_base.py

"""
Base IO code for all datasets
"""

# Authors: The scikit-learn developers
# SPDX-License-Identifier: BSD-3-Clause

import csv
import gzip
import hashlib
import os
import re
import shutil
import time
import unicodedata
import warnings
from collections import namedtuple
from importlib import resources
from numbers import Integral
from os import environ, listdir, makedirs
from os.path import expanduser, isdir, join, splitext
from pathlib import Path
from tempfile import NamedTemporaryFile
from urllib.error import URLError
from urllib.parse import urlparse
from urllib.request import urlretrieve

import numpy as np

from ..preprocessing import scale
from ..utils import Bunch, check_random_state
from ..utils._optional_dependencies import check_pandas_support
from ..utils._param_validation import Interval, StrOptions, validate_params

DATA_MODULE = "sklearn.datasets.data"
DESCR_MODULE = "sklearn.datasets.descr"
IMAGES_MODULE = "sklearn.datasets.images"

RemoteFileMetadata = namedtuple("RemoteFileMetadata", ["filename", "url", "checksum"])


@validate_params(
    {
        "data_home": [str, os.PathLike, None],
    },
    prefer_skip_nested_validation=True,
)
def get_data_home(data_home=None) -> str:
    """Return the path of the scikit-learn data directory.

    This folder is used by some large dataset loaders to avoid downloading the
    data several times.

    By default the data directory is set to a folder named 'scikit_learn_data' in the
    user home folder.

    Alternatively, it can be set by the 'SCIKIT_LEARN_DATA' environment
    variable or programmatically by giving an explicit folder path. The '~'
    symbol is expanded to the user home folder.

    If the folder does not already exist, it is automatically created.

    Parameters
    ----------
    data_home : str or path-like, default=None
        The path to scikit-learn data directory. If `None`, the default path
        is `~/scikit_learn_data`.

    Returns
    -------
    data_home: str
        The path to scikit-learn data directory.

    Examples
    --------
    >>> import os
    >>> from sklearn.datasets import get_data_home
    >>> data_home_path = get_data_home()
    >>> os.path.exists(data_home_path)
    True
    """
    if data_home is None:
        data_home = environ.get("SCIKIT_LEARN_DATA", join("~", "scikit_learn_data"))
    data_home = expanduser(data_home)
    makedirs(data_home, exist_ok=True)
    return data_home


@validate_params(
    {
        "data_home": [str, os.PathLike, None],
    },
    prefer_skip_nested_validation=True,
)
def clear_data_home(data_home=None):
    """Delete all the content of the data home cache.

    Parameters
    ----------
    data_home : str or path-like, default=None
        The path to scikit-learn data directory. If `None`, the default path
        is `~/scikit_learn_data`.

    Examples
    --------
    >>> from sklearn.datasets import clear_data_home
    >>> clear_data_home()  # doctest: +SKIP
    """
    data_home = get_data_home(data_home)
    shutil.rmtree(data_home)


def _convert_data_dataframe(
    caller_name, data, target, feature_names, target_names, sparse_data=False
):
    pd = check_pandas_support("{} with as_frame=True".format(caller_name))
    if not sparse_data:
        data_df = pd.DataFrame(data, columns=feature_names, copy=False)
    else:
        data_df = pd.DataFrame.sparse.from_spmatrix(data, columns=feature_names)

    target_df = pd.DataFrame(target, columns=target_names)
    combined_df = pd.concat([data_df, target_df], axis=1)
    X = combined_df[feature_names]
    y = combined_df[target_names]
    if y.shape[1] == 1:
        y = y.iloc[:, 0]
    return combined_df, X, y


@validate_params(
    {
        "container_path": [str, os.PathLike],
        "description": [str, None],
        "categories": [list, None],
        "load_content": ["boolean"],
        "shuffle": ["boolean"],
        "encoding": [str, None],
        "decode_error": [StrOptions({"strict", "ignore", "replace"})],
        "random_state": ["random_state"],
        "allowed_extensions": [list, None],
    },
    prefer_skip_nested_validation=True,
)
def load_files(
    container_path,
    *,
    description=None,
    categories=None,
    load_content=True,
    shuffle=True,
    encoding=None,
    decode_error="strict",
    random_state=0,
    allowed_extensions=None,
):
    """Load text files with categories as subfolder names.

    Individual samples are assumed to be files stored a two levels folder
    structure such as the following:

    .. code-block:: text

        container_folder/
            category_1_folder/
                file_1.txt
                file_2.txt
                ...
                file_42.txt
            category_2_folder/
                file_43.txt
                file_44.txt
                ...

    The folder names are used as supervised signal label names. The individual
    file names are not important.

    This function does not try to extract features into a numpy array or scipy
    sparse matrix. In addition, if load_content is false it does not try to
    load the files in memory.

    To use text files in a scikit-learn classification or clustering algorithm,
    you will need to use the :mod:`~sklearn.feature_extraction.text` module to
    build a feature extraction transformer that suits your problem.

    If you set load_content=True, you should also specify the encoding of the
    text using the 'encoding' parameter. For many modern text files, 'utf-8'
    will be the correct encoding. If you leave encoding equal to None, then the
    content will be made of bytes instead of Unicode, and you will not be able
    to use most functions in :mod:`~sklearn.feature_extraction.text`.

    Similar feature extractors should be built for other kind of unstructured
    data input such as images, audio, video, ...

    If you want files with a specific file extension (e.g. `.txt`) then you
    can pass a list of those file extensions to `allowed_extensions`.

    Read more in the :ref:`User Guide <datasets>`.

