reconnect moved files to git repo

venv/lib/python3.11/site-packages/sklearn/externals/README

@@ -0,0 +1,7 @@
+This directory contains bundled external dependencies that are updated
+every once in a while.
+
+Note for distribution packagers: if you want to remove the duplicated
+code and depend on a packaged version, we suggest that you simply do a
+symbolic link in this directory.
+

venv/lib/python3.11/site-packages/sklearn/externals/__init__.py

@@ -0,0 +1,5 @@
+
+"""
+External, bundled dependencies.
+
+"""

venv/lib/python3.11/site-packages/sklearn/externals/_packaging/_structures.py

@@ -0,0 +1,90 @@
+"""Vendoered from
+https://github.com/pypa/packaging/blob/main/packaging/_structures.py
+"""
+# Copyright (c) Donald Stufft and individual contributors.
+# All rights reserved.
+
+# Redistribution and use in source and binary forms, with or without
+# modification, are permitted provided that the following conditions are met:
+
+#     1. Redistributions of source code must retain the above copyright notice,
+#        this list of conditions and the following disclaimer.
+
+#     2. Redistributions in binary form must reproduce the above copyright
+#        notice, this list of conditions and the following disclaimer in the
+#        documentation and/or other materials provided with the distribution.
+
+# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
+# ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
+# WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
+# DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
+# FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
+# DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
+# SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
+# CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
+# OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
+# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+
+
+class InfinityType:
+    def __repr__(self) -> str:
+        return "Infinity"
+
+    def __hash__(self) -> int:
+        return hash(repr(self))
+
+    def __lt__(self, other: object) -> bool:
+        return False
+
+    def __le__(self, other: object) -> bool:
+        return False
+
+    def __eq__(self, other: object) -> bool:
+        return isinstance(other, self.__class__)
+
+    def __ne__(self, other: object) -> bool:
+        return not isinstance(other, self.__class__)
+
+    def __gt__(self, other: object) -> bool:
+        return True
+
+    def __ge__(self, other: object) -> bool:
+        return True
+
+    def __neg__(self: object) -> "NegativeInfinityType":
+        return NegativeInfinity
+
+
+Infinity = InfinityType()
+
+
+class NegativeInfinityType:
+    def __repr__(self) -> str:
+        return "-Infinity"
+
+    def __hash__(self) -> int:
+        return hash(repr(self))
+
+    def __lt__(self, other: object) -> bool:
+        return True
+
+    def __le__(self, other: object) -> bool:
+        return True
+
+    def __eq__(self, other: object) -> bool:
+        return isinstance(other, self.__class__)
+
+    def __ne__(self, other: object) -> bool:
+        return not isinstance(other, self.__class__)
+
+    def __gt__(self, other: object) -> bool:
+        return False
+
+    def __ge__(self, other: object) -> bool:
+        return False
+
+    def __neg__(self: object) -> InfinityType:
+        return Infinity
+
+
+NegativeInfinity = NegativeInfinityType()

venv/lib/python3.11/site-packages/sklearn/externals/_packaging/version.py

@@ -0,0 +1,535 @@
+"""Vendoered from
+https://github.com/pypa/packaging/blob/main/packaging/version.py
+"""
+# Copyright (c) Donald Stufft and individual contributors.
+# All rights reserved.
+
+# Redistribution and use in source and binary forms, with or without
+# modification, are permitted provided that the following conditions are met:
+
+#     1. Redistributions of source code must retain the above copyright notice,
+#        this list of conditions and the following disclaimer.
+
+#     2. Redistributions in binary form must reproduce the above copyright
+#        notice, this list of conditions and the following disclaimer in the
+#        documentation and/or other materials provided with the distribution.
+
+# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
+# ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
+# WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
+# DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
+# FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
+# DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
+# SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
+# CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
+# OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
+# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+
+import collections
+import itertools
+import re
+import warnings
+from typing import Callable, Iterator, List, Optional, SupportsInt, Tuple, Union
+
+from ._structures import Infinity, InfinityType, NegativeInfinity, NegativeInfinityType
+
+__all__ = ["parse", "Version", "LegacyVersion", "InvalidVersion", "VERSION_PATTERN"]
+
+InfiniteTypes = Union[InfinityType, NegativeInfinityType]
+PrePostDevType = Union[InfiniteTypes, Tuple[str, int]]
+SubLocalType = Union[InfiniteTypes, int, str]
+LocalType = Union[
+    NegativeInfinityType,
+    Tuple[
+        Union[
+            SubLocalType,
+            Tuple[SubLocalType, str],
+            Tuple[NegativeInfinityType, SubLocalType],
+        ],
+        ...,
+    ],
+]
+CmpKey = Tuple[
+    int, Tuple[int, ...], PrePostDevType, PrePostDevType, PrePostDevType, LocalType
+]
+LegacyCmpKey = Tuple[int, Tuple[str, ...]]
