使用Python对LibSVM中的多项式内核进行交叉验证

Question

I am using LibSVM with Python. Prior to building my classifier, I want to plot the average error of cross validation for different values of d and C to find the best (d, C) combination in terms of average accuracy. I wrote a Python script to cross-validate for a particular d, C value and the output for each of the ten iterations of my cross-validation appeared on the screen. I have 2 problems now:

1. How to write a Python script that takes in variables for d and C values as parameters in the svm_parameter function. svm_parameter('-d dval') gives the error:

    ValueError : invalid literal for int() with base 10:dval 

2. How to record the output data - I can't find a way to save the accuracy for each classification unless I manually copy it from the UNIX screen. Is there a way to access and save the output file?

Thank you. I am new to Python.

Answer 1

I strongly recommend to use the excellent sklearn library for your task. It also has a wrapper for LibSVM (see svm.SVC ), but in addition gives you all the necessary tools for cross-validation , finding optimal d and C with grid-search , easy way to measure accuracy with the metrics module and a huge number of other useful tools.

Regarding the valuerror, python attempts to cast the string dval into an integer. You probably want to do something like svm_parameter('-d %s' % dval) , if dval is the variable holding the value of d .