    Parameters
    ----------
    container_path : str
        Path to the main folder holding one subfolder per category.

    description : str, default=None
        A paragraph describing the characteristic of the dataset: its source,
        reference, etc.

    categories : list of str, default=None
        If None (default), load all the categories. If not None, list of
        category names to load (other categories ignored).

    load_content : bool, default=True
        Whether to load or not the content of the different files. If true a
        'data' attribute containing the text information is present in the data
        structure returned. If not, a filenames attribute gives the path to the
        files.

    shuffle : bool, default=True
        Whether or not to shuffle the data: might be important for models that
        make the assumption that the samples are independent and identically
        distributed (i.i.d.), such as stochastic gradient descent.

    encoding : str, default=None
        If None, do not try to decode the content of the files (e.g. for images
        or other non-text content). If not None, encoding to use to decode text
        files to Unicode if load_content is True.

    decode_error : {'strict', 'ignore', 'replace'}, default='strict'
        Instruction on what to do if a byte sequence is given to analyze that
        contains characters not of the given `encoding`. Passed as keyword
        argument 'errors' to bytes.decode.

    random_state : int, RandomState instance or None, default=0
        Determines random number generation for dataset shuffling. Pass an int
        for reproducible output across multiple function calls.
        See :term:`Glossary <random_state>`.

    allowed_extensions : list of str, default=None
        List of desired file extensions to filter the files to be loaded.

    Returns
    -------
    data : :class:`~sklearn.utils.Bunch`
        Dictionary-like object, with the following attributes.

        data : list of str
            Only present when `load_content=True`.
            The raw text data to learn.
        target : ndarray
            The target labels (integer index).
        target_names : list
            The names of target classes.
        DESCR : str
            The full description of the dataset.
        filenames: ndarray
            The filenames holding the dataset.

    Examples
    --------
    >>> from sklearn.datasets import load_files
    >>> container_path = "./"
    >>> load_files(container_path)  # doctest: +SKIP
    """

    target = []
    target_names = []
    filenames = []

    folders = [
        f for f in sorted(listdir(container_path)) if isdir(join(container_path, f))
    ]

    if categories is not None:
        folders = [f for f in folders if f in categories]

    if allowed_extensions is not None:
        allowed_extensions = frozenset(allowed_extensions)

    for label, folder in enumerate(folders):
        target_names.append(folder)
        folder_path = join(container_path, folder)
        files = sorted(listdir(folder_path))
        if allowed_extensions is not None:
            documents = [
                join(folder_path, file)
                for file in files
                if os.path.splitext(file)[1] in allowed_extensions
            ]
        else:
            documents = [join(folder_path, file) for file in files]
        target.extend(len(documents) * [label])
        filenames.extend(documents)

    # convert to array for fancy indexing
    filenames = np.array(filenames)
    target = np.array(target)

    if shuffle:
        random_state = check_random_state(random_state)
        indices = np.arange(filenames.shape[0])
        random_state.shuffle(indices)
        filenames = filenames[indices]
        target = target[indices]

    if load_content:
        data = []
        for filename in filenames:
            data.append(Path(filename).read_bytes())
        if encoding is not None:
            data = [d.decode(encoding, decode_error) for d in data]
        return Bunch(
            data=data,
            filenames=filenames,
            target_names=target_names,
            target=target,
            DESCR=description,
        )

    return Bunch(
        filenames=filenames, target_names=target_names, target=target, DESCR=description
    )


def load_csv_data(
    data_file_name,
    *,
    data_module=DATA_MODULE,
    descr_file_name=None,
    descr_module=DESCR_MODULE,
    encoding="utf-8",
):
    """Loads `data_file_name` from `data_module with `importlib.resources`.

    Parameters
    ----------
    data_file_name : str
        Name of csv file to be loaded from `data_module/data_file_name`.
        For example `'wine_data.csv'`.

    data_module : str or module, default='sklearn.datasets.data'
        Module where data lives. The default is `'sklearn.datasets.data'`.

    descr_file_name : str, default=None
        Name of rst file to be loaded from `descr_module/descr_file_name`.
        For example `'wine_data.rst'`. See also :func:`load_descr`.
        If not None, also returns the corresponding description of
        the dataset.

    descr_module : str or module, default='sklearn.datasets.descr'
        Module where `descr_file_name` lives. See also :func:`load_descr`.
        The default is `'sklearn.datasets.descr'`.

    Returns
    -------
    data : ndarray of shape (n_samples, n_features)
        A 2D array with each row representing one sample and each column
        representing the features of a given sample.

    target : ndarry of shape (n_samples,)
        A 1D array holding target variables for all the samples in `data`.
        For example target[0] is the target variable for data[0].

    target_names : ndarry of shape (n_samples,)
        A 1D array containing the names of the classifications. For example
        target_names[0] is the name of the target[0] class.

    descr : str, optional
        Description of the dataset (the content of `descr_file_name`).
        Only returned if `descr_file_name` is not None.

    encoding : str, optional
        Text encoding of the CSV file.

        .. versionadded:: 1.4
    """
    data_path = resources.files(data_module) / data_file_name
    with data_path.open("r", encoding="utf-8") as csv_file:
        data_file = csv.reader(csv_file)
        temp = next(data_file)
        n_samples = int(temp[0])
        n_features = int(temp[1])
        target_names = np.array(temp[2:])
        data = np.empty((n_samples, n_features))
        target = np.empty((n_samples,), dtype=int)

        for i, ir in enumerate(data_file):
            data[i] = np.asarray(ir[:-1], dtype=np.float64)
            target[i] = np.asarray(ir[-1], dtype=int)

    if descr_file_name is None:
        return data, target, target_names
    else:
        assert descr_module is not None
        descr = load_descr(descr_module=descr_module, descr_file_name=descr_file_name)
        return data, target, target_names, descr


def load_gzip_compressed_csv_data(
    data_file_name,
    *,
    data_module=DATA_MODULE,
    descr_file_name=None,
    descr_module=DESCR_MODULE,
    encoding="utf-8",
    **kwargs,
):
    """Loads gzip-compressed with `importlib.resources`.

