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Description:

coreax 0.2.1

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×d data set and
reduces it to m≪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, wi for i=1,...,m, are often
chosen from the simplex. In this case, they are non-negative and sum to 1:
wi>0 ∀i and ∑iwi=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these versions may be rather outdated, although we endeavour to avoid versions with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 recombination, as means
to reducing a large dataset whilst maintaining properties of the underlying distribution.