+VersionComparisonMethod = Callable[
+    [Union[CmpKey, LegacyCmpKey], Union[CmpKey, LegacyCmpKey]], bool
+]
+
+_Version = collections.namedtuple(
+    "_Version", ["epoch", "release", "dev", "pre", "post", "local"]
+)
+
+
+def parse(version: str) -> Union["LegacyVersion", "Version"]:
+    """Parse the given version from a string to an appropriate class.
+
+    Parameters
+    ----------
+    version : str
+        Version in a string format, eg. "0.9.1" or "1.2.dev0".
+
+    Returns
+    -------
+    version : :class:`Version` object or a :class:`LegacyVersion` object
+        Returned class depends on the given version: if is a valid
+        PEP 440 version or a legacy version.
+    """
+    try:
+        return Version(version)
+    except InvalidVersion:
+        return LegacyVersion(version)
+
+
+class InvalidVersion(ValueError):
+    """
+    An invalid version was found, users should refer to PEP 440.
+    """
+
+
+class _BaseVersion:
+    _key: Union[CmpKey, LegacyCmpKey]
+
+    def __hash__(self) -> int:
+        return hash(self._key)
+
+    # Please keep the duplicated `isinstance` check
+    # in the six comparisons hereunder
+    # unless you find a way to avoid adding overhead function calls.
+    def __lt__(self, other: "_BaseVersion") -> bool:
+        if not isinstance(other, _BaseVersion):
+            return NotImplemented
+
+        return self._key < other._key
+
+    def __le__(self, other: "_BaseVersion") -> bool:
+        if not isinstance(other, _BaseVersion):
+            return NotImplemented
+
+        return self._key <= other._key
+
+    def __eq__(self, other: object) -> bool:
+        if not isinstance(other, _BaseVersion):
+            return NotImplemented
+
+        return self._key == other._key
+
+    def __ge__(self, other: "_BaseVersion") -> bool:
+        if not isinstance(other, _BaseVersion):
+            return NotImplemented
+
+        return self._key >= other._key
+
+    def __gt__(self, other: "_BaseVersion") -> bool:
+        if not isinstance(other, _BaseVersion):
+            return NotImplemented
+
+        return self._key > other._key
+
+    def __ne__(self, other: object) -> bool:
+        if not isinstance(other, _BaseVersion):
+            return NotImplemented
+
+        return self._key != other._key
+
+
+class LegacyVersion(_BaseVersion):
+    def __init__(self, version: str) -> None:
+        self._version = str(version)
+        self._key = _legacy_cmpkey(self._version)
+
+        warnings.warn(
+            "Creating a LegacyVersion has been deprecated and will be "
+            "removed in the next major release",
+            DeprecationWarning,
+        )
+
+    def __str__(self) -> str:
+        return self._version
+
+    def __repr__(self) -> str:
+        return f"<LegacyVersion('{self}')>"
+
+    @property
+    def public(self) -> str:
+        return self._version
+
+    @property
+    def base_version(self) -> str:
+        return self._version
+
+    @property
+    def epoch(self) -> int:
+        return -1
+
+    @property
+    def release(self) -> None:
+        return None
+
+    @property
+    def pre(self) -> None:
+        return None
+
+    @property
+    def post(self) -> None:
+        return None
+
+    @property
+    def dev(self) -> None:
+        return None
+
+    @property
+    def local(self) -> None:
+        return None
+
+    @property
+    def is_prerelease(self) -> bool:
+        return False
+
+    @property
+    def is_postrelease(self) -> bool:
+        return False
+
+    @property
+    def is_devrelease(self) -> bool:
+        return False
+
+
+_legacy_version_component_re = re.compile(r"(\d+ | [a-z]+ | \.| -)", re.VERBOSE)
+
+_legacy_version_replacement_map = {
+    "pre": "c",
+    "preview": "c",
+    "-": "final-",
+    "rc": "c",
+    "dev": "@",
+}
+
+
+def _parse_version_parts(s: str) -> Iterator[str]:
+    for part in _legacy_version_component_re.split(s):
+        part = _legacy_version_replacement_map.get(part, part)
+
+        if not part or part == ".":
+            continue
+
+        if part[:1] in "0123456789":
+            # pad for numeric comparison
+            yield part.zfill(8)
+        else:
+            yield "*" + part
+
+    # ensure that alpha/beta/candidate are before final
+    yield "*final"
+
+
+def _legacy_cmpkey(version: str) -> LegacyCmpKey:
+
+    # We hardcode an epoch of -1 here. A PEP 440 version can only have a epoch
+    # greater than or equal to 0. This will effectively put the LegacyVersion,
+    # which uses the defacto standard originally implemented by setuptools,
+    # as before all PEP 440 versions.
+    epoch = -1
+
+    # This scheme is taken from pkg_resources.parse_version setuptools prior to
+    # it's adoption of the packaging library.
+    parts: List[str] = []
+    for part in _parse_version_parts(version.lower()):
+        if part.startswith("*"):
+            # remove "-" before a prerelease tag
+            if part < "*final":
+                while parts and parts[-1] == "*final-":
+                    parts.pop()
+
+            # remove trailing zeros from each series of numeric parts
+            while parts and parts[-1] == "00000000":
+                parts.pop()
+
+        parts.append(part)
+
+    return epoch, tuple(parts)
+
+
+# Deliberately not anchored to the start and end of the string, to make it
+# easier for 3rd party code to reuse
+VERSION_PATTERN = r"""
+    v?
