OBP Package v0.3.3 released

[Open Bandit Project]

Dear all,

We have released the OBP package version 0.3.3: https://github.com/st-tech/zr-obp/releases/tag/0.3.3 

The changes are summarized below.

• add `sample_action` method to `obp.policy.IPWLearner` which trains an offline bandit policy that samples a non-repetitive set of actions for new data. Thus, it can be used in practice even when the action interface has a list structure.
  • relevant PR: https://github.com/st-tech/zr-obp/pull/22
  • detailed description: https://zr-obp.readthedocs.io/en/latest/_autosummary/obp.policy.offline.html#module-obp.policy.offline
• fix a bug in the `fit_predict` method of `obp.ope.RegressonModel`
  • relevant PR: https://github.com/st-tech/zr-obp/pull/23
• complete the benchmark experiments on a wide variety of OPE estimators using the full size of Open Bandit Dataset. The detailed results and discussions can be found in the coming arXiv updates.
  • https://github.com/st-tech/zr-obp/tree/master/benchmark/ope 

Please update your package accordingly. We continue to improve and expand the software; stay tuned!

Best Regards,
Open Bandit Project Team