--- Begin Message ---
Source: umap-learn
Version: 0.4.5+dfsg-2
Severity: serious
Justification: FTBFS
Tags: bookworm sid ftbfs
User: [email protected]
Usertags: ftbfs-20211220 ftbfs-bookworm
Hi,
During a rebuild of all packages in sid, your package failed to build
on amd64.
Relevant part (hopefully):
> make[1]: Entering directory '/<<PKGBUILDDIR>>'
> dh_auto_test -- --system=custom --test-args="PYTHONPATH={build_dir}
> {interpreter} -m pytest"
> I: pybuild base:237: PYTHONPATH=/<<PKGBUILDDIR>>/.pybuild/cpython3_3.9/build
> python3.9 -m pytest
> ============================= test session starts
> ==============================
> platform linux -- Python 3.9.9, pytest-6.2.5, py-1.10.0, pluggy-0.13.0
> rootdir: /<<PKGBUILDDIR>>
> collected 128 items
>
> umap/tests/test_plot.py .xx [
> 2%]
> umap/tests/test_umap_df_validation_params.py ................... [
> 17%]
> umap/tests/test_umap_metrics.py ........................................ [
> 48%]
> . [
> 49%]
> umap/tests/test_umap_nn.py ............ [
> 58%]
> umap/tests/test_umap_on_iris.py FFFFFF.. [
> 64%]
> umap/tests/test_umap_ops.py .... [
> 67%]
> umap/tests/test_umap_repeated_data.py ......... [
> 75%]
> umap/tests/test_umap_trustworthiness.py FFFFFFFFF [
> 82%]
> umap/tests/test_umap_validation_params.py .......................
> [100%]
>
> =================================== FAILURES
> ===================================
> ______________________ test_umap_trustworthiness_on_iris
> _______________________
>
> iris = {'data': array([[5.1, 3.5, 1.4, 0.2],
> [4.9, 3. , 1.4, 0.2],
> [4.7, 3.2, 1.3, 0.2],
> [4.6, 3.1, 1.5,... width (cm)', 'petal length (cm)', 'petal width
> (cm)'], 'filename': 'iris.csv', 'data_module': 'sklearn.datasets.data'}
> iris_model = UMAP(min_dist=0.01, n_neighbors=10, random_state=42)
>
> def test_umap_trustworthiness_on_iris(iris, iris_model):
> embedding = iris_model.embedding_
> > trust = trustworthiness(iris.data, embedding, 10)
> E TypeError: trustworthiness() takes 2 positional arguments but 3 were
> given
>
> umap/tests/test_umap_on_iris.py:27: TypeError
> ________________ test_initialized_umap_trustworthiness_on_iris
> _________________
>
> iris = {'data': array([[5.1, 3.5, 1.4, 0.2],
> [4.9, 3. , 1.4, 0.2],
> [4.7, 3.2, 1.3, 0.2],
> [4.6, 3.1, 1.5,... width (cm)', 'petal length (cm)', 'petal width
> (cm)'], 'filename': 'iris.csv', 'data_module': 'sklearn.datasets.data'}
>
> def test_initialized_umap_trustworthiness_on_iris(iris):
> data = iris.data
> embedding = UMAP(
> n_neighbors=10, min_dist=0.01, init=data[:, 2:], n_epochs=200,
> random_state=42
> ).fit_transform(data)
> > trust = trustworthiness(iris.data, embedding, 10)
> E TypeError: trustworthiness() takes 2 positional arguments but 3 were
> given
>
> umap/tests/test_umap_on_iris.py:40: TypeError
> ___________________ test_umap_trustworthiness_on_sphere_iris
> ___________________
>
> iris = {'data': array([[5.1, 3.5, 1.4, 0.2],
> [4.9, 3. , 1.4, 0.2],
> [4.7, 3.2, 1.3, 0.2],
> [4.6, 3.1, 1.5,... width (cm)', 'petal length (cm)', 'petal width
> (cm)'], 'filename': 'iris.csv', 'data_module': 'sklearn.datasets.data'}
>
> def test_umap_trustworthiness_on_sphere_iris(iris,):
> data = iris.data
> embedding = UMAP(
> n_neighbors=10,
> min_dist=0.01,
> n_epochs=200,
> random_state=42,
> output_metric="haversine",
> ).fit_transform(data)
> # Since trustworthiness doesn't support haversine, project onto
> # a 3D embedding of the sphere and use cosine distance
> r = 3
> projected_embedding = np.vstack(
> [
> r * np.sin(embedding[:, 0]) * np.cos(embedding[:, 1]),
> r * np.sin(embedding[:, 0]) * np.sin(embedding[:, 1]),
> r * np.cos(embedding[:, 0]),
> ]
> ).T
> > trust = trustworthiness(iris.data, projected_embedding, 10,
> > metric="cosine")
> E TypeError: trustworthiness() takes 2 positional arguments but 3
> positional arguments (and 1 keyword-only argument) were given
>
> umap/tests/test_umap_on_iris.py:67: TypeError
> _________________________ test_umap_transform_on_iris
> __________________________
>
> iris = {'data': array([[5.1, 3.5, 1.4, 0.2],
> [4.9, 3. , 1.4, 0.2],
> [4.7, 3.2, 1.3, 0.2],
> [4.6, 3.1, 1.5,... width (cm)', 'petal length (cm)', 'petal width
> (cm)'], 'filename': 'iris.csv', 'data_module': 'sklearn.datasets.data'}
> iris_selection = array([ True, True, True, True, False, True, True,
> False, True,
> True, True, True, True, True, True,... True, True,
> True, True, True, True, True, False, False,
> True, True, False, True, False, False])
>
> def test_umap_transform_on_iris(iris, iris_selection):
> data = iris.data[iris_selection]
> fitter = UMAP(n_neighbors=10, min_dist=0.01, n_epochs=200,
> random_state=42).fit(
> data
> )
>
> new_data = iris.data[~iris_selection]
> embedding = fitter.transform(new_data)
>
> > trust = trustworthiness(new_data, embedding, 10)
> E TypeError: trustworthiness() takes 2 positional arguments but 3 were
> given
>
> umap/tests/test_umap_on_iris.py:88: TypeError
> __________________ test_umap_transform_on_iris_modified_dtype
> __________________
>
> iris = {'data': array([[5.1, 3.5, 1.4, 0.2],
> [4.9, 3. , 1.4, 0.2],
> [4.7, 3.2, 1.3, 0.2],
> [4.6, 3.1, 1.5,... width (cm)', 'petal length (cm)', 'petal width
> (cm)'], 'filename': 'iris.csv', 'data_module': 'sklearn.datasets.data'}
> iris_selection = array([ True, True, True, True, False, True, True,
> False, True,
> True, True, True, True, True, True,... True, True,
> True, True, True, True, True, False, False,
> True, True, False, True, False, False])
>
> def test_umap_transform_on_iris_modified_dtype(iris, iris_selection):
> data = iris.data[iris_selection]
> fitter = UMAP(n_neighbors=10, min_dist=0.01,
> random_state=42).fit(data)
