shapiq: Shapley Interactions for Machine Learning

TL;DR

shapiq is an open-source Python package that efficiently computes Shapley Values and Interactions for various machine learning models, enabling detailed explanation and visualization of feature interactions across diverse applications.

Contribution

it introduces shapiq, a unified framework for computing and benchmarking Shapley Values and Interactions in an application-agnostic manner, extending explainability tools in machine learning.

Findings

Efficient algorithms for computing SIs and SVs across multiple models.

Benchmark suite with 11 applications for performance assessment.

Visualization tools for feature interactions in complex models.

Abstract

Originally rooted in game theory, the Shapley Value (SV) has recently become an important tool in machine learning research. Perhaps most notably, it is used for feature attribution and data valuation in explainable artificial intelligence. Shapley Interactions (SIs) naturally extend the SV and address its limitations by assigning joint contributions to groups of entities, which enhance understanding of black box machine learning models. Due to the exponential complexity of computing SVs and SIs, various methods have been proposed that exploit structural assumptions or yield probabilistic estimates given limited resources. In this work, we introduce shapiq, an open-source Python package that unifies state-of-the-art algorithms to efficiently compute SVs and any-order SIs in an application-agnostic framework. Moreover, it includes a benchmarking suite containing 11 machine learning applications of SIs with pre-computed games and ground-truth values to systematically assess computational performance across domains. For practitioners, shapiq is able to explain and visualize any-order feature interactions in predictions of models, including vision transformers, language models, as well as XGBoost and LightGBM with TreeSHAP-IQ. With shapiq, we extend shap beyond feature attributions and consolidate the application of SVs and SIs in machine learning that facilitates future research. The source code and documentation are available at https://github.com/mmschlk/shapiq.