eggp 1.0.5

Python Wheels for eggp algorithm.

eggp - e-graph GP

eggp (e-graph genetic programming), follows the same structure as the traditional GP. The initial population is created using ramped half-and-half respecting a maximum size and maximum depth parameter and, for a number of generations, it will choose two parents using tournament selection, apply the subtree crossover with probability $pc$ followed by the subtree mutation with probability $pm$, when the offsprings replace the current population following a dominance criteria.

The key differences of eggp are:

- new solutions are inserted into the e-graph followed by one step of equality saturation to find and store some of the  equivalent expressions of the new offspring.
- the current population is replaced by the set of individuals formed by: the Pareto front, the next front after excluding the first Pareto-front, and a selection of the last offspring at random until it reaches the desired population size.
- the subtree crossover and mutation are modified to try to generate an unvisited exp

This repository provides a CLI and a Python package for eggp with a scikit-learn compatible API for symbolic regression.

Instructions:

• CLI version
• Python version

CLI

How to use

eggp - E-graph Genetic Programming for Symbolic Regression.

Usage: eggp (-d|--dataset INPUT-FILE) [-t|--test ARG] [-g|--generations GENS]
            (-s|--maxSize ARG) [-k|--split ARG] [--print-pareto] [--trace] 
            [--loss ARG] [--opt-iter ARG] [--opt-retries ARG] 
            [--number-params ARG] [--nPop ARG] [--tournament-size ARG] 
            [--pc ARG] [--pm ARG] [--non-terminals ARG] [--dump-to ARG] 
            [--load-from ARG] [--moo]

  An implementation of GP with modified crossover and mutation operators
  designed to exploit equality saturation and e-graphs.
  https://arxiv.org/abs/2501.17848

Available options:
  -d,--dataset INPUT-FILE  CSV dataset.
  -t,--test ARG            test data (default: "")
  -g,--generations GENS    Number of generations. (default: 100)
  -s,--maxSize ARG         max-size.
  -k,--split ARG           k-split ratio training-validation (default: 1)
  --print-pareto           print Pareto front instead of best found expression
  --trace                  print all evaluated expressions.
  --loss ARG               loss function: MSE, Gaussian, Poisson, Bernoulli.
                           (default: MSE)
  --opt-iter ARG           number of iterations in parameter optimization.
                           (default: 30)
  --opt-retries ARG        number of retries of parameter fitting. (default: 1)
  --number-params ARG      maximum number of parameters in the model. If this
                           argument is absent, the number is bounded by the
                           maximum size of the expression and there will be no
                           repeated parameter. (default: -1)
  --nPop ARG               population size (Default: 100). (default: 100)
  --tournament-size ARG    tournament size. (default: 2)
  --pc ARG                 probability of crossover. (default: 1.0)
  --pm ARG                 probability of mutation. (default: 0.3)
  --non-terminals ARG      set of non-terminals to use in the search.
                           (default: "Add,Sub,Mul,Div,PowerAbs,Recip")
  --dump-to ARG            dump final e-graph to a file. (default: "")
  --load-from ARG          load initial e-graph from a file. (default: "")
  --moo                    replace the current population with the pareto front
                           instead of replacing it with the generated children.
  -h,--help                Show this help text

The dataset file must contain a header with each features name, and the --dataset and --test arguments can be accompanied by arguments separated by ':' following the format:

filename.ext:start_row:end_row:target:features

where each ':' field is optional. The fields are:

• start_row:end_row is the range of the training rows (default 0:nrows-1). every other row not included in this range will be used as validation
• target is either the name of the (if the datafile has headers) or the index of the target variable
• features is a comma separated list of names or indices to be used as input variables of the regression model.

Example of valid names: dataset.csv, mydata.tsv, dataset.csv:20:100, dataset.tsv:20:100:price:m2,rooms,neighborhood, dataset.csv:::5:0,1,2.

The format of the file will be determined by the extension (e.g., csv, tsv,...). To use multi-view, simply pass multiple filenames in double-quotes:

eggp --dataset "dataset1.csv dataset2.csv dataset3.csv" ...

Installation

To install eggp you'll need:

• libz
• libnlopt
• libgmp
• ghc-9.6.6
• cabal or stack

Method 1: PIP

Simply run:

pip install eggp 

under your Python environment.

