Overview | Installation | Tutorials | Documentation | Citing ReLax
Important
📣 This repository is migrated to a new link: https://github.com/BirkhoffG/jax-relax.
ReLax (Recourse Explanation Library in Jax) is a library
built on top of jax to generate counterfactual and recourse
explanations for Machine Learning algorithms. By leveraging
vectorization though vmap/pmap and just-in-time compilation in
jax (a high-performance
auto-differentiation library). ReLax offers massive speed improvements
in generating individual (or local) explanations for predictions made by
Machine Learning algorithms.
Some of the key features are as follows:
-
🏃 Fast recourse generation via
jax.jit,jax.vmap/jax.pmap. -
🚀 Accelerated over
cpu,gpu,tpu. -
🪓 Comprehensive set of recourse methods implemented for benchmarking.
-
👐 Customizable API to enable the building of entire modeling
-
and interpretation pipelines for new recourse algorithms.
The latest ReLax release can directly be installed from PyPI:
pip install jax-relaxor installed directly from the repository:
pip install git+https://github.com/BirkhoffG/ReLax.git To futher unleash the power of accelerators (i.e., GPU/TPU), we suggest
to first install this library via pip install jax-relax. Then, follow
steps in the official install
guidelines to install the
right version for GPU or TPU.
See Getting Started with ReLax.
To cite this repository:
@software{relax2023github,
author = {Hangzhi Guo and Xinchang Xiong and Amulya Yadav},
title = {{R}e{L}ax: Recourse Explanation Library in Jax},
url = {http://github.com/birkhoffg/ReLax},
version = {0.1.0},
year = {2023},
}