kulprit 0.4.0

Kullback-Leibler projections for Bayesian model selection.

Kullback-Leibler projections for Bayesian model selection in Python.

Overview

Kulprit (Pronounced: kuːl.prɪt) is a package for variable selection for Bambi models. Kulprit is under active development so use it with care. If you find any bugs or have any feature requests, please open an issue.

Installation

Kulprit requires a working Python interpreter (3.10+). We recommend installing Python and key numerical libraries using the Anaconda Distribution, which has one-click installers available on all major platforms.

Assuming a standard Python environment is installed on your machine (including pip), Kulprit itself can be installed in one line using pip:

pip install kulprit

By default Kulprit performs a forward search, if you want to use Lasso (L1 search) you need to install scikit-learn package. You can install it using pip:

pip install kulprit[lasso]

Alternatively, if you want the bleeding edge version of the package you can install it from GitHub:

pip install git+https://github.com/bambinos/kulprit.git

Documentation

The Kulprit documentation can be found in the official docs. The examples provides a quick overview of variable selection and how this problem is tackled by Kulprit. A more detailed discussion of the theory, but also practical advice, we recommend you read the paper Advances in Projection Predictive Inference.

Contributions

Kulprit is a community project and welcomes contributions. Additional information can be found in the CONTRIBUTING.md page.

For a list of contributors see the GitHub contributor page

Citation

If you use Kulprit and want to cite it please use

@article{mclatchie2024,
    author = {Yann McLatchie and S{\"o}lvi R{\"o}gnvaldsson and Frank Weber and Aki Vehtari},
    title = {{Advances in Projection Predictive Inference}},
    volume = {40},
    journal = {Statistical Science},
    number = {1},
    publisher = {Institute of Mathematical Statistics},
    pages = {128 -- 147},
    keywords = {Bayesian model selection, cross-validation, projection predictive inference},
    year = {2025},
    doi = {10.1214/24-STS949},
    URL = {https://doi.org/10.1214/24-STS949}
}

Donations

If you want to support Kulprit financially, you can make a donation to our sister project PyMC.

Code of Conduct

Kulprit wishes to maintain a positive community. Additional details can be found in the Code of Conduct

License

MIT License