Change for scikit-learn example of dbscan clustering. Improve performance of plot_dbscan.py by minimizing calls to plot.

plot_dbscan.py

# -*- coding: utf-8 -*-
"""
===================================
Demo of DBSCAN clustering algorithm
===================================

Finds core samples of high density and expands clusters from them.

"""
print(__doc__)

import numpy as np

from sklearn.cluster import DBSCAN
from sklearn import metrics
from sklearn.datasets.samples_generator import make_blobs
from sklearn.preprocessing import StandardScaler
import time

##############################################################################
# Generate sample data
centers = [[1, 1], [-1, -1], [1, -1]]
X, labels_true = make_blobs(n_samples=750, centers=centers, cluster_std=0.4,
                            random_state=0)

X = StandardScaler().fit_transform(X)

##############################################################################
# Compute DBSCAN
db = DBSCAN(eps=0.3, min_samples=10).fit(X)
core_samples = db.core_sample_indices_
core_samples_mask = np.zeros_like(db.labels_,dtype=bool)
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('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))

##############################################################################
# Plot result
import pylab as pl

begTime = time.clock()

# Black removed and is used for noise instead.
unique_labels = set(labels)
colors = pl.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'

    if 0:
        #
        # Call plot two times for each class (label),
        # to distinguish between the core and non-core members by marker size.
        # Use numpy boolean arrays to create the subset passed to pl.plot.
        #
        # For  200 elements this takes .2 sec.
        # For 2000 elements this takes .2 sec.
        class_member_mask = labels==k

        xy = X[class_member_mask & core_samples_mask]
        pl.plot(xy[:,0], xy[:,1], 'o', markerfacecolor=col,
                markeredgecolor='k', markersize=14)

        xy = X[class_member_mask & np.logical_not(core_samples_mask)]
        pl.plot(xy[:,0], xy[:,1], 'o', markerfacecolor=col,
                markeredgecolor='k', markersize=6)

    if 1:
        # For  200 elements this takes .2 sec.
        # For 2000 elements this takes .2 sec.
        class_members = set(index[0] for index in np.argwhere(labels == k))
        cluster_core_samples = set(index for index in core_samples
                                if labels[index] == k)

        xy = X[list(class_members.intersection(cluster_core_samples))]
        pl.plot(xy[:,0], xy[:,1], 'o', markerfacecolor=col,
                markeredgecolor='k', markersize=14)

        xy = X[list(class_members.difference(cluster_core_samples))]
        pl.plot(xy[:,0], xy[:,1], 'o', markerfacecolor=col,
                markeredgecolor='k', markersize=6)

    if 0:
        # The cost per call to plot is expensive, so better to pass a numpy array.
        # For  200 elements this takes 0.9 sec.
        # For 2000 elements this takes 7.7 sec.
        class_members = [index[0] for index in np.argwhere(labels == k)]
        cluster_core_samples = [index for index in core_samples
                                if labels[index] == k]
        for index in class_members:
            x = X[index]
            if index in core_samples and k != -1:
                markersize = 14
            else:
                markersize = 6
            pl.plot(x[0], x[1], 'o', markerfacecolor=col,
                    markeredgecolor='k', markersize=markersize)

endTime = time.clock()
print "Time: %.1f sec" % (endTime-begTime)

pl.title('Estimated number of clusters: %d' % n_clusters_)
pl.show()