%global _empty_manifest_terminate_build 0
Name: python-gpjax
Version: 0.6.1
Release: 1
Summary: Gaussian processes in JAX.
License: Apache-2.0
URL: https://github.com/JaxGaussianProcesses/GPJax
Source0: https://mirrors.nju.edu.cn/pypi/web/packages/39/0a/4296668bec6e4468018d9898615e84ce92abd76ba70cbe1b64d286049388/gpjax-0.6.1.tar.gz
BuildArch: noarch
Requires: python3-jax
Requires: python3-optax
Requires: python3-jaxtyping
Requires: python3-tqdm
Requires: python3-simple-pytree
Requires: python3-tensorflow-probability
Requires: python3-orbax-checkpoint
Requires: python3-beartype
Requires: python3-jaxlib
%description
[](https://codecov.io/gh/JaxGaussianProcesses/GPJax)
[](https://www.codefactor.io/repository/github/jaxgaussianprocesses/gpjax)
[](https://gpjax.readthedocs.io/en/latest/?badge=latest)
[](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://gpjax.readthedocs.io/en/latest/)
| [**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 support
GPJax was founded by [Thomas Pinder](https://github.com/thomaspinder). Today,
the maintenance of GPJax is undertaken by [Thomas
Pinder](https://github.com/thomaspinder) and [Daniel
Dodd](https://github.com/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](https://github.com/JaxGaussianProcesses/GPJax/blob/master/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).
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.
# Supported methods and interfaces
## Notebook examples
> - [**Conjugate Inference**](https://gpjax.readthedocs.io/en/latest/examples/regression.html)
> - [**Classification with MCMC**](https://gpjax.readthedocs.io/en/latest/examples/classification.html)
> - [**Sparse Variational Inference**](https://gpjax.readthedocs.io/en/latest/examples/uncollapsed_vi.html)
> - [**BlackJax Integration**](https://gpjax.readthedocs.io/en/latest/examples/classification.html)
> - [**Laplace Approximation**](https://gpjax.readthedocs.io/en/latest/examples/classification.html#Laplace-approximation)
> - [**Inference on Non-Euclidean Spaces**](https://gpjax.readthedocs.io/en/latest/examples/kernels.html#Custom-Kernel)
> - [**Inference on Graphs**](https://gpjax.readthedocs.io/en/latest/examples/graph_kernels.html)
> - [**Learning Gaussian Process Barycentres**](https://gpjax.readthedocs.io/en/latest/examples/barycentres.html)
> - [**Deep Kernel Regression**](https://gpjax.readthedocs.io/en/latest/examples/haiku.html)
## Guides for customisation
>
> - [**Custom kernels**](https://gpjax.readthedocs.io/en/latest/examples/kernels.html#Custom-Kernel)
> - [**UCI regression**](https://gpjax.readthedocs.io/en/latest/examples/yacht.html)
## Conversion between `.ipynb` and `.py`
Above examples are stored in [examples](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
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:
```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.
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
```
> **Note**
>
> We advise you create virtual environment before installing:
> ```
> conda create -n gpjax_experimental python=3.10.0
> conda activate gpjax_experimental
> ```
>
> and 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}
}
```
%package -n python3-gpjax
Summary: Gaussian processes in JAX.
Provides: python-gpjax
BuildRequires: python3-devel
BuildRequires: python3-setuptools
BuildRequires: python3-pip
%description -n python3-gpjax
[](https://codecov.io/gh/JaxGaussianProcesses/GPJax)
[](https://www.codefactor.io/repository/github/jaxgaussianprocesses/gpjax)
[](https://gpjax.readthedocs.io/en/latest/?badge=latest)
[](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://gpjax.readthedocs.io/en/latest/)
| [**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 support
GPJax was founded by [Thomas Pinder](https://github.com/thomaspinder). Today,
the maintenance of GPJax is undertaken by [Thomas
Pinder](https://github.com/thomaspinder) and [Daniel
Dodd](https://github.com/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](https://github.com/JaxGaussianProcesses/GPJax/blob/master/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).
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.
# Supported methods and interfaces
## Notebook examples
> - [**Conjugate Inference**](https://gpjax.readthedocs.io/en/latest/examples/regression.html)
> - [**Classification with MCMC**](https://gpjax.readthedocs.io/en/latest/examples/classification.html)
> - [**Sparse Variational Inference**](https://gpjax.readthedocs.io/en/latest/examples/uncollapsed_vi.html)
> - [**BlackJax Integration**](https://gpjax.readthedocs.io/en/latest/examples/classification.html)
> - [**Laplace Approximation**](https://gpjax.readthedocs.io/en/latest/examples/classification.html#Laplace-approximation)
> - [**Inference on Non-Euclidean Spaces**](https://gpjax.readthedocs.io/en/latest/examples/kernels.html#Custom-Kernel)
> - [**Inference on Graphs**](https://gpjax.readthedocs.io/en/latest/examples/graph_kernels.html)
> - [**Learning Gaussian Process Barycentres**](https://gpjax.readthedocs.io/en/latest/examples/barycentres.html)
> - [**Deep Kernel Regression**](https://gpjax.readthedocs.io/en/latest/examples/haiku.html)
## Guides for customisation
>
> - [**Custom kernels**](https://gpjax.readthedocs.io/en/latest/examples/kernels.html#Custom-Kernel)
> - [**UCI regression**](https://gpjax.readthedocs.io/en/latest/examples/yacht.html)
## Conversion between `.ipynb` and `.py`
Above examples are stored in [examples](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
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:
```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.
