Speed Up Sampling Of Kernel Estimate

Here's a MWE of a much larger code I'm using. Basically, it performs a Monte Carlo integration over a KDE (kernel density estimate) for all values located below a certain threshold

Solution 1:

Probably the easiest way to speed this up is to parallelize kernel(sample):

Taking this code fragment:

tik = time.time()
insample = kernel(sample) < iso
print'filter/sample: ', time.time()-tik
#filter/sample:  1.94065904617

Change this to use multiprocessing:

from multiprocessing import Pool
tik = time.time()

#Create definition.defcalc_kernel(samp):
    return kernel(samp)

#Choose number of cores and split input array.
cores = 4
torun = np.array_split(sample, cores, axis=1)

#Calculate
pool = Pool(processes=cores)
results = pool.map(calc_kernel, torun)

#Reintegrate and calculate results
insample_mp = np.concatenate(results) < iso

print'multiprocessing filter/sample: ', time.time()-tik
#multiprocessing filter/sample:  0.496874094009

Double check they are returning the same answer:

print np.all(insample==insample_mp)
#True

A 3.9x improvement on 4 cores. Not sure what you are running this on, but after about 6 processors your input array size is not large enough to get considerably gains. For example using 20 processors its only about 5.8x faster.

Solution 2:

The claim in the comments section of this article (link below) is

"SciPy’s gaussian_kde doesn’t use FFT, while there is a statsmodels implementation that does"

…which is a possible cause of the observed poor performance. It goes on to report orders of magnitude improvement using FFT. See @jseabold's reply.

http://slendrmeans.wordpress.com/2012/05/01/will-it-python-machine-learning-for-hackers-chapter-2-part-1-summary-stats-and-density-estimators/

Disclaimer: I have no experience with statsmodels or scipy.