Python Sub String Engine and SVM Using Python

Question

How can I use Subringence String Kernel (SSK) [Lodhi 2002] to train SVM (vector machine support) in Python?

Answer 1

Recently, the kernel substring String (SSK) [Lodhi. et al., 2002] was added to the “Shogun Machine Learning Tool” and is available for use in all modular interfaces, including Python. You can find a working example of using this kernel for the DNA classification problem here using LibSVM.

Answer 2

, Shogun. commit 0891f5a38bcb, .

:

from shogun.Features import *
from shogun.Kernel import *
from shogun.Classifier import *
from shogun.Evaluation import *
from modshogun import StringCharFeatures, RAWBYTE
from shogun.Kernel import SSKStringKernel


strings = ['cat', 'doom', 'car', 'boom']
test = ['bat', 'soon']

train_labels  = numpy.array([1, -1, 1, -1])
test_labels = numpy.array([1, -1])

features = StringCharFeatures(strings, RAWBYTE)
test_features = StringCharFeatures(test, RAWBYTE)

# 1 is n and 0.5 is lambda as described in Lodhi 2002
sk = SSKStringKernel(features, features, 1, 0.5)

# Train the Support Vector Machine
labels = BinaryLabels(train_labels)
C = 1.0
svm = LibSVM(C, sk, labels)
svm.train()

# Prediction
predicted_labels = svm.apply(test_features).get_labels()
print predicted_labels

Answer 3

Further, for reference, the kernel name in the current version of Shogun (3.2.0) StringSubsequenceKernel .

Source: https://code.google.com/p/shogun-toolbox/source/browse/src/shogun/kernel/string/StringSubsequenceKernel.h