Robusta-HMF

jax implementation of robust heteroskedastic matrix factorisation. Robusta like the coffee bean, get it?

Installation

Easiest is from PyPI either with pip

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 dask and batching support for SGD
  • 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.
• 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