> fitter.embedding_ = fitter.embedding_.astype(np.float64)
>
> new_data = iris.data[~iris_selection]
> embedding = fitter.transform(new_data)
>
> > trust = trustworthiness(new_data, embedding, 10)
> E TypeError: trustworthiness() takes 2 positional arguments but 3 were
> given
>
> umap/tests/test_umap_on_iris.py:104: TypeError
> ______________________ test_umap_sparse_transform_on_iris
> ______________________
>
> iris = {'data': array([[5.1, 3.5, 1.4, 0.2],
> [4.9, 3. , 1.4, 0.2],
> [4.7, 3.2, 1.3, 0.2],
> [4.6, 3.1, 1.5,... width (cm)', 'petal length (cm)', 'petal width
> (cm)'], 'filename': 'iris.csv', 'data_module': 'sklearn.datasets.data'}
> iris_selection = array([ True, True, True, True, False, True, True,
> False, True,
> True, True, True, True, True, True,... True, True,
> True, True, True, True, True, False, False,
> True, True, False, True, False, False])
>
> def test_umap_sparse_transform_on_iris(iris, iris_selection):
> data = sparse.csr_matrix(iris.data[iris_selection])
> assert sparse.issparse(data)
> fitter = UMAP(
> n_neighbors=10,
> min_dist=0.01,
> random_state=42,
> n_epochs=100,
> force_approximation_algorithm=True,
> ).fit(data)
>
> new_data = sparse.csr_matrix(iris.data[~iris_selection])
> assert sparse.issparse(new_data)
> embedding = fitter.transform(new_data)
>
> > trust = trustworthiness(new_data, embedding, 10)
> E TypeError: trustworthiness() takes 2 positional arguments but 3 were
> given
>
> umap/tests/test_umap_on_iris.py:127: TypeError
> _______________________ test_umap_sparse_trustworthiness
> _______________________
>
> sparse_test_data = <1002x5 sparse matrix of type '<class 'numpy.float64'>'
> with 1672 stored elements in Compressed Sparse Row format>
>
> def test_umap_sparse_trustworthiness(sparse_test_data):
> embedding = UMAP(n_neighbors=10).fit_transform(sparse_test_data[:100])
> > trust = trustworthiness(sparse_test_data[:100].toarray(), embedding,
> > 10)
> E TypeError: trustworthiness() takes 2 positional arguments but 3 were
> given
>
> umap/tests/test_umap_trustworthiness.py:23: TypeError
> ____________________ test_umap_trustworthiness_fast_approx
> _____________________
>
> nn_data = array([[0.37454012, 0.95071431, 0.73199394, 0.59865848, 0.15601864],
> [0.15599452, 0.05808361, 0.86617615, 0.601... 0. , 0. ,
> 0. , 0. ],
> [0. , 0. , 0. , 0. , 0. ]])
>
> def test_umap_trustworthiness_fast_approx(nn_data):
> data = nn_data[:50]
> embedding = UMAP(
> n_neighbors=10,
> min_dist=0.01,
> random_state=42,
> n_epochs=100,
> force_approximation_algorithm=True,
> ).fit_transform(data)
> > trust = trustworthiness(data, embedding, 10)
> E TypeError: trustworthiness() takes 2 positional arguments but 3 were
> given
>
> umap/tests/test_umap_trustworthiness.py:41: TypeError
> ____________________ test_umap_trustworthiness_random_init
> _____________________
>
> nn_data = array([[0.37454012, 0.95071431, 0.73199394, 0.59865848, 0.15601864],
> [0.15599452, 0.05808361, 0.86617615, 0.601... 0. , 0. ,
> 0. , 0. ],
> [0. , 0. , 0. , 0. , 0. ]])
>
> def test_umap_trustworthiness_random_init(nn_data):
> data = nn_data[:50]
> embedding = UMAP(
> n_neighbors=10, min_dist=0.01, random_state=42, init="random"
> ).fit_transform(data)
> > trust = trustworthiness(data, embedding, 10)
> E TypeError: trustworthiness() takes 2 positional arguments but 3 were
> given
>
> umap/tests/test_umap_trustworthiness.py:54: TypeError
> _____________________ test_supervised_umap_trustworthiness
> _____________________
>
> def test_supervised_umap_trustworthiness():
> data, labels = make_blobs(50, cluster_std=0.5, random_state=42)
> embedding = UMAP(n_neighbors=10, min_dist=0.01,
> random_state=42).fit_transform(
> data, labels
> )
> > trust = trustworthiness(data, embedding, 10)
> E TypeError: trustworthiness() takes 2 positional arguments but 3 were
> given
>
> umap/tests/test_umap_trustworthiness.py:67: TypeError
> ___________________ test_semisupervised_umap_trustworthiness
> ___________________
>
> def test_semisupervised_umap_trustworthiness():
> data, labels = make_blobs(50, cluster_std=0.5, random_state=42)
> labels[10:30] = -1
> embedding = UMAP(n_neighbors=10, min_dist=0.01,
> random_state=42).fit_transform(
> data, labels
> )
> > trust = trustworthiness(data, embedding, 10)
> E TypeError: trustworthiness() takes 2 positional arguments but 3 were
> given
>
> umap/tests/test_umap_trustworthiness.py:81: TypeError
> _________________ test_metric_supervised_umap_trustworthiness
> __________________
>
> def test_metric_supervised_umap_trustworthiness():
> data, labels = make_blobs(50, cluster_std=0.5, random_state=42)
> embedding = UMAP(
> n_neighbors=10,
> min_dist=0.01,
> target_metric="l1",
> target_weight=0.8,
> n_epochs=100,
> random_state=42,
> ).fit_transform(data, labels)
> > trust = trustworthiness(data, embedding, 10)
> E TypeError: trustworthiness() takes 2 positional arguments but 3 were
> given
>
> umap/tests/test_umap_trustworthiness.py:99: TypeError
> ______________ test_string_metric_supervised_umap_trustworthiness
> ______________
>
> array = array(['this', 'this', 'that', 'other', 'this', 'this', 'this',
> 'this',
> 'that', 'other', 'that', 'that', 'that'...other',
> 'other', 'this', 'other', 'this', 'this', 'other', 'this', 'other',
> 'that', 'that'], dtype='<U5')
> accept_sparse = False
>
> def check_array(
> array,
> accept_sparse=False,
> *,
> accept_large_sparse=True,
> dtype="numeric",
> order=None,
> copy=False,
> force_all_finite=True,
> ensure_2d=True,
> allow_nd=False,
> ensure_min_samples=1,
> ensure_min_features=1,
> estimator=None,
> ):
>
> """Input validation on an array, list, sparse matrix or similar.
