137 lines
4.5 KiB
Python
137 lines
4.5 KiB
Python
from collections import defaultdict
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from time import time
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import numpy as np
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from numpy import random as nr
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from sklearn.cluster import KMeans, MiniBatchKMeans
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def compute_bench(samples_range, features_range):
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it = 0
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results = defaultdict(lambda: [])
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chunk = 100
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max_it = len(samples_range) * len(features_range)
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for n_samples in samples_range:
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for n_features in features_range:
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it += 1
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print('==============================')
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print('Iteration %03d of %03d' % (it, max_it))
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print('==============================')
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print()
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data = nr.randint(-50, 51, (n_samples, n_features))
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print('K-Means')
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tstart = time()
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kmeans = KMeans(init='k-means++', n_clusters=10).fit(data)
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delta = time() - tstart
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print("Speed: %0.3fs" % delta)
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print("Inertia: %0.5f" % kmeans.inertia_)
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print()
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results['kmeans_speed'].append(delta)
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results['kmeans_quality'].append(kmeans.inertia_)
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print('Fast K-Means')
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# let's prepare the data in small chunks
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mbkmeans = MiniBatchKMeans(init='k-means++',
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n_clusters=10,
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batch_size=chunk)
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tstart = time()
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mbkmeans.fit(data)
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delta = time() - tstart
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print("Speed: %0.3fs" % delta)
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print("Inertia: %f" % mbkmeans.inertia_)
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print()
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print()
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results['MiniBatchKMeans Speed'].append(delta)
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results['MiniBatchKMeans Quality'].append(mbkmeans.inertia_)
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return results
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def compute_bench_2(chunks):
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results = defaultdict(lambda: [])
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n_features = 50000
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means = np.array([[1, 1], [-1, -1], [1, -1], [-1, 1],
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[0.5, 0.5], [0.75, -0.5], [-1, 0.75], [1, 0]])
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X = np.empty((0, 2))
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for i in range(8):
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X = np.r_[X, means[i] + 0.8 * np.random.randn(n_features, 2)]
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max_it = len(chunks)
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it = 0
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for chunk in chunks:
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it += 1
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print('==============================')
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print('Iteration %03d of %03d' % (it, max_it))
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print('==============================')
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print()
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print('Fast K-Means')
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tstart = time()
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mbkmeans = MiniBatchKMeans(init='k-means++',
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n_clusters=8,
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batch_size=chunk)
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mbkmeans.fit(X)
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delta = time() - tstart
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print("Speed: %0.3fs" % delta)
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print("Inertia: %0.3fs" % mbkmeans.inertia_)
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print()
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results['MiniBatchKMeans Speed'].append(delta)
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results['MiniBatchKMeans Quality'].append(mbkmeans.inertia_)
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return results
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if __name__ == '__main__':
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from mpl_toolkits.mplot3d import axes3d # register the 3d projection
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import matplotlib.pyplot as plt
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samples_range = np.linspace(50, 150, 5).astype(int)
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features_range = np.linspace(150, 50000, 5).astype(int)
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chunks = np.linspace(500, 10000, 15).astype(int)
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results = compute_bench(samples_range, features_range)
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results_2 = compute_bench_2(chunks)
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max_time = max([max(i) for i in [t for (label, t) in results.items()
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if "speed" in label]])
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max_inertia = max([max(i) for i in [
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t for (label, t) in results.items()
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if "speed" not in label]])
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fig = plt.figure('scikit-learn K-Means benchmark results')
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for c, (label, timings) in zip('brcy',
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sorted(results.items())):
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if 'speed' in label:
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ax = fig.add_subplot(2, 2, 1, projection='3d')
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ax.set_zlim3d(0.0, max_time * 1.1)
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else:
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ax = fig.add_subplot(2, 2, 2, projection='3d')
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ax.set_zlim3d(0.0, max_inertia * 1.1)
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X, Y = np.meshgrid(samples_range, features_range)
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Z = np.asarray(timings).reshape(samples_range.shape[0],
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features_range.shape[0])
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ax.plot_surface(X, Y, Z.T, cstride=1, rstride=1, color=c, alpha=0.5)
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ax.set_xlabel('n_samples')
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ax.set_ylabel('n_features')
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i = 0
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for c, (label, timings) in zip('br',
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sorted(results_2.items())):
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i += 1
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ax = fig.add_subplot(2, 2, i + 2)
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y = np.asarray(timings)
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ax.plot(chunks, y, color=c, alpha=0.8)
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ax.set_xlabel('Chunks')
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ax.set_ylabel(label)
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plt.show()
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