From fc5da45c9e94bc18a55cd592a70bf1c21da75852 Mon Sep 17 00:00:00 2001
From: Olivier Grisel <olivier.grisel@ensta.org>
Date: Sun, 28 Nov 2010 16:27:47 +0100
Subject: [PATCH] new benchmark: comparing SVD implementations
---
benchmarks/plot_bench_svd.py | 87 ++++++++++++++++++++++++++++++++++++
1 file changed, 87 insertions(+)
create mode 100644 benchmarks/plot_bench_svd.py
diff --git a/benchmarks/plot_bench_svd.py b/benchmarks/plot_bench_svd.py
new file mode 100644
index 0000000000..bf65bde79b
--- /dev/null
+++ b/benchmarks/plot_bench_svd.py
@@ -0,0 +1,87 @@
+"""Benchmarks of Singular Values Decomposition (Exact and Approximate)"""
+import gc
+from time import time
+import numpy as np
+from collections import defaultdict
+
+from scipy.linalg import svd
+from scikits.learn.utils.extmath import fast_svd
+
+
+def make_data(n_samples, n_features, rank=None):
+ """Build a fixed rank matrix with logspaced singular values"""
+ np.random.seed(42)
+ if rank is None:
+ rank = min(n_samples, n_samples) / 10 + 1
+
+ s = np.identity(rank) * np.logspace(10, 10e-5, rank)
+ X = np.dot(np.random.randn(n_samples, rank), s)
+ X = np.dot(X, np.random.randn(rank, n_features))
+ return X
+
+
+def compute_bench(data_gen, samples_range, features_range, q=3):
+
+ it = 0
+
+ results = defaultdict(lambda: [])
+
+ 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 '===================='
+ X = make_data(n_samples, n_features)
+ rank = min(n_samples, n_samples) / 10 + 1
+
+ gc.collect()
+ print "benching scipy svd: "
+ tstart = time()
+ svd(X, full_matrices=False)
+ results['scipy svd'].append(time() - tstart)
+
+ gc.collect()
+ print "benching scikit-learn fast_svd: q=0"
+ tstart = time()
+ fast_svd(X, rank, q=0)
+ results['scikit-learn fast_svd (q=0)'].append(time() - tstart)
+
+ gc.collect()
+ print "benching scikit-learn fast_svd: q=%d " % q
+ tstart = time()
+ fast_svd(X, rank, q=q)
+ results['scikit-learn fast_svd (q=%d)' % q].append(time() - tstart)
+
+ return results
+
+
+if __name__ == '__main__':
+ from scikits.learn.linear_model import Lasso, LassoLARS
+ from mpl_toolkits.mplot3d import axes3d
+ import matplotlib.pyplot as plt
+
+ samples_range = np.linspace(2, 1000, 4)
+ features_range = np.linspace(2, 1000, 4)
+ results = compute_bench(make_data, samples_range, features_range)
+
+ fig = plt.figure()
+ ax = fig.gca(projection='3d')
+ for c, (label, timings) in zip('rbg', sorted(results.iteritems())):
+ X, Y = np.meshgrid(samples_range, features_range)
+ Z = np.asarray(timings).reshape(samples_range.shape[0],
+ features_range.shape[0])
+ # plot the actual surface
+ ax.plot_surface(X, Y, Z, rstride=8, cstride=8, alpha=0.3,
+ color=c)
+ # dummy point plot to stick the legend to since surface plot do not
+ # support legends (yet?)
+ ax.plot([1], [1], [1], color=c, label=label)
+
+ ax.set_xlabel('n_samples')
+ ax.set_ylabel('n_features')
+ ax.set_zlabel('time (s)')
+ ax.legend()
+ plt.show()
+
--
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