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from time import time
from collections import defaultdict
import numpy as np
from numpy import random as nr
from sklearn.cluster.k_means_ import KMeans, MiniBatchKMeans
def compute_bench(samples_range, features_range):
it = 0
iterations = 200
results = defaultdict(lambda: [])
chunk = 100
max_it = len(samples_range) * len(features_range)
for n_samples in samples_range:
for n_features in features_range:
it += 1
print '=============================='
print 'Iteration %03d of %03d' % (it, max_it)
print '=============================='
print ''
data = nr.random_integers(-50, 50, (n_samples, n_features))
print 'K-Means'
tstart = time()
kmeans = KMeans(init='k-means++',
k=10).fit(data)
delta = time() - tstart
print "Speed: %0.3fs" % delta
print "Inertia: %0.5f" % kmeans.inertia_
print ''
results['kmeans_speed'].append(delta)
results['kmeans_quality'].append(kmeans.inertia_)
print 'Fast K-Means'
# let's prepare the data in small chunks
mbkmeans = MiniBatchKMeans(init='k-means++',
k=10,
chunk_size=chunk)
tstart = time()
mbkmeans.fit(data)
delta = time() - tstart
print "Speed: %0.3fs" % delta
print "Inertia: %f" % mbkmeans.inertia_
print ''
print ''
results['minibatchkmeans_speed'].append(delta)
results['minibatchkmeans_quality'].append(mbkmeans.inertia_)
return results
def compute_bench_2(chunks):
results = defaultdict(lambda: [])
n_features = 50000
means = np.array([[1, 1], [-1, -1], [1, -1], [-1, 1],
[0.5, 0.5], [0.75, -0.5], [-1, 0.75], [1, 0]])
X = np.empty((0, 2))
for i in xrange(8):
X = np.r_[X, means[i] + 0.8 * np.random.randn(n_features, 2)]
max_it = len(chunks)
it = 0
for chunk in chunks:
it += 1
print '=============================='
print 'Iteration %03d of %03d' % (it, max_it)
print '=============================='
print ''
print 'Fast K-Means'
tstart = time()
mbkmeans = MiniBatchKMeans(init='k-means++',
k=8,
chunk_size=chunk)
mbkmeans.fit(X)
delta = time() - tstart
print "Speed: %0.3fs" % delta
print "Inertia: %0.3fs" % mbkmeans.inertia_
print ''
results['minibatchkmeans_speed'].append(delta)
results['minibatchkmeans_quality'].append(mbkmeans.inertia_)
return results
if __name__ == '__main__':
from mpl_toolkits.mplot3d import axes3d # register the 3d projection
import matplotlib.pyplot as plt
samples_range = np.linspace(50, 150, 5).astype(np.int)
features_range = np.linspace(150, 50000, 5).astype(np.int)
chunks = np.linspace(500, 10000, 15).astype(np.int)
results = compute_bench(samples_range, features_range)
results_2 = compute_bench_2(chunks)
max_time = max([max(i) for i in [t for (label, t) in results.iteritems()
if "speed" in label]])
max_inertia = max([max(i) for i in [
t for (label, t) in results.iteritems()
if "speed" not in label]])
fig = plt.figure()
for c, (label, timings) in zip('brcy',
sorted(results.iteritems())):
if 'speed' in label:
ax = fig.add_subplot(2, 2, 1, projection='3d')
ax.set_zlim3d(0.0, max_time * 1.1)
else:
ax = fig.add_subplot(2, 2, 2, projection='3d')
ax.set_zlim3d(0.0, max_inertia * 1.1)
X, Y = np.meshgrid(samples_range, features_range)
Z = np.asarray(timings).reshape(samples_range.shape[0],
features_range.shape[0])
ax.plot_surface(X, Y, Z.T, cstride=1, rstride=1, color=c, alpha=0.5)
ax.set_xlabel('n_samples')
ax.set_ylabel('n_features')
i = 0
for c, (label, timings) in zip('br',
sorted(results_2.iteritems())):
i += 1
ax = fig.add_subplot(2, 2, i + 2)
y = np.asarray(timings)
ax.plot(chunks, y, color=c, alpha=0.8)
ax.set_xlabel('chunks')
ax.set_ylabel(label)
plt.show()
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