I'm new to SVMs, and I'm trying to use the Python interface to libsvm to classify a sample containing a mean and stddev. However, I'm getting nonsensical results.
Is this task inappropriate for SVMs or is there an error in my use of libsvm? Below is the simple Python script I'm using to test:
#!/usr/bin/env python
# Simple classifier test.
# Adapted from the svm_test.py file included in the standard libsvm distribution.
from collections import defaultdict
from svm import *
# Define our sparse data formatted training and testing sets.
labels = [1,2,3,4]
train = [ # key: 0=mean, 1=stddev
{0:2.5,1:3.5},
{0:5,1:1.2},
{0:7,1:3.3},
{0:10.3,1:0.3},
]
problem = svm_problem(labels, train)
test = [
({0:3, 1:3.11},1),
({0:7.3,1:3.1},3),
({0:7,1:3.3},3),
({0:9.8,1:0.5},4),
]
# Test classifiers.
kernels = [LINEAR, POLY, RBF]
kname = ['linear','polynomial','rbf']
correct = defaultdict(int)
for kn,kt in zip(kname,kernels):
print kt
param = svm_parameter(kernel_type = kt, C=10, probability = 1)
model = svm_model(problem, param)
for test_sample,correct_label in test:
pred_label, pred_probability = model.predict_probability(test_sample)
correct[kn] += pred_label == correct_label
# Show results.
print '-'*80
print 'Accuracy:'
for kn,correct_count in correct.iteritems():
print '\t',kn, '%.6f (%i of %i)' % (correct_count/float(len(test)), correct_count, len(test))
The domain seems fairly simple. I'd expect that if it's trained to know a mean of 2.5 means label 1, then when it sees a mean of 2.4, it should return label 1 as the most likely classification. However, each kernel has an accuracy of 0%. Why is this?
On a side note, is there a way to hide all the verbose training output dumped by libsvm in the terminal? I've searched libsvm's docs and code, but I can't find any way to turn this off.
