GPy - A Gaussian Process (GP) framework in Python

Introduction

GPy is a Gaussian Process (GP) framework written in Python, from the Sheffield machine learning group. It includes support for basic GP regression, multiple output GPs (using coregionalization), various noise models, sparse GPs, non-parametric regression and latent variables.

The GPy homepage contains tutorials for users and further information on the project, including installation instructions.

The documentation hosted here is mostly aimed at developers interacting closely with the code-base.

Source Code

The code can be found on our Github project page. It is open source and provided under the BSD license.

Installation

Installation instructions can currently be found on our Github project page.

Tutorials

Several tutorials have been developed in the form of Jupyter Notebooks.

Architecture

GPy is a big, powerful package, with many features. The concept of how to use GPy in general terms is roughly as follows. A model (GPy.models) is created - this is at the heart of GPy from a user perspective. A kernel (GPy.kern), data and, usually, a representation of noise are assigned to the model. Specific models require, or can make use of, additional information. The kernel and noise are controlled by hyperparameters - calling the optimize (GPy.core.gp.GP.optimize) method against the model invokes an iterative process which seeks optimal hyperparameter values. The model object can be used to make plots and predictions (GPy.core.gp.GP.predict).

For developers

• Creating new Models
• Creating new kernels
• Defining a new plotting function in GPy
• Parameterization handling

API Documentation

• GPy.core package
• GPy.core.parameterization package
• GPy.models package
• GPy.kern package
• GPy.likelihoods package
• GPy.mappings package
• GPy.examples package
• GPy.util package
• GPy.plotting package
• GPy.inference.optimization package
• GPy.inference.latent_function_inference package
• GPy.inference.mcmc package