parametric-sr 0.1.0

Parametric Symbolic Regression for Modeling a Series of Datasets via Genetic Programming

parametric-symbolic-regression

Parametric Symbolic Regression with Genetic Algorithm

Table of Contents

• parametric-symbolic-regression
  • Table of Contents
  • Overview
  • Features
  • Installation
  • Quick Start
  • Contributing
  • License
  • Citation

Overview

Parametric Symbolic Regression (PSR) is a Python package that evolves symbolic expressions whose parameters can be optimized (e.g. with curve_fit or minimize). It combines genetic-programming style search over expression structure with numeric optimization of continuous parameters, which can be applied as classical symbolic regression for closed-form equations or more advanced scenarios like system (series of equations) identification.

This repository provides:

• Tree/FuncUnit/Expression primitives for building symbolic expressions.
• Generation, mutation and crossover operators for evolving populations of expressions.
• Fitting utilities using SciPy (curve_fit / minimize) to tune numeric parameters.
• Scoring, selection and simple utilities for batch evaluation and parallel execution.

Features

• Mixed discrete (structure) + continuous (parameters) optimization.
• Batch scoring and configurable scoring strategies.
• Pluggable optimization backends (SciPy).
• Multi-processing support for parallel evaluation.
• Helpers for logging and reproducible random seeds.

Installation

Python 3.12+ is required for the latest type hint features. We may consider supporting older versions in the future.

Install from source (to get the latest features):

git clone https://github.com/SijieFu/parametric-symbolic-regression.git
cd parametric-symbolic-regression
python -m pip install -e .

For development, you may also want to install testing dependencies using pip install -e ".[dev]", followed by pre-commit install to set up pre-commit hooks.

Or install from PyPI (to get the latest stable release):

pip install parametric-sr

Quick Start

Basic usage example:

import numpy as np

from psr import ParametricSR

# Generate synthetic data
X = np.random.random((100, 2))
y = 3.14 * X[:, 0] + X[:, 1] ** 2 + np.random.normal(size=100) * 0.1

# Initialize and fit the model
psr_model = ParametricSR(
    n_gen=10, n_per_gen=1000, n_survivor=30, n_jobs=-1, random_state=42, verbose=10
)
psr_model.fit(X, y)

# Evaluate the model
print("Best Expression:", psr_model.best_estimator_.format())

To enable logging, you can set the log file as follows:

from psr import add_file_handler

add_file_handler(log_file="test.log")

To save the model and reload it later:

import pickle

# Save the model
with open("psr_model.pkl", "wb") as f:
    pickle.dump(psr_model, f)

# Load the model
from psr import config

with config.inplace_update(), open("psr_model.pkl", "rb") as f:
    # inplace update is needed to sync the function registry
    loaded_model = pickle.load(f)

Contributing

Contributions are welcome! Please open issues or pull requests on the GitHub repository.

License

This project is licensed under the Apache-2.0 License. See the LICENSE file for details.

Citation

We are working on a paper that will be available soon (preprint forthcoming).

If you find this repository useful, please consider citing it.