Metadata-Version: 2.4
Name: torch-symbolic
Version: 1.0.1.post1
Summary: Deep Learning Interpretability with Symbolic Regression.
Author: Liz Tan
Author-email: eszt2@cam.ac.uk
Classifier: Programming Language :: Python :: 3.11
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Requires-Python: >=3.11
Description-Content-Type: text/markdown
Requires-Dist: torch
Requires-Dist: pysr
Requires-Dist: numpy
Requires-Dist: pandas
Requires-Dist: scikit-learn
Requires-Dist: matplotlib
Requires-Dist: sympy
Requires-Dist: dill
Provides-Extra: dev
Requires-Dist: ipython; extra == "dev"
Requires-Dist: ipdb; extra == "dev"
Requires-Dist: pytest; extra == "dev"
Dynamic: author
Dynamic: author-email
Dynamic: classifier
Dynamic: description
Dynamic: description-content-type
Dynamic: provides-extra
Dynamic: requires-dist
Dynamic: requires-python
Dynamic: summary

![logo](https://raw.githubusercontent.com/elizabethsztan/InterpretSR/main/docs/_static/symtorch_logo.png)

*SymTorch* allows you to approximate the behaviour of components within deep learning models with symbolic equations using [PySR](https://ai.damtp.cam.ac.uk/pysr/).

## Installation

SymTorch is available on [PyPI](https://pypi.org/) as `torch-symbolic`:

```bash
pip install torch-symbolic
```

You can also install directly from the source:

```bash
pip install git+https://github.com/elizabethsztan/SymTorch
```

## Documentation

Full documentation is available at [ReadTheDocs](https://symtorch.readthedocs.io/en/latest/).

## License

MIT License
