157 lines
5.2 KiB
Python
157 lines
5.2 KiB
Python
"""Modified Olivetti faces dataset.
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The original database was available from (now defunct)
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https://www.cl.cam.ac.uk/research/dtg/attarchive/facedatabase.html
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The version retrieved here comes in MATLAB format from the personal
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web page of Sam Roweis:
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https://cs.nyu.edu/~roweis/
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"""
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# Copyright (c) 2011 David Warde-Farley <wardefar at iro dot umontreal dot ca>
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# License: BSD 3 clause
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from os import PathLike, makedirs, remove
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from os.path import exists
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import joblib
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import numpy as np
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from scipy.io import loadmat
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from ..utils import Bunch, check_random_state
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from ..utils._param_validation import validate_params
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from . import get_data_home
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from ._base import RemoteFileMetadata, _fetch_remote, _pkl_filepath, load_descr
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# The original data can be found at:
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# https://cs.nyu.edu/~roweis/data/olivettifaces.mat
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FACES = RemoteFileMetadata(
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filename="olivettifaces.mat",
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url="https://ndownloader.figshare.com/files/5976027",
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checksum="b612fb967f2dc77c9c62d3e1266e0c73d5fca46a4b8906c18e454d41af987794",
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)
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@validate_params(
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{
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"data_home": [str, PathLike, None],
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"shuffle": ["boolean"],
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"random_state": ["random_state"],
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"download_if_missing": ["boolean"],
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"return_X_y": ["boolean"],
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},
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prefer_skip_nested_validation=True,
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)
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def fetch_olivetti_faces(
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*,
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data_home=None,
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shuffle=False,
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random_state=0,
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download_if_missing=True,
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return_X_y=False,
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):
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"""Load the Olivetti faces data-set from AT&T (classification).
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Download it if necessary.
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================= =====================
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Classes 40
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Samples total 400
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Dimensionality 4096
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Features real, between 0 and 1
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================= =====================
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Read more in the :ref:`User Guide <olivetti_faces_dataset>`.
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Parameters
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----------
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data_home : str or path-like, default=None
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Specify another download and cache folder for the datasets. By default
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all scikit-learn data is stored in '~/scikit_learn_data' subfolders.
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shuffle : bool, default=False
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If True the order of the dataset is shuffled to avoid having
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images of the same person grouped.
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random_state : int, RandomState instance or None, default=0
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Determines random number generation for dataset shuffling. Pass an int
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for reproducible output across multiple function calls.
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See :term:`Glossary <random_state>`.
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download_if_missing : bool, default=True
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If False, raise an OSError if the data is not locally available
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instead of trying to download the data from the source site.
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return_X_y : bool, default=False
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If True, returns `(data, target)` instead of a `Bunch` object. See
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below for more information about the `data` and `target` object.
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.. versionadded:: 0.22
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Returns
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-------
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data : :class:`~sklearn.utils.Bunch`
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Dictionary-like object, with the following attributes.
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data: ndarray, shape (400, 4096)
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Each row corresponds to a ravelled
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face image of original size 64 x 64 pixels.
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images : ndarray, shape (400, 64, 64)
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Each row is a face image
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corresponding to one of the 40 subjects of the dataset.
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target : ndarray, shape (400,)
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Labels associated to each face image.
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Those labels are ranging from 0-39 and correspond to the
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Subject IDs.
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DESCR : str
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Description of the modified Olivetti Faces Dataset.
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(data, target) : tuple if `return_X_y=True`
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Tuple with the `data` and `target` objects described above.
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.. versionadded:: 0.22
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"""
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data_home = get_data_home(data_home=data_home)
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if not exists(data_home):
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makedirs(data_home)
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filepath = _pkl_filepath(data_home, "olivetti.pkz")
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if not exists(filepath):
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if not download_if_missing:
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raise OSError("Data not found and `download_if_missing` is False")
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print("downloading Olivetti faces from %s to %s" % (FACES.url, data_home))
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mat_path = _fetch_remote(FACES, dirname=data_home)
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mfile = loadmat(file_name=mat_path)
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# delete raw .mat data
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remove(mat_path)
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faces = mfile["faces"].T.copy()
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joblib.dump(faces, filepath, compress=6)
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del mfile
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else:
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faces = joblib.load(filepath)
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# We want floating point data, but float32 is enough (there is only
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# one byte of precision in the original uint8s anyway)
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faces = np.float32(faces)
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faces = faces - faces.min()
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faces /= faces.max()
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faces = faces.reshape((400, 64, 64)).transpose(0, 2, 1)
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# 10 images per class, 400 images total, each class is contiguous.
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target = np.array([i // 10 for i in range(400)])
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if shuffle:
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random_state = check_random_state(random_state)
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order = random_state.permutation(len(faces))
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faces = faces[order]
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target = target[order]
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faces_vectorized = faces.reshape(len(faces), -1)
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fdescr = load_descr("olivetti_faces.rst")
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if return_X_y:
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return faces_vectorized, target
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return Bunch(data=faces_vectorized, images=faces, target=target, DESCR=fdescr)
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