Notebook updates overview

Welcome to pymc-examples. This wiki page is closely tied to the example updating project. You should also consider reading the contributing guide

To be in a column different than "To Do", a notebook must follow all the recommendations in its respective section in this wiki.

General updates

• Use matplotlib style arviz-darkgrid. If you want to use a different style, explain why and make sure colorblind people have no trouble distinguishing the colors in the palette.
• Use the new numpy Generator instead of the global state generators. We want to follow the recommendations on numpy docs about random number generation.
• Check for other outdated code. If you are lucky you can still find a try except from the times of python2 & python3 compatibility.
• Use proper priors wherever possible. You can find some guidance on sensible prior defaults at this other wiki of the Stan group.
• Only use pm.Deterministic is you are interested in the variable. If it's not used afterwards in the notebook, you should not store it.
• Unless specifically working on convergence issues, run multiple chains and make sure there are no clear convergence issues.
• Check for deprecated PyMC3 arguments when constructing models.

ArviZ related

• Use named coords and dims already within PyMC3. If they are not used, explain why in the respective issue.
• Use return_inferencedata=True. If it can't be used, consider opening an issue on the main pymc3 repo so we can fix this before making InferenceData the default return or comment in the respective issue.
• Add data to InferenceData as it's generated. You can use InferenceData.extend, InferenceData.add_groups, az.from_pymc3 and az.from_pymc3_predictions or combinations of these.
• Use xarray and label based indexing unless a clear limitation on ArviZ/xarray side limit usability or a bug is triggered. Take a look at this example for a quick overview of xarray capabilities when applied to PyMC3 results.
  • A key advantage of xarray is automatic broadcasting. Use this feature to work with samples until the very end.
  • You may also want to consider creating a "trace" for convenience and code conciseness. You can do so from an InferenceData called idata with trace = idata.posterior optionally also add .stack(sample=("chain", "draw"))
  • Don't be afraid to submit the PR early (again, once you have started working, it's never too early to submit a PR) and ask for help.

Best practices

Make sure the notebook follows all the recommendations in "General Updates" and "ArviZ" section.

v4

Adapt and run the notebook on pymc3>=4.0. See the upgrade guide for guidance on upgrading (page still in construction, for now follow pymc3#4515)