Fix import of issparse (#3047)

docs/conf.py

@@ -196,6 +196,8 @@ def setup(app: Sphinx):
 nitpick_ignore = [
     # Technical issues
     ("py:class", "numpy.int64"),  # documented as “attribute”
+    ("py:class", "numpy._typing._dtype_like._SupportsDType"),
+    ("py:class", "numpy._typing._dtype_like._DTypeDict"),
     # Will probably be documented
     ("py:class", "scanpy._settings.Verbosity"),
     ("py:class", "scanpy.neighbors.OnFlySymMatrix"),

scanpy/_compat.py

@@ -5,7 +5,7 @@
 from pathlib import Path
 
 from legacy_api_wrap import legacy_api
-from packaging import version
+from packaging.version import Version
 
 try:
     from functools import cache
@@ -68,16 +68,16 @@ def __exit__(self, *_excinfo) -> None:
 
 
 def pkg_metadata(package):
-    from importlib.metadata import metadata as m
+    from importlib.metadata import metadata
 
-    return m(package)
+    return metadata(package)
 
 
 @cache
 def pkg_version(package):
-    from importlib.metadata import version as v
+    from importlib.metadata import version
 
-    return version.parse(v(package))
+    return Version(version(package))
 
 
 old_positionals = partial(legacy_api, category=FutureWarning)

scanpy/_utils/__init__.py

@@ -34,7 +34,7 @@
 from anndata import AnnData
 from anndata import __version__ as anndata_version
 from numpy.typing import NDArray
-from packaging import version
+from packaging.version import Version
 from scipy import sparse
 from sklearn.utils import check_random_state
 
@@ -47,6 +47,8 @@
     from collections.abc import Mapping
     from pathlib import Path
 
+    from numpy.typing import DTypeLike
+
 
 class Empty(Enum):
     token = 0
@@ -102,7 +104,7 @@ def set_igraph_random_state(random_state: int):
 
 
 def check_versions():
-    if version.parse(anndata_version) < version.parse("0.6.10"):
+    if Version(anndata_version) < Version("0.6.10"):
         from .. import __version__
 
         raise ImportError(
@@ -712,7 +714,7 @@ def axis_sum(
     X: sparse.spmatrix,
     *,
     axis: tuple[Literal[0, 1], ...] | Literal[0, 1] | None = None,
-    dtype: np.typing.DTypeLike | None = None,
+    dtype: DTypeLike | None = None,
 ) -> np.matrix: ...
 
 
@@ -721,7 +723,7 @@ def axis_sum(
     X: np.ndarray,
     *,
     axis: tuple[Literal[0, 1], ...] | Literal[0, 1] | None = None,
-    dtype: np.typing.DTypeLike | None = None,
+    dtype: DTypeLike | None = None,
 ) -> np.ndarray:
     return np.sum(X, axis=axis, dtype=dtype)
 
@@ -731,7 +733,7 @@ def _(
     X: DaskArray,
     *,
     axis: tuple[Literal[0, 1], ...] | Literal[0, 1] | None = None,
-    dtype: np.typing.DTypeLike | None = None,
+    dtype: DTypeLike | None = None,
 ) -> DaskArray:
     import dask.array as da
 

scanpy/datasets/_utils.py

@@ -4,7 +4,7 @@
 from functools import wraps
 
 import anndata as ad
-from packaging import version
+from packaging.version import Version
 
 from .._settings import settings
 
@@ -26,7 +26,7 @@ def filter_oldformatwarning(f):
     @wraps(f)
     def wrapper(*args, **kwargs):
         with warnings.catch_warnings():
-            if version.parse(ad.__version__).release >= (0, 8):
+            if Version(ad.__version__).release >= (0, 8):
                 warnings.filterwarnings(
                     "ignore", category=ad.OldFormatWarning, module="anndata"
                 )

scanpy/external/pp/_magic.py

@@ -6,7 +6,7 @@
 
 from typing import TYPE_CHECKING, Literal
 
-from packaging import version
+from packaging.version import Version
 
 from ... import logging as logg
 from ..._settings import settings
@@ -145,7 +145,7 @@ def magic(
             "git+git://github.com/KrishnaswamyLab/MAGIC.git#subdirectory=python`"
         )
     else:
-        if not version.parse(__version__) >= version.parse(MIN_VERSION):
+        if Version(__version__) < Version(MIN_VERSION):
             raise ImportError(
                 "scanpy requires magic-impute >= "
                 f"v{MIN_VERSION} (detected: v{__version__}). "

scanpy/external/tl/_pypairs.py

@@ -7,7 +7,7 @@
 from collections.abc import Collection, Mapping
 from typing import TYPE_CHECKING, Union
 
-from packaging import version
+from packaging.version import Version
 
 from ..._settings import settings
 from ...testing._doctests import doctest_needs
@@ -154,6 +154,6 @@ def _check_import():
     except ImportError:
         raise ImportError("You need to install the package `pypairs`.")
 