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
```
> **Note**
>
> We advise you create virtual environment before installing:
> ```
> conda create -n gpjax_experimental python=3.10.0
> conda activate gpjax_experimental
> ```
>
> and 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}
}
```
%package help
Summary: Development documents and examples for gpjax
Provides: python3-gpjax-doc
%description help
[](https://codecov.io/gh/JaxGaussianProcesses/GPJax)
[](https://www.codefactor.io/repository/github/jaxgaussianprocesses/gpjax)
[](https://gpjax.readthedocs.io/en/latest/?badge=latest)
[](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://gpjax.readthedocs.io/en/latest/)
| [**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 support
GPJax was founded by [Thomas Pinder](https://github.com/thomaspinder). Today,
the maintenance of GPJax is undertaken by [Thomas
Pinder](https://github.com/thomaspinder) and [Daniel
Dodd](https://github.com/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](https://github.com/JaxGaussianProcesses/GPJax/blob/master/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).
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.
# Supported methods and interfaces
## Notebook examples
> - [**Conjugate Inference**](https://gpjax.readthedocs.io/en/latest/examples/regression.html)
> - [**Classification with MCMC**](https://gpjax.readthedocs.io/en/latest/examples/classification.html)
> - [**Sparse Variational Inference**](https://gpjax.readthedocs.io/en/latest/examples/uncollapsed_vi.html)
> - [**BlackJax Integration**](https://gpjax.readthedocs.io/en/latest/examples/classification.html)
> - [**Laplace Approximation**](https://gpjax.readthedocs.io/en/latest/examples/classification.html#Laplace-approximation)
> - [**Inference on Non-Euclidean Spaces**](https://gpjax.readthedocs.io/en/latest/examples/kernels.html#Custom-Kernel)
> - [**Inference on Graphs**](https://gpjax.readthedocs.io/en/latest/examples/graph_kernels.html)
> - [**Learning Gaussian Process Barycentres**](https://gpjax.readthedocs.io/en/latest/examples/barycentres.html)
> - [**Deep Kernel Regression**](https://gpjax.readthedocs.io/en/latest/examples/haiku.html)
## Guides for customisation
>
> - [**Custom kernels**](https://gpjax.readthedocs.io/en/latest/examples/kernels.html#Custom-Kernel)
> - [**UCI regression**](https://gpjax.readthedocs.io/en/latest/examples/yacht.html)
## Conversion between `.ipynb` and `.py`
Above examples are stored in [examples](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
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:
```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.
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
```
> **Note**
>
> We advise you create virtual environment before installing:
> ```
> conda create -n gpjax_experimental python=3.10.0
> conda activate gpjax_experimental
> ```
>
> and 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}
}
```
%prep
%autosetup -n gpjax-0.6.1
%build
%py3_build
%install
%py3_install
install -d -m755 %{buildroot}/%{_pkgdocdir}
if [ -d doc ]; then cp -arf doc %{buildroot}/%{_pkgdocdir}; fi
if [ -d docs ]; then cp -arf docs %{buildroot}/%{_pkgdocdir}; fi
if [ -d example ]; then cp -arf example %{buildroot}/%{_pkgdocdir}; fi
if [ -d examples ]; then cp -arf examples %{buildroot}/%{_pkgdocdir}; fi
pushd %{buildroot}
if [ -d usr/lib ]; then
find usr/lib -type f -printf "/%h/%f\n" >> filelist.lst
fi
if [ -d usr/lib64 ]; then
find usr/lib64 -type f -printf "/%h/%f\n" >> filelist.lst
fi
if [ -d usr/bin ]; then
find usr/bin -type f -printf "/%h/%f\n" >> filelist.lst
fi
if [ -d usr/sbin ]; then
find usr/sbin -type f -printf "/%h/%f\n" >> filelist.lst
fi
touch doclist.lst
if [ -d usr/share/man ]; then
find usr/share/man -type f -printf "/%h/%f.gz\n" >> doclist.lst
fi
popd
mv %{buildroot}/filelist.lst .
mv %{buildroot}/doclist.lst .
%files -n python3-gpjax -f filelist.lst
%dir %{python3_sitelib}/*
%files help -f doclist.lst
%{_docdir}/*
%changelog
* Mon May 29 2023 Python_Bot - 0.6.1-1
- Package Spec generated