse_spatial_check[braycurtis] _____________________
pynndescent>
pynndescent> sparse_spatial_data = <12x20 sparse matrix of type '<class 'numpy.float32'>'
pynndescent> with 64 stored elements in Compressed Sparse Row format>
pynndescent> metric = 'braycurtis', decimal = 6
pynndescent>
pynndescent> @pytest.mark.parametrize(
pynndescent> "metric",
pynndescent> [
pynndescent> "euclidean",
pynndescent> "manhattan",
pynndescent> "chebyshev",
pynndescent> "minkowski",
pynndescent> "hamming",
pynndescent> "canberra",
pynndescent> "cosine",
pynndescent> "braycurtis",
pynndescent> "correlation",
pynndescent> ],
pynndescent> )
pynndescent> def test_sparse_spatial_check(sparse_spatial_data, metric, decimal=6):
pynndescent> if metric in spdist.sparse_named_distances:
pynndescent> > dist_matrix = pairwise_distances(
pynndescent> sparse_spatial_data.todense().astype(np.float32), metric=metric
pynndescent> )
pynndescent>
pynndescent> pynndescent/tests/test_distances.py:111:
pynndescent> _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
pynndescent> /nix/store/cn7gxxzfngznz7kkwcmcaa379sg8w8vb-python3.10-scikit-learn-1.2.0/lib/python3.10/site-packages/sklearn/metrics/pairwise.py:2027: in pairwise_distances
pynndescent> X, Y = check_pairwise_arrays(
pynndescent> /nix/store/cn7gxxzfngznz7kkwcmcaa379sg8w8vb-python3.10-scikit-learn-1.2.0/lib/python3.10/site-packages/sklearn/metrics/pairwise.py:146: in check_pairwise_arrays
pynndescent> X = Y = check_array(
pynndescent> _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
pynndescent>
pynndescent> array = matrix([[ 0. , 0. , 0.08100726, 0. , -0.64761156,
pynndescent> 0. , 0. , 0. ... , 0. ,
pynndescent> 0. , 0. , 0. , 0. , 0. ]],
pynndescent> dtype=float32)
pynndescent> accept_sparse = 'csr'
pynndescent>
pynndescent> def check_array(
pynndescent> array,
pynndescent> accept_sparse=False,
pynndescent> *,
pynndescent> accept_large_sparse=True,
pynndescent> dtype="numeric",
pynndescent> order=None,
pynndescent> copy=False,
pynndescent> force_all_finite=True,
pynndescent> ensure_2d=True,
pynndescent> allow_nd=False,
pynndescent> ensure_min_samples=1,
pynndescent> ensure_min_features=1,
pynndescent> estimator=None,
pynndescent> input_name="",
pynndescent> ):
pynndescent>
pynndescent> """Input validation on an array, list, sparse matrix or similar.
pynndescent>
pynndescent> By default, the input is checked to be a non-empty 2D array containing
pynndescent> only finite values. If the dtype of the array is object, attempt
pynndescent> converting to float, raising on failure.
pynndescent>
pynndescent> Parameters
pynndescent> ----------
pynndescent> array : object
pynndescent> Input object to check / convert.
pynndescent>
pynndescent> accept_sparse : str, bool or list/tuple of str, default=False
pynndescent> String[s] representing allowed sparse matrix formats, such as 'csc',
pynndescent> 'csr', etc. If the input is sparse but not in the allowed format,
pynndescent> it will be converted to the first listed format. True allows the input
pynndescent> to be any format. False means that a sparse matrix input will
pynndescent> raise an error.
pynndescent>
pynndescent> accept_large_sparse : bool, default=True
pynndescent> If a CSR, CSC, COO or BSR sparse matrix is supplied and accepted by
pynndescent> accept_sparse, accept_large_sparse=False will cause it to be accepted
pynndescent> only if its indices are stored with a 32-bit dtype.
pynndescent>
pynndescent> .. versionadded:: 0.20
pynndescent>
pynndescent> dtype : 'numeric', type, list of type or None, default='numeric'
pynndescent> Data type of result. If None, the dtype of the input is preserved.
pynndescent> If "numeric", dtype is preserved unless array.dtype is object.
pynndescent> If dtype is a list of types, conversion on the first type is only
pynndescent> performed if the dtype of the input is not in the list.
