XGP is a machine learning library for performing symbolic regression. It can be used both for regression and classification tasks. Please refer to the documentation for an in-depth introduction to symbolic regression.

Interfaces

The core library is written in Go but it can be used in different ways:

• Command-line interface (CLI)
• Go API
• Python API

Usage examples

Command-line interface (CLI)

>>> xgp fit train.csv
>>> xgp predict test.csv

Go

package main

import "github.com/MaxHalford/xgp"

func main() {
    config := xgp.NewDefaultGPConfig()
    estimator := config.NewGP()

    estimator.Fit(XTrain, YTrain)
    yPred := estimator.Predict()
}

Python

import xgp

model = xgp.XGPRegressor()

model.fit(X_train, y_train)
y_pred = model.predict(X_test)

Dependencies

The core of XGP has the following dependencies.

• gonum/gonum for SIMD operations and calculating metrics
• gosuri/uiprogress for displaying progress bars
• MaxHalford/eaopt for the genetic algorithm part

License

The MIT License (MIT). Please see the LICENSE file for more information.