86 lines
2.6 KiB
Python
86 lines
2.6 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 AgglomerativeClustering
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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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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('n_samples %05d; n_features %02d' % (n_samples, n_features))
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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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for linkage in ("single", "average", "complete", "ward"):
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print(linkage.capitalize())
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tstart = time()
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AgglomerativeClustering(
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linkage=linkage,
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n_clusters=10
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).fit(data)
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delta = time() - tstart
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print("Speed: %0.3fs" % delta)
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print()
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results[linkage].append(delta)
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return results
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if __name__ == '__main__':
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import matplotlib.pyplot as plt
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samples_range = np.linspace(1000, 15000, 8).astype(int)
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features_range = np.array([2, 10, 20, 50])
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results = compute_bench(samples_range, features_range)
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max_time = max([max(i) for i in [t for (label, t) in results.items()]])
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colors = plt.get_cmap('tab10')(np.linspace(0, 1, 10))[:4]
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lines = {linkage: None for linkage in results.keys()}
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fig, axs = plt.subplots(2, 2, sharex=True, sharey=True)
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fig.suptitle(
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'Scikit-learn agglomerative clustering benchmark results',
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fontsize=16
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)
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for c, (label, timings) in zip(colors,
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sorted(results.items())):
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timing_by_samples = np.asarray(timings).reshape(
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samples_range.shape[0],
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features_range.shape[0]
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)
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for n in range(timing_by_samples.shape[1]):
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ax = axs.flatten()[n]
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lines[label], = ax.plot(
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samples_range,
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timing_by_samples[:, n],
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color=c,
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label=label
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)
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ax.set_title('n_features = %d' % features_range[n])
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if n >= 2:
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ax.set_xlabel('n_samples')
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if n % 2 == 0:
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ax.set_ylabel('time (s)')
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fig.subplots_adjust(right=0.8)
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fig.legend([lines[link] for link in sorted(results.keys())],
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sorted(results.keys()), loc="center right", fontsize=8)
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plt.show()
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