banditml is a lightweight contextual bandit & reinforcement learning library designed to be used in production Python services.

Related tags

Deep Learningreinforcement-learningpytorchpersonalizationneural-networksbanditscontextual-bandits
Overview

Bandit ML

PyPI version

What's banditml?

banditml is a lightweight contextual bandit & reinforcement learning library designed to be used in production Python services. This library is developed by Bandit ML and ex-authors of Facebook's applied reinforcement learning platform, Reagent.

Specifically, this repo contains:

  • Feature engineering & preprocessing
  • Model implementations
  • Model training workflows
  • Model serving code for Python services

Supported models

Models supported:

4 feature types supported:

  • Numeric: standard floating point features
    • e.g. {totalCartValue: 39.99}
  • Categorical: low-cardinality discrete features
    • e.g. {currentlyViewingCategory: "men's jeans"}
  • ID list: high-cardinality discrete features
    • e.g. {productsInCart: ["productId022", "productId109"...]}
    • Handled via. learned embedding tables
  • "Dense" ID list: high-cardinality discrete features, manually mapped to dense feature vectors
    • e.g {productId022: [0.5, 1.3, ...], productId109: [1.9, 0.1, ...], ...}

Docs

pip install banditml

Get started

License

GNU General Public License v3.0 or later

See COPYING to see the full text.

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Comments
  • Adapting ABTest data to contextual bandit setting

    Adapting ABTest data to contextual bandit setting

    Hi, and thanks for open sourcing this project.

    I wanted to dive into it by testing some ABTesting data with the implemented neural bandit.

    In my setting I have only 2 choices, 121 features as context, a reward range of [0.0, 120], and only 11% rows have non-zero reward. After training for a few epoch I see the testing loss decreasing a bit. But at test time, scores of the two choices are always equals, and the ucb_scores always equal to 0.

    opened by virgile-blg 0
  • Model input dimension does not update when keeping top n features

    Model input dimension does not update when keeping top n features

    Setting : Neural Bandit

    When setting keep_only_top_n to True, the model keeps the original number of features, resulting in a Pytorch matmul error for the first linear layer:

    RuntimeError: mat1 and mat2 shapes cannot be multiplied (256x10 and 121x64)

    opened by virgile-blg 0
Releases(1.0.2)
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Bandit ML
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