efaces_w_scilearn_pca.py

from sklearn.datasets import fetch_lfw_people

faces = fetch_lfw_people(min_faces_per_person=60)
face_data_raw = faces.data
average_face = face_data_raw.mean(axis=0)
face_data = face_data_raw - average_face

pca = PCA(100).fit(face_data)
PC = pca.transform(face_data)

inversed = pca.inverse_transform(PC)
inversed_faces = inversed + average_face

fig, ax = plt.subplots(2, 5, figsize=(5, 2.5),
                       subplot_kw={'xticks':[], 'yticks':[]},
                       gridspec_kw=dict(hspace=0.1, wspace=0.1))
for i in range(5, 10):
    ax[0, i-5].imshow(faces.data[i].reshape(62, 47), cmap='binary_r')
    ax[1, i-5].imshow(inversed_faces[i].reshape(62, 47), cmap='binary_r')