Coreax

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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 Stein kernel herding, with point size a function of weight. (Right) 8,000 points chosen randomly. Run examples/david_map_reduce_weighted.py to replicate.

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

Before installing coreax, make sure JAX is installed. Be sure to install the preferred version of JAX for your system.

Install JAX noting that there are (currently) different setup paths for CPU and GPU use:

$ python3 -m pip install jax

Install Coreax:

$ python3 -m pip install coreax

Optionally, install additional dependencies required to run the examples:

$ python3 -m pip install coreax[test]

Should the installation fail, try again using stable pinned package versions. Note that these versions may be rather outdated, although we endeavour to avoid versions with known vulnerabilities. To install Coreax:

$ python3 -m pip install --no-dependencies -r requirements.txt

To run the examples, use requirements-test.txt instead.

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.

Coming soon

Some features coming soon include:

• Coordinate bootstrapping for high-dimensional data.
• Other coreset-style algorithms, including kernel thinning and recombination, as means to reducing a large dataset whilst maintaining properties of the underlying distribution.