Testing machine learning code using polyhedral region

Abstract

To date, although machine learning has been successful in various practical applications, generic methods of testing machine learning code have not been established yet. Here we present a new approach to test machine learning code using the possible input region obtained as a polyhedron. If an ML system generates different output for multiple input in the polyhedron, it is ensured that there exists a bug in the code. This property is known as one of theoretical fundamentals in statistical inference, for example, sparse regression models such as the lasso, and a wide range of machine learning algorithms satisfy this polyhedral condition, to which our testing procedure can be applied. We empirically show that the existence of bugs in lasso code can be effectively detected by our method in the mutation testing framework.