ShaRP is an open source library with the implementation of the ShaRP
algorithm (Shapley for Rankings and Preferences), a framework that can be used
to explain the contributions of features to different aspects of a ranked
outcome, based on Shapley values.
A Python distribution of version >= 3.9 is required to run this
project. ShaRP requires:
- numpy (>= 1.20.0)
- pandas (>= 1.3.5)
- scikit-learn (>= 1.2.0)
- ml-research (>= 0.4.2)
Some functions require Matplotlib (>= 2.2.3) for plotting.
The easiest way to install sharp is using pip :
pip install -U git+https://github.com/DataResponsibly/ShaRP
The documentation includes more detailed installation instructions.
The following commands should allow you to setup the development version of the project with minimal effort:
# Clone the project.
git clone https://github.com/DataResponsibly/sharp.git
cd sharp
# Create and activate an environment
make environment
conda activate sharp # Assuming you are have conda set up
# Install project requirements and the research package. Dependecy group
# "all" will also install the dependency groups shown below.
pip install .[optional,tests,docs]
If you use sharp in a scientific publication, we would appreciate citations to the following paper:
@article{pliatsika2024sharp,
title={ShaRP: Explaining Rankings with Shapley Values},
author={Pliatsika, Venetia and Fonseca, Joao and Wang, Tilun and Stoyanovich, Julia},
journal={arXiv preprint arXiv:2401.16744},
year={2024}
}