Metadata-Version: 2.1
Name: mabwiser
Version: 2.2.0
Summary: MABWiser: Parallelizable Contextual Multi-Armed Bandits Library
Home-page: https://github.com/fidelity/mabwiser
Author: FMR LLC
License: UNKNOWN
Project-URL: Documentation, https://fidelity.github.io/mabwiser/
Project-URL: Source, https://github.com/fidelity/mabwiser
Platform: UNKNOWN
Classifier: License :: OSI Approved :: Apache Software License
Classifier: Programming Language :: Python :: 3.6
Classifier: Operating System :: OS Independent
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: joblib
Requires-Dist: numpy (>=1.17.0)
Requires-Dist: pandas
Requires-Dist: scikit-learn (>=0.24.0)
Requires-Dist: scipy
Requires-Dist: seaborn (>=0.9.0)

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# MABWiser: Parallelizable Contextual Multi-Armed Bandits 

MABWiser ([IJAIT 2021](https://www.worldscientific.com/doi/10.1142/S0218213021500214), [ICTAI 2019](https://ieeexplore.ieee.org/document/8995418)) 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. 

MABWiser is developed by the Artificial Intelligence Center of Excellence at Fidelity Investments. Documentation is available at [fidelity.github.io/mabwiser](https://fidelity.github.io/mabwiser).

## 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 [1, 2]
* LinTS [3]
* LinUCB [4]
* Popularity [2]
* Random [2]
* Softmax [2]
* Thompson Sampling (TS) [5]
* Upper Confidence Bound (UCB1) [2]

Available Neighborhood Policies: 
* Clusters [6]
* K-Nearest [7, 8]
* LSH Nearest [9]
* Radius [7, 8]
* TreeBandit [10]

## Installation

MABWiser is available to install as `pip install mabwiser`. It can also be installed by building from source by following the instructions in the [documentation](https://fidelity.github.io/mabwiser/installation.html).

## Support

Please submit bug reports and feature requests as [Issues](https://github.com/fidelity/mabwiser/issues).

## Citation

If you use MABWiser in a publication, please cite it as:

* **[IJAIT 2021]** [E. Strong,  B. Kleynhans, and S. Kadioglu, "MABWiser: Parallelizable Contextual Multi-Armed Bandits"](https://www.worldscientific.com/doi/abs/10.1142/S0218213021500214)
* **[ICTAI 2019]** [E. Strong,  B. Kleynhans, and S. Kadioglu, "MABWiser: A Parallelizable Contextual Multi-Armed Bandit Library for Python"](https://ieeexplore.ieee.org/document/8995418)

```bibtex
    @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},
    }
```

## License

MABWiser is licensed under the [Apache License 2.0](LICENSE).

## References

1. John Langford and Tong Zhang. The epoch-greedy algorithm for contextual multi-armed bandits
2. Volodymyr Kuleshov and Doina Precup. Algorithms for multi-armed bandit problems
3. Agrawal, Shipra and Navin Goyal. Thompson sampling for contextual bandits with linear payoffs
4. Chu, Wei, Li, Lihong, Reyzin Lev, and Schapire Robert. Contextual bandits with linear payoff functions
5. Osband, Ian, Daniel Russo, and Benjamin Van Roy. More efficient reinforcement learning via posterior sampling
6. Nguyen, Trong T. and Hady W. Lauw. Dynamic clustering of contextual multi-armed bandits
7. Melody Y. Guan and Heinrich Jiang, Nonparametric stochastic contextual bandits
8. Philippe Rigollet and Assaf Zeevi. Nonparametric bandits with covariates 
9. Indyk, Piotr, Motwani, Rajeev, Raghavan, Prabhakar, Vempala, Santosh. Locality-preserving hashing in multidimensional spaces
10. Adam N. Elmachtoub, Ryan McNellis, Sechan Oh, Marek Petrik, A practical method for solving contextual bandit problems using decision trees

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