Scripts for evaluation of contextual bandit algorithms in Vowpal Wabbit. The precise branch of VW used in the experiments is available here. See the following paper for details:
A. Bietti, A. Agarwal, and J. Lanford. A Contextual Bandit Bake-Off. arXiv preprint, 2018.
For making VW datasets, see oml_to_vw.py (for multiclass), multilabel_to_vw.py (for multilabel), make_full.py/full_to_ldf.py (for learning-to-rank).
Note that these require different VW options for simulating bandit feedback (--cbify <num_actions> for multiclass, --cbify <num_actions> --cbify_cs for multilabel/cost-sensitive, --cbify_ldf for datasets with label-dependent-features)
Here is an example bash script for running 100 jobs on multiclass datasets with -1/0 encoding:
name='resultsneg10'
for i in `seq 0 99`; do # these should be run on different cores/machines with your own parallelization mechanism
python2 run_vw_job.py $i 100 --name ${name} --flags '--loss0 -1 --loss1 0';
done;