PyXAB - Python X-Armed Bandit

PyXAB is a Python open-source library for X-armed bandit algorithms, a prestigious set of optimizers for online black-box optimization and hyperparameter optimization.

PyXAB includes implementations of different algorithms for X-armed bandit, such as Zooming, StoSOO, and HCT, and the most recent works such as GPO and VHCT. PyXAB also provides the most commonly-used synthetic objectives to evaluate the performance of different algorithms and the implementations for different hierarchical partitions

PyXAB is featured for:

• User-friendly APIs, clear documentation, and detailed examples

• Comprehensive library of optimization algorithms, partitions and synthetic objectives

• High standard code quality and high testing coverage

• Low dependency for flexible combination with other packages such as PyTorch, Scikit-Learn

Reminder: The algorithms are maximization algorithms!

Quick Example

PyXAB follows a natural and straightforward API design completely aligned with the online blackbox optimization paradigm. The following is a simple 6-line usage example.

First, we define the parameter domain and the algorithm to run. At every round t, call algo.pull(t) to get a point and call algo.receive_reward(t, reward) to give the algorithm the objective evaluation (reward)

domain = [[0, 1]]               # Parameter is 1-D and between 0 and 1
algo = T_HOO(rounds=1000, domain=domain)
for t in range(1000):
    point = algo.pull(t)
    reward = 1                  #TODO: User-defined objective returns the reward
    algo.receive_reward(t, reward)

Citations

If you use our package in your research or projects, we kindly ask you to cite our work

@article{Li2023PyXAB,
    doi = {10.21105/joss.06507},
    url = {https://joss.theoj.org/papers/10.21105/joss.06507},
    author = {Li, Wenjie and Li, Haoze and Song, Qifan and Honorio, Jean},
    title = {PyXAB -- A Python Library for $\mathcal{X}$-Armed Bandit and Online Blackbox Optimization Algorithms},
    journal={Journal of Open Source Software},
    year = {2024},
    issn={2475-9066},
}

We would appreciate it if you could cite our related works.

@article{li2023optimumstatistical,
    title={Optimum-statistical Collaboration Towards General and Efficient Black-box Optimization},
    author={Wenjie Li and Chi-Hua Wang and Guang Cheng and Qifan Song},
    journal={Transactions on Machine Learning Research},
    issn={2835-8856},
    year={2023},
    url={https://openreview.net/forum?id=ClIcmwdlxn},
    note={}
}

@article{Li2024Federated,
    title={Federated $\chi$-armed Bandit},
    volume={38},
    url={https://ojs.aaai.org/index.php/AAAI/article/view/29267},
    DOI={10.1609/aaai.v38i12.29267},
    number={12},
    journal={Proceedings of the AAAI Conference on Artificial Intelligence},
    author={Li, Wenjie and Song, Qifan and Honorio, Jean and Lin, Guang},
    year={2024},
    month={Mar.},
    pages={13628-13636}

    }

@InProceedings{Li2024Personalized,
    title =          {Personalized Federated $\chi$-armed Bandit},
    author =       {Li, Wenjie and Song, Qifan and Honorio, Jean},
    booktitle =      {Proceedings of The 27th International Conference on Artificial Intelligence and Statistics},
    pages =          {37--45},
    year =   {2024},
    editor =         {Dasgupta, Sanjoy and Mandt, Stephan and Li, Yingzhen},
    volume =         {238},
    series =         {Proceedings of Machine Learning Research},
    month =          {02--04 May},
    publisher =    {PMLR},
    pdf =    {https://proceedings.mlr.press/v238/li24a/li24a.pdf},
    url =    {https://proceedings.mlr.press/v238/li24a.html},
    }