73 lines
2.4 KiB
Python
73 lines
2.4 KiB
Python
# -*- coding: utf-8 -*-
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"""
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===================================
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Demo of DBSCAN clustering algorithm
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===================================
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Finds core samples of high density and expands clusters from them.
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"""
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print(__doc__)
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import numpy as np
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from sklearn.cluster import DBSCAN
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from sklearn import metrics
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from sklearn.datasets.samples_generator import make_blobs
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from sklearn.preprocessing import StandardScaler
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##############################################################################
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# Generate sample data
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centers = [[1, 1], [-1, -1], [1, -1]]
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X, labels_true = make_blobs(n_samples=750, centers=centers, cluster_std=0.4,
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random_state=0)
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X = StandardScaler().fit_transform(X)
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##############################################################################
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# Compute DBSCAN
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db = DBSCAN(eps=0.3, min_samples=10).fit(X)
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core_samples_mask = np.zeros_like(db.labels_, dtype=bool)
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core_samples_mask[db.core_sample_indices_] = True
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labels = db.labels_
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# Number of clusters in labels, ignoring noise if present.
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n_clusters_ = len(set(labels)) - (1 if -1 in labels else 0)
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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"
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% metrics.adjusted_rand_score(labels_true, labels))
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print("Adjusted Mutual Information: %0.3f"
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% metrics.adjusted_mutual_info_score(labels_true, labels))
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print("Silhouette Coefficient: %0.3f"
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% metrics.silhouette_score(X, labels))
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##############################################################################
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# Plot result
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import matplotlib.pyplot as plt
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# Black removed and is used for noise instead.
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unique_labels = set(labels)
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colors = plt.cm.Spectral(np.linspace(0, 1, len(unique_labels)))
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for k, col in zip(unique_labels, colors):
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if k == -1:
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# Black used for noise.
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col = 'k'
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class_member_mask = (labels == k)
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xy = X[class_member_mask & core_samples_mask]
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plt.plot(xy[:, 0], xy[:, 1], 'o', markerfacecolor=col,
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markeredgecolor='k', markersize=14)
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xy = X[class_member_mask & ~core_samples_mask]
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plt.plot(xy[:, 0], xy[:, 1], 'o', markerfacecolor=col,
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markeredgecolor='k', markersize=6)
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plt.title('Estimated number of clusters: %d' % n_clusters_)
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plt.show()
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