+    (?:
+        (?:(?P<epoch>[0-9]+)!)?                           # epoch
+        (?P<release>[0-9]+(?:\.[0-9]+)*)                  # release segment
+        (?P<pre>                                          # pre-release
+            [-_\.]?
+            (?P<pre_l>(a|b|c|rc|alpha|beta|pre|preview))
+            [-_\.]?
+            (?P<pre_n>[0-9]+)?
+        )?
+        (?P<post>                                         # post release
+            (?:-(?P<post_n1>[0-9]+))
+            |
+            (?:
+                [-_\.]?
+                (?P<post_l>post|rev|r)
+                [-_\.]?
+                (?P<post_n2>[0-9]+)?
+            )
+        )?
+        (?P<dev>                                          # dev release
+            [-_\.]?
+            (?P<dev_l>dev)
+            [-_\.]?
+            (?P<dev_n>[0-9]+)?
+        )?
+    )
+    (?:\+(?P<local>[a-z0-9]+(?:[-_\.][a-z0-9]+)*))?       # local version
+"""
+
+
+class Version(_BaseVersion):
+
+    _regex = re.compile(r"^\s*" + VERSION_PATTERN + r"\s*$", re.VERBOSE | re.IGNORECASE)
+
+    def __init__(self, version: str) -> None:
+
+        # Validate the version and parse it into pieces
+        match = self._regex.search(version)
+        if not match:
+            raise InvalidVersion(f"Invalid version: '{version}'")
+
+        # Store the parsed out pieces of the version
+        self._version = _Version(
+            epoch=int(match.group("epoch")) if match.group("epoch") else 0,
+            release=tuple(int(i) for i in match.group("release").split(".")),
+            pre=_parse_letter_version(match.group("pre_l"), match.group("pre_n")),
+            post=_parse_letter_version(
+                match.group("post_l"), match.group("post_n1") or match.group("post_n2")
+            ),
+            dev=_parse_letter_version(match.group("dev_l"), match.group("dev_n")),
+            local=_parse_local_version(match.group("local")),
+        )
+
+        # Generate a key which will be used for sorting
+        self._key = _cmpkey(
+            self._version.epoch,
+            self._version.release,
+            self._version.pre,
+            self._version.post,
+            self._version.dev,
+            self._version.local,
+        )
+
+    def __repr__(self) -> str:
+        return f"<Version('{self}')>"
+
+    def __str__(self) -> str:
+        parts = []
+
+        # Epoch
+        if self.epoch != 0:
+            parts.append(f"{self.epoch}!")
+
+        # Release segment
+        parts.append(".".join(str(x) for x in self.release))
+
+        # Pre-release
+        if self.pre is not None:
+            parts.append("".join(str(x) for x in self.pre))
+
+        # Post-release
+        if self.post is not None:
+            parts.append(f".post{self.post}")
+
+        # Development release
+        if self.dev is not None:
+            parts.append(f".dev{self.dev}")
+
+        # Local version segment
+        if self.local is not None:
+            parts.append(f"+{self.local}")
+
+        return "".join(parts)
+
+    @property
+    def epoch(self) -> int:
+        _epoch: int = self._version.epoch
+        return _epoch
+
+    @property
+    def release(self) -> Tuple[int, ...]:
+        _release: Tuple[int, ...] = self._version.release
+        return _release
+
+    @property
+    def pre(self) -> Optional[Tuple[str, int]]:
+        _pre: Optional[Tuple[str, int]] = self._version.pre
+        return _pre
+
+    @property
+    def post(self) -> Optional[int]:
+        return self._version.post[1] if self._version.post else None
+
+    @property
+    def dev(self) -> Optional[int]:
+        return self._version.dev[1] if self._version.dev else None
+
+    @property
+    def local(self) -> Optional[str]:
+        if self._version.local:
+            return ".".join(str(x) for x in self._version.local)
+        else:
+            return None
+
+    @property
+    def public(self) -> str:
+        return str(self).split("+", 1)[0]
+
+    @property
+    def base_version(self) -> str:
+        parts = []
+
+        # Epoch
+        if self.epoch != 0:
+            parts.append(f"{self.epoch}!")
+
+        # Release segment
+        parts.append(".".join(str(x) for x in self.release))
+
+        return "".join(parts)
+
+    @property
+    def is_prerelease(self) -> bool:
+        return self.dev is not None or self.pre is not None
+
+    @property
+    def is_postrelease(self) -> bool:
+        return self.post is not None
+
+    @property
+    def is_devrelease(self) -> bool:
+        return self.dev is not None
+
+    @property
+    def major(self) -> int:
+        return self.release[0] if len(self.release) >= 1 else 0
+
+    @property
+    def minor(self) -> int:
+        return self.release[1] if len(self.release) >= 2 else 0
+
+    @property
+    def micro(self) -> int:
+        return self.release[2] if len(self.release) >= 3 else 0
+
+
+def _parse_letter_version(
+    letter: str, number: Union[str, bytes, SupportsInt]
+) -> Optional[Tuple[str, int]]:
+
+    if letter:
+        # We consider there to be an implicit 0 in a pre-release if there is
+        # not a numeral associated with it.
