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