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https://github.com/thomaspinder/GPJax
Gaussian processes in JAX.
https://github.com/thomaspinder/GPJax
bayesian-inference gaussian-processes jax machine-learning probabilistic-programming
Last synced: 3 months ago
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Gaussian processes in JAX.
- Host: GitHub
- URL: https://github.com/thomaspinder/GPJax
- Owner: JaxGaussianProcesses
- License: apache-2.0
- Created: 2020-09-27T20:40:59.000Z (about 4 years ago)
- Default Branch: main
- Last Pushed: 2024-04-23T09:21:37.000Z (7 months ago)
- Last Synced: 2024-05-01T11:24:49.477Z (6 months ago)
- Topics: bayesian-inference, gaussian-processes, jax, machine-learning, probabilistic-programming
- Language: Python
- Homepage: https://docs.jaxgaussianprocesses.com/
- Size: 237 MB
- Stars: 387
- Watchers: 4
- Forks: 47
- Open Issues: 63
-
Metadata Files:
- Readme: README.md
- Contributing: docs/contributing.md
- License: LICENSE
- Code of conduct: .github/CODE_OF_CONDUCT.md
- Citation: CITATION.bib
- Governance: docs/GOVERNANCE.md
Awesome Lists containing this project
- awesome-jax - GPJax - Gaussian processes in JAX. (Libraries / New Libraries)
README
[](https://codecov.io/gh/JaxGaussianProcesses/GPJax)
[](https://www.codefactor.io/repository/github/jaxgaussianprocesses/gpjax)
[](https://app.netlify.com/sites/endearing-crepe-c2d5fe/deploys)
[](https://badge.fury.io/py/GPJax)
[](https://doi.org/10.21105/joss.04455)
[](https://pepy.tech/project/gpjax)
[](https://join.slack.com/t/gpjax/shared_invite/zt-1da57pmjn-rdBCVg9kApirEEn2E5Q2Zw)
[**Quickstart**](#simple-example)
| [**Install guide**](#installation)
| [**Documentation**](https://docs.jaxgaussianprocesses.com/)
| [**Slack Community**](https://join.slack.com/t/gpjax/shared_invite/zt-1da57pmjn-rdBCVg9kApirEEn2E5Q2Zw)
GPJax aims to provide a low-level interface to Gaussian process (GP) models in
[Jax](https://github.com/google/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 organisation
## Contributions
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](https://github.com/JaxGaussianProcesses/GPJax/blob/main/docs/contributing.md).
If you have any questions, we encourage you to [open an
issue](https://github.com/JaxGaussianProcesses/GPJax/issues/new/choose). For
broader conversations, such as best GP fitting practices or questions about the
mathematics of GPs, we invite you to [open a
discussion](https://github.com/JaxGaussianProcesses/GPJax/discussions).
Another way you can contribute to GPJax is through [issue
triaging](https://www.codetriage.com/what). This can include reproducing bug reports,
asking for vital information such as version numbers and reproduction instructions, or
identifying stale issues. If you would like to begin triaging issues, an easy way to get
started is to
[subscribe to GPJax on CodeTriage](https://www.codetriage.com/jaxgaussianprocesses/gpjax).
As a contributor to GPJax, you are expected to abide by our [code of
conduct](docs/CODE_OF_CONDUCT.md). If you feel that you have either experienced or
witnessed behaviour that violates this standard, then we ask that you report any such
behaviours through [this form](https://jaxgaussianprocesses.com/contact/) or reach out to
one of the project's [_gardeners_](https://docs.jaxgaussianprocesses.com/GOVERNANCE/#roles).
Feel free to join our [Slack
Channel](https://join.slack.com/t/gpjax/shared_invite/zt-1da57pmjn-rdBCVg9kApirEEn2E5Q2Zw),
where we can discuss the development of GPJax and broader support for Gaussian
process modelling.
