Benchmark code for the paper Efficient Shapley Performance Attribution for Least-Squares Regression by Logan Bell, Nikhil Devanathan, and Stephen Boyd. This code was written with primarily performance in mind. A more elegant, easy-to-use (but slightly less performant) library implementation of the reference paper can be found at cvxgrp/ls-spa. We recommend using the library implementaton.

The code has the following dependencies:

• ls_spa
• numpy
• scipy
• pandas
• jax
• matplotlib

JAX is a dependency of ls_spa, but its installation varies by platform (do not try to blindly pip install jax). Follow these instructions to correctly install JAX. The other packages are safely pip installable.

To run the benchmark code, clone this repository and install the dependencies. Afterwards, the two experiment files can be executed with Python.

If you use this code for research, please cite the associated paper.

@article{Bell2024,
  title = {Efficient Shapley performance attribution for least-squares regression},
  volume = {34},
  ISSN = {1573-1375},
  url = {http://dx.doi.org/10.1007/s11222-024-10459-9},
  DOI = {10.1007/s11222-024-10459-9},
  number = {5},
  journal = {Statistics and Computing},
  publisher = {Springer Science and Business Media LLC},
  author = {Bell,  Logan and Devanathan,  Nikhil and Boyd,  Stephen},
  year = {2024},
  month = jul 
}