Note

Go to the end to download the full example code. or to run this example in your browser via Binder

在线学习人脸部件的字典#

本示例使用一个大型人脸数据集来学习一组20 x 20的图像块，这些图像块构成人脸。

从编程的角度来看，这很有趣，因为它展示了如何使用scikit-learn的在线API通过分块处理一个非常大的数据集。我们的处理方式是一次加载一张图像，并从这张图像中随机提取50个图像块。当我们积累了500个这样的图像块（使用10张图像）后，我们运行在线KMeans对象MiniBatchKMeans的:func:~sklearn.cluster.MiniBatchKMeans.partial_fit 方法。

MiniBatchKMeans的详细设置使我们能够看到在连续调用partial_fit时，一些聚类被重新分配。这是因为它们所代表的图像块数量变得太少，选择一个新的随机聚类会更好。

加载数据#

from sklearn import datasets

faces = datasets.fetch_olivetti_faces()

学习图像的字典#

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()

# 在线学习部分：对整个数据集循环6次
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 0.83s.

绘制结果#

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()