An example using python bindings for SVM library, LIBSVM

Question

I am in dire need of a classification task example using LibSVM in python. I don't know how the Input should look like and which function is responsible for training and which one for testing Thanks

Answer 1

The code examples listed here don't work with LibSVM 3.1, so I've more or less ported the example by mossplix:

from svmutil import *
svm_model.predict = lambda self, x: svm_predict([0], [x], self)[0][0]

prob = svm_problem([1,-1], [[1,0,1], [-1,0,-1]])

param = svm_parameter()
param.kernel_type = LINEAR
param.C = 10

m=svm_train(prob, param)

m.predict([1,1,1])

Answer 2

This example demonstrates a one-class SVM classifier; it's about as simple as possible while still showing the complete LIBSVM workflow.

Step 1: Import NumPy & LIBSVM

  import numpy as NP
    from svm import *

Step 2: Generate synthetic data: for this example, 500 points within a given boundary (note: quite a few real data sets are are provided on the LIBSVM website)

Data = NP.random.randint(-5, 5, 1000).reshape(500, 2)

Step 3: Now, choose some non-linear decision boundary for a one-class classifier:

rx = [ (x**2 + y**2) < 9 and 1 or 0 for (x, y) in Data ]

Step 4: Next, arbitrarily partition the data w/r/t this decision boundary:

• Class I: those that lie on or within an arbitrary circle

• Class II: all points outside the decision boundary (circle)

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The SVM Model Building begins here; all steps before this one were just to prepare some synthetic data.

Step 5: Construct the problem description by calling svm_problem, passing in the decision boundary function and the data, then bind this result to a variable.

px = svm_problem(rx, Data)

Step 6: Select a kernel function for the non-linear mapping

For this exmaple, i chose RBF (radial basis function) as my kernel function

pm = svm_parameter(kernel_type=RBF)

Step 7: Train the classifier, by calling svm_model, passing in the problem description (px) & kernel (pm)

v = svm_model(px, pm)

Step 8: Finally, test the trained classifier by calling predict on the trained model object ('v')

v.predict([3, 1])
# returns the class label (either '1' or '0')

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For the example above, I used version 3.0 of LIBSVM (the current stable release at the time this answer was posted).

Finally, w/r/t the part of your question regarding the choice of kernel function, Support Vector Machines are not specific to a particular kernel function--e.g., i could have chosen a different kernel (gaussian, polynomial, etc.).

LIBSVM includes all of the most commonly used kernel functions--which is a big help because you can see all plausible alternatives and to select one for use in your model, is just a matter of calling svm_parameter and passing in a value for kernel_type (a three-letter abbreviation for the chosen kernel).

Finally, the kernel function you choose for training must match the kernel function used against the testing data.