127 lines
4.4 KiB
Python
127 lines
4.4 KiB
Python
"""
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===========================================================
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A demo of K-Means clustering on the handwritten digits data
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===========================================================
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In this example we compare the various initialization strategies for
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K-means in terms of runtime and quality of the results.
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As the ground truth is known here, we also apply different cluster
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quality metrics to judge the goodness of fit of the cluster labels to the
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ground truth.
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Cluster quality metrics evaluated (see :ref:`clustering_evaluation` for
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definitions and discussions of the metrics):
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=========== ========================================================
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Shorthand full name
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=========== ========================================================
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homo homogeneity score
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compl completeness score
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v-meas V measure
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ARI adjusted Rand index
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AMI adjusted mutual information
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silhouette silhouette coefficient
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=========== ========================================================
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"""
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print(__doc__)
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from time import time
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import numpy as np
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import matplotlib.pyplot as plt
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from sklearn import metrics
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from sklearn.cluster import KMeans
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from sklearn.datasets import load_digits
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from sklearn.decomposition import PCA
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from sklearn.preprocessing import scale
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np.random.seed(42)
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X_digits, y_digits = load_digits(return_X_y=True)
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data = scale(X_digits)
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n_samples, n_features = data.shape
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n_digits = len(np.unique(y_digits))
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labels = y_digits
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sample_size = 300
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print("n_digits: %d, \t n_samples %d, \t n_features %d"
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% (n_digits, n_samples, n_features))
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print(82 * '_')
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print('init\t\ttime\tinertia\thomo\tcompl\tv-meas\tARI\tAMI\tsilhouette')
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def bench_k_means(estimator, name, data):
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t0 = time()
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estimator.fit(data)
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print('%-9s\t%.2fs\t%i\t%.3f\t%.3f\t%.3f\t%.3f\t%.3f\t%.3f'
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% (name, (time() - t0), estimator.inertia_,
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metrics.homogeneity_score(labels, estimator.labels_),
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metrics.completeness_score(labels, estimator.labels_),
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metrics.v_measure_score(labels, estimator.labels_),
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metrics.adjusted_rand_score(labels, estimator.labels_),
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metrics.adjusted_mutual_info_score(labels, estimator.labels_),
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metrics.silhouette_score(data, estimator.labels_,
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metric='euclidean',
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sample_size=sample_size)))
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bench_k_means(KMeans(init='k-means++', n_clusters=n_digits, n_init=10),
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name="k-means++", data=data)
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bench_k_means(KMeans(init='random', n_clusters=n_digits, n_init=10),
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name="random", data=data)
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# in this case the seeding of the centers is deterministic, hence we run the
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# kmeans algorithm only once with n_init=1
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pca = PCA(n_components=n_digits).fit(data)
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bench_k_means(KMeans(init=pca.components_, n_clusters=n_digits, n_init=1),
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name="PCA-based",
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data=data)
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print(82 * '_')
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# #############################################################################
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# Visualize the results on PCA-reduced data
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reduced_data = PCA(n_components=2).fit_transform(data)
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kmeans = KMeans(init='k-means++', n_clusters=n_digits, n_init=10)
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kmeans.fit(reduced_data)
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# Step size of the mesh. Decrease to increase the quality of the VQ.
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h = .02 # point in the mesh [x_min, x_max]x[y_min, y_max].
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# Plot the decision boundary. For that, we will assign a color to each
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x_min, x_max = reduced_data[:, 0].min() - 1, reduced_data[:, 0].max() + 1
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y_min, y_max = reduced_data[:, 1].min() - 1, reduced_data[:, 1].max() + 1
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xx, yy = np.meshgrid(np.arange(x_min, x_max, h), np.arange(y_min, y_max, h))
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# Obtain labels for each point in mesh. Use last trained model.
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Z = kmeans.predict(np.c_[xx.ravel(), yy.ravel()])
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# Put the result into a color plot
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Z = Z.reshape(xx.shape)
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plt.figure(1)
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plt.clf()
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plt.imshow(Z, interpolation='nearest',
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extent=(xx.min(), xx.max(), yy.min(), yy.max()),
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cmap=plt.cm.Paired,
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aspect='auto', origin='lower')
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plt.plot(reduced_data[:, 0], reduced_data[:, 1], 'k.', markersize=2)
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# Plot the centroids as a white X
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centroids = kmeans.cluster_centers_
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plt.scatter(centroids[:, 0], centroids[:, 1],
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marker='x', s=169, linewidths=3,
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color='w', zorder=10)
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plt.title('K-means clustering on the digits dataset (PCA-reduced data)\n'
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'Centroids are marked with white cross')
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plt.xlim(x_min, x_max)
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plt.ylim(y_min, y_max)
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plt.xticks(())
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plt.yticks(())
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plt.show()
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