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.

Installation

A Python distribution of version >= 3.10 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.

User Installation

The easiest way to install sharp is using pip :

# Install latest release
pip install -U xai-sharp

# Install additional dependencies (matplotlib) for plotting
pip install -U "xai-sharp[optional]"

# Install unreleased version (may be unstable)
pip install -U git+https://github.com/DataResponsibly/ShaRP

Installation instruction can also be found in the documentation pages.

Installing from source

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]"

Citing ShaRP

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}
}