    1) Open resource file with `importlib.resources.open_binary`
    2) Decompress file obj with `gzip.open`
    3) Load decompressed data with `np.loadtxt`

    Parameters
    ----------
    data_file_name : str
        Name of gzip-compressed csv file  (`'*.csv.gz'`) to be loaded from
        `data_module/data_file_name`. For example `'diabetes_data.csv.gz'`.

    data_module : str or module, default='sklearn.datasets.data'
        Module where data lives. The default is `'sklearn.datasets.data'`.

    descr_file_name : str, default=None
        Name of rst file to be loaded from `descr_module/descr_file_name`.
        For example `'wine_data.rst'`. See also :func:`load_descr`.
        If not None, also returns the corresponding description of
        the dataset.

    descr_module : str or module, default='sklearn.datasets.descr'
        Module where `descr_file_name` lives. See also :func:`load_descr`.
        The default  is `'sklearn.datasets.descr'`.

    encoding : str, default="utf-8"
        Name of the encoding that the gzip-decompressed file will be
        decoded with. The default is 'utf-8'.

    **kwargs : dict, optional
        Keyword arguments to be passed to `np.loadtxt`;
        e.g. delimiter=','.

    Returns
    -------
    data : ndarray of shape (n_samples, n_features)
        A 2D array with each row representing one sample and each column
        representing the features and/or target of a given sample.

    descr : str, optional
        Description of the dataset (the content of `descr_file_name`).
        Only returned if `descr_file_name` is not None.
    """
    data_path = resources.files(data_module) / data_file_name
    with data_path.open("rb") as compressed_file:
        compressed_file = gzip.open(compressed_file, mode="rt", encoding=encoding)
        data = np.loadtxt(compressed_file, **kwargs)

    if descr_file_name is None:
        return data
    else:
        assert descr_module is not None
        descr = load_descr(descr_module=descr_module, descr_file_name=descr_file_name)
        return data, descr


def load_descr(descr_file_name, *, descr_module=DESCR_MODULE, encoding="utf-8"):
    """Load `descr_file_name` from `descr_module` with `importlib.resources`.

    Parameters
    ----------
    descr_file_name : str, default=None
        Name of rst file to be loaded from `descr_module/descr_file_name`.
        For example `'wine_data.rst'`. See also :func:`load_descr`.
        If not None, also returns the corresponding description of
        the dataset.

    descr_module : str or module, default='sklearn.datasets.descr'
        Module where `descr_file_name` lives. See also :func:`load_descr`.
        The default  is `'sklearn.datasets.descr'`.

    encoding : str, default="utf-8"
        Name of the encoding that `descr_file_name` will be decoded with.
        The default is 'utf-8'.

        .. versionadded:: 1.4

    Returns
    -------
    fdescr : str
        Content of `descr_file_name`.
    """
    path = resources.files(descr_module) / descr_file_name
    return path.read_text(encoding=encoding)


@validate_params(
    {
        "return_X_y": ["boolean"],
        "as_frame": ["boolean"],
    },
    prefer_skip_nested_validation=True,
)
def load_wine(*, return_X_y=False, as_frame=False):
    """Load and return the wine dataset (classification).

    .. versionadded:: 0.18

    The wine dataset is a classic and very easy multi-class classification
    dataset.

    =================   ==============
    Classes                          3
    Samples per class        [59,71,48]
    Samples total                  178
    Dimensionality                  13
    Features            real, positive
    =================   ==============

    The copy of UCI ML Wine Data Set dataset is downloaded and modified to fit
    standard format from:
    https://archive.ics.uci.edu/ml/machine-learning-databases/wine/wine.data

    Read more in the :ref:`User Guide <wine_dataset>`.

    Parameters
    ----------
    return_X_y : bool, default=False
        If True, returns ``(data, target)`` instead of a Bunch object.
        See below for more information about the `data` and `target` object.

    as_frame : bool, default=False
        If True, the data is a pandas DataFrame including columns with
        appropriate dtypes (numeric). The target is
        a pandas DataFrame or Series depending on the number of target columns.
        If `return_X_y` is True, then (`data`, `target`) will be pandas
        DataFrames or Series as described below.

        .. versionadded:: 0.23

    Returns
    -------
    data : :class:`~sklearn.utils.Bunch`
        Dictionary-like object, with the following attributes.

        data : {ndarray, dataframe} of shape (178, 13)
            The data matrix. If `as_frame=True`, `data` will be a pandas
            DataFrame.
        target: {ndarray, Series} of shape (178,)
            The classification target. If `as_frame=True`, `target` will be
            a pandas Series.
        feature_names: list
            The names of the dataset columns.
        target_names: list
            The names of target classes.
        frame: DataFrame of shape (178, 14)
            Only present when `as_frame=True`. DataFrame with `data` and
            `target`.

            .. versionadded:: 0.23
        DESCR: str
            The full description of the dataset.

    (data, target) : tuple if ``return_X_y`` is True
        A tuple of two ndarrays by default. The first contains a 2D array of shape
        (178, 13) with each row representing one sample and each column representing
        the features. The second array of shape (178,) contains the target samples.

    Examples
    --------
    Let's say you are interested in the samples 10, 80, and 140, and want to
    know their class name.

    >>> from sklearn.datasets import load_wine
    >>> data = load_wine()
    >>> data.target[[10, 80, 140]]
    array([0, 1, 2])
    >>> list(data.target_names)
    [np.str_('class_0'), np.str_('class_1'), np.str_('class_2')]
    """

    data, target, target_names, fdescr = load_csv_data(
        data_file_name="wine_data.csv", descr_file_name="wine_data.rst"
    )

    feature_names = [
        "alcohol",
        "malic_acid",
        "ash",
        "alcalinity_of_ash",
        "magnesium",
        "total_phenols",
        "flavanoids",
        "nonflavanoid_phenols",
        "proanthocyanins",
        "color_intensity",
        "hue",
        "od280/od315_of_diluted_wines",
        "proline",
    ]

    frame = None
    target_columns = [
        "target",
    ]
    if as_frame:
        frame, data, target = _convert_data_dataframe(
            "load_wine", data, target, feature_names, target_columns
        )

    if return_X_y:
        return data, target

    return Bunch(
        data=data,
        target=target,
        frame=frame,
        target_names=target_names,
        DESCR=fdescr,
        feature_names=feature_names,
    )


@validate_params(
    {"return_X_y": ["boolean"], "as_frame": ["boolean"]},
    prefer_skip_nested_validation=True,
)
def load_iris(*, return_X_y=False, as_frame=False):
    """Load and return the iris dataset (classification).

