Traktor/myenv/Lib/site-packages/sklearn/datasets/_olivetti_faces.py

"""Modified Olivetti faces dataset.

The original database was available from (now defunct)

    https://www.cl.cam.ac.uk/research/dtg/attarchive/facedatabase.html

The version retrieved here comes in MATLAB format from the personal
web page of Sam Roweis:

    https://cs.nyu.edu/~roweis/
"""

# Copyright (c) 2011 David Warde-Farley <wardefar at iro dot umontreal dot ca>
# License: BSD 3 clause

from numbers import Integral, Real
from os import PathLike, makedirs, remove
from os.path import exists

import joblib
import numpy as np
from scipy.io import loadmat

from ..utils import Bunch, check_random_state
from ..utils._param_validation import Interval, validate_params
from . import get_data_home
from ._base import RemoteFileMetadata, _fetch_remote, _pkl_filepath, load_descr

# The original data can be found at:
# https://cs.nyu.edu/~roweis/data/olivettifaces.mat
FACES = RemoteFileMetadata(
    filename="olivettifaces.mat",
    url="https://ndownloader.figshare.com/files/5976027",
    checksum="b612fb967f2dc77c9c62d3e1266e0c73d5fca46a4b8906c18e454d41af987794",
)


@validate_params(
    {
        "data_home": [str, PathLike, None],
        "shuffle": ["boolean"],
        "random_state": ["random_state"],
        "download_if_missing": ["boolean"],
        "return_X_y": ["boolean"],
        "n_retries": [Interval(Integral, 1, None, closed="left")],
        "delay": [Interval(Real, 0.0, None, closed="neither")],
    },
    prefer_skip_nested_validation=True,
)
def fetch_olivetti_faces(
    *,
    data_home=None,
    shuffle=False,
    random_state=0,
    download_if_missing=True,
    return_X_y=False,
    n_retries=3,
    delay=1.0,
):
    """Load the Olivetti faces data-set from AT&T (classification).

    Download it if necessary.

    =================   =====================
    Classes                                40
    Samples total                         400
    Dimensionality                       4096
    Features            real, between 0 and 1
    =================   =====================

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

    Parameters
    ----------
    data_home : str or path-like, default=None
        Specify another download and cache folder for the datasets. By default
        all scikit-learn data is stored in '~/scikit_learn_data' subfolders.

    shuffle : bool, default=False
        If True the order of the dataset is shuffled to avoid having
        images of the same person grouped.

    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>`.

    download_if_missing : bool, default=True
        If False, raise an OSError if the data is not locally available
        instead of trying to download the data from the source site.

    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:: 0.22

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

        .. versionadded:: 1.5

    delay : float, default=1.0
        Number of seconds between retries.

        .. versionadded:: 1.5

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

        data: ndarray, shape (400, 4096)
            Each row corresponds to a ravelled
            face image of original size 64 x 64 pixels.
        images : ndarray, shape (400, 64, 64)
            Each row is a face image
            corresponding to one of the 40 subjects of the dataset.
        target : ndarray, shape (400,)
            Labels associated to each face image.
            Those labels are ranging from 0-39 and correspond to the
            Subject IDs.
        DESCR : str
            Description of the modified Olivetti Faces Dataset.

    (data, target) : tuple if `return_X_y=True`
        Tuple with the `data` and `target` objects described above.

        .. versionadded:: 0.22

    Examples
    --------
    >>> from sklearn.datasets import fetch_olivetti_faces
    >>> olivetti_faces = fetch_olivetti_faces()
    >>> olivetti_faces.data.shape
    (400, 4096)
    >>> olivetti_faces.target.shape
    (400,)
    >>> olivetti_faces.images.shape
    (400, 64, 64)
    """
    data_home = get_data_home(data_home=data_home)
    if not exists(data_home):
        makedirs(data_home)
    filepath = _pkl_filepath(data_home, "olivetti.pkz")
    if not exists(filepath):
        if not download_if_missing:
            raise OSError("Data not found and `download_if_missing` is False")

        print("downloading Olivetti faces from %s to %s" % (FACES.url, data_home))
        mat_path = _fetch_remote(
            FACES, dirname=data_home, n_retries=n_retries, delay=delay
        )
        mfile = loadmat(file_name=mat_path)
        # delete raw .mat data
        remove(mat_path)

        faces = mfile["faces"].T.copy()
        joblib.dump(faces, filepath, compress=6)
        del mfile
    else:
        faces = joblib.load(filepath)

    # We want floating point data, but float32 is enough (there is only
    # one byte of precision in the original uint8s anyway)
    faces = np.float32(faces)
    faces = faces - faces.min()
    faces /= faces.max()
    faces = faces.reshape((400, 64, 64)).transpose(0, 2, 1)
    # 10 images per class, 400 images total, each class is contiguous.
    target = np.array([i // 10 for i in range(400)])
    if shuffle:
        random_state = check_random_state(random_state)
        order = random_state.permutation(len(faces))
        faces = faces[order]
        target = target[order]
    faces_vectorized = faces.reshape(len(faces), -1)

    fdescr = load_descr("olivetti_faces.rst")

    if return_X_y:
        return faces_vectorized, target

    return Bunch(data=faces_vectorized, images=faces, target=target, DESCR=fdescr)