>
> By default, the input is checked to be a non-empty 2D array containing
> only finite values. If the dtype of the array is object, attempt
> converting to float, raising on failure.
>
> Parameters
> ----------
> array : object
> Input object to check / convert.
>
> accept_sparse : str, bool or list/tuple of str, default=False
> String[s] representing allowed sparse matrix formats, such as
> 'csc',
> 'csr', etc. If the input is sparse but not in the allowed format,
> it will be converted to the first listed format. True allows the
> input
> to be any format. False means that a sparse matrix input will
> raise an error.
>
> accept_large_sparse : bool, default=True
> If a CSR, CSC, COO or BSR sparse matrix is supplied and accepted
> by
> accept_sparse, accept_large_sparse=False will cause it to be
> accepted
> only if its indices are stored with a 32-bit dtype.
>
> .. versionadded:: 0.20
>
> dtype : 'numeric', type, list of type or None, default='numeric'
> Data type of result. If None, the dtype of the input is preserved.
> If "numeric", dtype is preserved unless array.dtype is object.
> If dtype is a list of types, conversion on the first type is only
> performed if the dtype of the input is not in the list.
>
> order : {'F', 'C'} or None, default=None
> Whether an array will be forced to be fortran or c-style.
> When order is None (default), then if copy=False, nothing is
> ensured
> about the memory layout of the output array; otherwise (copy=True)
> the memory layout of the returned array is kept as close as
> possible
> to the original array.
>
> copy : bool, default=False
> Whether a forced copy will be triggered. If copy=False, a copy
> might
> be triggered by a conversion.
>
> force_all_finite : bool or 'allow-nan', default=True
> Whether to raise an error on np.inf, np.nan, pd.NA in array. The
> possibilities are:
>
> - True: Force all values of array to be finite.
> - False: accepts np.inf, np.nan, pd.NA in array.
> - 'allow-nan': accepts only np.nan and pd.NA values in array.
> Values
> cannot be infinite.
>
> .. versionadded:: 0.20
> ``force_all_finite`` accepts the string ``'allow-nan'``.
>
> .. versionchanged:: 0.23
> Accepts `pd.NA` and converts it into `np.nan`
>
> ensure_2d : bool, default=True
> Whether to raise a value error if array is not 2D.
>
> allow_nd : bool, default=False
> Whether to allow array.ndim > 2.
>
> ensure_min_samples : int, default=1
> Make sure that the array has a minimum number of samples in its
> first
> axis (rows for a 2D array). Setting to 0 disables this check.
>
> ensure_min_features : int, default=1
> Make sure that the 2D array has some minimum number of features
> (columns). The default value of 1 rejects empty datasets.
> This check is only enforced when the input data has effectively 2
> dimensions or is originally 1D and ``ensure_2d`` is True. Setting
> to 0
> disables this check.
>
> estimator : str or estimator instance, default=None
> If passed, include the name of the estimator in warning messages.
>
> Returns
> -------
> array_converted : object
> The converted and validated array.
> """
> if isinstance(array, np.matrix):
> warnings.warn(
> "np.matrix usage is deprecated in 1.0 and will raise a
> TypeError "
> "in 1.2. Please convert to a numpy array with np.asarray. For
> "
> "more information see: "
>
> "https://numpy.org/doc/stable/reference/generated/numpy.matrix.html";, # noqa
> FutureWarning,
> )
>
> # store reference to original array to check if copy is needed when
> # function returns
> array_orig = array
>
> # store whether originally we wanted numeric dtype
> dtype_numeric = isinstance(dtype, str) and dtype == "numeric"
>
> dtype_orig = getattr(array, "dtype", None)
> if not hasattr(dtype_orig, "kind"):
> # not a data type (e.g. a column named dtype in a pandas
> DataFrame)
> dtype_orig = None
>
> # check if the object contains several dtypes (typically a pandas
> # DataFrame), and store them. If not, store None.
> dtypes_orig = None
> has_pd_integer_array = False
> if hasattr(array, "dtypes") and hasattr(array.dtypes, "__array__"):
> # throw warning if columns are sparse. If all columns are sparse,
> then
> # array.sparse exists and sparsity will be preserved (later).
> with suppress(ImportError):
> from pandas.api.types import is_sparse
>
> if not hasattr(array, "sparse") and
> array.dtypes.apply(is_sparse).any():
> warnings.warn(
> "pandas.DataFrame with sparse columns found."
> "It will be converted to a dense numpy array."