Method 2: cabal

After installing the dependencies (e.g., apt install libz libnlopt libgmp), install ghcup

For Linux, macOS, FreeBSD or WSL2:

curl --proto '=https' --tlsv1.2 -sSf https://get-ghcup.haskell.org | sh

For Windows, run the following in a PowerShell:

Set-ExecutionPolicy Bypass -Scope Process -Force;[System.Net.ServicePointManager]::SecurityProtocol = [System.Net.ServicePointManager]::SecurityProtocol -bor 3072; try { & ([ScriptBlock]::Create((Invoke-WebRequest https://www.haskell.org/ghcup/sh/bootstrap-haskell.ps1 -UseBasicParsing))) -Interactive -DisableCurl } catch { Write-Error $_ }

After the installation, run ghcup tui and install the latest stack or cabal together with ghc-9.6.6 (select the items and press i). To install srsimplify simply run:

cabal install

Python

Features

• Scikit-learn compatible API with fit() and predict() methods
• Genetic programming approach with e-graph representation
• Support for multi-view symbolic regression see here
• Customizable evolutionary parameters (population size, tournament selection, etc.)
• Flexible function set selection
• Various loss functions for different problem types
• Parameter optimization with multiple restarts
• Optional expression simplification through equality saturation
• Ability to save and load e-graphs

Usage

Basic Example

from pyeggp import PyEGGP
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 = PyEGGP(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)

Multi-View Symbolic Regression

from pyeggp import PyEGGP
import numpy as np

# Create multiple views of data
X1 = np.linspace(-5, 5, 50).reshape(-1, 1)
y1 = np.sin(X1.ravel()) + np.random.normal(0, 0.1, 50)

X2 = np.linspace(0, 10, 100).reshape(-1, 1)
y2 = np.sin(X2.ravel()) + np.random.normal(0, 0.2, 100)

# Create and fit multi-view model
model = PyEGGP(gen=150, nPop=200)
model.fit_mvsr([X1, X2], [y1, y2])

# Make predictions for each view
y_pred1 = model.predict_mvsr(X1, view=0)
y_pred2 = model.predict_mvsr(X2, view=1)

Integration with scikit-learn

from sklearn.model_selection import train_test_split
from sklearn.metrics import mean_squared_error
from pyeggp import PyEGGP

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

# Create and fit model
model = PyEGGP(gen=150, nPop=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
nPop	int	100	Population size
maxSize	int	15	Maximum allowed size for expressions (max 100)
nTournament	int	3	Tournament size for parent selection
pc	float	0.9	Probability of performing crossover
pm	float	0.3	Probability of performing mutation
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
simplify	bool	False	Whether to apply equality saturation to simplify expressions
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

Core 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

Multi-View Methods

• fit_mvsr(Xs, ys): Fits a multi-view regression model
• predict_mvsr(X, view): Generates predictions for a specific view
• evaluate_best_model_view(X, view): Evaluates the best model on a specific view
• evaluate_model_view(X, ix, view): Evaluates a specific model on a specific view

Utility Methods

• 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
• get_model(idx): Returns a model function and its visual representation

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 PyEGGP in your research, please cite:

@inproceedings{eggp,
author = {de Franca, Fabricio Olivetti and Kronberger, Gabriel},
title = {Improving Genetic Programming for Symbolic Regression with Equality Graphs},
year = {2025},
isbn = {9798400714658},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3712256.3726383},
doi = {10.1145/3712256.3726383},
booktitle = {Proceedings of the Genetic and Evolutionary Computation Conference},
pages = {},
numpages = {9},
keywords = {Symbolic regression, Genetic programming, Equality saturation, Equality graphs},
location = {Malaga, Spain},
series = {GECCO '25},
archivePrefix = {arXiv},
       eprint = {2501.17848},
 primaryClass = {cs.LG}, 
}

Acknowledgments

The bindings were created following the amazing example written by wenkokke

Fabricio Olivetti de Franca is supported by Conselho Nacional de Desenvolvimento Cient'{i}fico e Tecnol'{o}gico (CNPq) grant 301596/2022-0.

Gabriel Kronberger is supported by the Austrian Federal Ministry for Climate Action, Environment, Energy, Mobility, Innovation and Technology, the Federal Ministry for Labour and Economy, and the regional government of Upper Austria within the COMET project ProMetHeus (904919) supported by the Austrian Research Promotion Agency (FFG).