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# MABWiser: Parallelizable Contextual Multi-Armed Bandits MABWiser is a research library written in Python for rapid prototyping of multi-armed bandit algorithms. It supports **context-free**, **parametric** and **non-parametric** **contextual** bandit models and provides built-in parallelization for both training and testing components. The library also provides a simulation utility for comparing different policies and performing hyper-parameter tuning. MABWiser follows a scikit-learn style public interface, adheres to [PEP-8 standards](https://www.python.org/dev/peps/pep-0008/), and is tested heavily. Full documentation is available at [fidelity.github.io/mabwiser](https://fidelity.github.io/mabwiser). MABWiser is developed by the Artificial Intelligence Center of Excellence at Fidelity Investments. ## Quick Start ```python # An example that shows how to use the UCB1 learning policy # to choose between two arms based on their expected rewards. # Import MABWiser Library from mabwiser.mab import MAB, LearningPolicy, NeighborhoodPolicy # Data arms = ['Arm1', 'Arm2'] decisions = ['Arm1', 'Arm1', 'Arm2', 'Arm1'] rewards = [20, 17, 25, 9] # Model mab = MAB(arms, LearningPolicy.UCB1(alpha=1.25)) # Train mab.fit(decisions, rewards) # Test mab.predict() ``` ## Available Bandit Policies Available Learning Policies: * Epsilon Greedy * LinTS * LinUCB * Popularity * Random * Softmax * Thompson Sampling (TS) * Upper Confidence Bound (UCB1) Available Neighborhood Policies: * Clusters * K-Nearest * Radius ## Installation MABWiser can be installed from the wheel file or building from source by following the instructions in our [documentation](https://fidelity.github.io/mabwiser/installation.html). ## Support Please submit bug reports and feature requests as [Issues](https://github.com/fidelity/mabwiser/issues). For additional questions and feedback, please contact us at [mabwiser@fmr.com](mailto:mabwiser@fmr.com?subject=[Github]%20MABWiser%20Feedback). ## Citation If you use MABWiser in a publication, please cite it as: E. Strong, B. Kleynhans, and S. Kadioglu, "MABWiser: A Parallelizable Contextual Multi-Armed Bandit Library for Python," in 2019 IEEE 31st International Conference on Tools with Artificial Intelligence (ICTAI 2019) (pp.885-890). IEEE, 2019. ```bibtex @inproceedings{mabwiser2019, author = {Strong, Emily and Kleynhans, Bernard and Kadioglu, Serdar}, title = {MABWiser: A Parallelizable Contextual Multi-Armed Bandit Library for Python}, booktitle = {2019 IEEE 31st International Conference on Tools with Artificial Intelligence (ICTAI 2019)}, year = {2019}, pages = {885-890}, organization = {IEEE}, url = {https://github.com/fidelity/mabwiser} } ``` ## License MABWiser is licensed under the [Apache License 2.0](LICENSE). <br>