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December 10, 2012 14:10
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Timings and memory consumption for the singular value decomposition implemented in scipy. The script svd_memory.py generates a plot about the memory consumption (requires the package `memory_profiler`) and the second one will plot the timings. The scripts will perform several iterations for matrices of different sizes and will take about 30 minu…
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# .. Memory benchmarks for SciPy's Singular Value Decomposition .. | |
# .. Author: Fabian Pedregosa <fabian@fseoane.net> | |
import numpy as np | |
from scipy.sparse import linalg as splinalg | |
from scipy import sparse, linalg | |
import pylab as pl | |
from memory_profiler import memory_usage | |
dims = np.arange(500, 1500, 20) | |
n_iter = 3 | |
LAPACK_mem = np.zeros((n_iter, dims.size)) | |
ARPACK_mem = np.zeros((n_iter, dims.size)) | |
for i_iter in range(n_iter): | |
for i_dim, k in enumerate(dims): | |
x = np.random.randn(k, k) | |
tmp = memory_usage((linalg.svd, (x,))) | |
LAPACK_mem[i_iter, i_dim] = np.max(tmp) | |
tmp = memory_usage((splinalg.svds, (x,))) | |
ARPACK_mem[i_iter, i_dim] = np.max(tmp) | |
def errorfill(x, y, yerr, color=None, alpha_fill=0.3, ax=None, label=None): | |
# helper function, stolen from http://tonysyu.github.com/plotting-error-bars.html | |
ax = ax if ax is not None else pl.gca() | |
if color is None: | |
color = ax._get_lines.color_cycle.next() | |
if np.isscalar(yerr) or len(yerr) == len(y): | |
ymin = y - yerr | |
ymax = y + yerr | |
elif len(yerr) == 2: | |
ymin, ymax = yerr | |
ax.plot(x, y, color=color, label=label) | |
ax.fill_between(x, ymax, ymin, color=color, alpha=alpha_fill) | |
errorfill(dims, LAPACK_mem.mean(0), LAPACK_mem.std(0), label='linalg.svd') | |
errorfill(dims, ARPACK_mem.mean(0), ARPACK_mem.std(0), label='sparse.linalg.svds') | |
pl.ylabel('Memory (in MB)') | |
pl.legend(loc='upper left') | |
pl.axis('tight') | |
pl.show() |
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# .. Benchmarks for SciPy's Singular Value Decomposition .. | |
# .. Author: Fabian Pedregosa <fabian@fseoane.net> | |
import time | |
import numpy as np | |
from scipy.sparse import linalg as splinalg | |
from scipy import sparse, linalg | |
import pylab as pl | |
dims = np.arange(500, 1500, 20) | |
n_iter = 3 | |
LAPACK_time = np.zeros((n_iter, dims.size)) | |
ARPACK_time = np.zeros((n_iter, dims.size)) | |
# .. we perform several runs for greater stability .. | |
for i_iter in range(n_iter): | |
for i_dim, k in enumerate(dims): | |
# .. create the data .. | |
x = np.random.randn(k, k) | |
t0 = time.time() | |
linalg.svd(x) | |
LAPACK_time[i_iter, i_dim] = time.time() - t0 | |
t0 = time.time() | |
splinalg.svds(x, k-1) | |
ARPACK_time[i_iter, i_dim] = time.time() - t0 | |
# .. plot the result .. | |
def errorfill(x, y, yerr, color=None, alpha_fill=0.3, ax=None, label=None): | |
# helper function, stolen from http://tonysyu.github.com/plotting-error-bars.html | |
ax = ax if ax is not None else pl.gca() | |
if color is None: | |
color = ax._get_lines.color_cycle.next() | |
if np.isscalar(yerr) or len(yerr) == len(y): | |
ymin = y - yerr | |
ymax = y + yerr | |
elif len(yerr) == 2: | |
ymin, ymax = yerr | |
ax.plot(x, y, color=color, label=label) | |
ax.fill_between(x, ymax, ymin, color=color, alpha=alpha_fill) | |
errorfill(dims, LAPACK_time.sum(0), LAPACK_time.std(0), label='linalg.svd') | |
errorfill(dims, ARPACK_time.sum(0), LAPACK_time.std(0), label='sparse.linalg.svds') | |
pl.ylabel('Time (in seconds)') | |
pl.xlabel('Size of matrices') | |
pl.legend(loc='upper left') | |
pl.axis('tight') | |
# pl.savefig('svd_timing.png') | |
pl.show() |
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