pynndescent>
pynndescent> order : {'F', 'C'} or None, default=None
pynndescent> Whether an array will be forced to be fortran or c-style.
pynndescent> When order is None (default), then if copy=False, nothing is ensured
pynndescent> about the memory layout of the output array; otherwise (copy=True)
pynndescent> the memory layout of the returned array is kept as close as possible
pynndescent> to the original array.
pynndescent>
pynndescent> copy : bool, default=False
pynndescent> Whether a forced copy will be triggered. If copy=False, a copy might
pynndescent> be triggered by a conversion.
pynndescent>
pynndescent> force_all_finite : bool or 'allow-nan', default=True
pynndescent> Whether to raise an error on np.inf, np.nan, pd.NA in array. The
pynndescent> possibilities are:
pynndescent>
pynndescent> - True: Force all values of array to be finite.
pynndescent> - False: accepts np.inf, np.nan, pd.NA in array.
pynndescent> - 'allow-nan': accepts only np.nan and pd.NA values in array. Values
pynndescent> cannot be infinite.
pynndescent>
pynndescent> .. versionadded:: 0.20
pynndescent> ``force_all_finite`` accepts the string ``'allow-nan'``.
pynndescent>
pynndescent> .. versionchanged:: 0.23
pynndescent> Accepts `pd.NA` and converts it into `np.nan`
pynndescent>
pynndescent> ensure_2d : bool, default=True
pynndescent> Whether to raise a value error if array is not 2D.
pynndescent>
pynndescent> allow_nd : bool, default=False
pynndescent> Whether to allow array.ndim > 2.
pynndescent>
pynndescent> ensure_min_samples : int, default=1
pynndescent> Make sure that the array has a minimum number of samples in its first
pynndescent> axis (rows for a 2D array). Setting to 0 disables this check.
pynndescent>
pynndescent> ensure_min_features : int, default=1
pynndescent> Make sure that the 2D array has some minimum number of features
pynndescent> (columns). The default value of 1 rejects empty datasets.
pynndescent> This check is only enforced when the input data has effectively 2
pynndescent> dimensions or is originally 1D and ``ensure_2d`` is True. Setting to 0
pynndescent> disables this check.
pynndescent>
pynndescent> estimator : str or estimator instance, default=None
pynndescent> If passed, include the name of the estimator in warning messages.
pynndescent>
pynndescent> input_name : str, default=""
pynndescent> The data name used to construct the error message. In particular
pynndescent> if `input_name` is "X" and the data has NaN values and
pynndescent> allow_nan is False, the error message will link to the imputer
pynndescent> documentation.
pynndescent>
pynndescent> .. versionadded:: 1.1.0
pynndescent>
pynndescent> Returns
pynndescent> -------
pynndescent> array_converted : object
pynndescent> The converted and validated array.
pynndescent> """
pynndescent> if isinstance(array, np.matrix):
pynndescent> > raise TypeError(
pynndescent> "np.matrix is not supported. Please convert to a numpy array with "
pynndescent> "np.asarray. For more information see: "
pynndescent> "https://numpy.org/doc/stable/reference/generated/numpy.matrix.html"
pynndescent> )
pynndescent> 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
pynndescent>
pynndescent> /nix/store/cn7gxxzfngznz7kkwcmcaa379sg8w8vb-python3.10-scikit-learn-1.2.0/lib/python3.10/site-packages/sklearn/utils/validation.py:737: TypeError
pynndescent> ____________________ test_sparse_spatial_check[correlation] ____________________
pynndescent>
pynndescent> sparse_spatial_data = <12x20 sparse matrix of type '<class 'numpy.float32'>'
pynndescent> with 68 stored elements in Compressed Sparse Row format>
pynndescent> metric = 'correlation', decimal = 6
pynndescent>
pynndescent> @pytest.mark.parametrize(
pynndescent> "metric",
pynndescent> [
pynndescent> "euclidean",
pynndescent> "manhattan",
pynndescent> "chebyshev",
pynndescent> "minkowski",
pynndescent> "hamming",
pynndescent> "canberra",
pynndescent> "cosine",
pynndescent> "braycurtis",
pynndescent> "correlation",
pynndescent> ],
pynndescent> )
pynndescent> def test_sparse_spatial_check(sparse_spatial_data, metric, decimal=6):
