OBP Package v0.5.2 released

[Open Bandit Project]

Dear all,

We have released the OBP package version 0.5.2: https://github.com/st-tech/zr-obp/releases/tag/0.5.2 

The changes are summarized below:

Updates

• Implement `obp.policy.QLearner` (https://github.com/st-tech/zr-obp/pull/144 )
• Implement the Balanced IPW Estimator as `obp.ope.BalancedInverseProbabilityWeighting`. See Sondhi et al.(2020) for details. (https://github.com/st-tech/zr-obp/pull/146 ).
• Implement the Cascade Doubly Robust estimator for the combinatorial action OPE as `obp.ope.CascadeDR`. See Kiyohara et al.(2022) for details. (https://github.com/st-tech/zr-obp/pull/142 )
• Implement a data-driven hyperparameter tuning method for OPE called SLOPE proposed by Su et al.(2020) and Tucker et al.(2021) (https://github.com/st-tech/zr-obp/pull/148 )
• Implement new estimators for the standard OPE based on a power-mean transformation of importance weights proposed by Metelli et al.(2021) (https://github.com/st-tech/zr-obp/pull/149 )
• Implement dataset class for generating synthetic logged bandit data with multiple loggers. Corresponding estimators will be added in the next update (https://github.com/st-tech/zr-obp/pull/150 ) 
• Implement an argument to control the number of deficient actions in `obp.dataset.SyntheticBanditDataset` and `obp.dataset.MultiClassToBanditReduction`. See Sachdeva et al.(2020) for details. (https://github.com/st-tech/zr-obp/pull/150 ) 
• Implement some flexible functions to synthesize reward function and behavior policy (https://github.com/st-tech/zr-obp/pull/145 )

References

• Haruka Kiyohara, Yuta Saito, Tatsuya Matsuhiro, Yusuke Narita, Nobuyuki Shimizu, and Yasuo Yamamoto. Doubly Robust Off-Policy Evaluation for Ranking Policies under the Cascade Behavior Model. WSDM2022.
• Arjun Sondhi, David Arbour, Drew Dimmery. Balanced Off-Policy Evaluation in General Action Spaces. AISTATS2020.
• Yi Su, Pavithra Srinath, Akshay Krishnamurthy. Adaptive Estimator Selection for Off-Policy Evaluation. ICML2020.
• George Tucker and Jonathan Lee. Improved Estimator Selection for Off-Policy Evaluation. ICML2021 Workshop.
• Alberto Maria Metelli, Alessio Russo, Marcello Restelli. Subgaussian and Differentiable Importance Sampling for Off-Policy Evaluation and Learning. NeurIPS2021.
• Noveen Sachdeva, Yi Su, and Thorsten Joachims. "Off-policy Bandits with Deficient Support.", KDD2020.
• Aman Agarwal, Soumya Basu, Tobias Schnabel, Thorsten Joachims. "Effective Evaluation using Logged Bandit Feedback from Multiple Loggers.", KDD2018.
• Nathan Kallus, Yuta Saito, and Masatoshi Uehara. "Optimal Off-Policy Evaluation from Multiple Logging Policies.", ICML2021.

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

Best Regards,
Open Bandit Project Team