mabwiser 1.11.2

MABWiser: Parallelizable Contextual Multi-Armed Bandits Library

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, and is tested heavily.

MABWiser is developed by the Artificial Intelligence Center of Excellence at Fidelity Investments. Full documentation is available at fidelity.github.io/mabwiser.

Quick Start

# 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
• LSH Nearest
• Radius

Installation

MABWiser is available to install as pip install mabwiser. It can also be installed by building from source by following the instructions in our documentation.

Support

Please submit bug reports and feature requests as Issues.

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.

    @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.