Powering up python as a data analysis platform
When working with Machine Learning algorithms we face large data movement, but in many algorithms the most important part is a heavy use of linear algebra operations and other mathematical/vectorial computations.
Intel has a math library that is optimized for the latest processors (MKL), including programmer-made optimizations for multiple core counts, wider vector units and more varied architectures which yield a performance that could not be achieved only with compiler automated optimization for routines such as highly vectorized and threaded linear algebra, fast Fourier transforms, and vector math and Statistics. These functions are royalty-free, so including them statically in the program comes at no cost.
Cristoph Gohlke and collaborators have a MKL license and have taken the effort to compile a series of Python modules compiled agaist them. In particular, Numpy and Scipy include these powerful libraries. Add to this that he has already compiled the binaries for Windows 64 bits which are very rare on the internet.
The following are two tests with a positive definite matrix. We compute the eigenvalues in R and Python, using the symmetric eigenvalue solver in each case. The processor is a i5 3210M not plugged in to the socket (losing approx. half its performance). Note that this version of R is compiled against standard Atlas libraries.
**B=read.csv("B.csv",header=F)
st=proc.time(); eigB=eigen(B,symmetric=T); en=proc.time()
en-st
user system elapsed
0.58 0.00 0.58 **
In Python
from time import time
import numpy
**B=numpy.loadtxt("B.csv", delimiter=",")
st = time(); U, E = numpy.linalg.eigh(B); en = time()
en-st
0.13400006294250488
**A final remark is that there exists an opensource alternative to high-performance CPU computing, and it is the OpenBLAS libraries. Their performance is comparable to MKL.
Link to the positive definite matrix used in the experiments here. Link to Christoph Gohlke's page here.
Despite the fact that I've been aware of Scikits Learn (sklearn) for some time during my postgraduate years, I never got the chance to really use Python for data analysis and, instead, I had been a victim of my own inertia and limited myself to use R and especially Matlab.
I must say, in the beginning, Python looks awkward: it was inconceivable for me to use an invisible element (spaces or tabs) as a structural construction of a program (defining blocks), in a way much similar to Fortran, which I always considered weird (coming from the C world). This and the lack of the omnipresent, C-syntax end-of-line semicolon, prove to be a major boosting element when programming in Python. I must say that whatever lack in computer performance is overcome by the speed the programmer experiences when writing the software. This applies to general software, such as the App server that I am preparing, which is being written in Python using the Google App Engine, and I have to say that it just runs smoothly, no need for recompilations, clear syntax and one-line complex data-processing pieces of code.
