Created
April 8, 2015 06:49
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Running DBSCAN for generated data set
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import matplotlib.pyplot as plt | |
import numpy as np | |
from sklearn.cluster import DBSCAN | |
from sklearn import metrics | |
from sklearn.datasets.samples_generator import make_blobs | |
# generating sampl data | |
centers = [[5, 5], [0, 0], [1, 5],[5, -1]] | |
X, labels_true =make_blobs(n_samples=500, n_features=5, centers=centers, cluster_std=0.9, center_box=(1, 10.0), shuffle=True, random_state=0) | |
# Compute DBSCAN | |
db = DBSCAN(eps=0.5, min_samples=10).fit(X) | |
#zeros_like :Return an array of zeros with the same shape and type as a given array., dtype will overrides the data type of the result. | |
core_samples_mask = np.zeros_like(db.labels_, dtype=bool) | |
#core_sample_indices_ : Attributes and it is index of core samples (array, shape = [n_core_samples]) | |
core_samples_mask[db.core_sample_indices_] = True | |
labels = db.labels_ | |
# Number of clusters in labels, ignoring noise if present. | |
n_clusters_ = len(set(labels)) - (1 if -1 in labels else 0) | |
#print results | |
print('Estimated number of clusters: %d' % n_clusters_) | |
print("Homogeneity: %0.3f" % metrics.homogeneity_score(labels_true, labels)) | |
print("Completeness: %0.3f" % metrics.completeness_score(labels_true, labels)) | |
print("V-measure: %0.3f" % metrics.v_measure_score(labels_true, labels)) | |
print("Adjusted Rand Index: %0.3f"% metrics.adjusted_rand_score(labels_true, labels)) | |
print("Adjusted Mutual Information: %0.3f"% metrics.adjusted_mutual_info_score(labels_true, labels)) | |
print("Silhouette Coefficient: %0.3f"% metrics.silhouette_score(X, labels)) | |
# Drawing chart | |
# Black removed and is used for noise instead. | |
unique_labels = set(labels) | |
colors = plt.cm.Spectral(np.linspace(0, 1, len(unique_labels))) | |
for k, col in zip(unique_labels, colors): | |
if k == -1: | |
# Black used for noise. | |
col = 'k' | |
class_member_mask = (labels == k) | |
xy = X[class_member_mask & core_samples_mask] | |
plt.plot(xy[:, 0], xy[:, 1], 'o', markerfacecolor=col, | |
markeredgecolor='k', markersize=14) | |
xy = X[class_member_mask & ~core_samples_mask] | |
plt.plot(xy[:, 0], xy[:, 1], 'o', markerfacecolor=col, | |
markeredgecolor='k', markersize=6) | |
plt.title('Estimated number of clusters: %d' % n_clusters_) | |
plt.show() |
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