https://github.com/smeznar/symbolicregressiontoolkit
Toolkit for symbolic regression/equation discovery
https://github.com/smeznar/symbolicregressiontoolkit
equation-discovery parameter-estimation symbolic-regression
Last synced: about 1 month ago
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Toolkit for symbolic regression/equation discovery
- Host: GitHub
- URL: https://github.com/smeznar/symbolicregressiontoolkit
- Owner: smeznar
- License: gpl-3.0
- Created: 2024-12-03T15:18:51.000Z (over 1 year ago)
- Default Branch: master
- Last Pushed: 2026-04-06T18:18:37.000Z (about 2 months ago)
- Last Synced: 2026-04-06T20:20:12.414Z (about 2 months ago)
- Topics: equation-discovery, parameter-estimation, symbolic-regression
- Language: Python
- Homepage: https://smeznar.github.io/SymbolicRegressionToolkit/
- Size: 129 MB
- Stars: 5
- Watchers: 2
- Forks: 1
- Open Issues: 1
-
Metadata Files:
- Readme: README.md
- Changelog: CHANGELOG.md
- License: LICENSE
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README
# SRToolkit: Symbolic Regression / Equation Discovery Benchmark Toolkit
**Documentation:**: [https://smeznar.github.io/SymbolicRegressionToolkit](https://smeznar.github.io/SymbolicRegressionToolkit/)
## What is SRToolkit?
The **SRToolkit** is a **comprehensive Python toolkit** designed to accelerate research and development in
**Symbolic Regression (SR)** / **Equation Discovery (ED)**. It provides a robust, easy-to-use framework for
**benchmarking, rapid prototyping, and mathematical expression manipulation**.
## Core Features
SRToolkit provides a straightforward interface for:
* **Benchmarking** Symbolic Regression algorithms using built-in datasets (currently **Feynman** and **Nguyen**) or **custom data**.
* **Converting expressions** into **expression trees** or **fast, callable NumPy functions**.
* **Generating random expressions** by defining the symbol space or a grammar.
* **Estimating constant parameters** of expressions against real-world data.
* **Comparing** and **measuring the distance** between expressions.
## Installation
To install the latest stable release of the package, run the following command in your terminal:
```
pip install symbolic-regression-toolkit
```
Alternatively, you can install the latest build directly from the repository with the command:
```
pip install git+https://github.com/smeznar/SymbolicRegressionToolkit
```
## Examples
### 1. Expression Manipulation (The Toolkit Core)
SRToolkit offers fundamental utilities for working with mathematical expressions as tokens, trees, and executable code—the building blocks for any SR approach.
```python
import numpy as np
from SRToolkit.utils import expr_to_executable_function, tokens_to_tree, SymbolLibrary, expr_to_latex
# Create an executable function from the expression
expr = expr_to_executable_function(["X_0", "+", "X_1", "*", "C"])
# Calculate the output at two points (1, 2) and (2, 5) with C=3
data_points = np.array([[1, 2], [2, 5]])
constants = [3]
output = expr(data_points, constants)
# Variable "output" should now contain np.array([7, 17])
# Create a SymbolLibrary defining the symbol space for 2 variables
sl = SymbolLibrary.default_symbols(num_variables=2)
# Create an expression tree from the token list
expr_tree = tokens_to_tree(["X_0", "+", "X_1", "*", "C"], sl)
# Transform the expression into a list of symbols in postfix notation
postfix_expr = expr_tree.to_list(notation="postfix")
# Create a LaTeX string of the expression for clear presentation
expr_latex = expr_to_latex(expr_tree, sl)
```
### 2. Benchmarking and Evaluation (The Main Use Case)
The primary advantage of SRToolkit is its robust benchmarking framework, allowing you to quickly evaluate and compare different Symbolic Regression approaches.
```python
from SRToolkit.dataset import Feynman
from SRToolkit.utils import generate_n_expressions
# Create the Feynman benchmark suite
feynman = Feynman()
# List datasets in the benchmark and select the first 2-variable one
dataset_name = feynman.list_datasets(verbose=False, num_variables=2)[0]
# Create the dataset and the dedicated evaluator object
dataset = feynman.create_dataset(dataset_name)
evaluator = dataset.create_evaluator()
# Generate 100 random expressions for a baseline evaluation
expressions = generate_n_expressions(dataset.symbol_library, 100)
# Evaluate the expressions and print their error
for expr in expressions:
rmse = evaluator.evaluate_expr(expr)
print(f"Expr: {''.join(expr)}, Error: {rmse}")
# Get structured results of the evaluation, focusing on the 20 best expressions
results = evaluator.get_results(top_k=20)
```
Additional examples can be found in the `examples` folder or in the official documentation.
## Roadmap 🗺️
In future releases, our primary focus will be on benchmarking and comparability:
* **Benchmarking Core:** Add the ability to save/load benchmark runs and automatically evaluate multiple ED/SR approaches.
* **SR Library:** Create a library of easy-to-use and comparable ED/SR approach implementations.
* **Advanced Expressions (Distant Plan):** Implement support for different types of expressions, such as **ODEs and PDEs**.
* **Constraints:** Implement more robust expression generation constraints using techniques like attribute grammars.
## Contributing 🤝
We welcome contributions! Whether you're adding a new benchmark, implementing an SR approach, fixing a bug, or improving the documentation, please feel free to submit a **Pull Request (PR)** with a clear description of your changes.
We are especially looking for contributions of:
* New **Benchmarks** and **Datasets** (e.g., datasets from physics, finance, etc.).
* Implementations of additional **Symbolic Regression Approaches** (once the core framework for comparison is finalized).