Interpretable Machine Learning for Science with PySR and SymbolicRegression.jl

• URL: http://arxiv.org/abs/2305.01582v3
• Date: Fri, 5 May 2023 17:44:07 GMT
• Title: Interpretable Machine Learning for Science with PySR and SymbolicRegression.jl
• Authors: Miles Cranmer (Princeton University and Flatiron Institute)
• Abstract summary: PySR is an open-source library for practical symbolic regression. It is built on a high-performance distributed back-end, a flexible search algorithm, and interfaces with several deep learning packages. In describing this software, we also introduce a new benchmark, "EmpiricalBench," to quantify the applicability of symbolic regression algorithms in science.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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