    The iris dataset is a classic and very easy multi-class classification
    dataset.

    =================   ==============
    Classes                          3
    Samples per class               50
    Samples total                  150
    Dimensionality                   4
    Features            real, positive
    =================   ==============

    Read more in the :ref:`User Guide <iris_dataset>`.

    .. versionchanged:: 0.20
        Fixed two wrong data points according to Fisher's paper.
        The new version is the same as in R, but not as in the UCI
        Machine Learning Repository.

    Parameters
    ----------
    return_X_y : bool, default=False
        If True, returns ``(data, target)`` instead of a Bunch object. See
        below for more information about the `data` and `target` object.

        .. versionadded:: 0.18

    as_frame : bool, default=False
        If True, the data is a pandas DataFrame including columns with
        appropriate dtypes (numeric). The target is
        a pandas DataFrame or Series depending on the number of target columns.
        If `return_X_y` is True, then (`data`, `target`) will be pandas
        DataFrames or Series as described below.

        .. versionadded:: 0.23

    Returns
    -------
    data : :class:`~sklearn.utils.Bunch`
        Dictionary-like object, with the following attributes.

        data : {ndarray, dataframe} of shape (150, 4)
            The data matrix. If `as_frame=True`, `data` will be a pandas
            DataFrame.
        target: {ndarray, Series} of shape (150,)
            The classification target. If `as_frame=True`, `target` will be
            a pandas Series.
        feature_names: list
            The names of the dataset columns.
        target_names: ndarray of shape (3, )
            The names of target classes.
        frame: DataFrame of shape (150, 5)
            Only present when `as_frame=True`. DataFrame with `data` and
            `target`.

            .. versionadded:: 0.23
        DESCR: str
            The full description of the dataset.
        filename: str
            The path to the location of the data.

            .. versionadded:: 0.20

    (data, target) : tuple if ``return_X_y`` is True
        A tuple of two ndarray. The first containing a 2D array of shape
        (n_samples, n_features) with each row representing one sample and
        each column representing the features. The second ndarray of shape
        (n_samples,) containing the target samples.

        .. versionadded:: 0.18

    Examples
    --------
    Let's say you are interested in the samples 10, 25, and 50, and want to
    know their class name.

    >>> from sklearn.datasets import load_iris
    >>> data = load_iris()
    >>> data.target[[10, 25, 50]]
    array([0, 0, 1])
    >>> list(data.target_names)
    [np.str_('setosa'), np.str_('versicolor'), np.str_('virginica')]

    See :ref:`sphx_glr_auto_examples_decomposition_plot_pca_iris.py` for a more
    detailed example of how to work with the iris dataset.
    """
    data_file_name = "iris.csv"
    data, target, target_names, fdescr = load_csv_data(
        data_file_name=data_file_name, descr_file_name="iris.rst"
    )

    feature_names = [
        "sepal length (cm)",
        "sepal width (cm)",
        "petal length (cm)",
        "petal width (cm)",
    ]

    frame = None
    target_columns = [
        "target",
    ]
    if as_frame:
        frame, data, target = _convert_data_dataframe(
            "load_iris", data, target, feature_names, target_columns
        )

    if return_X_y:
        return data, target

    return Bunch(
        data=data,
        target=target,
        frame=frame,
        target_names=target_names,
        DESCR=fdescr,
        feature_names=feature_names,
        filename=data_file_name,
        data_module=DATA_MODULE,
    )


@validate_params(
    {"return_X_y": ["boolean"], "as_frame": ["boolean"]},
    prefer_skip_nested_validation=True,
)
def load_breast_cancer(*, return_X_y=False, as_frame=False):
    """Load and return the breast cancer Wisconsin dataset (classification).

    The breast cancer dataset is a classic and very easy binary classification
    dataset.

    =================   ==============
    Classes                          2
    Samples per class    212(M),357(B)
    Samples total                  569
    Dimensionality                  30
    Features            real, positive
    =================   ==============

    The copy of UCI ML Breast Cancer Wisconsin (Diagnostic) dataset is
    downloaded from:
    https://archive.ics.uci.edu/dataset/17/breast+cancer+wisconsin+diagnostic

    Read more in the :ref:`User Guide <breast_cancer_dataset>`.

    Parameters
    ----------
    return_X_y : bool, default=False
        If True, returns ``(data, target)`` instead of a Bunch object.
        See below for more information about the `data` and `target` object.

        .. versionadded:: 0.18

    as_frame : bool, default=False
        If True, the data is a pandas DataFrame including columns with
        appropriate dtypes (numeric). The target is
        a pandas DataFrame or Series depending on the number of target columns.
        If `return_X_y` is True, then (`data`, `target`) will be pandas
        DataFrames or Series as described below.

        .. versionadded:: 0.23

    Returns
    -------
    data : :class:`~sklearn.utils.Bunch`
        Dictionary-like object, with the following attributes.

        data : {ndarray, dataframe} of shape (569, 30)
            The data matrix. If `as_frame=True`, `data` will be a pandas
            DataFrame.
        target : {ndarray, Series} of shape (569,)
            The classification target. If `as_frame=True`, `target` will be
            a pandas Series.
        feature_names : ndarray of shape (30,)
            The names of the dataset columns.
        target_names : ndarray of shape (2,)
            The names of target classes.
        frame : DataFrame of shape (569, 31)
            Only present when `as_frame=True`. DataFrame with `data` and
            `target`.

            .. versionadded:: 0.23
        DESCR : str
            The full description of the dataset.
        filename : str
            The path to the location of the data.

