Equation Tree

The Equation Tree package is an equation toolbox with symbolic regression in mind. It represents expressions as an incomplete binary equation tree and has various features tailored towards testing symbolic regression algorithms or training models. The most notable features are:

• Equation Sampling
• Calculating Distance Metrics between equations

It also encompasses a variety of additional features including the capability to analyse distribution parameters for a given set of equations. For example, to obtain information about a specific field one can use the equation scraper to scrape equations from wikipedia and then use the sampler to generate equations that resemble a equations of a scientific field.

Relevant Publication

For reference and information about the evaluation of our package, read our NeuroIPS 2023 paper:

Marinescu*, I., Strittmatter*, Y, Williams, C, Musslick, S. "Expression Sampler as a Dynamic Benchmark for Symbolic Regression." In NeurIPS 2023 AI for Science Workshop. (2023), . [*equal contribution]

About

This project is in active development by the Autonomous Empirical Research Group (package developer: Ioana Marinescu and Younes Strittmatter, PI: Sebastian Musslick. This research program is supported by Schmidt Science Fellows, in partnership with the Rhodes Trust, as well as the Carney BRAINSTORM program at Brown University.