Interpretable Machine Learning for Science with PySR and SymbolicRegression.jl
This work addresses the need for interpretable machine learning tools in science, though it is incremental as it builds on existing symbolic regression methods with software improvements.
The authors introduced PySR, an open-source library for symbolic regression aimed at discovering human-interpretable models for scientific applications, and developed a new benchmark called EmpiricalBench to evaluate the recovery of historical empirical equations from datasets.
PySR is an open-source library for practical symbolic regression, a type of machine learning which aims to discover human-interpretable symbolic models. PySR was developed to democratize and popularize symbolic regression for the sciences, and is built on a high-performance distributed back-end, a flexible search algorithm, and interfaces with several deep learning packages. PySR's internal search algorithm is a multi-population evolutionary algorithm, which consists of a unique evolve-simplify-optimize loop, designed for optimization of unknown scalar constants in newly-discovered empirical expressions. PySR's backend is the extremely optimized Julia library SymbolicRegression.jl, which can be used directly from Julia. It is capable of fusing user-defined operators into SIMD kernels at runtime, performing automatic differentiation, and distributing populations of expressions to thousands of cores across a cluster. In describing this software, we also introduce a new benchmark, "EmpiricalBench," to quantify the applicability of symbolic regression algorithms in science. This benchmark measures recovery of historical empirical equations from original and synthetic datasets.