            .. versionadded:: 0.20

    (data, target) : tuple if ``return_X_y`` is True
        A tuple of two ndarrays by default. The first contains a 2D ndarray of
        shape (569, 30) with each row representing one sample and each column
        representing the features. The second ndarray of shape (569,) contains
        the target samples.  If `as_frame=True`, both arrays are pandas objects,
        i.e. `X` a dataframe and `y` a series.

        .. versionadded:: 0.18

    Examples
    --------
    Let's say you are interested in the samples 10, 50, and 85, and want to
    know their class name.

    >>> from sklearn.datasets import load_breast_cancer
    >>> data = load_breast_cancer()
    >>> data.target[[10, 50, 85]]
    array([0, 1, 0])
    >>> list(data.target_names)
    [np.str_('malignant'), np.str_('benign')]
    """
    data_file_name = "breast_cancer.csv"
    data, target, target_names, fdescr = load_csv_data(
        data_file_name=data_file_name, descr_file_name="breast_cancer.rst"
    )

    feature_names = np.array(
        [
            "mean radius",
            "mean texture",
            "mean perimeter",
            "mean area",
            "mean smoothness",
            "mean compactness",
            "mean concavity",
            "mean concave points",
            "mean symmetry",
            "mean fractal dimension",
            "radius error",
            "texture error",
            "perimeter error",
            "area error",
            "smoothness error",
            "compactness error",
            "concavity error",
            "concave points error",
            "symmetry error",
            "fractal dimension error",
            "worst radius",
            "worst texture",
            "worst perimeter",
            "worst area",
            "worst smoothness",
            "worst compactness",
            "worst concavity",
            "worst concave points",
            "worst symmetry",
            "worst fractal dimension",
        ]
    )

    frame = None
    target_columns = [
        "target",
    ]
    if as_frame:
        frame, data, target = _convert_data_dataframe(
            "load_breast_cancer", data, target, feature_names, target_columns
        )

    if return_X_y:
        return data, target

    return Bunch(
        data=data,
        target=target,
        frame=frame,
        target_names=target_names,
        DESCR=fdescr,
        feature_names=feature_names,
        filename=data_file_name,
        data_module=DATA_MODULE,
    )


@validate_params(
    {
        "n_class": [Interval(Integral, 1, 10, closed="both")],
        "return_X_y": ["boolean"],
        "as_frame": ["boolean"],
    },
    prefer_skip_nested_validation=True,
)
def load_digits(*, n_class=10, return_X_y=False, as_frame=False):
    """Load and return the digits dataset (classification).

    Each datapoint is a 8x8 image of a digit.

    =================   ==============
    Classes                         10
    Samples per class             ~180
    Samples total                 1797
    Dimensionality                  64
    Features             integers 0-16
    =================   ==============

    This is a copy of the test set of the UCI ML hand-written digits datasets
    https://archive.ics.uci.edu/ml/datasets/Optical+Recognition+of+Handwritten+Digits

    Read more in the :ref:`User Guide <digits_dataset>`.

    Parameters
    ----------
    n_class : int, default=10
        The number of classes to return. Between 0 and 10.

    return_X_y : bool, default=False
        If True, returns ``(data, target)`` instead of a Bunch object.
        See below for more information about the `data` and `target` object.

        .. versionadded:: 0.18

    as_frame : bool, default=False
        If True, the data is a pandas DataFrame including columns with
        appropriate dtypes (numeric). The target is
        a pandas DataFrame or Series depending on the number of target columns.
        If `return_X_y` is True, then (`data`, `target`) will be pandas
        DataFrames or Series as described below.

        .. versionadded:: 0.23

    Returns
    -------
    data : :class:`~sklearn.utils.Bunch`
        Dictionary-like object, with the following attributes.

        data : {ndarray, dataframe} of shape (1797, 64)
            The flattened data matrix. If `as_frame=True`, `data` will be
            a pandas DataFrame.
        target: {ndarray, Series} of shape (1797,)
            The classification target. If `as_frame=True`, `target` will be
            a pandas Series.
        feature_names: list
            The names of the dataset columns.
        target_names: list
            The names of target classes.

            .. versionadded:: 0.20

        frame: DataFrame of shape (1797, 65)
            Only present when `as_frame=True`. DataFrame with `data` and
            `target`.

            .. versionadded:: 0.23
        images: {ndarray} of shape (1797, 8, 8)
            The raw image data.
        DESCR: str
            The full description of the dataset.

    (data, target) : tuple if ``return_X_y`` is True
        A tuple of two ndarrays by default. The first contains a 2D ndarray of
        shape (1797, 64) with each row representing one sample and each column
        representing the features. The second ndarray of shape (1797) contains
        the target samples.  If `as_frame=True`, both arrays are pandas objects,
        i.e. `X` a dataframe and `y` a series.

        .. versionadded:: 0.18

    Examples
    --------
    To load the data and visualize the images::

        >>> from sklearn.datasets import load_digits
        >>> digits = load_digits()
        >>> print(digits.data.shape)
        (1797, 64)
        >>> import matplotlib.pyplot as plt
        >>> plt.matshow(digits.images[0], cmap="gray")
        <...>
        >>> plt.show()
    """

    data, fdescr = load_gzip_compressed_csv_data(
        data_file_name="digits.csv.gz", descr_file_name="digits.rst", delimiter=","
    )

    target = data[:, -1].astype(int, copy=False)
    flat_data = data[:, :-1]
    images = flat_data.view()
    images.shape = (-1, 8, 8)

    if n_class < 10:
        idx = target < n_class
        flat_data, target = flat_data[idx], target[idx]
        images = images[idx]

    feature_names = [
        "pixel_{}_{}".format(row_idx, col_idx)
        for row_idx in range(8)
        for col_idx in range(8)
    ]

    frame = None
    target_columns = [
        "target",
    ]
    if as_frame:
        frame, flat_data, target = _convert_data_dataframe(
            "load_digits", flat_data, target, feature_names, target_columns
        )

    if return_X_y:
        return flat_data, target

    return Bunch(
        data=flat_data,
        target=target,
        frame=frame,
        feature_names=feature_names,
        target_names=np.arange(10),
        images=images,
        DESCR=fdescr,
    )


@validate_params(
    {"return_X_y": ["boolean"], "as_frame": ["boolean"], "scaled": ["boolean"]},
    prefer_skip_nested_validation=True,
)
def load_diabetes(*, return_X_y=False, as_frame=False, scaled=True):
    """Load and return the diabetes dataset (regression).

