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venv/Lib/site-packages/sklearn/datasets/_olivetti_faces.py

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+"""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 os.path import dirname, exists, join
+from os import makedirs, remove
+
+import numpy as np
+from scipy.io.matlab import loadmat
+import joblib
+
+from . import get_data_home
+from ._base import _fetch_remote
+from ._base import RemoteFileMetadata
+from ._base import _pkl_filepath
+from ..utils import check_random_state, Bunch
+from ..utils.validation import _deprecate_positional_args
+
+# 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=('b612fb967f2dc77c9c62d3e1266e0c73'
+              'd5fca46a4b8906c18e454d41af987794'))
+
+
+@_deprecate_positional_args
+def fetch_olivetti_faces(*, data_home=None, shuffle=False, random_state=0,
+                         download_if_missing=True, return_X_y=False):
+    """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 : optional, 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 : boolean, optional
+        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 : optional, True by default
+        If False, raise a IOError if the data is not locally available
+        instead of trying to download the data from the source site.
+
+    return_X_y : boolean, 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
+
+    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`
+        .. versionadded:: 0.22
+    """
+    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 IOError("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)
+        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)
+
+    module_path = dirname(__file__)
+    with open(join(module_path, 'descr', 'olivetti_faces.rst')) as rst_file:
+        fdescr = rst_file.read()
+
+    if return_X_y:
+        return faces_vectorized, target
+
+    return Bunch(data=faces_vectorized,
+                 images=faces,
+                 target=target,
+                 DESCR=fdescr)