pynndescent> if metric in spdist.sparse_named_distances:
pynndescent> > dist_matrix = pairwise_distances(
pynndescent> sparse_spatial_data.todense().astype(np.float32), metric=metric
pynndescent> )
pynndescent>
pynndescent> pynndescent/tests/test_distances.py:111:
pynndescent> _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
pynndescent> /nix/store/cn7gxxzfngznz7kkwcmcaa379sg8w8vb-python3.10-scikit-learn-1.2.0/lib/python3.10/site-packages/sklearn/metrics/pairwise.py:2027: in pairwise_distances
pynndescent> X, Y = check_pairwise_arrays(
pynndescent> /nix/store/cn7gxxzfngznz7kkwcmcaa379sg8w8vb-python3.10-scikit-learn-1.2.0/lib/python3.10/site-packages/sklearn/metrics/pairwise.py:146: in check_pairwise_arrays
pynndescent> X = Y = check_array(
pynndescent> _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
pynndescent>
pynndescent> array = matrix([[ 0.0000000e+00, 0.0000000e+00, 4.1397369e-01, -5.2132380e-01,
pynndescent> 1.3164551e+00, 0.0000000e+00, 0.0...0e+00, 0.0000000e+00,
pynndescent> 0.0000000e+00, 0.0000000e+00, 0.0000000e+00, 0.0000000e+00]],
pynndescent> dtype=float32)
pynndescent> accept_sparse = 'csr'
pynndescent>
pynndescent> def check_array(
pynndescent> array,
pynndescent> accept_sparse=False,
pynndescent> *,
pynndescent> accept_large_sparse=True,
pynndescent> dtype="numeric",
pynndescent> order=None,
pynndescent> copy=False,
pynndescent> force_all_finite=True,
pynndescent> ensure_2d=True,
pynndescent> allow_nd=False,
pynndescent> ensure_min_samples=1,
pynndescent> ensure_min_features=1,
pynndescent> estimator=None,
pynndescent> input_name="",
pynndescent> ):
pynndescent>
pynndescent> """Input validation on an array, list, sparse matrix or similar.
pynndescent>
pynndescent> By default, the input is checked to be a non-empty 2D array containing
pynndescent> only finite values. If the dtype of the array is object, attempt
pynndescent> converting to float, raising on failure.
pynndescent>
pynndescent> Parameters
pynndescent> ----------
pynndescent> array : object
pynndescent> Input object to check / convert.
pynndescent>
pynndescent> accept_sparse : str, bool or list/tuple of str, default=False
pynndescent> String[s] representing allowed sparse matrix formats, such as 'csc',
pynndescent> 'csr', etc. If the input is sparse but not in the allowed format,
pynndescent> it will be converted to the first listed format. True allows the input
pynndescent> to be any format. False means that a sparse matrix input will
pynndescent> raise an error.
pynndescent>
pynndescent> accept_large_sparse : bool, default=True
pynndescent> If a CSR, CSC, COO or BSR sparse matrix is supplied and accepted by
pynndescent> accept_sparse, accept_large_sparse=False will cause it to be accepted
pynndescent> only if its indices are stored with a 32-bit dtype.
pynndescent>
pynndescent> .. versionadded:: 0.20
pynndescent>
pynndescent> dtype : 'numeric', type, list of type or None, default='numeric'
pynndescent> Data type of result. If None, the dtype of the input is preserved.
pynndescent> If "numeric", dtype is preserved unless array.dtype is object.
pynndescent> If dtype is a list of types, conversion on the first type is only
pynndescent> performed if the dtype of the input is not in the list.
pynndescent>
pynndescent> order : {'F', 'C'} or None, default=None
pynndescent> Whether an array will be forced to be fortran or c-style.
pynndescent> When order is None (default), then if copy=False, nothing is ensured
pynndescent> about the memory layout of the output array; otherwise (copy=True)
pynndescent> the memory layout of the returned array is kept as close as possible
pynndescent> to the original array.
pynndescent>
pynndescent> copy : bool, default=False
pynndescent> Whether a forced copy will be triggered. If copy=False, a copy might
pynndescent> be triggered by a conversion.
pynndescent>
pynndescent> force_all_finite : bool or 'allow-nan', default=True
pynndescent> Whether to raise an error on np.inf, np.nan, pd.NA in array. The
pynndescent> possibilities are:
pynndescent>
pynndescent> - True: Force all values of array to be finite.
