zhenhuixiang / PhySO

Physical Symbolic Optimization

$\Phi$-SO : Physical Symbolic Optimization

The physical symbolic regression ( $\Phi$-SO ) package physo is a symbolic regression package that fully leverages physical units constraints. For more details see: [Tenachi et al 2023].

Installation

Virtual environment

The package has been tested on Unix and OSX. To install the package it is recommend to first create a conda virtual environment:

conda create -n PhySO python=3.8

And activate it:

conda activate PhySO

Dependencies

From the repository root:

Installing essential dependencies :

conda install --file requirements.txt

Installing optional dependencies (for monitoring plots) :

pip install -r requirements_display.txt

Side note for ARM users:

The file requirements_display.txt contains dependencies that can be installed via pip only. However, it also contains pygraphviz which can be installed via conda which avoids compiler issues on ARM.

It is recommended to run:

conda install pygraphviz==1.9

before running:

pip install -r requirements_display.txt

Installing $\Phi$-SO

Installing physo (from the repository root):

pip install -e .

Testing install

Import test:

python3
>>> import physo

This should result in physo being successfully imported.

Unit tests:

From the repository root:

python -m unittest discover -p "*UnitTest.py"

This should result in all tests being successfully passed (except for plots tests if dependencies were not installed).

Getting started

Symbolic regression with default hyperparameters

[Coming soon] In the meantime you can have a look a our demo folder ! :)

Symbolic regression

[Coming soon]

Custom symbolic optimization task

[Coming soon]

Using custom functions

[Coming soon]

Open training loop

[Coming soon]

Citing this work

@ARTICLE{2023arXiv230303192T,
       author = {{Tenachi}, Wassim and {Ibata}, Rodrigo and {Diakogiannis}, Foivos I.},
        title = "{Deep symbolic regression for physics guided by units constraints: toward the automated discovery of physical laws}",
      journal = {arXiv e-prints},
     keywords = {Astrophysics - Instrumentation and Methods for Astrophysics, Computer Science - Machine Learning, Physics - Computational Physics},
         year = 2023,
        month = mar,
          eid = {arXiv:2303.03192},
        pages = {arXiv:2303.03192},
          doi = {10.48550/arXiv.2303.03192},
archivePrefix = {arXiv},
       eprint = {2303.03192},
 primaryClass = {astro-ph.IM},
       adsurl = {https://ui.adsabs.harvard.edu/abs/2023arXiv230303192T},
      adsnote = {Provided by the SAO/NASA Astrophysics Data System}
}