pysymregg 1.0.1

Python Wheels for SymRegg algorithm.

PySymRegg - Python bindings for SymRegg

A Python package for symbolic regression using e-graphs. PySymRegg is built on top of the SymRegg algorithm and provides a scikit-learn compatible API for symbolic regression tasks.

More info here

Installation

pip install pysymregg

Features

• Scikit-learn compatible API with fit() and predict() methods
• Support for multiple optimization algorithms
• Flexible function set selection
• Various loss functions for different problem types
• Parameter optimization with multiple restarts
• Ability to save and load e-graphs

Usage

Basic Example

from pysymregg import PySymRegg
import numpy as np

# Create sample data
X = np.linspace(-10, 10, 100).reshape(-1, 1)
y = 2 * X.ravel() + 3 * np.sin(X.ravel()) + np.random.normal(0, 1, 100)

# Create and fit the model
model = PySymRegg(gen=100, nonterminals="add,sub,mul,div,sin,cos")
model.fit(X, y)

# Make predictions
y_pred = model.predict(X)

# Examine the results
print(model.results)

Integration with scikit-learn

from sklearn.model_selection import train_test_split
from sklearn.metrics import mean_squared_error
from pysymregg import PySymRegg

# Split data
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)

# Create and fit model
model = PySymRegg(gen=150, optIter=100)
model.fit(X_train, y_train)

# Evaluate on test set
y_pred = model.predict(X_test)
mse = mean_squared_error(y_test, y_pred)
print(f"Test MSE: {mse}")

Parameters

Parameter	Type	Default	Description
gen	int	100	Number of generations to run
alg	str	"BestFirst"	Algorithm type: "BestFirst" or "OnlyRandom"
maxSize	int	15	Maximum allowed size for expressions (max 100)
nonterminals	str	"add,sub,mul,div"	Comma-separated list of allowed functions
loss	str	"MSE"	Loss function: "MSE", "Gaussian", "Bernoulli", or "Poisson"
optIter	int	50	Number of iterations for parameter optimization
optRepeat	int	2	Number of restarts for parameter optimization
nParams	int	-1	Maximum number of parameters (-1 for unlimited)
split	int	1	Data splitting ratio for validation
dumpTo	str	""	Filename to save the final e-graph
loadFrom	str	""	Filename to load an e-graph to resume search

Available Functions

The following functions can be used in the nonterminals parameter:

• Basic operations: add, sub, mul, div
• Powers: power, powerabs, square, cube
• Roots: sqrt, sqrtabs, cbrt
• Trigonometric: sin, cos, tan, asin, acos, atan
• Hyperbolic: sinh, cosh, tanh, asinh, acosh, atanh
• Others: abs, log, logabs, exp, recip, aq (analytical quotient)

Methods

• fit(X, y): Fits the symbolic regression model
• predict(X): Generates predictions using the best model
• score(X, y): Computes R² score of the best model
• evaluate_best_model(X): Evaluates the best model on the given data
• evaluate_model(ix, X): Evaluates the model with index ix on the given data

Results

After fitting, the results attribute contains a pandas DataFrame with details about the discovered models, including:

• Mathematical expressions
• Model complexity
• Parameter values
• Error metrics
• NumPy-compatible expressions

License

[LICENSE]

Citation

If you use PySymRegg in your research, please cite:

TBD

Acknowledgments

The bindings were created following the amazing example written by wenkokke