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"""
Online learning of a dictionary of parts of faces
=================================================
This example uses a large dataset of faces to learn a set of 20 x 20
images patches that constitute faces.
From the programming standpoint, it is interesting because it shows how
to use the online API of the scikit-learn to process a very large
dataset by chunks. The way we proceed is that we load an image at a time
and extract randomly 50 patches from this image. Once we have accumulated
500 of these patches (using 10 images), we run the
:func:`~sklearn.cluster.MiniBatchKMeans.partial_fit` method
of the online KMeans object, MiniBatchKMeans.
The verbose setting on the MiniBatchKMeans enables us to see that some
clusters are reassigned during the successive calls to
partial-fit. This is because the number of patches that they represent
has become too low, and it is better to choose a random new
cluster.
"""
# %%
# Load the data
# -------------
from sklearn import datasets
faces = datasets.fetch_olivetti_faces()
# %%
# Learn the dictionary of images
# ------------------------------
import time
import numpy as np
from sklearn.cluster import MiniBatchKMeans
from sklearn.feature_extraction.image import extract_patches_2d
print("Learning the dictionary... ")
rng = np.random.RandomState(0)
kmeans = MiniBatchKMeans(n_clusters=81, random_state=rng, verbose=True, n_init=3)
patch_size = (20, 20)
buffer = []
t0 = time.time()
# The online learning part: cycle over the whole dataset 6 times
index = 0
for _ in range(6):
for img in faces.images:
data = extract_patches_2d(img, patch_size, max_patches=50, random_state=rng)
data = np.reshape(data, (len(data), -1))
buffer.append(data)
index += 1
if index % 10 == 0:
data = np.concatenate(buffer, axis=0)
data -= np.mean(data, axis=0)
data /= np.std(data, axis=0)
kmeans.partial_fit(data)
buffer = []
if index % 100 == 0:
print("Partial fit of %4i out of %i" % (index, 6 * len(faces.images)))
dt = time.time() - t0
print("done in %.2fs." % dt)
# %%
# Plot the results
# ----------------
import matplotlib.pyplot as plt
plt.figure(figsize=(4.2, 4))
for i, patch in enumerate(kmeans.cluster_centers_):
plt.subplot(9, 9, i + 1)
plt.imshow(patch.reshape(patch_size), cmap=plt.cm.gray, interpolation="nearest")
plt.xticks(())
plt.yticks(())
plt.suptitle(
"Patches of faces\nTrain time %.1fs on %d patches" % (dt, 8 * len(faces.images)),
fontsize=16,
)
plt.subplots_adjust(0.08, 0.02, 0.92, 0.85, 0.08, 0.23)
plt.show()
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