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()
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
Bug Reports¶
You can send feedback to mabwiser@fmr.com. Please use the GitHub Issues tracking for bug reports and feature requests.
Citation¶
You can cite MABWiser as:
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/fmr-llc/mabwiser}
}