+        if number is None:
+            number = 0
+
+        # We normalize any letters to their lower case form
+        letter = letter.lower()
+
+        # We consider some words to be alternate spellings of other words and
+        # in those cases we want to normalize the spellings to our preferred
+        # spelling.
+        if letter == "alpha":
+            letter = "a"
+        elif letter == "beta":
+            letter = "b"
+        elif letter in ["c", "pre", "preview"]:
+            letter = "rc"
+        elif letter in ["rev", "r"]:
+            letter = "post"
+
+        return letter, int(number)
+    if not letter and number:
+        # We assume if we are given a number, but we are not given a letter
+        # then this is using the implicit post release syntax (e.g. 1.0-1)
+        letter = "post"
+
+        return letter, int(number)
+
+    return None
+
+
+_local_version_separators = re.compile(r"[\._-]")
+
+
+def _parse_local_version(local: str) -> Optional[LocalType]:
+    """
+    Takes a string like abc.1.twelve and turns it into ("abc", 1, "twelve").
+    """
+    if local is not None:
+        return tuple(
+            part.lower() if not part.isdigit() else int(part)
+            for part in _local_version_separators.split(local)
+        )
+    return None
+
+
+def _cmpkey(
+    epoch: int,
+    release: Tuple[int, ...],
+    pre: Optional[Tuple[str, int]],
+    post: Optional[Tuple[str, int]],
+    dev: Optional[Tuple[str, int]],
+    local: Optional[Tuple[SubLocalType]],
+) -> CmpKey:
+
+    # When we compare a release version, we want to compare it with all of the
+    # trailing zeros removed. So we'll use a reverse the list, drop all the now
+    # leading zeros until we come to something non zero, then take the rest
+    # re-reverse it back into the correct order and make it a tuple and use
+    # that for our sorting key.
+    _release = tuple(
+        reversed(list(itertools.dropwhile(lambda x: x == 0, reversed(release))))
+    )
+
+    # We need to "trick" the sorting algorithm to put 1.0.dev0 before 1.0a0.
+    # We'll do this by abusing the pre segment, but we _only_ want to do this
+    # if there is not a pre or a post segment. If we have one of those then
+    # the normal sorting rules will handle this case correctly.
+    if pre is None and post is None and dev is not None:
+        _pre: PrePostDevType = NegativeInfinity
+    # Versions without a pre-release (except as noted above) should sort after
+    # those with one.
+    elif pre is None:
+        _pre = Infinity
+    else:
+        _pre = pre
+
+    # Versions without a post segment should sort before those with one.
+    if post is None:
+        _post: PrePostDevType = NegativeInfinity
+
+    else:
+        _post = post
+
+    # Versions without a development segment should sort after those with one.
+    if dev is None:
+        _dev: PrePostDevType = Infinity
+
+    else:
+        _dev = dev
+
+    if local is None:
+        # Versions without a local segment should sort before those with one.
+        _local: LocalType = NegativeInfinity
+    else:
+        # Versions with a local segment need that segment parsed to implement
+        # the sorting rules in PEP440.
+        # - Alpha numeric segments sort before numeric segments
+        # - Alpha numeric segments sort lexicographically
+        # - Numeric segments sort numerically
+        # - Shorter versions sort before longer versions when the prefixes
+        #   match exactly
+        _local = tuple(
+            (i, "") if isinstance(i, int) else (NegativeInfinity, i) for i in local
+        )
+
+    return epoch, _release, _pre, _post, _dev, _local

venv/lib/python3.11/site-packages/sklearn/externals/_scipy/sparse/csgraph/__init__.py

@@ -0,0 +1 @@
+from ._laplacian import laplacian

venv/lib/python3.11/site-packages/sklearn/externals/_scipy/sparse/csgraph/_laplacian.py

@@ -0,0 +1,557 @@
+"""
+This file is a copy of the scipy.sparse.csgraph._laplacian module from SciPy 1.12
+
+scipy.sparse.csgraph.laplacian supports sparse arrays only starting from Scipy 1.12,
+see https://github.com/scipy/scipy/pull/19156. This vendored file can be removed as
+soon as Scipy 1.12 becomes the minimum supported version.
+
+Laplacian of a compressed-sparse graph
+"""
+
+# License: BSD 3 clause
+
+import numpy as np
+from scipy.sparse import issparse
+from scipy.sparse.linalg import LinearOperator
+
+
+###############################################################################
+# Graph laplacian
+def laplacian(
+    csgraph,
+    normed=False,
+    return_diag=False,
+    use_out_degree=False,
+    *,
+    copy=True,
+    form="array",
+    dtype=None,
+    symmetrized=False,
+):
+    """
+    Return the Laplacian of a directed graph.
