76 lines
2.1 KiB
Python
76 lines
2.1 KiB
Python
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"""
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=================================================
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Demo of affinity propagation clustering algorithm
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=================================================
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Reference:
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Brendan J. Frey and Delbert Dueck, "Clustering by Passing Messages
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Between Data Points", Science Feb. 2007
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"""
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import numpy as np
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from sklearn import metrics
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from sklearn.cluster import AffinityPropagation
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from sklearn.datasets import make_blobs
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# %%
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# Generate sample data
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# --------------------
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centers = [[1, 1], [-1, -1], [1, -1]]
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X, labels_true = make_blobs(
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n_samples=300, centers=centers, cluster_std=0.5, random_state=0
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)
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# %%
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# Compute Affinity Propagation
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# ----------------------------
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af = AffinityPropagation(preference=-50, random_state=0).fit(X)
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cluster_centers_indices = af.cluster_centers_indices_
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labels = af.labels_
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n_clusters_ = len(cluster_centers_indices)
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print("Estimated number of clusters: %d" % n_clusters_)
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print("Homogeneity: %0.3f" % metrics.homogeneity_score(labels_true, labels))
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print("Completeness: %0.3f" % metrics.completeness_score(labels_true, labels))
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print("V-measure: %0.3f" % metrics.v_measure_score(labels_true, labels))
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print("Adjusted Rand Index: %0.3f" % metrics.adjusted_rand_score(labels_true, labels))
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print(
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"Adjusted Mutual Information: %0.3f"
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% metrics.adjusted_mutual_info_score(labels_true, labels)
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)
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print(
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"Silhouette Coefficient: %0.3f"
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% metrics.silhouette_score(X, labels, metric="sqeuclidean")
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)
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# %%
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# Plot result
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# -----------
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import matplotlib.pyplot as plt
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plt.close("all")
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plt.figure(1)
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plt.clf()
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colors = plt.cycler("color", plt.cm.viridis(np.linspace(0, 1, 4)))
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for k, col in zip(range(n_clusters_), colors):
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class_members = labels == k
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cluster_center = X[cluster_centers_indices[k]]
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plt.scatter(
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X[class_members, 0], X[class_members, 1], color=col["color"], marker="."
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)
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plt.scatter(
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cluster_center[0], cluster_center[1], s=14, color=col["color"], marker="o"
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)
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for x in X[class_members]:
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plt.plot(
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[cluster_center[0], x[0]], [cluster_center[1], x[1]], color=col["color"]
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)
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plt.title("Estimated number of clusters: %d" % n_clusters_)
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plt.show()
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