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fetch_olivetti_faces — scikit-learn 1.7.0 documentation

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)

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