equiboots

Pre-release

This release is a pre-release and may not be stable for production use.

The equiboots library is a fairness-aware model evaluation toolkit designed to audit performance disparities across demographic groups. It provides robust, bootstrapped metrics for binary, multi-class, and multi-label classification, as well as regression models. The library supports group-wise performance slicing, fairness diagnostics, and customizable visualizations to support equitable AI/ML development.

equiboots is particularly useful in clinical, social, and policy domains where transparency, bias mitigation, and outcome fairness are critical for responsible deployment.

Prerequisites

Before installing equiboots, ensure your system meets the following requirements:

Python Version

equiboots requires Python 3.7.4 or higher. Specific dependency versions vary depending on your Python version.

Dependencies

The following dependencies will be automatically installed with equiboots:

matplotlib>=3.5.3, <=3.10.1
numpy>=1.21.6, <=2.2.4
pandas>=1.3.5, <=2.2.3
scikit-learn>=1.0.2, <=1.5.2
scipy>=1.8.0, <=1.15.2
seaborn>=0.11.2, <=0.13.2
statsmodels>=0.13, <=0.14.4
tqdm>=4.66.4, <=4.67.1

💾 Installation

You can install equiboots directly from PyPI:

pip install equiboots

📄 Official Documentation

https://uclamii.github.io/equiboots_docs

🌐 Author Website

https://www.mii.ucla.edu/

⚖️ License

equiboots is distributed under the Apache License. See LICENSE for more information.

📚 Citing `equiboots`

If you use equiboots in your research or projects, please consider citing it.

@software{shpaner_2025_15086941,
   author       = {Shpaner, Leonid and
                   Funnell, Arthur and
                   Rahrooh, Al and
                   Beam, Colin and
                   Petousis, Panayiotis},
   title        = {EquiBoots},
   month        = mar,
   year         = 2025,
   publisher    = {Zenodo},
   version      = {0.0.1a11},
   doi          = {10.5281/zenodo.15086941},
   url          = {https://doi.org/10.5281/zenodo.15086941}
}

Support

If you have any questions or issues with equiboots, please open an issue on this GitHub repository.

Acknowledgements

This work was supported by the UCLA Medical Informatics Institute (MII) and the Clinical and Translational Science Institute (CTSI). Special thanks to Alex Bui, PhD, for his invaluable guidance and support. Many thanks to David Elashoff, PhD, and Sitaram Vangala, M.S., for their statistical consultation. Thanks to Jayleen Mendoza for her contribution to model healing.

Metadata

Release files for equiboots 0.0.1a11

For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.

Source distribution (sdist)

Source distribution for equiboots 0.0.1a11
File	Size	Uploaded
equiboots-0.0.1a11.tar.gz	42.1 kB	Dec 16, 2025	Details

Built distribution (wheel)

Table of built distributions (wheels) for equiboots 0.0.1a11
File	Interpreter	ABI	Platform	Reset
equiboots-0.0.1a11-py3-none-any.whl 40.7 kB Dec 16, 2025	Python 3	none	any	Details

Total release size: 82.8 kB

Release files / equiboots-0.0.1a11.tar.gz

Download URL	equiboots-0.0.1a11.tar.gz
Size	42.1 kB
Tags	Source
SHA-256 checksum How to use checksums	`a6dfa342db48ede686e7bfcbecfe318c0adf42dcef25b4e0afd64fa1597093af`
BLAKE2b-256 checksum How to use checksums	`1f03db20d59d8a80526e9531d99488bcb060456bb016159739d3cb486bc6afdb`
Upload date	Dec 16, 2025
Uploaded using Trusted Publishing? What is trusted publishing?	No
Uploaded via	`twine/6.2.0 CPython/3.14.2`

Release files / equiboots-0.0.1a11-py3-none-any.whl

Download URL	equiboots-0.0.1a11-py3-none-any.whl
Size	40.7 kB
Tags	Python 3
SHA-256 checksum How to use checksums	`52edf79334a70f8a424a127e6d95add29ec42782ea535c900087aff64f58343d`
BLAKE2b-256 checksum How to use checksums	`eef85f2b357688e1033865a4bf60b252eb12814afa07d9245958f849211318b0`
Upload date	Dec 16, 2025
Uploaded using Trusted Publishing? What is trusted publishing?	No
Uploaded via	`twine/6.2.0 CPython/3.14.2`