probabilistic matrix factorization

[OriolAbril]

File: https://github.com/pymc-devs/pymc-examples/blob/main/examples/case_studies/probabilistic_matrix_factorization.ipynb Reviewers: @colcarroll

Known changes needed

Changes listed in this section should all be done at some point in order to get this notebook to a "Best Practices" state. However, these are probably not enough! Make sure to thoroughly review the notebook and search for other updates.

General updates

• Use numpy Generator. See also https://numpy.org/doc/stable/reference/random/index.html?highlight=random%20sampling%20numpy%20random#quick-start

Changes for discussion

Changes listed in this section are up for discussion, these are ideas on how to improve the notebook but may not have a clear implementation, or fix some know issue only partially.

ArviZ related

• Use ArviZ and xarray for postprocessing. This will probably be challenging. I'd recommend familiarizing with xarray before working on that. Some ideas:
  • _norms in code cell 23 looks like it could be replaced by xr.apply_ufunc (using input_core_dims)

Notes

Exotic dependencies

None

Computing requirements

Model samples in roughly 1 hour

[OriolAbril]

Note: the "good first issue" label only applies to updating the notebook from To Do to General Updates.

[chiral-carbon]

Hi @OriolAbril , can I take this issue up? I am going through the probabilistic matrix factorization notebook at the moment and thought I could attempt to make the recommended changes.

[OriolAbril]

Definitely, I'll assign it to you. Let me know if you have any doubts, especially on xarray which can be challenging even for seasoned pymc3 users

[chiral-carbon]

sure, thanks! I'll get back to you on this.

[percevalve]

Looking into this one.