gplite 3.1.1

Lightweight Gaussian Process regression library

gplite

A lightweight Gaussian Process regression library built on NumPy and SciPy, originally designed for Δ-Machine Learning.

Features

• Gaussian Process regression with automatic hyperparameter optimization
• Multiple kernels: RBF, Matérn, Periodic, Constant (combinable via + and *)
• Active learning with multiple selection strategies (uncertainty, maximum absolute error, expected improvement)
• Model saving and loading with pickle
• Anisotropic kernels (support for per-dimension hyperparameters)
• String export for integration with external tools

Installation

pip install gplite

or for optional dependencies to run the example files

pip install "gplite[examples]"

Quick Start

The core workflow of gplite is designed to be highly intuitive. More detailed, end-to-end examples can be found in the examples directory.

1. Standard Regression & Composable Kernels

import numpy as np
from gplite import GaussianProcess, RBFKernel, PeriodicKernel

X_train, y_train = ... # load your data

# easily combine kernels for complex data
kernel = (
    RBFKernel(length_scale=2.0) + 
    PeriodicKernel(length_scale=1.0, period=2*np.pi)
)

# fit your model and use it for predictions
gp = GaussianProcess(kernel)
gp.fit(X_train, y_train, optimize=True)

y_mean, y_std = gp.predict(X_test, return_std=True)

2. Automated Active Learning

from gplite import ActiveLearner

learner = ActiveLearner(kernel=kernel, x_full=X_full, y_full=y_full)

# automatically train the model where it is most uncertain
learner.learn(
    learning_strategy="uncertainty", 
    rmse_threshold=0.1, 
    max_points=50
)

y_pred = learner.gp.predict(X_test)

Modules

• GaussianProcess - core GP regression
• Kernels - RBF, Matérn, Periodic, Constant, composites
• ActiveLearning - automated model training
• Optimization - hyperparameter optimization

Requirements

• Python >= 3.10
• NumPy >= 2.2.6
• SciPy >= 1.15.3

License

GPLv3