> )
>
> dtypes_orig = list(array.dtypes)
> # pandas boolean dtype __array__ interface coerces bools to
> objects
> for i, dtype_iter in enumerate(dtypes_orig):
> if dtype_iter.kind == "b":
> dtypes_orig[i] = np.dtype(object)
> elif dtype_iter.name.startswith(("Int", "UInt")):
> # name looks like an Integer Extension Array, now check
> for
> # the dtype
> with suppress(ImportError):
> from pandas import (
> Int8Dtype,
> Int16Dtype,
> Int32Dtype,
> Int64Dtype,
> UInt8Dtype,
> UInt16Dtype,
> UInt32Dtype,
> UInt64Dtype,
> )
>
> if isinstance(
> dtype_iter,
> (
> Int8Dtype,
> Int16Dtype,
> Int32Dtype,
> Int64Dtype,
> UInt8Dtype,
> UInt16Dtype,
> UInt32Dtype,
> UInt64Dtype,
> ),
> ):
> has_pd_integer_array = True
>
> if all(isinstance(dtype, np.dtype) for dtype in dtypes_orig):
> dtype_orig = np.result_type(*dtypes_orig)
>
> if dtype_numeric:
> if dtype_orig is not None and dtype_orig.kind == "O":
> # if input is object, convert to float.
> dtype = np.float64
> else:
> dtype = None
>
> if isinstance(dtype, (list, tuple)):
> if dtype_orig is not None and dtype_orig in dtype:
> # no dtype conversion required
> dtype = None
> else:
> # dtype conversion required. Let's select the first element
> of the
> # list of accepted types.
> dtype = dtype[0]
>
> if has_pd_integer_array:
> # If there are any pandas integer extension arrays,
> array = array.astype(dtype)
>
> if force_all_finite not in (True, False, "allow-nan"):
> raise ValueError(
> 'force_all_finite should be a bool or "allow-nan". Got {!r}
> instead'.format(
> force_all_finite
> )
> )
>
> if estimator is not None:
> if isinstance(estimator, str):
> estimator_name = estimator
> else:
> estimator_name = estimator.__class__.__name__
> else:
> estimator_name = "Estimator"
> context = " by %s" % estimator_name if estimator is not None else ""
>
> # When all dataframe columns are sparse, convert to a sparse array
> if hasattr(array, "sparse") and array.ndim > 1:
> # DataFrame.sparse only supports `to_coo`
> array = array.sparse.to_coo()
> if array.dtype == np.dtype("object"):
> unique_dtypes = set([dt.subtype.name for dt in
> array_orig.dtypes])
> if len(unique_dtypes) > 1:
> raise ValueError(
> "Pandas DataFrame with mixed sparse extension arrays "
> "generated a sparse matrix with object dtype which "
> "can not be converted to a scipy sparse matrix."
> "Sparse extension arrays should all have the same "
> "numeric type."
> )
>
> if sp.issparse(array):
> _ensure_no_complex_data(array)
> array = _ensure_sparse_format(
> array,
> accept_sparse=accept_sparse,
> dtype=dtype,
> copy=copy,
> force_all_finite=force_all_finite,
> accept_large_sparse=accept_large_sparse,
> )
> else:
> # If np.array(..) gives ComplexWarning, then we convert the
> warning
> # to an error. This is needed because specifying a non complex
> # dtype to the function converts complex to real dtype,
> # thereby passing the test made in the lines following the scope
> # of warnings context manager.
> with warnings.catch_warnings():
> try:
> warnings.simplefilter("error", ComplexWarning)
> if dtype is not None and np.dtype(dtype).kind in "iu":
> # Conversion float -> int should not contain NaN or
> # inf (numpy#14412). We cannot use casting='safe'
> because
> # then conversion float -> int would be disallowed.
> array = np.asarray(array, order=order)
> if array.dtype.kind == "f":
> _assert_all_finite(array, allow_nan=False,
> msg_dtype=dtype)
> array = array.astype(dtype, casting="unsafe",
> copy=False)
> else:
> array = np.asarray(array, order=order, dtype=dtype)
> except ComplexWarning as complex_warning:
> raise ValueError(
> "Complex data not supported\n{}\n".format(array)
> ) from complex_warning
>
> # It is possible that the np.array(..) gave no warning. This
> happens
> # when no dtype conversion happened, for example dtype = None. The
> # result is that np.array(..) produces an array of complex dtype
> # and we need to catch and raise exception for such cases.
> _ensure_no_complex_data(array)
>
> if ensure_2d:
> # If input is scalar raise error
> if array.ndim == 0:
> raise ValueError(
> "Expected 2D array, got scalar array
> instead:\narray={}.\n"
> "Reshape your data either using array.reshape(-1, 1)
> if "
> "your data has a single feature or array.reshape(1,
> -1) "
> "if it contains a single sample.".format(array)
> )
> # If input is 1D raise error
> if array.ndim == 1:
> raise ValueError(
> "Expected 2D array, got 1D array
> instead:\narray={}.\n"
> "Reshape your data either using array.reshape(-1, 1)
> if "
> "your data has a single feature or array.reshape(1,
> -1) "
> "if it contains a single sample.".format(array)
> )
>
> # make sure we actually converted to numeric:
> if dtype_numeric and array.dtype.kind in "OUSV":
> warnings.warn(
> "Arrays of bytes/strings is being converted to decimal "
> "numbers if dtype='numeric'. This behavior is deprecated
> in "
> "0.24 and will be removed in 1.1 (renaming of 0.26).
> Please "
> "convert your data to numeric values explicitly instead.",
> FutureWarning,
> stacklevel=2,
> )
> try:
> > array = array.astype(np.float64)
> E ValueError: could not convert string to float: 'this'
>
> /usr/lib/python3/dist-packages/sklearn/utils/validation.py:779: ValueError
>
> The above exception was the direct cause of the following exception:
>
> def test_string_metric_supervised_umap_trustworthiness():
> data, labels = make_blobs(50, cluster_std=0.5, random_state=42)
> labels = np.array(["this", "that", "other"])[labels]
> > embedding = UMAP(
> n_neighbors=10,
> min_dist=0.01,
> target_metric="string",
> target_weight=0.8,
> n_epochs=100,
> random_state=42,
> ).fit_transform(data, labels)
>
> umap/tests/test_umap_trustworthiness.py:110:
> _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
> _
> umap/umap_.py:2014: in fit_transform
> self.fit(X, y)
> umap/umap_.py:1867: in fit
> y_ = check_array(y, ensure_2d=False)[index]
> _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
> _
>
> array = array(['this', 'this', 'that', 'other', 'this', 'this', 'this',
> 'this',
> 'that', 'other', 'that', 'that', 'that'...other',
> 'other', 'this', 'other', 'this', 'this', 'other', 'this', 'other',
> 'that', 'that'], dtype='<U5')
> accept_sparse = False
>
> def check_array(
> array,
> accept_sparse=False,
> *,
> accept_large_sparse=True,
> dtype="numeric",
> order=None,
> copy=False,
> force_all_finite=True,
> ensure_2d=True,
> allow_nd=False,
> ensure_min_samples=1,
> ensure_min_features=1,
> estimator=None,
> ):
>
> """Input validation on an array, list, sparse matrix or similar.
