Metadata-Version: 2.1
Name: mabwiser
Version: 1.11.1
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
Description: # 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
        * 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](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:
        
        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>
        
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
