Quickstart | Install guide | Documentation | Slack Community
GPJax aims to provide a low-level interface to Gaussian process (GP) models in Jax, structured to give researchers maximum flexibility in extending the code to suit their own needs. The idea is that the code should be as close as possible to the maths we write on paper when working with GP models.
Package support
GPJax was founded by Thomas Pinder. Today, the maintenance of GPJax is undertaken by Thomas Pinder and Daniel Dodd.
We would be delighted to receive contributions from interested individuals and groups. To learn how you can get involved, please read our guide for contributing. If you have any questions, we encourage you to open an issue. For broader conversations, such as best GP fitting practices or questions about the mathematics of GPs, we invite you to open a discussion.
Feel free to join our Slack Channel, where we can discuss the development of GPJax and broader support for Gaussian process modelling.
Supported methods and interfaces
Notebook examples
Guides for customisation
Conversion between .ipynb and .py
Above examples are stored in examples directory in the double
percent (py:percent) format. Checkout jupytext
using-cli for more
info.
- To convert
example.pytoexample.ipynb, run:
jupytext --to notebook example.py
- To convert
example.ipynbtoexample.py, run:
jupytext --to py:percent example.ipynb
Simple example
Let us import some dependencies and simulate a toy dataset $\mathcal{D}$.
import gpjax as gpx
from jax import grad, jit
import jax.numpy as jnp
import jax.random as jr
import optax as ox
key = jr.PRNGKey(123)
f = lambda x: 10 * jnp.sin(x)
n = 50
x = jr.uniform(key=key, minval=-3.0, maxval=3.0, shape=(n,1)).sort()
y = f(x) + jr.normal(key, shape=(n,1))
D = gpx.Dataset(X=x, y=y)
# Construct the prior
meanf = gpx.mean_functions.Zero()
kernel = gpx.kernels.RBF()
prior = gpx.Prior(mean_function=meanf, kernel = kernel)
# Define a likelihood
likelihood = gpx.Gaussian(num_datapoints = n)
# Construct the posterior
posterior = prior * likelihood
# Define an optimiser
optimiser = ox.adam(learning_rate=1e-2)
# Define the marginal log-likelihood
negative_mll = jit(gpx.objectives.ConjugateMLL(negative=True))
# Obtain Type 2 MLEs of the hyperparameters
opt_posterior, history = gpx.fit(
model=posterior,
objective=negative_mll,
train_data=D,
optim=optimiser,
num_iters=500,
safe=True,
key=key,
)
# Infer the predictive posterior distribution
xtest = jnp.linspace(-3., 3., 100).reshape(-1, 1)
latent_dist = opt_posterior(xtest, D)
predictive_dist = opt_posterior.likelihood(latent_dist)
# Obtain the predictive mean and standard deviation
pred_mean = predictive_dist.mean()
pred_std = predictive_dist.stddev()
Installation
Stable version
The latest stable version of GPJax can be installed via pip:
pip install gpjax
Note
We recommend you check your installation version:
python -c 'import gpjax; print(gpjax.__version__)'
Development version
Warning
This version is possibly unstable and may contain bugs.
Clone a copy of the repository to your local machine and run the setup configuration in development mode.
git clone https://github.com/JaxGaussianProcesses/GPJax.git
cd GPJax
poetry install
Note
We advise you create virtual environment before installing:
conda create -n gpjax_experimental python=3.10.0 conda activate gpjax_experimentaland recommend you check your installation passes the supplied unit tests:
poetry run pytest
Citing GPJax
If you use GPJax in your research, please cite our JOSS paper.
@article{Pinder2022,
doi = {10.21105/joss.04455},
url = {https://doi.org/10.21105/joss.04455},
year = {2022},
publisher = {The Open Journal},
volume = {7},
number = {75},
pages = {4455},
author = {Thomas Pinder and Daniel Dodd},
title = {GPJax: A Gaussian Process Framework in JAX},
journal = {Journal of Open Source Software}
}
1 maintainer
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