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
  • #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
  • #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 at the coming arXiv updates.
  • https://github.com/st-tech/zr-obp/tree/master/benchmark/ope