libsvm and Python

[Andrs]

I am not familiar with libsvm.
I have succeeded to install the libsvm software for Python/windows. However I have not found any good documentation about the functions supported by Libsvm in Python. How to call the different functions, parameters, examples etc. Has anybody found a good tutorial that provides information about libsvm/Python??
I would like to check some very basic question to see if i getting it right.
It seems that there are changes in the Python interface.
I have seen in the Web some old examples using the following import statement in Python. But I cannot use this import statement (Python 2.73, libsvm 3.12).

PHP Code:

 from libsvm import *

Traceback (most recent call last):
  File "<pyshell#14>", line 1, in <module>
    from libsvm import *
ImportError: No module named libsvm 


Instead I am able to use this import statements:

PHP Code:

>>>from svm import *
>>>
>>> from svmutil import * 


It seems that the package "svmutil" contains the user interface functions.
Any help about finding more info is really appreciated

[doclogan]

Originally Posted by Andrs

I am not familiar with libsvm.
I have succeeded to install the libsvm software for Python/windows. However I have not found any good documentation about the functions supported by Libsvm in Python. How to call the different functions, parameters, examples etc. Has anybody found a good tutorial that provides information about libsvm/Python??
I would like to check some very basic question to see if i getting it right.
It seems that there are changes in the Python interface.
I have seen in the Web some old examples using the following import statement in Python. But I cannot use this import statement (Python 2.73, libsvm 3.12).
Any help about finding more info is really appreciated

There is a readme file in the directory which does provide documentation on the interface. I switched to the scikit-learn package which has better documentation and several examples (and is faster too - at least out of the box). It is based on libsvm. I am using ubuntu so I do not know if they are issues with using this package in a windows environment.

[invis]

Hi !
All you need for this HW is svm_train and a bit svm_predict.
So how to use them ?

I found that svm_train cant take data from numpy.array, so this is what I do:

with open('features.train') as f:
	lines=f.read().splitlines()

numbers=[]
numbers =[[float(e.strip().split()[0]), float(e.strip().split()[1]), float(e.strip().split()[2])] for e in lines]
# or same, but with loop
# for e in lines:
# 	temp = e.strip().split()
#	numbers.append([float(temp[0]), float(temp[1]), float(temp[2])])

Y_train=[]
X_train=[]
for i in range(len(numbers)):
	Y_train.append(numbers[i][0])
	X_train.append({1 : numbers[i][1], 2:numbers[i][2]})

After you can train your model:

import svmutil as svm
options = 'your options, for example -c 2 -q 3 ...' 
model = svm.svm_train(Y_train, X_train, options)

To use you model on another data sets (or on the same) use:

p_label, p_acc, p_val = svm.svm_predict(Y_train, X_train, model)

try help(svm.svm_predict) or help(svm.svm_train) for more information.

Hope this helps.

edit: one important note. If you train your model with '-v' parameter (for cross validation) function will not return the Model, but only a float number - 'Cross Validation Accuracy'

[Andrs]

Originally Posted by doclogan

There is a readme file in the directory which does provide documentation on the interface. I switched to the scikit-learn package which has better documentation and several examples (and is faster too - at least out of the box). It is based on libsvm. I am using ubuntu so I do not know if they are issues with using this package in a windows environment.

Thanks doclogan, scikit-learn seems to be a very good package. When I look into the parameters for the kernel = "poly", they talk about gamma (gamma*u'*v + coef0)^degree).
Gamma is not clearly defined in our formula and it is multiplicating the inner product. Are you setting the values 1 to gamma and coeff0 ? Any other parameters that I should pay attention (other than the degree = 2)?

[doclogan]

Originally Posted by Andrs

Thanks doclogan, scikit-learn seems to be a very good package. When I look into the parameters for the kernel = "poly", they talk about gamma (gamma*u'*v + coef0)^degree).
Gamma is not clearly defined in our formula and it is multiplicating the inner product. Are you setting the values 1 to gamma and coeff0 ? Any other parameters that I should pay attention (other than the degree = 2)?

Only used the default values but have the problem with duplicate C values mentioned elsewhere in the forum. Maybe it would be a good idea to explore these parameters.