damo-di-ml / neurips2021-submodular-ruleset

Source code of NeurIPS'21 paper: Learning Interpretable Decision Rule Sets: A Submodular Optimization Approach

Source code of NeurIPS2021 XAI Paper

Learning Interpretable Decision Rule Sets: A Submodular Optimization Approach [paper]

Abstract

Rule sets are highly interpretable logical models in which the predicates for decision are expressed in disjunctive normal form (DNF, OR-of-ANDs), or, equivalently, the overall model comprises an unordered collection of if-then decision rules. In this paper, we consider a submodular optimization based approach for learning rule sets. The learning problem is framed as a subset selection task in which a subset of all possible rules needs to be selected to form an accurate and interpretable rule set. We employ an objective function that exhibits submodularity and thus is amenable to submodular optimization techniques. To overcome the difficulty arose from dealing with the exponential-sized ground set of rules, the subproblem of searching a rule is casted as another subset selection task that asks for a subset of features. We show it is possible to write the induced objective function for the subproblem as a difference of two submodular (DS) functions to make it approximately solvable by DS optimization algorithms. Overall, the proposed approach is simple, scalable, and likely to be benefited from further research on submodular optimization. Experiments on real datasets demonstrate the effectiveness of our method.

neurips2021-submodular-ruleset's Issues

Inquiry

Dear Author:
I tried to use Python to reproduce the DS-OPT and SwapLocalSearch algorithms, but the two algorithms I implemented always failed to get R==R'. I am wondering if you could kindly send me the code about DS-OPT and SwapLocalSearch algorithms and the necessary information about it. I promise they will be used only for research purposed.