MABWiser Contextual Multi-Armed Bandits

MABWiser is a research library for fast prototyping of multi-armed bandit algorithms. It supports context-free, parametric and non-parametric contextual bandit models. It provides built-in parallelization for both training and testing components and a simulation utility for algorithm comparisons and hyper-parameter tuning. The library follows the scikit-learn style, adheres to PEP-8 standards, and is tested heavily. MABWiser is released by Fidelity Investments Artificial Intelligence Center of Excellence.

Quick Start

# An example that shows how to use the UCB1 learning policy
# to make decisions 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()

Source Code

The source code is hosted on GitHub.

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

  • TreeBandit

Bug Reports

Please use the GitHub Issues tracking for bug reports and feature requests.

Citation

You can cite MABWiser as:

   @article{DBLP:journals/ijait/StrongKK21,
  author    = {Emily Strong and Bernard Kleynhans and Serdar Kadioglu},
  title     = {{MABWiser:} Parallelizable Contextual Multi-armed Bandits},
  journal   = {Int. J. Artif. Intell. Tools},
  volume    = {30},
  number    = {4},
  pages     = {2150021:1--2150021:19},
  year      = {2021},
  url       = {https://doi.org/10.1142/S0218213021500214},
  doi       = {10.1142/S0218213021500214},
}

@inproceedings{DBLP:conf/ictai/StrongKK19,
author    = {Emily Strong and Bernard Kleynhans and Serdar Kadioglu},
title     = {MABWiser: {A} Parallelizable Contextual Multi-Armed Bandit Library for Python},
booktitle = {31st {IEEE} International Conference on Tools with Artificial Intelligence, {ICTAI} 2019, Portland, OR, USA, November 4-6, 2019},
pages     = {909--914},
publisher = {{IEEE}},
year      = {2019},
url       = {https://doi.org/10.1109/ICTAI.2019.00129},
doi       = {10.1109/ICTAI.2019.00129},
}

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