astroautomata / PySR

PySR Symbolic Regression in Python and Julia Docs  ×  Examples  ×  API  ×  Forum  ×  Agents 
 [code] 
If you find PySR useful, please cite the paper arXiv:2305.01582.
If you've finished a project with PySR, please submit a PR to showcase your work on the research showcase page!
**Contents**:
- Why PySR?
- Installation
- Quickstart
- → Documentation
- Contributors
## Why PySR?
PySR is an open-source tool for *Symbolic Regression*: a machine learning
task where the goal is to find an interpretable symbolic expression that optimizes some objective.
Over a period of several years, PySR has been engineered from the ground up
to be (1) as high-performance as possible,
(2) as configurable as possible, and (3) easy to use.
PySR is developed alongside the Julia library SymbolicRegression.jl,
which forms the powerful search engine of PySR.
The details of these algorithms are described in the PySR paper.
Symbolic regression works best on low-dimensional datasets, but
one can also extend these approaches to higher-dimensional
spaces by using "*Symbolic Distillation*" of Neural Networks, as explained in
2006.11287, where we apply
it to N-body problems. Here, one essentially uses
symbolic regression to convert a neural net
to an analytic equation. Thus, these tools simultaneously present
an explicit and powerful way to interpret deep neural networks.
## Installation
You can install PySR with pip or conda:
 [code] 
 [code] 
Julia dependencies will be installed at first import.
### AI Agent Skill
Install the PySR skill to provide your coding
agents guidance using PySR as part of a larger agentic loop:
 [code] 
Add `--global` to install the skill globally.
### Docker
You can also use the `Dockerfile` to install PySR in a docker container
1. Clone this repo.
2. Within the repo's directory, build the docker container:
 [code] 
3. You can then start the container with an IPython execution with:
 [code] 
For more details, see the docker section.
### Apptainer
If you are using PySR on a cluster where you do not have root access,
you can use Apptainer to build a container
instead of Docker. The `Apptainer.def` file is analogous to the `Dockerfile`,
and can be built with:
 [code] 
and launched with
 [code] 
### Troubleshooting
One issue you might run into can result in a hard crash at import with
a message like "`GLIBCXX_...` not found". This is due to another one of the Python dependencies
loading an incorrect `libstdc++` library. To fix this, you should modify your
`LD_LIBRARY_PATH` variable to reference the Julia libraries. For example, if the Julia
version of `libstdc++.so` is located in `$HOME/.julia/juliaup/julia-1.10.0+0.x64.linux.gnu/lib/julia/`
(which likely differs on your system!), you could add:
 [code] 
to your `.bashrc` or `.zshrc` file.
## Quickstart
You might wish to try the interactive tutorial here, which uses the notebook in `examples/pysr_demo.ipynb`.
In practice, I highly recommend using IPython rather than Jupyter, as the printing is much nicer.
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