https://github.com/maxreiss123/pygep
Python interface for utilizing GeneExpressionProgramming.jl
https://github.com/maxreiss123/pygep
equation-discovery explainable-ai gene-expression-programming machine-learning scientific-machine-learning symbolic-regression
Last synced: 3 months ago
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Python interface for utilizing GeneExpressionProgramming.jl
- Host: GitHub
- URL: https://github.com/maxreiss123/pygep
- Owner: maxreiss123
- License: mit
- Created: 2025-07-06T23:37:36.000Z (3 months ago)
- Default Branch: main
- Last Pushed: 2025-07-10T03:00:20.000Z (3 months ago)
- Last Synced: 2025-07-10T11:29:17.825Z (3 months ago)
- Topics: equation-discovery, explainable-ai, gene-expression-programming, machine-learning, scientific-machine-learning, symbolic-regression
- Language: Python
- Homepage:
- Size: 6.84 KB
- Stars: 0
- Watchers: 0
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# PyGEP: Python Interface for GeneExpressionProgramming.jl
A Python interface for [GeneExpressionProgramming.jl](https://github.com/maxreiss123/GeneExpressionProgramming.jl) package.
This package provides a Python wrapper for the Julia implementation, which performs symbolic regression using Gene Expression Programming.
## Features
- Underlying Julia implementation for fast optimization
- Standard GEP symbolic regression
- Multi-objective optimization (not available yet for the interface)
- Physics-aware regression with dimensional constraints (not available yet for the interface)
- Tensor/matrix regression support (not available yet for the interface)
## Installation
### 1. Install Julia
First, install Julia from [https://julialang.org/downloads/](https://julialang.org/downloads/)
### 2. Install PyGEP
```bash
cd pygep
pip install .
```
### 3. Install Julia Dependencies
```bash
pygep-setup
```
or
```python
import pygep
pygep.install_julia_dependencies()
```
## Quick Start
### Basic Symbolic Regression
```python
import numpy as np
from pygep import GepRegressor
# Generate test data
X = np.random.randn(100, 2)
y = X[:, 0]**2 + X[:, 0] * X[:, 1] - 2 * X[:, 0] * X[:, 1]
# Create and fit regressor
regressor = GepRegressor(number_features=2)
regressor.fit(X, y, epochs=1000, population_size=1000, loss_function="mse")
# Make predictions
predictions = regressor.predict(X)
# Get results
print(f"Best expression: {regressor.best_expression_}")
print(f"Best fitness: {regressor.best_fitness_}")
```
## API Reference
### GepRegressor
Main class for symbolic regression.
#### Parameters:
- `number_features` (int): Number of input features
#### Methods:
- `fit(X, y, epochs=1000, population_size=1000, loss_function="mse", ...)`: Fit the regressor
- `predict(X)`: Make predictions
- `get_best_models(n=1)`: Get n best models
#### Properties:
- `best_expression_`: Best symbolic expression as string
- `best_fitness_`: Fitness of best expression
## Requirements
- Python ≥ 3.8
- Julia ≥ 1.6
- numpy ≥ 1.20.0
- julia ≥ 0.6.0 (Python package)
## Troubleshooting
### Julia Not Found
```bash
# Make sure Julia is in your PATH
julia --version
# If not found, add Julia to PATH or install from:
# https://julialang.org/downloads/
```
### PyCall Issues
```python
# In Julia REPL:
using Pkg
Pkg.add("PyCall")
Pkg.build("PyCall")
```
### Package Installation Issues
```python
# Force reinstall Julia dependencies
import pygep
pygep.install_julia_dependencies()
```
## Citation
If you use this package, please cite the original Julia implementation:
```bibtex
@article{Reissmann2025,
author = {Maximilian Reissmann and Yuan Fang and Andrew S. H. Ooi and Richard D. Sandberg},
title = {Constraining Genetic Symbolic Regression via Semantic Backpropagation},
journal = {Genetic Programming and Evolvable Machines},
year = {2025},
volume = {26},
number = {1},
pages = {12},
doi = {10.1007/s10710-025-09510-z}
}
```
## License
MIT License - see LICENSE file for details.