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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 exists
from os import makedirs, remove
import numpy as np
from scipy.io import loadmat
import joblib
from . import get_data_home
from ._base import _fetch_remote
from ._base import RemoteFileMetadata
from ._base import _pkl_filepath
from ._base import load_descr
from ..utils import check_random_state, Bunch
# 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",
)
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 : str, 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 a IOError 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
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
"""
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)
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)
|
