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139 lines (103 loc) · 4.11 KB
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# This code is supporting material for the book
# Building Machine Learning Systems with Python
# by Willi Richert and Luis Pedro Coelho
# published by PACKT Publishing
#
# It is made available under the MIT License
# inspired by http://scikit-
# learn.org/dev/auto_examples/cluster/plot_kmeans_digits.html#example-
# cluster-plot-kmeans-digits-py
import os
import scipy as sp
from scipy.stats import norm
from matplotlib import pylab
from sklearn.cluster import KMeans
from utils import DATA_DIR, CHART_DIR
seed = 2
sp.random.seed(seed) # to reproduce the data later on
num_clusters = 3
def plot_clustering(x, y, title, mx=None, ymax=None, xmin=None, km=None):
pylab.figure(num=None, figsize=(8, 6))
if km:
pylab.scatter(x, y, s=50, c=km.predict(list(zip(x, y))))
else:
pylab.scatter(x, y, s=50)
pylab.title(title)
pylab.xlabel("Occurrence word 1")
pylab.ylabel("Occurrence word 2")
# pylab.xticks([w*7*24 for w in range(10)], ['week %i'%w for w in range(10)])
pylab.autoscale(tight=True)
pylab.ylim(ymin=0, ymax=1)
pylab.xlim(xmin=0, xmax=1)
pylab.grid(True, linestyle='-', color='0.75')
return pylab
xw1 = norm(loc=0.3, scale=.15).rvs(20)
yw1 = norm(loc=0.3, scale=.15).rvs(20)
xw2 = norm(loc=0.7, scale=.15).rvs(20)
yw2 = norm(loc=0.7, scale=.15).rvs(20)
xw3 = norm(loc=0.2, scale=.15).rvs(20)
yw3 = norm(loc=0.8, scale=.15).rvs(20)
x = sp.append(sp.append(xw1, xw2), xw3)
y = sp.append(sp.append(yw1, yw2), yw3)
i = 1
plot_clustering(x, y, "Vectors")
pylab.savefig(os.path.join(CHART_DIR, "1400_03_0%i.png" % i))
pylab.clf()
i += 1
# 1 iteration ####################
mx, my = sp.meshgrid(sp.arange(0, 1, 0.001), sp.arange(0, 1, 0.001))
km = KMeans(init='random', n_clusters=num_clusters, verbose=1,
n_init=1, max_iter=1,
random_state=seed)
km.fit(sp.array(list(zip(x, y))))
Z = km.predict(sp.c_[mx.ravel(), my.ravel()]).reshape(mx.shape)
plot_clustering(x, y, "Clustering iteration 1", km=km)
pylab.imshow(Z, interpolation='nearest',
extent=(mx.min(), mx.max(), my.min(), my.max()),
cmap=pylab.cm.Blues,
aspect='auto', origin='lower')
c1a, c1b, c1c = km.cluster_centers_
pylab.scatter(km.cluster_centers_[:, 0], km.cluster_centers_[:, 1],
marker='x', linewidth=2, s=100, color='black')
pylab.savefig(os.path.join(CHART_DIR, "1400_03_0%i.png" % i))
pylab.clf()
i += 1
# 2 iterations ####################
km = KMeans(init='random', n_clusters=num_clusters, verbose=1,
n_init=1, max_iter=2,
random_state=seed)
km.fit(sp.array(list(zip(x, y))))
Z = km.predict(sp.c_[mx.ravel(), my.ravel()]).reshape(mx.shape)
plot_clustering(x, y, "Clustering iteration 2", km=km)
pylab.imshow(Z, interpolation='nearest',
extent=(mx.min(), mx.max(), my.min(), my.max()),
cmap=pylab.cm.Blues,
aspect='auto', origin='lower')
c2a, c2b, c2c = km.cluster_centers_
pylab.scatter(km.cluster_centers_[:, 0], km.cluster_centers_[:, 1],
marker='x', linewidth=2, s=100, color='black')
pylab.gca().add_patch(
pylab.Arrow(c1a[0], c1a[1], c2a[0] - c1a[0], c2a[1] - c1a[1], width=0.1))
pylab.gca().add_patch(
pylab.Arrow(c1b[0], c1b[1], c2b[0] - c1b[0], c2b[1] - c1b[1], width=0.1))
pylab.gca().add_patch(
pylab.Arrow(c1c[0], c1c[1], c2c[0] - c1c[0], c2c[1] - c1c[1], width=0.1))
pylab.savefig(os.path.join(CHART_DIR, "1400_03_0%i.png" % i))
pylab.clf()
i += 1
# 3 iterations ####################
km = KMeans(init='random', n_clusters=num_clusters, verbose=1,
n_init=1, max_iter=10,
random_state=seed)
km.fit(sp.array(list(zip(x, y))))
Z = km.predict(sp.c_[mx.ravel(), my.ravel()]).reshape(mx.shape)
plot_clustering(x, y, "Clustering iteration 10", km=km)
pylab.imshow(Z, interpolation='nearest',
extent=(mx.min(), mx.max(), my.min(), my.max()),
cmap=pylab.cm.Blues,
aspect='auto', origin='lower')
pylab.scatter(km.cluster_centers_[:, 0], km.cluster_centers_[:, 1],
marker='x', linewidth=2, s=100, color='black')
pylab.savefig(os.path.join(CHART_DIR, "1400_03_0%i.png" % i))
pylab.clf()
i += 1