Robust heteroskedastic matrix factorisation in JAX.
Project description
Robusta-HMF
jax implementation of robust heteroskedastic matrix factorisation. Robusta like the coffee bean, get it?
Installation
Easiest is from PyPI either with pip (CURRENTLY NOT ON PYPI YET)
pip install robusta-hmf
or uv (recommended)
uv add robusta-hmf
Or, you can clone and build from source
git clone git@github.com:TomHilder/robusta-hmf.git
cd robusta-hmf
pip install -e .
Usage
TODO
Citation
TODO
Help
TODO
TODOs
- Port Hogg's existing code and make sure it builds/installs*
- Port to
equinox* - Type checking with
mypy* - Add dependency injection for the following:*
- Optimisation method, IRLS, SGD (directly optimising objective, see robust_hmf_notes.pdf)
- Potentially
daskand batching support for SGD
- Potentially
- w-steps. Each w-step corresponds to a different likelihood. Hogg's is Cauchy. We should let this flexible*
- Initialisation.
- Re-orientation. Can easily imagine wanting something cheaper for really big data.
- Optimisation method, IRLS, SGD (directly optimising objective, see robust_hmf_notes.pdf)
- Add a save and restore method. Probably avoid pickle/dill and instead encapsulate info in serialisable way and then rebuild model upon loading
- Eh maybe, maybe not
- Tests!*
- CI, automated tests, automated relases, and PyPI*
- Relax version requirements since uv by default is newest everything
(*) = Priority
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