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scikit-learn 1.3.2
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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 `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()

    downloading Olivetti faces from https://ndownloader.figshare.com/files/5976027 to /root/scikit_learn_data

## 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)

    Learning the dictionary...
    [MiniBatchKMeans] Reassigning 8 cluster centers.
    [MiniBatchKMeans] Reassigning 5 cluster centers.
    Partial fit of  100 out of 2400
    [MiniBatchKMeans] Reassigning 3 cluster centers.
    Partial fit of  200 out of 2400
    [MiniBatchKMeans] Reassigning 1 cluster centers.
    Partial fit of  300 out of 2400
    [MiniBatchKMeans] Reassigning 3 cluster centers.
    Partial fit of  400 out of 2400
    Partial fit of  500 out of 2400
    Partial fit of  600 out of 2400
    Partial fit of  700 out of 2400
    Partial fit of  800 out of 2400
    Partial fit of  900 out of 2400
    Partial fit of 1000 out of 2400
    Partial fit of 1100 out of 2400
    Partial fit of 1200 out of 2400
    Partial fit of 1300 out of 2400
    Partial fit of 1400 out of 2400
    Partial fit of 1500 out of 2400
    Partial fit of 1600 out of 2400
    Partial fit of 1700 out of 2400
    Partial fit of 1800 out of 2400
    Partial fit of 1900 out of 2400
    Partial fit of 2000 out of 2400
    Partial fit of 2100 out of 2400
    Partial fit of 2200 out of 2400
    Partial fit of 2300 out of 2400
    Partial fit of 2400 out of 2400
    done in 0.84s.

## 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()

Total running time of the script: (0 minutes 29.199 seconds)

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