Obp

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

This release enhances the OBP package in the following ways.
- add some new contents to the obp document: https://zr-obp.readthedocs.io/en/latest/index.html
- In particular, you can use "off-policy evaluation" section as a textbook about this area
- add `obp.dataset.MultiClassToBanditReduction` class for handling multi-class classification datasets as bandit feedback https://github.com/st-tech/zr-obp/pull/19
- https://zr-obp.readthedocs.io/en/latest/_autosummary/obp.dataset.multiclass.html#module-obp.dataset.multiclass
- this will allow researchers to run their synthetic experiments with some multi-class classification datasets easily
- relevant quickstart and example will be added to the repository soon
- add continuous reward option to `obp.dataset.SyntheticBanditDataset`
- add squared error (se) option for the evaluation of OPE with `obp.ope.OffPolicyEvaluation`
- fix some README and docstring inconsistencies
- refactor the dataset and ope modules

In this release, we fix some bugs in the cross-fitting procedure

This release enhances the OBP package in the following ways.

- allowing evaluation policy to be stochastic, which makes the package more consistent with the formulation of OPE
- adding some advanced estimation techniques such as cross-fitting and doubly robust with shrinkage
- modifying examples to evaluate offline bandit policies (not online ones), which again makes the package more consistent with the formulation of OPE: https://github.com/st-tech/zr-obp/tree/master/examples
- adding some slides: https://github.com/st-tech/zr-obp/tree/master/slides