70 lines
2.4 KiB
Python
70 lines
2.4 KiB
Python
"""
|
|
===========================================================
|
|
A demo of K-Means clustering on the handwritten digits data
|
|
===========================================================
|
|
|
|
Comparing various initialization strategies in terms of runtime and quality of
|
|
the results.
|
|
|
|
TODO: explode the ouput of the cluster labeling and digits.target groundtruth
|
|
as categorical boolean arrays of shape (n_sample, n_unique_labels) and measure
|
|
the Pearson correlation as an additional measure of the clustering quality.
|
|
"""
|
|
print __doc__
|
|
|
|
from time import time
|
|
import numpy as np
|
|
|
|
from scikits.learn import metrics
|
|
from scikits.learn.cluster import KMeans
|
|
from scikits.learn.datasets import load_digits
|
|
from scikits.learn.decomposition import PCA
|
|
from scikits.learn.preprocessing import scale
|
|
|
|
np.random.seed(42)
|
|
|
|
digits = load_digits()
|
|
data = scale(digits.data)
|
|
|
|
n_samples, n_features = data.shape
|
|
n_digits = len(np.unique(digits.target))
|
|
labels = digits.target
|
|
|
|
print "n_digits: %d" % n_digits
|
|
print "n_features: %d" % n_features
|
|
print "n_samples: %d" % n_samples
|
|
print
|
|
|
|
print "Raw k-means with k-means++ init..."
|
|
t0 = time()
|
|
km = KMeans(init='k-means++', k=n_digits, n_init=10).fit(data)
|
|
print "done in %0.3fs" % (time() - t0)
|
|
print "Inertia: %f" % km.inertia_
|
|
print "Homogeneity: %0.3f" % metrics.homogeneity_score(labels, km.labels_)
|
|
print "Completeness: %0.3f" % metrics.completeness_score(labels, km.labels_)
|
|
print "V-measure: %0.3f" % metrics.v_measure_score(labels, km.labels_)
|
|
print
|
|
|
|
print "Raw k-means with random centroid init..."
|
|
t0 = time()
|
|
km = KMeans(init='random', k=n_digits, n_init=10).fit(data)
|
|
print "done in %0.3fs" % (time() - t0)
|
|
print "Inertia: %f" % km.inertia_
|
|
print "Homogeneity: %0.3f" % metrics.homogeneity_score(labels, km.labels_)
|
|
print "Completeness: %0.3f" % metrics.completeness_score(labels, km.labels_)
|
|
print "V-measure: %0.3f" % metrics.v_measure_score(labels, km.labels_)
|
|
print
|
|
|
|
print "Raw k-means with PCA-based centroid init..."
|
|
# in this case the seeding of the centers is deterministic, hence we run the
|
|
# kmeans algorithm only once with n_init=1
|
|
t0 = time()
|
|
pca = PCA(n_components=n_digits).fit(data)
|
|
km = KMeans(init=pca.components_, k=n_digits, n_init=1).fit(data)
|
|
print "done in %0.3fs" % (time() - t0)
|
|
print "Inertia: %f" % km.inertia_
|
|
print "Homogeneity: %0.3f" % metrics.homogeneity_score(labels, km.labels_)
|
|
print "Completeness: %0.3f" % metrics.completeness_score(labels, km.labels_)
|
|
print "V-measure: %0.3f" % metrics.v_measure_score(labels, km.labels_)
|
|
print
|