Coreax

© Crown Copyright GCHQ

Coreax is a library for coreset algorithms, written in JAX for fast execution and GPU support.

About Coresets

For $n$ points in $d$ dimensions, a coreset algorithm takes an $n \times d$ data set and reduces it to $m \ll n$ points whilst attempting to preserve the statistical properties of the full data set. The algorithm maintains the dimension of the original data set. Thus the $m$ points, referred to as the coreset, are also $d$-dimensional.

The $m$ points need not be in the original data set. We refer to the special case where all selected points are in the original data set as a coresubset.

Some algorithms return the $m$ points with weights, so that importance can be attributed to each point in the coreset. The weights, $w_i$ for $i=1,...,m$, are often chosen from the simplex. In this case, they are non-negative and sum to 1: $w_i >0$ $\forall i$ and $\sum_{i} w_i =1$.

Please see the documentation for some in-depth examples.

Example applications

Choosing pixels from an image

In the example below, we reduce the original 180x215 pixel image (38,700 pixels in total) to a coreset approximately 20% of this size. (Left) original image. (Centre) 8,000 coreset points chosen using kernel thinning. (Right) 8,000 points chosen randomly. Run benchmark/david_benchmark.py to replicate.

Original	Coreset	Random

Video event detection

Here we identify representative frames such that most of the useful information in a video is preserved. Run examples/pounce.py to replicate.

Original	Coreset

Setup

Install Coreax from PyPI by adding coreax to your project dependencies or running

pip install coreax

Coreax uses JAX. It installs the CPU version by default, but if you have a GPU or TPU, see the JAX installation instructions for options available to take advantage of the power of your system. For example, if you have an NVIDIA GPU on Linux, add jax[cuda12] to your project dependencies or run

pip install jax[cuda12]

There are additional dependency groups:

• dev includes all the tools and packages a developer of Coreax might need (includes all the below groups).
• doc is for compiling the Sphinx documentation;
• tests is used to run the tests (includes benchmark);
• benchmark is required to run benchmarking (includes example);
• example contains all dependencies for the example scripts;

Note that the test and dev dependencies include opencv-python-headless, which is the headless version of OpenCV and is incompatible with other versions of OpenCV. If you wish to use an alternative version, remove opencv-python-headless and select an alternative from the OpenCV documentation.

Should the installation of Coreax fail, you can see the versions used by the Coreax development team in uv.lock. You can transfer these to your own project as follows. First, install UV. Then, clone the repo from GitHub. Next, run

uv export --format requirements-txt

which will generate a requirements.txt. Install this in your own project before trying to install Coreax itself,

pip install -r requirements.txt
pip install coreax

Release cycle

We anticipate two release types: feature releases and security releases. Security releases will be issued as needed in accordance with the security policy. Feature releases will be issued as appropriate, dependent on the feature pipeline and development priorities.