+
+    Parameters
+    ----------
+    csgraph : array_like or sparse matrix, 2 dimensions
+        Compressed-sparse graph, with shape (N, N).
+    normed : bool, optional
+        If True, then compute symmetrically normalized Laplacian.
+        Default: False.
+    return_diag : bool, optional
+        If True, then also return an array related to vertex degrees.
+        Default: False.
+    use_out_degree : bool, optional
+        If True, then use out-degree instead of in-degree.
+        This distinction matters only if the graph is asymmetric.
+        Default: False.
+    copy : bool, optional
+        If False, then change `csgraph` in place if possible,
+        avoiding doubling the memory use.
+        Default: True, for backward compatibility.
+    form : 'array', or 'function', or 'lo'
+        Determines the format of the output Laplacian:
+
+        * 'array' is a numpy array;
+        * 'function' is a pointer to evaluating the Laplacian-vector
+          or Laplacian-matrix product;
+        * 'lo' results in the format of the `LinearOperator`.
+
+        Choosing 'function' or 'lo' always avoids doubling
+        the memory use, ignoring `copy` value.
+        Default: 'array', for backward compatibility.
+    dtype : None or one of numeric numpy dtypes, optional
+        The dtype of the output. If ``dtype=None``, the dtype of the
+        output matches the dtype of the input csgraph, except for
+        the case ``normed=True`` and integer-like csgraph, where
+        the output dtype is 'float' allowing accurate normalization,
+        but dramatically increasing the memory use.
+        Default: None, for backward compatibility.
+    symmetrized : bool, optional
+        If True, then the output Laplacian is symmetric/Hermitian.
+        The symmetrization is done by ``csgraph + csgraph.T.conj``
+        without dividing by 2 to preserve integer dtypes if possible
+        prior to the construction of the Laplacian.
+        The symmetrization will increase the memory footprint of
+        sparse matrices unless the sparsity pattern is symmetric or
+        `form` is 'function' or 'lo'.
+        Default: False, for backward compatibility.
+
+    Returns
+    -------
+    lap : ndarray, or sparse matrix, or `LinearOperator`
+        The N x N Laplacian of csgraph. It will be a NumPy array (dense)
+        if the input was dense, or a sparse matrix otherwise, or
+        the format of a function or `LinearOperator` if
+        `form` equals 'function' or 'lo', respectively.
+    diag : ndarray, optional
+        The length-N main diagonal of the Laplacian matrix.
+        For the normalized Laplacian, this is the array of square roots
+        of vertex degrees or 1 if the degree is zero.
+
+    Notes
+    -----
+    The Laplacian matrix of a graph is sometimes referred to as the
+    "Kirchhoff matrix" or just the "Laplacian", and is useful in many
+    parts of spectral graph theory.
+    In particular, the eigen-decomposition of the Laplacian can give
+    insight into many properties of the graph, e.g.,
+    is commonly used for spectral data embedding and clustering.
+
+    The constructed Laplacian doubles the memory use if ``copy=True`` and
+    ``form="array"`` which is the default.
+    Choosing ``copy=False`` has no effect unless ``form="array"``
+    or the matrix is sparse in the ``coo`` format, or dense array, except
+    for the integer input with ``normed=True`` that forces the float output.
+
+    Sparse input is reformatted into ``coo`` if ``form="array"``,
+    which is the default.
+
+    If the input adjacency matrix is not symmetric, the Laplacian is
+    also non-symmetric unless ``symmetrized=True`` is used.
+
+    Diagonal entries of the input adjacency matrix are ignored and
+    replaced with zeros for the purpose of normalization where ``normed=True``.
+    The normalization uses the inverse square roots of row-sums of the input
+    adjacency matrix, and thus may fail if the row-sums contain
+    negative or complex with a non-zero imaginary part values.
+
+    The normalization is symmetric, making the normalized Laplacian also
+    symmetric if the input csgraph was symmetric.