>
> By default, the input is checked to be a non-empty 2D array containing
> only finite values. If the dtype of the array is object, attempt
> converting to float, raising on failure.
>
> Parameters
> ----------
> array : object
> Input object to check / convert.
>
> accept_sparse : str, bool or list/tuple of str, default=False
> String[s] representing allowed sparse matrix formats, such as
> 'csc',
> 'csr', etc. If the input is sparse but not in the allowed format,
> it will be converted to the first listed format. True allows the
> input
> to be any format. False means that a sparse matrix input will
> raise an error.
>
> accept_large_sparse : bool, default=True
> If a CSR, CSC, COO or BSR sparse matrix is supplied and accepted
> by
> accept_sparse, accept_large_sparse=False will cause it to be
> accepted
> only if its indices are stored with a 32-bit dtype.
>
> .. versionadded:: 0.20
>
> dtype : 'numeric', type, list of type or None, default='numeric'
> Data type of result. If None, the dtype of the input is preserved.
> If "numeric", dtype is preserved unless array.dtype is object.
> If dtype is a list of types, conversion on the first type is only
> performed if the dtype of the input is not in the list.
>
> order : {'F', 'C'} or None, default=None
> Whether an array will be forced to be fortran or c-style.
> When order is None (default), then if copy=False, nothing is
> ensured
> about the memory layout of the output array; otherwise (copy=True)
> the memory layout of the returned array is kept as close as
> possible
> to the original array.
>
> copy : bool, default=False
> Whether a forced copy will be triggered. If copy=False, a copy
> might
> be triggered by a conversion.
>
> force_all_finite : bool or 'allow-nan', default=True
> Whether to raise an error on np.inf, np.nan, pd.NA in array. The
> possibilities are:
>
> - True: Force all values of array to be finite.
> - False: accepts np.inf, np.nan, pd.NA in array.
> - 'allow-nan': accepts only np.nan and pd.NA values in array.
> Values
> cannot be infinite.
>
> .. versionadded:: 0.20
> ``force_all_finite`` accepts the string ``'allow-nan'``.
>
> .. versionchanged:: 0.23
> Accepts `pd.NA` and converts it into `np.nan`
>
> ensure_2d : bool, default=True
> Whether to raise a value error if array is not 2D.
>
> allow_nd : bool, default=False
> Whether to allow array.ndim > 2.
>
> ensure_min_samples : int, default=1
> Make sure that the array has a minimum number of samples in its
> first
> axis (rows for a 2D array). Setting to 0 disables this check.
>
> ensure_min_features : int, default=1
> Make sure that the 2D array has some minimum number of features
> (columns). The default value of 1 rejects empty datasets.
> This check is only enforced when the input data has effectively 2
> dimensions or is originally 1D and ``ensure_2d`` is True. Setting
> to 0
> disables this check.
>
> estimator : str or estimator instance, default=None
> If passed, include the name of the estimator in warning messages.
>
> Returns
> -------
> array_converted : object
> The converted and validated array.
> """
> if isinstance(array, np.matrix):
> warnings.warn(
> "np.matrix usage is deprecated in 1.0 and will raise a
> TypeError "
> "in 1.2. Please convert to a numpy array with np.asarray. For
> "
> "more information see: "
>
> "https://numpy.org/doc/stable/reference/generated/numpy.matrix.html";, # noqa
> FutureWarning,
> )
>
> # store reference to original array to check if copy is needed when
> # function returns
> array_orig = array
>
> # store whether originally we wanted numeric dtype
> dtype_numeric = isinstance(dtype, str) and dtype == "numeric"
>
> dtype_orig = getattr(array, "dtype", None)
> if not hasattr(dtype_orig, "kind"):
> # not a data type (e.g. a column named dtype in a pandas
> DataFrame)
> dtype_orig = None
>
> # check if the object contains several dtypes (typically a pandas
> # DataFrame), and store them. If not, store None.
> dtypes_orig = None
> has_pd_integer_array = False
> if hasattr(array, "dtypes") and hasattr(array.dtypes, "__array__"):
> # throw warning if columns are sparse. If all columns are sparse,
> then
> # array.sparse exists and sparsity will be preserved (later).
> with suppress(ImportError):
> from pandas.api.types import is_sparse
>
> if not hasattr(array, "sparse") and
> array.dtypes.apply(is_sparse).any():
> warnings.warn(
> "pandas.DataFrame with sparse columns found."
> "It will be converted to a dense numpy array."
> )
>
> dtypes_orig = list(array.dtypes)
> # pandas boolean dtype __array__ interface coerces bools to
> objects
> for i, dtype_iter in enumerate(dtypes_orig):
> if dtype_iter.kind == "b":
> dtypes_orig[i] = np.dtype(object)
> elif dtype_iter.name.startswith(("Int", "UInt")):
> # name looks like an Integer Extension Array, now check
> for
> # the dtype
> with suppress(ImportError):
> from pandas import (
> Int8Dtype,
> Int16Dtype,
> Int32Dtype,
> Int64Dtype,
> UInt8Dtype,
> UInt16Dtype,
> UInt32Dtype,
> UInt64Dtype,
> )
>
> if isinstance(
> dtype_iter,
> (
> Int8Dtype,
> Int16Dtype,
> Int32Dtype,
> Int64Dtype,
> UInt8Dtype,
> UInt16Dtype,
> UInt32Dtype,
> UInt64Dtype,
> ),
> ):
> has_pd_integer_array = True
>
> if all(isinstance(dtype, np.dtype) for dtype in dtypes_orig):
> dtype_orig = np.result_type(*dtypes_orig)
>
> if dtype_numeric:
> if dtype_orig is not None and dtype_orig.kind == "O":
> # if input is object, convert to float.
