symbolic-regression-toolkit 1.6.0

Symbolic Regression/Equation Discovery Toolkit

SRToolkit: Symbolic Regression / Equation Discovery Benchmark Toolkit

Documentation:: 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:

• Implementing new Symbolic Regression approaches and evaluating their performance against other approaches.

• Benchmarking Symbolic Regression approaches using built-in benchmarks (currently Feynman and Nguyen) or custom data.

• Experiment organization and postprocessing of results.

• 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

Some built-in approaches (currently EDHiE) require extra dependencies (PyTorch and pymoo). To install them alongside the package, use the approaches extra:

pip install 'symbolic-regression-toolkit[approaches]'

Alternatively, you can install the latest build directly from the repository with the command (Recommended):

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.

import numpy as np
from SRToolkit.utils import compile_expr, tokens_to_tree, SymbolLibrary, expr_to_latex

# Create an executable function from the expression
expr = compile_expr(["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])

# Use a SymbolLibrary as a context manager to avoid passing it to every call
with SymbolLibrary.default_symbols(num_variables=2) as sl:
    # Create an expression tree from the token list
    expr_tree = tokens_to_tree(["X_0", "+", "X_1", "*", "C"])

    # 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)

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.

from SRToolkit.approaches import EDHiE, ProGED
from SRToolkit.dataset import Feynman
from SRToolkit.evaluation import LoggingCallback
from SRToolkit.experiments import ExperimentGrid

# Load the Feynman benchmark and pick two 2-variable datasets to run on.
bm = Feynman()
ds_names = bm.list_datasets(num_variables=2, verbose=False)
dataset1 = bm.create_dataset(ds_names[0])
dataset2 = bm.create_dataset(ds_names[1])

# Define the SR approaches to benchmark.
# EDHiE requires a pre-trained/adapted model state; ProGED needs no time consuming adaptation.
edhie = EDHiE()
proged = ProGED()

# Map each (approach, dataset) pair to a file where the adapted model state will
# be saved. Both datasets reuse the same file here because they share the same
# number of variables, so one adapted state covers both.
adapted_states = {edhie.name: {ds_names[0]: "adapted_state_2_vars.pt", ds_names[1]: "adapted_state_2_vars.pt"}}

# Build the experiment grid: every combination of approach × dataset will be run
# num_experiments times (with different random seeds). Results are written under
# results_dir, specifically to "results_dir/{dataset}/{approach}/exp_{seed}.json".
eg = ExperimentGrid(
    approaches=[proged, edhie],
    datasets=[dataset1, dataset2],
    num_experiments=2,
    results_dir="../results/",
    adapted_states=adapted_states,
)

# Write a shell script of CLI commands that can be executed in parallel, e.g.:
#   cat commands.sh | parallel -j 4
eg.save_commands("commands.sh")

# Run adaptation for any approach/dataset pair whose adapted state file is missing.
# This is a no-op if all state files already exist.
eg.adapt_if_missing()

# Collect all pending jobs (skip any whose result file already exists on disk).
jobs = eg.create_jobs(skip_completed=True)

# Run each job sequentially in this process. To parallelize, use the generated
# commands.sh instead.
for job in jobs:
    job.run()

# See how many jobs are completed
eg.progress()

Additional examples can be found in the examples folder or in the official documentation.

Roadmap 🗺️

In future releases, our primary focus will:

• Expanded Library of Approaches: Add more Symbolic Regression approaches to the toolkit.

• Result Visualization: Implement a robust visualization and result aggregation framework for SR results.

• Simplification: Implement a better (more accurate, efficient, and stable) simplification system for expressions.

• Constraints: Implement more robust expression generation constraints using techniques like attribute grammars.

• Improved Benchmarking: Improve the robustness and efficiency of the benchmarking framework (continuous).

• Advanced Expressions (Distant Plan): Implement support for different types of expressions, such as ODEs and PDEs.

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 open a issue on the Github page or submit a Pull Request (PR) with a clear description of your changes.

We are especially looking for contributions of:

• New Benchmarks and Datasets.

• Implementations of additional Symbolic Regression Approaches (once the core framework for comparison is finalized).

Instructions on how to contribute can be found in the Contribution Guide.