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pep8 fixes
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| .DS_Store | ||
| build | ||
| scikits/learn/datasets/__config__.py | ||
| scikits/learn/**/*.html | ||
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| dist/ | ||
| doc/_build/ | ||
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| Original file line number | Diff line number | Diff line change |
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| @@ -0,0 +1,143 @@ | ||
| import gc | ||
| from time import time | ||
| import sys | ||
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| from collections import defaultdict | ||
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| import numpy as np | ||
| from numpy import random as nr | ||
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| from scikits.learn.cluster.k_means_ import KMeans, MiniBatchKMeans | ||
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| def compute_bench(samples_range, features_range): | ||
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| it = 0 | ||
| iterations = 200 | ||
| results = defaultdict(lambda: []) | ||
| chunk = 100 | ||
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| 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)) | ||
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| print 'K-Means' | ||
| tstart = time() | ||
| kmeans = KMeans(init='k-means++', | ||
| k=10).fit(data) | ||
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| delta = time() - tstart | ||
| print "Speed: %0.3fs" % delta | ||
| print "Inertia: %0.5f" % kmeans.inertia_ | ||
| print '' | ||
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| results['kmeans_speed'].append(delta) | ||
| results['kmeans_quality'].append(kmeans.inertia_) | ||
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| 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 '' | ||
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| results['minibatchkmeans_speed'].append(delta) | ||
| results['minibatchkmeans_quality'].append(mbkmeans.inertia_) | ||
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| return results | ||
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| 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 '' | ||
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| print 'Fast K-Means' | ||
| tstart = time() | ||
| mbkmeans = MiniBatchKMeans(init='k-means++', | ||
| k=8, | ||
| chunk_size=chunk) | ||
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| mbkmeans.fit(X) | ||
| delta = time() - tstart | ||
| print "Speed: %0.3fs" % delta | ||
| print "Inertia: %0.3fs" % mbkmeans.inertia_ | ||
| print '' | ||
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| results['minibatchkmeans_speed'].append(delta) | ||
| results['minibatchkmeans_quality'].append(mbkmeans.inertia_) | ||
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| return results | ||
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| if __name__ == '__main__': | ||
| from mpl_toolkits.mplot3d import axes3d # register the 3d projection | ||
| import matplotlib.pyplot as plt | ||
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| 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) | ||
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| results = compute_bench(samples_range, features_range) | ||
| results_2 = compute_bench_2(chunks) | ||
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| 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]]) | ||
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| 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) | ||
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| 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') | ||
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| 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) | ||
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| plt.show() |
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