+
+    References
+    ----------
+    .. [1] Laplacian matrix. https://en.wikipedia.org/wiki/Laplacian_matrix
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> from scipy.sparse import csgraph
+
+    Our first illustration is the symmetric graph
+
+    >>> G = np.arange(4) * np.arange(4)[:, np.newaxis]
+    >>> G
+    array([[0, 0, 0, 0],
+           [0, 1, 2, 3],
+           [0, 2, 4, 6],
+           [0, 3, 6, 9]])
+
+    and its symmetric Laplacian matrix
+
+    >>> csgraph.laplacian(G)
+    array([[ 0,  0,  0,  0],
+           [ 0,  5, -2, -3],
+           [ 0, -2,  8, -6],
+           [ 0, -3, -6,  9]])
+
+    The non-symmetric graph
+
+    >>> G = np.arange(9).reshape(3, 3)
+    >>> G
+    array([[0, 1, 2],
+           [3, 4, 5],
+           [6, 7, 8]])
+
+    has different row- and column sums, resulting in two varieties
+    of the Laplacian matrix, using an in-degree, which is the default
+
+    >>> L_in_degree = csgraph.laplacian(G)
+    >>> L_in_degree
+    array([[ 9, -1, -2],
+           [-3,  8, -5],
+           [-6, -7,  7]])
+
+    or alternatively an out-degree
+
+    >>> L_out_degree = csgraph.laplacian(G, use_out_degree=True)
+    >>> L_out_degree
+    array([[ 3, -1, -2],
+           [-3,  8, -5],
+           [-6, -7, 13]])
+
+    Constructing a symmetric Laplacian matrix, one can add the two as
+
+    >>> L_in_degree + L_out_degree.T
+    array([[ 12,  -4,  -8],
+            [ -4,  16, -12],
+            [ -8, -12,  20]])
+
+    or use the ``symmetrized=True`` option
+
+    >>> csgraph.laplacian(G, symmetrized=True)
+    array([[ 12,  -4,  -8],
+           [ -4,  16, -12],
+           [ -8, -12,  20]])
+
+    that is equivalent to symmetrizing the original graph
+
+    >>> csgraph.laplacian(G + G.T)
+    array([[ 12,  -4,  -8],
+           [ -4,  16, -12],
+           [ -8, -12,  20]])
+
+    The goal of normalization is to make the non-zero diagonal entries
+    of the Laplacian matrix to be all unit, also scaling off-diagonal
+    entries correspondingly. The normalization can be done manually, e.g.,
+
+    >>> G = np.array([[0, 1, 1], [1, 0, 1], [1, 1, 0]])
+    >>> L, d = csgraph.laplacian(G, return_diag=True)
+    >>> L
+    array([[ 2, -1, -1],
+           [-1,  2, -1],
+           [-1, -1,  2]])
+    >>> d
+    array([2, 2, 2])
+    >>> scaling = np.sqrt(d)
+    >>> scaling
+    array([1.41421356, 1.41421356, 1.41421356])
+    >>> (1/scaling)*L*(1/scaling)
+    array([[ 1. , -0.5, -0.5],
+           [-0.5,  1. , -0.5],
+           [-0.5, -0.5,  1. ]])
+
+    Or using ``normed=True`` option
+
+    >>> L, d = csgraph.laplacian(G, return_diag=True, normed=True)
+    >>> L
+    array([[ 1. , -0.5, -0.5],
+           [-0.5,  1. , -0.5],
+           [-0.5, -0.5,  1. ]])
+
+    which now instead of the diagonal returns the scaling coefficients
+
+    >>> d
+    array([1.41421356, 1.41421356, 1.41421356])
+
+    Zero scaling coefficients are substituted with 1s, where scaling
+    has thus no effect, e.g.,
+
+    >>> G = np.array([[0, 0, 0], [0, 0, 1], [0, 1, 0]])
+    >>> G
+    array([[0, 0, 0],
+           [0, 0, 1],
+           [0, 1, 0]])
+    >>> L, d = csgraph.laplacian(G, return_diag=True, normed=True)
+    >>> L
+    array([[ 0., -0., -0.],
+           [-0.,  1., -1.],
+           [-0., -1.,  1.]])
+    >>> d
+    array([1., 1., 1.])
+
+    Only the symmetric normalization is implemented, resulting
+    in a symmetric Laplacian matrix if and only if its graph is symmetric
+    and has all non-negative degrees, like in the examples above.
+
+    The output Laplacian matrix is by default a dense array or a sparse matrix
+    inferring its shape, format, and dtype from the input graph matrix:
+
+    >>> G = np.array([[0, 1, 1], [1, 0, 1], [1, 1, 0]]).astype(np.float32)
+    >>> G
+    array([[0., 1., 1.],
+           [1., 0., 1.],
+           [1., 1., 0.]], dtype=float32)
+    >>> csgraph.laplacian(G)
+    array([[ 2., -1., -1.],
+           [-1.,  2., -1.],
+           [-1., -1.,  2.]], dtype=float32)
+
+    but can alternatively be generated matrix-free as a LinearOperator:
+
+    >>> L = csgraph.laplacian(G, form="lo")
+    >>> L
+    <3x3 _CustomLinearOperator with dtype=float32>
+    >>> L(np.eye(3))
+    array([[ 2., -1., -1.],
+           [-1.,  2., -1.],
+           [-1., -1.,  2.]])
+
+    or as a lambda-function:
+
+    >>> L = csgraph.laplacian(G, form="function")
+    >>> L
+    <function _laplace.<locals>.<lambda> at 0x0000012AE6F5A598>
+    >>> L(np.eye(3))
+    array([[ 2., -1., -1.],
+           [-1.,  2., -1.],
+           [-1., -1.,  2.]])
+
+    The Laplacian matrix is used for
+    spectral data clustering and embedding
+    as well as for spectral graph partitioning.
+    Our final example illustrates the latter
+    for a noisy directed linear graph.