    ==============   ==================
    Samples total    442
    Dimensionality   10
    Features         real, -.2 < x < .2
    Targets          integer 25 - 346
    ==============   ==================

    .. note::
       The meaning of each feature (i.e. `feature_names`) might be unclear
       (especially for `ltg`) as the documentation of the original dataset is
       not explicit. We provide information that seems correct in regard with
       the scientific literature in this field of research.

    Read more in the :ref:`User Guide <diabetes_dataset>`.

    Parameters
    ----------
    return_X_y : bool, default=False
        If True, returns ``(data, target)`` instead of a Bunch object.
        See below for more information about the `data` and `target` object.

        .. versionadded:: 0.18

    as_frame : bool, default=False
        If True, the data is a pandas DataFrame including columns with
        appropriate dtypes (numeric). The target is
        a pandas DataFrame or Series depending on the number of target columns.
        If `return_X_y` is True, then (`data`, `target`) will be pandas
        DataFrames or Series as described below.

        .. versionadded:: 0.23

    scaled : bool, default=True
        If True, the feature variables are mean centered and scaled by the
        standard deviation times the square root of `n_samples`.
        If False, raw data is returned for the feature variables.

        .. versionadded:: 1.1

    Returns
    -------
    data : :class:`~sklearn.utils.Bunch`
        Dictionary-like object, with the following attributes.

        data : {ndarray, dataframe} of shape (442, 10)
            The data matrix. If `as_frame=True`, `data` will be a pandas
            DataFrame.
        target: {ndarray, Series} of shape (442,)
            The regression target. If `as_frame=True`, `target` will be
            a pandas Series.
        feature_names: list
            The names of the dataset columns.
        frame: DataFrame of shape (442, 11)
            Only present when `as_frame=True`. DataFrame with `data` and
            `target`.

            .. versionadded:: 0.23
        DESCR: str
            The full description of the dataset.
        data_filename: str
            The path to the location of the data.
        target_filename: str
            The path to the location of the target.

    (data, target) : tuple if ``return_X_y`` is True
        Returns a tuple of two ndarray of shape (n_samples, n_features)
        A 2D array with each row representing one sample and each column
        representing the features and/or target of a given sample.

        .. versionadded:: 0.18

    Examples
    --------
    >>> from sklearn.datasets import load_diabetes
    >>> diabetes = load_diabetes()
    >>> diabetes.target[:3]
    array([151.,  75., 141.])
    >>> diabetes.data.shape
    (442, 10)
    """
    data_filename = "diabetes_data_raw.csv.gz"
    target_filename = "diabetes_target.csv.gz"
    data = load_gzip_compressed_csv_data(data_filename)
    target = load_gzip_compressed_csv_data(target_filename)

    if scaled:
        data = scale(data, copy=False)
        data /= data.shape[0] ** 0.5

    fdescr = load_descr("diabetes.rst")

    feature_names = ["age", "sex", "bmi", "bp", "s1", "s2", "s3", "s4", "s5", "s6"]

    frame = None
    target_columns = [
        "target",
    ]
    if as_frame:
        frame, data, target = _convert_data_dataframe(
            "load_diabetes", data, target, feature_names, target_columns
        )

    if return_X_y:
        return data, target

    return Bunch(
        data=data,
        target=target,
        frame=frame,
        DESCR=fdescr,
        feature_names=feature_names,
        data_filename=data_filename,
        target_filename=target_filename,
        data_module=DATA_MODULE,
    )


@validate_params(
    {
        "return_X_y": ["boolean"],
        "as_frame": ["boolean"],
    },
    prefer_skip_nested_validation=True,
)
def load_linnerud(*, return_X_y=False, as_frame=False):
    """Load and return the physical exercise Linnerud dataset.

    This dataset is suitable for multi-output regression tasks.

    ==============   ============================
    Samples total    20
    Dimensionality   3 (for both data and target)
    Features         integer
    Targets          integer
    ==============   ============================

    Read more in the :ref:`User Guide <linnerrud_dataset>`.

    Parameters
    ----------
    return_X_y : bool, default=False
        If True, returns ``(data, target)`` instead of a Bunch object.
        See below for more information about the `data` and `target` object.

        .. versionadded:: 0.18

    as_frame : bool, default=False
        If True, the data is a pandas DataFrame including columns with
        appropriate dtypes (numeric, string or categorical). The target is
        a pandas DataFrame or Series depending on the number of target columns.
        If `return_X_y` is True, then (`data`, `target`) will be pandas
        DataFrames or Series as described below.

        .. versionadded:: 0.23

    Returns
    -------
    data : :class:`~sklearn.utils.Bunch`
        Dictionary-like object, with the following attributes.

        data : {ndarray, dataframe} of shape (20, 3)
            The data matrix. If `as_frame=True`, `data` will be a pandas
            DataFrame.
        target: {ndarray, dataframe} of shape (20, 3)
            The regression targets. If `as_frame=True`, `target` will be
            a pandas DataFrame.
        feature_names: list
            The names of the dataset columns.
        target_names: list
            The names of the target columns.
        frame: DataFrame of shape (20, 6)
            Only present when `as_frame=True`. DataFrame with `data` and
            `target`.

            .. versionadded:: 0.23
        DESCR: str
            The full description of the dataset.
        data_filename: str
            The path to the location of the data.
        target_filename: str
            The path to the location of the target.

