A Python binding to the SVM-Light support vector machine library by Thorsten Joachims.

Translation to Python by Ceri Stagg and by David Campbell, Hacker School project, NYC, summer 2012. Original PySVMLight by Bill Cauchois.

PySVMLight uses distutils for setup. Installation is as simple as

$ chmod +x setup.py
$ ./setup.py --help
$ ./setup.py build

If you want to install SVMLight to your PYTHONPATH, type:

$ ./setup.py install

(You may need to execute this command as the superuser.) Otherwise, look in the build/ directory to find svmlight.so and copy that file to the directory of your project. You should now be able to import svmlight.

See examples/simple.py for example usage.

If you type help(svmlight), you will see that there are currently three functions.

learn(training_data, **options) -> model

Train a model based on a set of training data. The training data should be in the following format:

>> (<label>, [(<feature>, <value>), ...])

or

>> (<label>, [(<feature>, <value>), ...], <queryid>)

See examples/data.py for an example of some training data. Available options include (corresponding roughly to the command-line options for svmlight detailed on this page under the section titled "How to use"):

• type: select between 'classification', 'regression', 'ranking' (preference ranking), and 'optimization'.
• kernel: select between 'linear', 'polynomial', 'rbf', and 'sigmoid'.
• verbosity: set the verbosity level (default 0).
• C: trade-off between training error and margin.
• poly_degree: parameter d in polynomial kernel.
• rbf_gamma: parameter gamma in rbf kernel.
• coef_lin
• coef_const

The result of this call is a model that you can pass to classify().

classify(model, test_data, **options) -> predictions

Classify a set of test data using the provided model. The test data should be in the same format as training data (see above). The result will be a list of floats, corresponding to predicted labels for each of the test instances.

write_model(model, filename) -> None

Write the provided model to the specified file. The file format used is the same format as that used by the command-line svmlight program.

read_model(filename) -> model

Read a model that was saved using write_model().