+
+    >>> from scipy.sparse import diags, random
+    >>> from scipy.sparse.linalg import lobpcg
+
+    Create a directed linear graph with ``N=35`` vertices
+    using a sparse adjacency matrix ``G``:
+
+    >>> N = 35
+    >>> G = diags(np.ones(N-1), 1, format="csr")
+
+    Fix a random seed ``rng`` and add a random sparse noise to the graph ``G``:
+
+    >>> rng = np.random.default_rng()
+    >>> G += 1e-2 * random(N, N, density=0.1, random_state=rng)
+
+    Set initial approximations for eigenvectors:
+
+    >>> X = rng.random((N, 2))
+
+    The constant vector of ones is always a trivial eigenvector
+    of the non-normalized Laplacian to be filtered out:
+
+    >>> Y = np.ones((N, 1))
+
+    Alternating (1) the sign of the graph weights allows determining
+    labels for spectral max- and min- cuts in a single loop.
+    Since the graph is undirected, the option ``symmetrized=True``
+    must be used in the construction of the Laplacian.
+    The option ``normed=True`` cannot be used in (2) for the negative weights
+    here as the symmetric normalization evaluates square roots.
+    The option ``form="lo"`` in (2) is matrix-free, i.e., guarantees
+    a fixed memory footprint and read-only access to the graph.
+    Calling the eigenvalue solver ``lobpcg`` (3) computes the Fiedler vector
+    that determines the labels as the signs of its components in (5).
+    Since the sign in an eigenvector is not deterministic and can flip,
+    we fix the sign of the first component to be always +1 in (4).
+
+    >>> for cut in ["max", "min"]:
+    ...     G = -G  # 1.
+    ...     L = csgraph.laplacian(G, symmetrized=True, form="lo")  # 2.
+    ...     _, eves = lobpcg(L, X, Y=Y, largest=False, tol=1e-3)  # 3.
+    ...     eves *= np.sign(eves[0, 0])  # 4.
+    ...     print(cut + "-cut labels:\\n", 1 * (eves[:, 0]>0))  # 5.
+    max-cut labels:
+    [1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1]
+    min-cut labels:
+    [1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]
+
+    As anticipated for a (slightly noisy) linear graph,
+    the max-cut strips all the edges of the graph coloring all
+    odd vertices into one color and all even vertices into another one,
+    while the balanced min-cut partitions the graph
+    in the middle by deleting a single edge.
+    Both determined partitions are optimal.
+    """
+    if csgraph.ndim != 2 or csgraph.shape[0] != csgraph.shape[1]:
+        raise ValueError("csgraph must be a square matrix or array")
+
+    if normed and (
+        np.issubdtype(csgraph.dtype, np.signedinteger)
+        or np.issubdtype(csgraph.dtype, np.uint)
+    ):
+        csgraph = csgraph.astype(np.float64)
+
+    if form == "array":
+        create_lap = _laplacian_sparse if issparse(csgraph) else _laplacian_dense
+    else:
+        create_lap = (
+            _laplacian_sparse_flo if issparse(csgraph) else _laplacian_dense_flo
+        )
+
+    degree_axis = 1 if use_out_degree else 0
+
+    lap, d = create_lap(
+        csgraph,
+        normed=normed,
+        axis=degree_axis,
+        copy=copy,
+        form=form,
+        dtype=dtype,
+        symmetrized=symmetrized,
+    )
+    if return_diag:
+        return lap, d
+    return lap
+
+
+def _setdiag_dense(m, d):
+    step = len(d) + 1
+    m.flat[::step] = d
+
+
+def _laplace(m, d):
+    return lambda v: v * d[:, np.newaxis] - m @ v
+
+
+def _laplace_normed(m, d, nd):
+    laplace = _laplace(m, d)
+    return lambda v: nd[:, np.newaxis] * laplace(v * nd[:, np.newaxis])
+
+
+def _laplace_sym(m, d):
+    return (
+        lambda v: v * d[:, np.newaxis]
+        - m @ v
+        - np.transpose(np.conjugate(np.transpose(np.conjugate(v)) @ m))
+    )
+
+
+def _laplace_normed_sym(m, d, nd):
+    laplace_sym = _laplace_sym(m, d)
+    return lambda v: nd[:, np.newaxis] * laplace_sym(v * nd[:, np.newaxis])
+
+
+def _linearoperator(mv, shape, dtype):
+    return LinearOperator(matvec=mv, matmat=mv, shape=shape, dtype=dtype)
+
+
+def _laplacian_sparse_flo(graph, normed, axis, copy, form, dtype, symmetrized):
+    # The keyword argument `copy` is unused and has no effect here.
