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Uncertainty Playground: A Fast and Simplified Python Library for Uncertainty Estimation

CI Test Suite Python Version PyPI License

This Python library provides fast (and easy) prediction intervals for regression tasks built on top of PyTorch & GPyTorch. Specifically, the library uses Sparse & Variational Gaussian Process Regression for Gaussian cases (normally distributed outcomes) and Mixed Density Networks for multi-modal distributions. Users can estimate the prediction interval for a given instance with either model (see the section usage).

Installation

Requirements:

  • Python >= 3.8
  • PyTorch == 2.0.1
  • GPyTorch == 1.10
  • Numpy == 1.24.0
  • Seaborn == 0.12.2

Use PyPI to install the package:

pip install uncertaintyplayground

or alterntively, to use the development version, install directly from GitHub:

pip install git+https://github.com/unco3892/UncertaintyPlayground.git

Then, you can import the module:

import uncertaintyplayground as up

Documentation layout

Aside from this page, there are three other sections in this documentation. The most important is the Code Reference which relates the source code and all the arguments for the functions. The Usage section contains a couple of examples of using this package with real and simulated data. Finally, the Bibliography section contains the list of papers that are used in this package.

Contributors

This library is maintained by Ilia Azizi (University of Lausanne). Any other contributors are welcome to join! Feel free to get in touch with (contact links on my website).

Citation

If you use this package in your research, please cite our work:

UncertaintyPlayground: A Fast and Simplified Python Library for Uncertainty Estimation, Ilia Azizi, arXiv:2310.15281

@misc{azizi2023uncertaintyplayground,
      title={UncertaintyPlayground: A Fast and Simplified Python Library for Uncertainty Estimation}, 
      author={Ilia Azizi},
      year={2023},
      eprint={2310.15281},
      archivePrefix={arXiv},
      primaryClass={stat.ML}
}

License

Please see the project MIT licensed here.