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

Key dates

PyPI data
Data sourced directly from PyPI's database.
  • Released:
Latest release

1 maintainer

PyPI data
Data sourced directly from PyPI's database.
Avatar for TomHilder from gravatar.com TomHilder

Credits

Author: Tom Hilder

Requires

Python >=3.10

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