            .. versionadded:: 0.20

    (data, target) : tuple if ``return_X_y`` is True
        Returns a tuple of two ndarrays or dataframe of shape
        `(20, 3)`. Each row represents one sample and each column represents the
        features in `X` and a target in `y` of a given sample.

        .. versionadded:: 0.18

    Examples
    --------
    >>> from sklearn.datasets import load_linnerud
    >>> linnerud = load_linnerud()
    >>> linnerud.data.shape
    (20, 3)
    >>> linnerud.target.shape
    (20, 3)
    """
    data_filename = "linnerud_exercise.csv"
    target_filename = "linnerud_physiological.csv"

    data_module_path = resources.files(DATA_MODULE)
    # Read header and data
    data_path = data_module_path / data_filename
    with data_path.open("r", encoding="utf-8") as f:
        header_exercise = f.readline().split()
        f.seek(0)  # reset file obj
        data_exercise = np.loadtxt(f, skiprows=1)

    target_path = data_module_path / target_filename
    with target_path.open("r", encoding="utf-8") as f:
        header_physiological = f.readline().split()
        f.seek(0)  # reset file obj
        data_physiological = np.loadtxt(f, skiprows=1)

    fdescr = load_descr("linnerud.rst")

    frame = None
    if as_frame:
        (frame, data_exercise, data_physiological) = _convert_data_dataframe(
            "load_linnerud",
            data_exercise,
            data_physiological,
            header_exercise,
            header_physiological,
        )
    if return_X_y:
        return data_exercise, data_physiological

    return Bunch(
        data=data_exercise,
        feature_names=header_exercise,
        target=data_physiological,
        target_names=header_physiological,
        frame=frame,
        DESCR=fdescr,
        data_filename=data_filename,
        target_filename=target_filename,
        data_module=DATA_MODULE,
    )


def load_sample_images():
    """Load sample images for image manipulation.

    Loads both, ``china`` and ``flower``.

    Read more in the :ref:`User Guide <sample_images>`.

    Returns
    -------
    data : :class:`~sklearn.utils.Bunch`
        Dictionary-like object, with the following attributes.

        images : list of ndarray of shape (427, 640, 3)
            The two sample image.
        filenames : list
            The filenames for the images.
        DESCR : str
            The full description of the dataset.

    Examples
    --------
    To load the data and visualize the images:

    >>> from sklearn.datasets import load_sample_images
    >>> dataset = load_sample_images()     #doctest: +SKIP
    >>> len(dataset.images)                #doctest: +SKIP
    2
    >>> first_img_data = dataset.images[0] #doctest: +SKIP
    >>> first_img_data.shape               #doctest: +SKIP
    (427, 640, 3)
    >>> first_img_data.dtype               #doctest: +SKIP
    dtype('uint8')
    """
    try:
        from PIL import Image
    except ImportError:
        raise ImportError(
            "The Python Imaging Library (PIL) is required to load data "
            "from jpeg files. Please refer to "
            "https://pillow.readthedocs.io/en/stable/installation.html "
            "for installing PIL."
        )

    descr = load_descr("README.txt", descr_module=IMAGES_MODULE)

    filenames, images = [], []

    jpg_paths = sorted(
        resource
        for resource in resources.files(IMAGES_MODULE).iterdir()
        if resource.is_file() and resource.match("*.jpg")
    )

    for path in jpg_paths:
        filenames.append(str(path))
        with path.open("rb") as image_file:
            pil_image = Image.open(image_file)
            image = np.asarray(pil_image)
        images.append(image)

    return Bunch(images=images, filenames=filenames, DESCR=descr)


@validate_params(
    {
        "image_name": [StrOptions({"china.jpg", "flower.jpg"})],
    },
    prefer_skip_nested_validation=True,
)
def load_sample_image(image_name):
    """Load the numpy array of a single sample image.

    Read more in the :ref:`User Guide <sample_images>`.

    Parameters
    ----------
    image_name : {`china.jpg`, `flower.jpg`}
        The name of the sample image loaded.

    Returns
    -------
    img : 3D array
        The image as a numpy array: height x width x color.

    Examples
    --------

    >>> from sklearn.datasets import load_sample_image
    >>> china = load_sample_image('china.jpg')   # doctest: +SKIP
    >>> china.dtype                              # doctest: +SKIP
    dtype('uint8')
    >>> china.shape                              # doctest: +SKIP
    (427, 640, 3)
    >>> flower = load_sample_image('flower.jpg') # doctest: +SKIP
    >>> flower.dtype                             # doctest: +SKIP
    dtype('uint8')
    >>> flower.shape                             # doctest: +SKIP
    (427, 640, 3)
    """
    images = load_sample_images()
    index = None
    for i, filename in enumerate(images.filenames):
        if filename.endswith(image_name):
            index = i
            break
    if index is None:
        raise AttributeError("Cannot find sample image: %s" % image_name)
    return images.images[index]


def _pkl_filepath(*args, **kwargs):
    """Return filename for Python 3 pickles

    args[-1] is expected to be the ".pkl" filename. For compatibility with
    older scikit-learn versions, a suffix is inserted before the extension.

    _pkl_filepath('/path/to/folder', 'filename.pkl') returns
    '/path/to/folder/filename_py3.pkl'

    """
    py3_suffix = kwargs.get("py3_suffix", "_py3")
    basename, ext = splitext(args[-1])
    basename += py3_suffix
    new_args = args[:-1] + (basename + ext,)
    return join(*new_args)


def _sha256(path):
    """Calculate the sha256 hash of the file at path."""
    sha256hash = hashlib.sha256()
    chunk_size = 8192
    with open(path, "rb") as f:
        while True:
            buffer = f.read(chunk_size)
            if not buffer:
                break
            sha256hash.update(buffer)
    return sha256hash.hexdigest()


def _fetch_remote(remote, dirname=None, n_retries=3, delay=1):
    """Helper function to download a remote dataset.

    Fetch a dataset pointed by remote's url, save into path using remote's
    filename and ensure its integrity based on the SHA256 checksum of the
    downloaded file.