## Governance
GPJax was founded by [Thomas Pinder](https://github.com/thomaspinder). Today, the
project's gardeners are [daniel-dodd@](https://github.com/daniel-dodd),
[henrymoss@](https://github.com/henrymoss), [st--@](https://github.com/st--), and
[thomaspinder@](https://github.com/thomaspinder), listed in alphabetical order. The full
governance structure of GPJax is detailed [here](docs/GOVERNANCE.md). We appreciate all
[the contributors to
GPJax](https://github.com/JaxGaussianProcesses/GPJax/graphs/contributors) who have
helped to shape GPJax into the package it is today.
# Supported methods and interfaces
## Notebook examples
> - [**Conjugate Inference**](https://docs.jaxgaussianprocesses.com/examples/regression/)
> - [**Classification with MCMC**](https://docs.jaxgaussianprocesses.com/examples/classification/)
> - [**Sparse Variational Inference**](https://docs.jaxgaussianprocesses.com/examples/collapsed_vi/)
> - [**Stochastic Variational Inference**](https://docs.jaxgaussianprocesses.com/examples/uncollapsed_vi/)
> - [**BlackJax Integration**](https://docs.jaxgaussianprocesses.com/examples/classification/#mcmc-inference)
> - [**Laplace Approximation**](https://docs.jaxgaussianprocesses.com/examples/classification/#laplace-approximation)
> - [**Inference on Non-Euclidean Spaces**](https://docs.jaxgaussianprocesses.com/examples/constructing_new_kernels/#custom-kernel)
> - [**Inference on Graphs**](https://docs.jaxgaussianprocesses.com/examples/graph_kernels/)
> - [**Pathwise Sampling**](https://docs.jaxgaussianprocesses.com/examples/spatial/)
> - [**Learning Gaussian Process Barycentres**](https://docs.jaxgaussianprocesses.com/examples/barycentres/)
> - [**Deep Kernel Regression**](https://docs.jaxgaussianprocesses.com/examples/deep_kernels/)
> - [**Poisson Regression**](https://docs.jaxgaussianprocesses.com/examples/poisson/)
> - [**Bayesian Optimisation**](https://docs.jaxgaussianprocesses.com/examples/bayesian_optimisation/)
## Guides for customisation
>
> - [**Custom kernels**](https://docs.jaxgaussianprocesses.com/examples/constructing_new_kernels/#custom-kernel)
> - [**UCI regression**](https://docs.jaxgaussianprocesses.com/examples/yacht/)
## Conversion between `.ipynb` and `.py`
Above examples are stored in [examples](docs/examples) directory in the double
percent (`py:percent`) format. Checkout [jupytext
using-cli](https://jupytext.readthedocs.io/en/latest/using-cli.html) for more
info.
* To convert `example.py` to `example.ipynb`, run:
```bash
jupytext --to notebook example.py
```
* To convert `example.ipynb` to `example.py`, run:
```bash
jupytext --to py:percent example.ipynb
```
# Simple example
Let us import some dependencies and simulate a toy dataset $\mathcal{D}$.
```python
from jax import config
config.update("jax_enable_x64", True)
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.key(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.gps.Prior(mean_function=meanf, kernel = kernel)
# Define a likelihood
likelihood = gpx.likelihoods.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:
```bash
pip install gpjax
```
> **Note**
>
> We recommend you check your installation version:
> ```python
> python -c 'import gpjax; print(gpjax.__version__)'
> ```
## Development version
> **Warning**
>
> This version is possibly unstable and may contain bugs.
> **Note**
>
> We advise you create virtual environment before installing:
> ```
> conda create -n gpjax_experimental python=3.10.0
> conda activate gpjax_experimental
> ```
Clone a copy of the repository to your local machine and run the setup
configuration in development mode.
```bash
git clone https://github.com/JaxGaussianProcesses/GPJax.git
cd GPJax
poetry install
```
> We recommend you check your installation passes the supplied unit tests:
>
> ```python
> poetry run pytest
> ```
# Citing GPJax
If you use GPJax in your research, please cite our [JOSS paper](https://joss.theoj.org/papers/10.21105/joss.04455#).
```
@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}
}
```