PY-SHARQ: A Holistic Python Library for Explaining Association Rules on Relational Data

Association rules are an important technique for gaining insights over large relational datasets consisting of tuples of elements (i.e., attribute-value pairs). However, it is difficult to explain the relative importance of data elements with respect to the rules in which they appear. This demo showcases SHARQ (ShApley Rules Quantification), a Shapley-based measure for quantifying an element's contribution to a set of association rules. SHARQ is implemented in a convenient Python library, PY-SHARQ, which supports various explainability scenarios for association rules-including importance analysis of individual elements, rules, and attributes.