83 lines
2.8 KiB
Python
83 lines
2.8 KiB
Python
"""Benchmarks of Singular Value Decomposition (Exact and Approximate)
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The data is mostly low rank but is a fat infinite tail.
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"""
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import gc
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from time import time
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import numpy as np
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from collections import defaultdict
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from scipy.linalg import svd
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from sklearn.utils.extmath import randomized_svd
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from sklearn.datasets import make_low_rank_matrix
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def compute_bench(samples_range, features_range, n_iter=3, rank=50):
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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('====================')
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X = make_low_rank_matrix(n_samples, n_features,
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effective_rank=rank,
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tail_strength=0.2)
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gc.collect()
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print("benchmarking scipy svd: ")
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tstart = time()
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svd(X, full_matrices=False)
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results['scipy svd'].append(time() - tstart)
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gc.collect()
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print("benchmarking scikit-learn randomized_svd: n_iter=0")
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tstart = time()
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randomized_svd(X, rank, n_iter=0)
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results['scikit-learn randomized_svd (n_iter=0)'].append(
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time() - tstart)
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gc.collect()
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print("benchmarking scikit-learn randomized_svd: n_iter=%d "
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% n_iter)
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tstart = time()
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randomized_svd(X, rank, n_iter=n_iter)
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results['scikit-learn randomized_svd (n_iter=%d)'
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% n_iter].append(time() - tstart)
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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(2, 1000, 4).astype(int)
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features_range = np.linspace(2, 1000, 4).astype(int)
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results = compute_bench(samples_range, features_range)
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label = 'scikit-learn singular value decomposition benchmark results'
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fig = plt.figure(label)
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ax = fig.gca(projection='3d')
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for c, (label, timings) in zip('rbg', sorted(results.items())):
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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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# plot the actual surface
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ax.plot_surface(X, Y, Z, rstride=8, cstride=8, alpha=0.3,
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color=c)
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# dummy point plot to stick the legend to since surface plot do not
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# support legends (yet?)
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ax.plot([1], [1], [1], color=c, label=label)
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ax.set_xlabel('n_samples')
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ax.set_ylabel('n_features')
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ax.set_zlabel('Time (s)')
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ax.legend()
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plt.show()
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