-    min_version = version.parse("3.0.9")
-    if version.parse(pypairs.__version__) < min_version:
+    min_version = Version("3.0.9")
+    if Version(pypairs.__version__) < min_version:
         raise ImportError(f"Please only use `pypairs` >= {min_version}")

scanpy/preprocessing/_pca.py

@@ -7,7 +7,7 @@
 import anndata as ad
 import numpy as np
 from anndata import AnnData
-from packaging import version
+from packaging.version import Version
 from scipy.sparse import issparse, spmatrix
 from scipy.sparse.linalg import LinearOperator, svds
 from sklearn.utils import check_array, check_random_state
@@ -22,7 +22,7 @@
 from ._utils import _get_mean_var
 
 if TYPE_CHECKING:
-    from numpy.typing import NDArray
+    from numpy.typing import DTypeLike, NDArray
 
 
 @_doc_params(
@@ -39,7 +39,7 @@ def pca(
     return_info: bool = False,
     mask_var: NDArray[np.bool_] | str | None | Empty = _empty,
     use_highly_variable: bool | None = None,
-    dtype: str = "float32",
+    dtype: DTypeLike = "float32",
     copy: bool = False,
     chunked: bool = False,
     chunk_size: int | None = None,
@@ -172,7 +172,7 @@ def pca(
     if data_is_AnnData:
         adata = data.copy() if copy else data
     else:
-        if pkg_version("anndata") < version.parse("0.8.0rc1"):
+        if pkg_version("anndata") < Version("0.8.0rc1"):
             adata = AnnData(data, dtype=data.dtype)
         else:
             adata = AnnData(data)
@@ -195,7 +195,7 @@ def pca(
 
     # See: https://github.com/scverse/scanpy/pull/2816#issuecomment-1932650529
     if (
-        version.parse(ad.__version__) < version.parse("0.9")
+        Version(ad.__version__) < Version("0.9")
         and mask_var is not None
         and isinstance(X, np.ndarray)
     ):
@@ -305,7 +305,8 @@ def pca(
         )
         X_pca = pca_.fit_transform(X)
     else:
-        raise Exception("This shouldn't happen. Please open a bug report.")
+        msg = "This shouldn’t happen. Please open a bug report."
+        raise AssertionError(msg)
 
     if X_pca.dtype.descr != np.dtype(dtype).descr:
         X_pca = X_pca.astype(dtype)
@@ -390,7 +391,14 @@ def _handle_mask_var(
     return mask_var, _check_mask(adata, mask_var, "var")
 
 
-def _pca_with_sparse(X, npcs, solver="arpack", mu=None, random_state=None):
+def _pca_with_sparse(
+    X: spmatrix,
+    n_pcs: int,
+    *,
+    solver: str = "arpack",
+    mu: NDArray[np.floating] | None = None,
+    random_state: AnyRandom = None,
+):
     random_state = check_random_state(random_state)
     np.random.set_state(random_state.get_state())
     random_init = np.random.rand(np.min(X.shape))
@@ -429,8 +437,11 @@ def rmatmat(x):
         rmatmat=rmatmat,
     )
 
-    u, s, v = svds(XL, solver=solver, k=npcs, v0=random_init)
-    u, v = svd_flip(u, v)
+    u, s, v = svds(XL, solver=solver, k=n_pcs, v0=random_init)
+    # u_based_decision was changed in https://github.com/scikit-learn/scikit-learn/pull/27491
+    u, v = svd_flip(
+        u, v, u_based_decision=pkg_version("scikit-learn") < Version("1.5.0rc1")
+    )
     idx = np.argsort(-s)
     v = v[idx, :]
 

scanpy/preprocessing/_utils.py

@@ -11,21 +11,19 @@
 from .._utils import AnyRandom, _SupportedArray, axis_sum, elem_mul
 
 if TYPE_CHECKING:
-    from numpy.typing import NDArray
+    from numpy.typing import DTypeLike, NDArray
 
     from .._compat import DaskArray
 
 
 @singledispatch
-def axis_mean(
-    X: DaskArray, *, axis: Literal[0, 1], dtype: np.typing.DTypeLike
-) -> DaskArray:
+def axis_mean(X: DaskArray, *, axis: Literal[0, 1], dtype: DTypeLike) -> DaskArray:
     total = axis_sum(X, axis=axis, dtype=dtype)
     return total / X.shape[axis]
 
 
 @axis_mean.register(np.ndarray)
-def _(X: np.ndarray, *, axis: Literal[0, 1], dtype: np.typing.DTypeLike) -> np.ndarray:
+def _(X: np.ndarray, *, axis: Literal[0, 1], dtype: DTypeLike) -> np.ndarray:
     return X.mean(axis=axis, dtype=dtype)
 
 

scanpy/testing/_pytest/fixtures/data.py

@@ -13,6 +13,7 @@
     from collections.abc import Callable
 
     from anndata import AnnData
+    from numpy.typing import DTypeLike
 
 
 @pytest.fixture(
@@ -47,7 +48,7 @@ def _prepare_pbmc_testdata(
     sparsity_func: Callable[
         [np.ndarray | sparse.spmatrix], np.ndarray | sparse.spmatrix
     ],
-    dtype: str | np.dtype,
+    dtype: DTypeLike,
     *,
     small: bool,
 ) -> AnnData:

scanpy/testing/_pytest/marks.py

@@ -12,7 +12,12 @@ def _next_val(name: str, start: int, count: int, last_values: list[str]) -> str:
     return name.replace("_", "-")
 
 
-class needs(pytest.MarkDecorator, Enum):
+class QuietMarkDecorator(pytest.MarkDecorator):
+    def __init__(self, mark: pytest.Mark) -> None:
+        super().__init__(mark, _ispytest=True)
+
+
+class needs(QuietMarkDecorator, Enum):
     """
     Pytest skip marker evaluated at module import.
 