> dtype = np.float64
> else:
> dtype = None
>
> if isinstance(dtype, (list, tuple)):
> if dtype_orig is not None and dtype_orig in dtype:
> # no dtype conversion required
> dtype = None
> else:
> # dtype conversion required. Let's select the first element
> of the
> # list of accepted types.
> dtype = dtype[0]
>
> if has_pd_integer_array:
> # If there are any pandas integer extension arrays,
> array = array.astype(dtype)
>
> if force_all_finite not in (True, False, "allow-nan"):
> raise ValueError(
> 'force_all_finite should be a bool or "allow-nan". Got {!r}
> instead'.format(
> force_all_finite
> )
> )
>
> if estimator is not None:
> if isinstance(estimator, str):
> estimator_name = estimator
> else:
> estimator_name = estimator.__class__.__name__
> else:
> estimator_name = "Estimator"
> context = " by %s" % estimator_name if estimator is not None else ""
>
> # When all dataframe columns are sparse, convert to a sparse array
> if hasattr(array, "sparse") and array.ndim > 1:
> # DataFrame.sparse only supports `to_coo`
> array = array.sparse.to_coo()
> if array.dtype == np.dtype("object"):
> unique_dtypes = set([dt.subtype.name for dt in
> array_orig.dtypes])
> if len(unique_dtypes) > 1:
> raise ValueError(
> "Pandas DataFrame with mixed sparse extension arrays "
> "generated a sparse matrix with object dtype which "
> "can not be converted to a scipy sparse matrix."
> "Sparse extension arrays should all have the same "
> "numeric type."
> )
>
> if sp.issparse(array):
> _ensure_no_complex_data(array)
> array = _ensure_sparse_format(
> array,
> accept_sparse=accept_sparse,
> dtype=dtype,
> copy=copy,
> force_all_finite=force_all_finite,
> accept_large_sparse=accept_large_sparse,
> )
> else:
> # If np.array(..) gives ComplexWarning, then we convert the
> warning
> # to an error. This is needed because specifying a non complex
> # dtype to the function converts complex to real dtype,
> # thereby passing the test made in the lines following the scope
> # of warnings context manager.
> with warnings.catch_warnings():
> try:
> warnings.simplefilter("error", ComplexWarning)
> if dtype is not None and np.dtype(dtype).kind in "iu":
> # Conversion float -> int should not contain NaN or
> # inf (numpy#14412). We cannot use casting='safe'
> because
> # then conversion float -> int would be disallowed.
> array = np.asarray(array, order=order)
> if array.dtype.kind == "f":
> _assert_all_finite(array, allow_nan=False,
> msg_dtype=dtype)
> array = array.astype(dtype, casting="unsafe",
> copy=False)
> else:
> array = np.asarray(array, order=order, dtype=dtype)
> except ComplexWarning as complex_warning:
> raise ValueError(
> "Complex data not supported\n{}\n".format(array)
> ) from complex_warning
>
> # It is possible that the np.array(..) gave no warning. This
> happens
> # when no dtype conversion happened, for example dtype = None. The
> # result is that np.array(..) produces an array of complex dtype
> # and we need to catch and raise exception for such cases.
> _ensure_no_complex_data(array)
>
> if ensure_2d:
> # If input is scalar raise error
> if array.ndim == 0:
> raise ValueError(
> "Expected 2D array, got scalar array
> instead:\narray={}.\n"
> "Reshape your data either using array.reshape(-1, 1)
> if "
> "your data has a single feature or array.reshape(1,
> -1) "
> "if it contains a single sample.".format(array)
> )
> # If input is 1D raise error
> if array.ndim == 1:
> raise ValueError(
> "Expected 2D array, got 1D array
> instead:\narray={}.\n"
> "Reshape your data either using array.reshape(-1, 1)
> if "
> "your data has a single feature or array.reshape(1,
> -1) "
> "if it contains a single sample.".format(array)
> )
>
> # make sure we actually converted to numeric:
> if dtype_numeric and array.dtype.kind in "OUSV":
> warnings.warn(
> "Arrays of bytes/strings is being converted to decimal "
> "numbers if dtype='numeric'. This behavior is deprecated
> in "
> "0.24 and will be removed in 1.1 (renaming of 0.26).
> Please "
> "convert your data to numeric values explicitly instead.",
> FutureWarning,
> stacklevel=2,
> )
> try:
> array = array.astype(np.float64)
> except ValueError as e:
> > raise ValueError(
> "Unable to convert array of bytes/strings "
> "into decimal numbers with dtype='numeric'"
> ) from e
> E ValueError: Unable to convert array of bytes/strings into
> decimal numbers with dtype='numeric'
>
> /usr/lib/python3/dist-packages/sklearn/utils/validation.py:781: ValueError
> _____________ test_discrete_metric_supervised_umap_trustworthiness
> _____________
>
> def test_discrete_metric_supervised_umap_trustworthiness():
> data, labels = make_blobs(50, cluster_std=0.5, random_state=42)
> embedding = UMAP(
> n_neighbors=10,
> min_dist=0.01,
> target_metric="ordinal",
> target_weight=0.8,
> n_epochs=100,
> random_state=42,
> ).fit_transform(data, labels)
> > trust = trustworthiness(data, embedding, 10)
> E TypeError: trustworthiness() takes 2 positional arguments but 3 were
> given
>
> umap/tests/test_umap_trustworthiness.py:136: TypeError
> ______________ test_count_metric_supervised_umap_trustworthiness
> _______________
>
> def test_count_metric_supervised_umap_trustworthiness():
> data, labels = make_blobs(50, cluster_std=0.5, random_state=42)
> labels = (labels ** 2) + 2 * labels
> embedding = UMAP(
> n_neighbors=10,
> min_dist=0.01,
> target_metric="count",
> target_weight=0.8,
> n_epochs=100,
> random_state=42,
> ).fit_transform(data, labels)
> > trust = trustworthiness(data, embedding, 10)
> E TypeError: trustworthiness() takes 2 positional arguments but 3 were
> given
>
> umap/tests/test_umap_trustworthiness.py:155: TypeError
> =============================== warnings summary
> ===============================
> ../../../usr/lib/python3/dist-packages/numba/core/types/__init__.py:108
> /usr/lib/python3/dist-packages/numba/core/types/__init__.py:108:
> DeprecationWarning: `np.long` is a deprecated alias for `np.compat.long`. To
> silence this warning, use `np.compat.long` by itself. In the likely event
> your code does not need to work on Python 2 you can use the builtin `int` for
> which `np.compat.long` is itself an alias. Doing this will not modify any
> behaviour and is safe. When replacing `np.long`, 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.
