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:

• 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, add an empty directory called plots to the root directory of the repository, and install the dependencies. Afterwards, the two experiment files can be executed with Python. The medium experiment generates Figure 2 in the companion paper, and the large experiment generates Figure 3 in the companion paper.

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

@misc{https://doi.org/10.48550/arxiv.2310.19245,
  doi = {10.48550/ARXIV.2310.19245},
  url = {https://arxiv.org/abs/2310.19245},
  author = {Bell,  Logan and Devanathan,  Nikhil and Boyd,  Stephen},
  keywords = {Computation (stat.CO),  FOS: Computer and information sciences,  FOS: Computer and information sciences,  62-08 (Primary),  62-04,  62J99 (Secondary)},
  title = {Efficient Shapley Performance Attribution for Least-Squares Regression},
  publisher = {arXiv},
  year = {2023},
  copyright = {arXiv.org perpetual,  non-exclusive license}
}