pynndescent> - False: accepts np.inf, np.nan, pd.NA in array.
pynndescent> - 'allow-nan': accepts only np.nan and pd.NA values in array. Values
pynndescent> cannot be infinite.
pynndescent>
pynndescent> .. versionadded:: 0.20
pynndescent> ``force_all_finite`` accepts the string ``'allow-nan'``.
pynndescent>
pynndescent> .. versionchanged:: 0.23
pynndescent> Accepts `pd.NA` and converts it into `np.nan`
pynndescent>
pynndescent> ensure_2d : bool, default=True
pynndescent> Whether to raise a value error if array is not 2D.
pynndescent>
pynndescent> allow_nd : bool, default=False
pynndescent> Whether to allow array.ndim > 2.
pynndescent>
pynndescent> ensure_min_samples : int, default=1
pynndescent> Make sure that the array has a minimum number of samples in its first
pynndescent> axis (rows for a 2D array). Setting to 0 disables this check.
pynndescent>
pynndescent> ensure_min_features : int, default=1
pynndescent> Make sure that the 2D array has some minimum number of features
pynndescent> (columns). The default value of 1 rejects empty datasets.
pynndescent> This check is only enforced when the input data has effectively 2
pynndescent> dimensions or is originally 1D and ``ensure_2d`` is True. Setting to 0
pynndescent> disables this check.
pynndescent>
pynndescent> estimator : str or estimator instance, default=None
pynndescent> If passed, include the name of the estimator in warning messages.
pynndescent>
pynndescent> input_name : str, default=""
pynndescent> The data name used to construct the error message. In particular
pynndescent> if `input_name` is "X" and the data has NaN values and
pynndescent> allow_nan is False, the error message will link to the imputer
pynndescent> documentation.
pynndescent>
pynndescent> .. versionadded:: 1.1.0
pynndescent>
pynndescent> Returns
pynndescent> -------
pynndescent> array_converted : object
pynndescent> The converted and validated array.
pynndescent> """
pynndescent> if isinstance(array, np.matrix):
pynndescent> > raise TypeError(
pynndescent> "np.matrix is not supported. Please convert to a numpy array with "
pynndescent> "np.asarray. For more information see: "
pynndescent> "https://numpy.org/doc/stable/reference/generated/numpy.matrix.html"
pynndescent> )
pynndescent> 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
pynndescent>
pynndescent> /nix/store/cn7gxxzfngznz7kkwcmcaa379sg8w8vb-python3.10-scikit-learn-1.2.0/lib/python3.10/site-packages/sklearn/utils/validation.py:737: TypeError
pynndescent> =========================== short test summary info ============================
pynndescent> FAILED pynndescent/tests/test_distances.py::test_sparse_spatial_check[euclidean] - TypeError: np.matrix is not supported. Please convert to a numpy array with...
pynndescent> FAILED pynndescent/tests/test_distances.py::test_sparse_spatial_check[manhattan] - TypeError: np.matrix is not supported. Please convert to a numpy array with...
pynndescent> FAILED pynndescent/tests/test_distances.py::test_sparse_spatial_check[chebyshev] - TypeError: np.matrix is not supported. Please convert to a numpy array with...
pynndescent> FAILED pynndescent/tests/test_distances.py::test_sparse_spatial_check[minkowski] - TypeError: np.matrix is not supported. Please convert to a numpy array with...
pynndescent> FAILED pynndescent/tests/test_distances.py::test_sparse_spatial_check[hamming] - TypeError: np.matrix is not supported. Please convert to a numpy array with...
pynndescent> FAILED pynndescent/tests/test_distances.py::test_sparse_spatial_check[canberra] - TypeError: np.matrix is not supported. Please convert to a numpy array with...
pynndescent> FAILED pynndescent/tests/test_distances.py::test_sparse_spatial_check[cosine] - TypeError: np.matrix is not supported. Please convert to a numpy array with...
pynndescent> FAILED pynndescent/tests/test_distances.py::test_sparse_spatial_check[braycurtis] - TypeError: np.matrix is not supported. Please convert to a numpy array with...
pynndescent> FAILED pynndescent/tests/test_distances.py::test_sparse_spatial_check[correlation] - TypeError: np.matrix is not supported. Please convert to a numpy array with...
pynndescent> =========== 9 failed, 130 passed, 4 deselected in 192.54s (0:03:12) ============
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