> Deprecated in NumPy 1.20; for more details and guidance:
> https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations
> long_ = _make_signed(np.long)
>
> ../../../usr/lib/python3/dist-packages/numba/core/types/__init__.py:109
> /usr/lib/python3/dist-packages/numba/core/types/__init__.py:109:
> DeprecationWarning: `np.long` is a deprecated alias for `np.compat.long`. To
> silence this warning, use `np.compat.long` by itself. In the likely event
> your code does not need to work on Python 2 you can use the builtin `int` for
> which `np.compat.long` is itself an alias. Doing this will not modify any
> behaviour and is safe. When replacing `np.long`, 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.
> Deprecated in NumPy 1.20; for more details and guidance:
> https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations
> ulong = _make_unsigned(np.long)
>
> umap/distances.py:24
> /<<PKGBUILDDIR>>/umap/distances.py:24: DeprecationWarning: invalid escape
> sequence \s
> """Standard euclidean distance.
>
> umap/distances.py:37
> /<<PKGBUILDDIR>>/umap/distances.py:37: DeprecationWarning: invalid escape
> sequence \s
> """Standard euclidean distance and its gradient.
>
> umap/distances.py:53
> /<<PKGBUILDDIR>>/umap/distances.py:53: DeprecationWarning: invalid escape
> sequence \s
> """Euclidean distance standardised against a vector of standard
>
> umap/distances.py:68
> /<<PKGBUILDDIR>>/umap/distances.py:68: DeprecationWarning: invalid escape
> sequence \s
> """Euclidean distance standardised against a vector of standard
>
> umap/distances.py:84
> /<<PKGBUILDDIR>>/umap/distances.py:84: DeprecationWarning: invalid escape
> sequence \s
> """Manhattan, taxicab, or l1 distance.
>
> umap/distances.py:98
> /<<PKGBUILDDIR>>/umap/distances.py:98: DeprecationWarning: invalid escape
> sequence \s
> """Manhattan, taxicab, or l1 distance with gradient.
>
> umap/distances.py:113
> /<<PKGBUILDDIR>>/umap/distances.py:113: DeprecationWarning: invalid escape
> sequence \m
> """Chebyshev or l-infinity distance.
>
> umap/distances.py:127
> /<<PKGBUILDDIR>>/umap/distances.py:127: DeprecationWarning: invalid escape
> sequence \m
> """Chebyshev or l-infinity distance with gradient.
>
> umap/distances.py:147
> /<<PKGBUILDDIR>>/umap/distances.py:147: DeprecationWarning: invalid escape
> sequence \l
> """Minkowski distance.
>
> umap/distances.py:166
> /<<PKGBUILDDIR>>/umap/distances.py:166: DeprecationWarning: invalid escape
> sequence \l
> """Minkowski distance with gradient.
>
> umap/distances.py:193
> /<<PKGBUILDDIR>>/umap/distances.py:193: DeprecationWarning: invalid escape
> sequence \d
> """Poincare distance.
>
> umap/distances.py:230
> /<<PKGBUILDDIR>>/umap/distances.py:230: DeprecationWarning: invalid escape
> sequence \l
> """A weighted version of Minkowski distance.
>
> umap/distances.py:248
> /<<PKGBUILDDIR>>/umap/distances.py:248: DeprecationWarning: invalid escape
> sequence \l
> """A weighted version of Minkowski distance with gradient.
>
> umap/distances.py:752
> /<<PKGBUILDDIR>>/umap/distances.py:752: DeprecationWarning: invalid escape
> sequence \l
> """
>
> ../../../usr/lib/python3/dist-packages/nose/importer.py:12
> /usr/lib/python3/dist-packages/nose/importer.py:12: DeprecationWarning: the
> imp module is deprecated in favour of importlib; see the module's
> documentation for alternative uses
> from imp import find_module, load_module, acquire_lock, release_lock
>
> umap/tests/test_plot.py::test_umap_plot_dependency
> /<<PKGBUILDDIR>>/umap/plot.py:20: UserWarning: The umap.plot package
> requires extra plotting libraries to be installed.
> You can install these via pip using
>
> pip install umap-learn[plot]
>
> or via conda using
>
> conda install pandas matplotlib datashader bokeh holoviews colorcet
>
> warn(
>
> umap/tests/test_umap_metrics.py: 16 warnings
> umap/tests/test_umap_nn.py: 4 warnings
> /usr/lib/python3/dist-packages/sklearn/utils/validation.py:585:
> FutureWarning: np.matrix usage is deprecated in 1.0 and will raise a
> TypeError in 1.2. Please convert to a numpy array with np.asarray. For more
> information see:
> https://numpy.org/doc/stable/reference/generated/numpy.matrix.html
> warnings.warn(
>
> umap/tests/test_umap_metrics.py::test_weighted_minkowski
> umap/tests/test_umap_metrics.py::test_grad_metrics_match_metrics
> /usr/lib/python3/dist-packages/scipy/spatial/distance.py:275:
> DeprecationWarning: 'wminkowski' metric is deprecated and will be removed in
> SciPy 1.8.0, use 'minkowski' instead.
> kwargs = _validate_kwargs(X, m, n, **kwargs)
>
> umap/tests/test_umap_metrics.py::test_hellinger
> /<<PKGBUILDDIR>>/umap/tests/test_umap_metrics.py:396: RuntimeWarning:
> invalid value encountered in sqrt
> dist_matrix = np.sqrt(dist_matrix)
>
> umap/tests/test_umap_nn.py::test_smooth_knn_dist_l1norms
> umap/tests/test_umap_on_iris.py::test_umap_trustworthiness_on_iris
> /usr/lib/python3/dist-packages/numba/core/consts.py:114:
> 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.
