def compute_bench(samples_range, features_range, q=3, rank=50):
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_low_rank_matrix(n_samples, n_features, effective_rank=rank,
tail_strength=0.2)
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
def compute_bench(samples_range, features_range, rank=50, tolerance=1e-7):
it = 0
timeset = defaultdict(lambda: [])
err = 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 = np.abs(make_low_rank_matrix(n_samples, n_features,
effective_rank=rank, tail_strength=0.2))
gc.collect()
print "benching nndsvd-nmf: "
tstart = time()
m = NMF(n_components=30, tol=tolerance, init='nndsvd').fit(X)
tend = time() - tstart
timeset['nndsvd-nmf'].append(tend)
err['nndsvd-nmf'].append(m.reconstruction_err_)
print m.reconstruction_err_, tend
gc.collect()
print "benching nndsvda-nmf: "
tstart = time()
m = NMF(n_components=30, init='nndsvda',
tol=tolerance).fit(X)
tend = time() - tstart
timeset['nndsvda-nmf'].append(tend)
err['nndsvda-nmf'].append(m.reconstruction_err_)
print m.reconstruction_err_, tend
gc.collect()
print "benching nndsvdar-nmf: "
tstart = time()
m = NMF(n_components=30, init='nndsvdar',
tol=tolerance).fit(X)
tend = time() - tstart
timeset['nndsvdar-nmf'].append(tend)
err['nndsvdar-nmf'].append(m.reconstruction_err_)
print m.reconstruction_err_, tend
gc.collect()
print "benching random-nmf"
tstart = time()
m = NMF(n_components=30, init=None, max_iter=1000,
tol=tolerance).fit(X)
tend = time() - tstart
timeset['random-nmf'].append(tend)
err['random-nmf'].append(m.reconstruction_err_)
print m.reconstruction_err_, tend
gc.collect()
print "benching alt-random-nmf"
tstart = time()
W, H = alt_nnmf(X, r=30, R=None, tol=tolerance)
tend = time() - tstart
timeset['alt-random-nmf'].append(tend)
err['alt-random-nmf'].append(np.linalg.norm(X - np.dot(W, H)))
print np.linalg.norm(X - np.dot(W, H)), tend
return timeset, err
