Script for the Robust Ensemble of SVMs (RESVM) method. For algorithm details, please refer to: ftp://ftp.esat.kuleuven.be/pub/SISTA/claesenm/reports/14-22.pdf

If you use this software in research, please cite the associated paper.

This script allows you to perform the following tasks:

• train : train an RESVM model.
• predict : predict with an existing RESVM model.
• cross-validate : perform k-fold cross-validation for a parameter tuple.
• grid-search : select an optimal parameter tuple (+optionally train model).

To perform a specific task, call ./resvm.py task options. An overview of task specific arguments is shown using ./resvm.py help task.

Training and testing data files must be provided in LIBSVM format, e.g.

label index_1:value_1 index_2:value_2 ... index_p:value_p

For additional information, updates or bug reports, please refer to: https://github.com/claesenm/resvm

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EnsembleSVM

This script generates intermediate files in a folder of your choosing. The EnsembleSVM library is used as a back-end and must be installed. EnsembleSVM is freely available at: http://esat.kuleuven.be/stadius/ensemblesvm/

General options related to EnsembleSVM, usable in all tasks listed above:

• esvm-prefix : prefix used in all EnsembleSVM executables (default '').
• esvm-suffix : suffix used in all EnsembleSVM executables (default '').
• work-dir : working directory to use for intermediate files (default '/tmp/').
• noclean : retain intermediate files (flag, default: remove intermediates).

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Example

As an example we have included one pair of training and test sets used in the associated paper. These data sets are based on the MNIST digit recognition data set (digit 7 is positive), with 50 positives and 2000 unlabeled instances. The provided positive and unlabeled sets contain 10% contamination.

Train an RESVM with 200 base models, each trained using 10 positives and 100 unlabeled instances. The resulting model will be saved in model.txt:

./resvm.py train data=data/train.libsvm nmodels=200 npos=10 nunl=100 c=1 wpos=1.6 model=model.txt

Predict the test set using the model we trained and save to predictions.txt:

./resvm.py predict data=data/test.libsvm model=model.txt predictions=predictions.txt

The output file contains predicted labels in the first column and decision values in the second. To compute area under the PR and ROC curves, please use ./evaluate.py (requires scikit-learn).