@@ -57,4 +62,4 @@ def __init__(self, mod: str) -> None:
         if self._name_.casefold() != mod.casefold().replace("-", "_"):
             reason = f"{reason} (`pip install {mod}`)"
         dec = pytest.mark.skipif(not find_spec(self._name_), reason=reason)
-        super().__init__(dec.mark, _ispytest=True)
+        super().__init__(dec.mark)

scanpy/tests/conftest.py

@@ -84,6 +84,8 @@ def check_same_image(
         tol: int,
         basename: str = "",
     ) -> None:
+        __tracebackhide__ = True
+
         def fmt_descr(descr):
             return f"{descr} ({basename})" if basename else descr
 
@@ -122,6 +124,8 @@ def image_comparer(check_same_image):
     from matplotlib import pyplot as plt
 
     def save_and_compare(*path_parts: Path | os.PathLike, tol: int):
+        __tracebackhide__ = True
+
         base_pth = Path(*path_parts)
 
         if not base_pth.is_dir():

scanpy/tests/test_normalization.py

@@ -7,8 +7,7 @@
 from anndata import AnnData
 from anndata.tests.helpers import assert_equal
 from scipy import sparse
-from scipy.sparse import csr_matrix
-from sklearn.utils import issparse
+from scipy.sparse import csr_matrix, issparse
 
 import scanpy as sc
 from scanpy._utils import axis_sum

scanpy/tests/test_pca.py

@@ -13,7 +13,7 @@
 )
 from packaging.version import Version
 from scipy import sparse
-from sklearn.utils import issparse
+from scipy.sparse import issparse
 
 import scanpy as sc
 from scanpy.testing._helpers import as_dense_dask_array, as_sparse_dask_array
@@ -231,12 +231,14 @@ def test_pca_sparse():
     implicit = sc.pp.pca(pbmc, dtype=np.float64, copy=True)
     explicit = sc.pp.pca(pbmc_dense, dtype=np.float64, copy=True)
 
-    assert np.allclose(implicit.uns["pca"]["variance"], explicit.uns["pca"]["variance"])
-    assert np.allclose(
+    np.testing.assert_allclose(
+        implicit.uns["pca"]["variance"], explicit.uns["pca"]["variance"]
+    )
+    np.testing.assert_allclose(
         implicit.uns["pca"]["variance_ratio"], explicit.uns["pca"]["variance_ratio"]
     )
-    assert np.allclose(implicit.obsm["X_pca"], explicit.obsm["X_pca"])
-    assert np.allclose(implicit.varm["PCs"], explicit.varm["PCs"])
+    np.testing.assert_allclose(implicit.obsm["X_pca"], explicit.obsm["X_pca"])
+    np.testing.assert_allclose(implicit.varm["PCs"], explicit.varm["PCs"])
 
 
 def test_pca_reproducible(array_type):

scanpy/tests/test_plotting.py

@@ -13,7 +13,7 @@
 import seaborn as sns
 from anndata import AnnData
 from matplotlib.testing.compare import compare_images
-from packaging import version
+from packaging.version import Version
 
 import scanpy as sc
 from scanpy._compat import pkg_version
@@ -152,7 +152,7 @@ def test_heatmap(image_comparer):
 
 
 @pytest.mark.skipif(
-    pkg_version("matplotlib") < version.parse("3.1"),
+    pkg_version("matplotlib") < Version("3.1"),
     reason="https://github.com/mwaskom/seaborn/issues/1953",
 )
 @pytest.mark.parametrize(
@@ -1288,7 +1288,10 @@ def test_binary_scatter(image_comparer):
     )
     sc.pp.pca(data)
     sc.pl.pca(data, color="binary")
-    save_and_compare_images("binary_pca")
+    if pkg_version("scikit-learn") >= Version("1.5.0rc1"):
+        save_and_compare_images("binary_pca")
+    else:
+        save_and_compare_images("binary_pca_old")
 
 
 def test_scatter_specify_layer_and_raw():

scanpy/tests/test_preprocessing.py

@@ -9,7 +9,7 @@
 from anndata.tests.helpers import asarray, assert_equal
 from numpy.testing import assert_allclose
 from scipy import sparse as sp
-from sklearn.utils import issparse
+from scipy.sparse import issparse
 
 import scanpy as sc
 from scanpy.testing._helpers import (