> Deprecated in NumPy 1.20; for more details and guidance:
> https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations
> return getattr(value, expr.attr)
>
> umap/tests/test_umap_nn.py::test_smooth_knn_dist_l1norms
> umap/tests/test_umap_nn.py::test_smooth_knn_dist_l1norms
> umap/tests/test_umap_on_iris.py::test_umap_trustworthiness_on_iris
> umap/tests/test_umap_on_iris.py::test_umap_trustworthiness_on_iris
> /usr/lib/python3/dist-packages/numba/core/ir_utils.py:2097:
> 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.
> Deprecated in NumPy 1.20; for more details and guidance:
> https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations
> defn = getattr(defn, x, False)
>
> umap/tests/test_umap_nn.py::test_smooth_knn_dist_l1norms
> umap/tests/test_umap_nn.py::test_smooth_knn_dist_l1norms
> umap/tests/test_umap_nn.py::test_smooth_knn_dist_l1norms
> umap/tests/test_umap_nn.py::test_smooth_knn_dist_l1norms
> umap/tests/test_umap_on_iris.py::test_umap_trustworthiness_on_iris
> umap/tests/test_umap_on_iris.py::test_umap_trustworthiness_on_iris
> umap/tests/test_umap_on_iris.py::test_umap_trustworthiness_on_iris
> umap/tests/test_umap_on_iris.py::test_umap_trustworthiness_on_iris
> /usr/lib/python3/dist-packages/numba/core/typing/context.py:338:
> 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.
> Deprecated in NumPy 1.20; for more details and guidance:
> https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations
> attrval = getattr(typ.pymod, attr)
>
> umap/tests/test_umap_on_iris.py::test_umap_trustworthiness_on_iris
> /usr/lib/python3/dist-packages/numba/np/arrayobj.py:3843:
> 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.
> Deprecated in NumPy 1.20; for more details and guidance:
> https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations
> NPY_TY = getattr(types, "int%s" % (8 * np.dtype(np.int).itemsize))
>
> umap/tests/test_umap_ops.py::test_multi_component_layout
> /usr/lib/python3/dist-packages/sklearn/manifold/_spectral_embedding.py:260:
> UserWarning: Graph is not fully connected, spectral embedding may not work as
> expected.
> warnings.warn(
>
> umap/tests/test_umap_repeated_data.py::test_repeated_points_large_sparse_spatial
> umap/tests/test_umap_repeated_data.py::test_repeated_points_small_sparse_spatial
> umap/tests/test_umap_repeated_data.py::test_repeated_points_large_sparse_binary
> umap/tests/test_umap_repeated_data.py::test_repeated_points_small_sparse_binary
> /usr/lib/python3/dist-packages/numpy/lib/arraysetops.py:270:
> 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.
> ar = np.asanyarray(ar)
>
> umap/tests/test_umap_repeated_data.py::test_repeated_points_large_n
> umap/tests/test_umap_validation_params.py::test_umap_too_many_neighbors_warns
> /<<PKGBUILDDIR>>/umap/umap_.py:1678: UserWarning: n_neighbors is larger
> than the dataset size; truncating to X.shape[0] - 1
> warn(
>
> umap/tests/test_umap_trustworthiness.py::test_string_metric_supervised_umap_trustworthiness
> /<<PKGBUILDDIR>>/umap/umap_.py:1867: FutureWarning: Arrays of bytes/strings
> is being converted to decimal numbers if dtype='numeric'. This behavior is
> deprecated in 0.24 and will be removed in 1.1 (renaming of 0.26). Please
> convert your data to numeric values explicitly instead.
> y_ = check_array(y, ensure_2d=False)[index]
>
> -- Docs: https://docs.pytest.org/en/stable/warnings.html
> =========================== short test summary info
> ============================
> FAILED umap/tests/test_umap_on_iris.py::test_umap_trustworthiness_on_iris -
> T...
> FAILED
> umap/tests/test_umap_on_iris.py::test_initialized_umap_trustworthiness_on_iris
> FAILED
> umap/tests/test_umap_on_iris.py::test_umap_trustworthiness_on_sphere_iris
> FAILED umap/tests/test_umap_on_iris.py::test_umap_transform_on_iris -
> TypeErr...
> FAILED
> umap/tests/test_umap_on_iris.py::test_umap_transform_on_iris_modified_dtype
> FAILED umap/tests/test_umap_on_iris.py::test_umap_sparse_transform_on_iris -
> ...
> FAILED
> umap/tests/test_umap_trustworthiness.py::test_umap_sparse_trustworthiness
> FAILED
> umap/tests/test_umap_trustworthiness.py::test_umap_trustworthiness_fast_approx
> FAILED
> umap/tests/test_umap_trustworthiness.py::test_umap_trustworthiness_random_init
> FAILED
> umap/tests/test_umap_trustworthiness.py::test_supervised_umap_trustworthiness
> FAILED
> umap/tests/test_umap_trustworthiness.py::test_semisupervised_umap_trustworthiness
> FAILED
> umap/tests/test_umap_trustworthiness.py::test_metric_supervised_umap_trustworthiness
> FAILED
> umap/tests/test_umap_trustworthiness.py::test_string_metric_supervised_umap_trustworthiness
> FAILED
> umap/tests/test_umap_trustworthiness.py::test_discrete_metric_supervised_umap_trustworthiness
> FAILED
> umap/tests/test_umap_trustworthiness.py::test_count_metric_supervised_umap_trustworthiness
> ====== 15 failed, 111 passed, 2 xfailed, 64 warnings in 94.86s (0:01:34)
> =======
> E: pybuild pybuild:355: test: plugin custom failed with: exit code=1:
> PYTHONPATH=/<<PKGBUILDDIR>>/.pybuild/cpython3_3.9/build python3.9 -m pytest
> dh_auto_test: error: pybuild --test --test-pytest -i python{version} -p 3.9
> --system=custom "--test-args=PYTHONPATH={build_dir} {interpreter} -m pytest"
> returned exit code 13
The full build log is available from:
http://qa-logs.debian.net/2021/12/20/umap-learn_0.4.5+dfsg-2_unstable.log
A list of current common problems and possible solutions is available at
http://wiki.debian.org/qa.debian.org/FTBFS . You're welcome to contribute!
If you reassign this bug to another package, please marking it as 'affects'-ing
this package. See https://www.debian.org/Bugs/server-control#affects
If you fail to reproduce this, please provide a build log and diff it with mine
so that we can identify if something relevant changed in the meantime.
--- End Message ---
