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 xai-sharp

Or conda :

conda install -c conda-forge xai-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}
}