fetch_olivetti_faces (original) (raw)
sklearn.datasets.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)[source]#
Load the Olivetti faces data-set from AT&T (classification).
Download it if necessary.
Read more in the User Guide.
Parameters:
data_homestr 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.
shufflebool, default=False
If True the order of the dataset is shuffled to avoid having images of the same person grouped.
random_stateint, RandomState instance or None, default=0
Determines random number generation for dataset shuffling. Pass an int for reproducible output across multiple function calls. See Glossary.
download_if_missingbool, 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_ybool, default=False
If True, returns (data, target) instead of a Bunch object. See below for more information about the data and target object.
Added in version 0.22.
n_retriesint, default=3
Number of retries when HTTP errors are encountered.
Added in version 1.5.
delayfloat, default=1.0
Number of seconds between retries.
Added in version 1.5.
Returns:
dataBunch
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.
imagesndarray, shape (400, 64, 64)
Each row is a face image corresponding to one of the 40 subjects of the dataset.
targetndarray, shape (400,)
Labels associated to each face image. Those labels are ranging from 0-39 and correspond to the Subject IDs.
DESCRstr
Description of the modified Olivetti Faces Dataset.
**(data, target)**tuple if return_X_y=True
Tuple with the data and target objects described above.
Added in version 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)