dbscan python

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23 Aug 2016

1print(__doc__)
2
3import numpy as np
4
5from sklearn.cluster import DBSCAN
6from sklearn import metrics
7from sklearn.datasets import make_blobs
8from sklearn.preprocessing import StandardScaler
9
10
11# #############################################################################
12# Generate sample data
13centers = [[1, 1], [-1, -1], [1, -1]]
14X, labels_true = make_blobs(n_samples=750, centers=centers, cluster_std=0.4,
15                            random_state=0)
16
17X = StandardScaler().fit_transform(X)
18
19# #############################################################################
20# Compute DBSCAN
21db = DBSCAN(eps=0.3, min_samples=10).fit(X)
22core_samples_mask = np.zeros_like(db.labels_, dtype=bool)
23core_samples_mask[db.core_sample_indices_] = True
24labels = db.labels_
25
26# Number of clusters in labels, ignoring noise if present.
27n_clusters_ = len(set(labels)) - (1 if -1 in labels else 0)
28n_noise_ = list(labels).count(-1)
29
30print('Estimated number of clusters: %d' % n_clusters_)
31print('Estimated number of noise points: %d' % n_noise_)
32print("Homogeneity: %0.3f" % metrics.homogeneity_score(labels_true, labels))
33print("Completeness: %0.3f" % metrics.completeness_score(labels_true, labels))
34print("V-measure: %0.3f" % metrics.v_measure_score(labels_true, labels))
35print("Adjusted Rand Index: %0.3f"
36      % metrics.adjusted_rand_score(labels_true, labels))
37print("Adjusted Mutual Information: %0.3f"
38      % metrics.adjusted_mutual_info_score(labels_true, labels))
39print("Silhouette Coefficient: %0.3f"
40      % metrics.silhouette_score(X, labels))
41
42# #############################################################################
43# Plot result
44import matplotlib.pyplot as plt
45
46# Black removed and is used for noise instead.
47unique_labels = set(labels)
48colors = [plt.cm.Spectral(each)
49          for each in np.linspace(0, 1, len(unique_labels))]
50for k, col in zip(unique_labels, colors):
51    if k == -1:
52        # Black used for noise.
53        col = [0, 0, 0, 1]
54
55    class_member_mask = (labels == k)
56
57    xy = X[class_member_mask & core_samples_mask]
58    plt.plot(xy[:, 0], xy[:, 1], 'o', markerfacecolor=tuple(col),
59             markeredgecolor='k', markersize=14)
60
61    xy = X[class_member_mask & ~core_samples_mask]
62    plt.plot(xy[:, 0], xy[:, 1], 'o', markerfacecolor=tuple(col),
63             markeredgecolor='k', markersize=6)
64
65plt.title('Estimated number of clusters: %d' % n_clusters_)
66plt.show()
67

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