+    del copy
+
+    if dtype is None:
+        dtype = graph.dtype
+
+    graph_sum = np.asarray(graph.sum(axis=axis)).ravel()
+    graph_diagonal = graph.diagonal()
+    diag = graph_sum - graph_diagonal
+    if symmetrized:
+        graph_sum += np.asarray(graph.sum(axis=1 - axis)).ravel()
+        diag = graph_sum - graph_diagonal - graph_diagonal
+
+    if normed:
+        isolated_node_mask = diag == 0
+        w = np.where(isolated_node_mask, 1, np.sqrt(diag))
+        if symmetrized:
+            md = _laplace_normed_sym(graph, graph_sum, 1.0 / w)
+        else:
+            md = _laplace_normed(graph, graph_sum, 1.0 / w)
+        if form == "function":
+            return md, w.astype(dtype, copy=False)
+        elif form == "lo":
+            m = _linearoperator(md, shape=graph.shape, dtype=dtype)
+            return m, w.astype(dtype, copy=False)
+        else:
+            raise ValueError(f"Invalid form: {form!r}")
+    else:
+        if symmetrized:
+            md = _laplace_sym(graph, graph_sum)
+        else:
+            md = _laplace(graph, graph_sum)
+        if form == "function":
+            return md, diag.astype(dtype, copy=False)
+        elif form == "lo":
+            m = _linearoperator(md, shape=graph.shape, dtype=dtype)
+            return m, diag.astype(dtype, copy=False)
+        else:
+            raise ValueError(f"Invalid form: {form!r}")
+
+
+def _laplacian_sparse(graph, normed, axis, copy, form, dtype, symmetrized):
+    # The keyword argument `form` is unused and has no effect here.
+    del form
+
+    if dtype is None:
+        dtype = graph.dtype
+
+    needs_copy = False
+    if graph.format in ("lil", "dok"):
+        m = graph.tocoo()
+    else:
+        m = graph
+        if copy:
+            needs_copy = True
+
+    if symmetrized:
+        m += m.T.conj()
+
+    w = np.asarray(m.sum(axis=axis)).ravel() - m.diagonal()
+    if normed:
+        m = m.tocoo(copy=needs_copy)
+        isolated_node_mask = w == 0
+        w = np.where(isolated_node_mask, 1, np.sqrt(w))
+        m.data /= w[m.row]
+        m.data /= w[m.col]
+        m.data *= -1
+        m.setdiag(1 - isolated_node_mask)
+    else:
+        if m.format == "dia":
+            m = m.copy()
+        else:
+            m = m.tocoo(copy=needs_copy)
+        m.data *= -1
+        m.setdiag(w)
+
+    return m.astype(dtype, copy=False), w.astype(dtype)
+
+
+def _laplacian_dense_flo(graph, normed, axis, copy, form, dtype, symmetrized):
+    if copy:
+        m = np.array(graph)
+    else:
+        m = np.asarray(graph)
+
+    if dtype is None:
+        dtype = m.dtype
+
+    graph_sum = m.sum(axis=axis)
+    graph_diagonal = m.diagonal()
+    diag = graph_sum - graph_diagonal
+    if symmetrized:
+        graph_sum += m.sum(axis=1 - axis)
+        diag = graph_sum - graph_diagonal - graph_diagonal
+
+    if normed:
+        isolated_node_mask = diag == 0
+        w = np.where(isolated_node_mask, 1, np.sqrt(diag))
+        if symmetrized:
+            md = _laplace_normed_sym(m, graph_sum, 1.0 / w)
+        else:
+            md = _laplace_normed(m, graph_sum, 1.0 / w)
+        if form == "function":
+            return md, w.astype(dtype, copy=False)
+        elif form == "lo":
+            m = _linearoperator(md, shape=graph.shape, dtype=dtype)
+            return m, w.astype(dtype, copy=False)
+        else:
+            raise ValueError(f"Invalid form: {form!r}")
+    else:
+        if symmetrized:
+            md = _laplace_sym(m, graph_sum)
+        else:
+            md = _laplace(m, graph_sum)
+        if form == "function":
+            return md, diag.astype(dtype, copy=False)
+        elif form == "lo":
+            m = _linearoperator(md, shape=graph.shape, dtype=dtype)
+            return m, diag.astype(dtype, copy=False)
+        else:
+            raise ValueError(f"Invalid form: {form!r}")
+
+
+def _laplacian_dense(graph, normed, axis, copy, form, dtype, symmetrized):
+    if form != "array":
+        raise ValueError(f'{form!r} must be "array"')
+
+    if dtype is None:
+        dtype = graph.dtype
+
+    if copy:
+        m = np.array(graph)
+    else:
+        m = np.asarray(graph)
+
+    if dtype is None:
+        dtype = m.dtype
+
+    if symmetrized:
+        m += m.T.conj()
+    np.fill_diagonal(m, 0)
+    w = m.sum(axis=axis)
+    if normed:
+        isolated_node_mask = w == 0
+        w = np.where(isolated_node_mask, 1, np.sqrt(w))
+        m /= w
+        m /= w[:, np.newaxis]
+        m *= -1
+        _setdiag_dense(m, 1 - isolated_node_mask)
+    else:
+        m *= -1
+        _setdiag_dense(m, w)
+
+    return m.astype(dtype, copy=False), w.astype(dtype, copy=False)

venv/lib/python3.11/site-packages/sklearn/externals/conftest.py

@@ -0,0 +1,6 @@
+# Do not collect any tests in externals. This is more robust than using
+# --ignore because --ignore needs a path and it is not convenient to pass in
+# the externals path (very long install-dependent path in site-packages) when
+# using --pyargs
+def pytest_ignore_collect(collection_path, config):
+    return True