    .. versionchanged:: 1.6

        If the file already exists locally and the SHA256 checksums match, the
        path to the local file is returned without re-downloading.

    Parameters
    ----------
    remote : RemoteFileMetadata
        Named tuple containing remote dataset meta information: url, filename
        and checksum.

    dirname : str or Path, default=None
        Directory to save the file to. If None, the current working directory
        is used.

    n_retries : int, default=3
        Number of retries when HTTP errors are encountered.

        .. versionadded:: 1.5

    delay : int, default=1
        Number of seconds between retries.

        .. versionadded:: 1.5

    Returns
    -------
    file_path: Path
        Full path of the created file.
    """
    if dirname is None:
        folder_path = Path(".")
    else:
        folder_path = Path(dirname)

    file_path = folder_path / remote.filename

    if file_path.exists():
        if remote.checksum is None:
            return file_path

        checksum = _sha256(file_path)
        if checksum == remote.checksum:
            return file_path
        else:
            warnings.warn(
                f"SHA256 checksum of existing local file {file_path.name} "
                f"({checksum}) differs from expected ({remote.checksum}): "
                f"re-downloading from {remote.url} ."
            )

    # We create a temporary file dedicated to this particular download to avoid
    # conflicts with parallel downloads. If the download is successful, the
    # temporary file is atomically renamed to the final file path (with
    # `shutil.move`). We therefore pass `delete=False` to `NamedTemporaryFile`.
    # Otherwise, garbage collecting temp_file would raise an error when
    # attempting to delete a file that was already renamed. If the download
    # fails or the result does not match the expected SHA256 digest, the
    # temporary file is removed manually in the except block.
    temp_file = NamedTemporaryFile(
        prefix=remote.filename + ".part_", dir=folder_path, delete=False
    )
    # Note that Python 3.12's `delete_on_close=True` is ignored as we set
    # `delete=False` explicitly. So after this line the empty temporary file still
    # exists on disk to make sure that it's uniquely reserved for this specific call of
    # `_fetch_remote` and therefore it protects against any corruption by parallel
    # calls.
    temp_file.close()
    try:
        temp_file_path = Path(temp_file.name)
        while True:
            try:
                urlretrieve(remote.url, temp_file_path)
                break
            except (URLError, TimeoutError):
                if n_retries == 0:
                    # If no more retries are left, re-raise the caught exception.
                    raise
                warnings.warn(f"Retry downloading from url: {remote.url}")
                n_retries -= 1
                time.sleep(delay)

        checksum = _sha256(temp_file_path)
        if remote.checksum is not None and remote.checksum != checksum:
            raise OSError(
                f"The SHA256 checksum of {remote.filename} ({checksum}) "
                f"differs from expected ({remote.checksum})."
            )
    except (Exception, KeyboardInterrupt):
        os.unlink(temp_file.name)
        raise

    # The following renaming is atomic whenever temp_file_path and
    # file_path are on the same filesystem. This should be the case most of
    # the time, but we still use shutil.move instead of os.rename in case
    # they are not.
    shutil.move(temp_file_path, file_path)

    return file_path


def _filter_filename(value, filter_dots=True):
    """Derive a name that is safe to use as filename from the given string.

    Adapted from the `slugify` function of django:
    https://github.com/django/django/blob/master/django/utils/text.py

    Convert spaces or repeated dashes to single dashes. Replace characters that
    aren't alphanumerics, underscores, hyphens or dots by underscores. Convert
    to lowercase. Also strip leading and trailing whitespace, dashes, and
    underscores.
    """
    value = unicodedata.normalize("NFKD", value).lower()
    if filter_dots:
        value = re.sub(r"[^\w\s-]+", "_", value)
    else:
        value = re.sub(r"[^.\w\s-]+", "_", value)
    value = re.sub(r"[\s-]+", "-", value)
    return value.strip("-_.")


def _derive_folder_and_filename_from_url(url):
    parsed_url = urlparse(url)
    if not parsed_url.hostname:
        raise ValueError(f"Invalid URL: {url}")
    folder_components = [_filter_filename(parsed_url.hostname, filter_dots=False)]
    path = parsed_url.path

    if "/" in path:
        base_folder, raw_filename = path.rsplit("/", 1)

        base_folder = _filter_filename(base_folder)
        if base_folder:
            folder_components.append(base_folder)
    else:
        raw_filename = path

    filename = _filter_filename(raw_filename, filter_dots=False)
    if not filename:
        filename = "downloaded_file"

    return "/".join(folder_components), filename


def fetch_file(
    url, folder=None, local_filename=None, sha256=None, n_retries=3, delay=1
):
    """Fetch a file from the web if not already present in the local folder.

    If the file already exists locally (and the SHA256 checksums match when
    provided), the path to the local file is returned without re-downloading.

    .. versionadded:: 1.6

    Parameters
    ----------
    url : str
        URL of the file to download.

    folder : str or Path, default=None
        Directory to save the file to. If None, the file is downloaded in a
        folder with a name derived from the URL host name and path under
        scikit-learn data home folder.

    local_filename : str, default=None
        Name of the file to save. If None, the filename is inferred from the
        URL.

    sha256 : str, default=None
        SHA256 checksum of the file. If None, no checksum is verified.

    n_retries : int, default=3
        Number of retries when HTTP errors are encountered.

    delay : int, default=1
        Number of seconds between retries.

    Returns
    -------
    file_path : Path
        Full path of the downloaded file.
    """
    folder_from_url, filename_from_url = _derive_folder_and_filename_from_url(url)

    if local_filename is None:
        local_filename = filename_from_url

    if folder is None:
        folder = Path(get_data_home()) / folder_from_url
        makedirs(folder, exist_ok=True)

    remote_metadata = RemoteFileMetadata(
        filename=local_filename, url=url, checksum=sha256
    )
    return _fetch_remote(
        remote_metadata, dirname=folder, n_retries=n_retries, delay=delay
    )