1. Haejoong Lee
  2. pysvmlight

Overview

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PySVMLight ========== A Python binding to the [SVM-Light](http://svmlight.joachims.org/) support vector machine library by Thorsten Joachims. Written by Bill Cauchois (<wcauchois@gmail.com>), with thanks to Lucas Beyer and n0mad for their contributions. Installation ------------ 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`. Getting Started --------------- See examples/simple.py for example usage. Reference --------- 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](http://svmlight.joachims.org/) 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` - `costratio` (corresponds to `-j` option to `svm_learn`) 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().