pymc-devs/pymc

Doc string terminology for unnamed distributions created via .dist()

Open

#6,095 opened on Sep 2, 2022

View on GitHub
 (9 comments) (0 reactions) (0 assignees)Python (1,902 forks)batch import
docshelp wanted

Repository metrics

Stars
 (7,926 stars)
PR merge metrics
 (Avg merge 11d 6h) (12 merged PRs in 30d)

Description

When updating the docstrings for pm.AR() distribution class (https://github.com/pymc-devs/pymc/pull/6080), it became clear that we have no clean, unified way to refer to the "unnamed distribution" arguments taken by some distributions (e.g., time series, pm.Mixture, etc.). @ricardoV94 suggested adopting something like the "tensor_like" nomenclature used elsewhere. This issue is intended to start discussion about whether that's a good idea and, if so, what a good term might be. While we're at it, we might try and figure out if there are any other argument types that we might want to name.

Contributor guide

Research direction
Review the discussion in this issue and the linked PR #6080, particularly @ricardoV94's suggestion to use a 'tensor like' terminology. Examine how 'tensor like' is currently used in PyMC's docstrings and codebase. Propose a consistent term for unnamed distributions (e.g., in pm.AR, pm.Mixture) and define it clearly. Consider other ambiguous argument types that might benefit from similar naming.
Tech stack
python
Domain
documentation
Issue type
Documentation
DifficultyEstimated implementation difficulty for a new contributor, from 1 for very small changes to 5 for expert-level work.
3
Estimated timeA rough time range for an experienced contributor to investigate, implement, test, and prepare a pull request.
1-3 hours
Activity statusHow available the issue appears right now: fresh, active, stale, blocked, or waiting on maintainer input.
Stale
ClarityHow clearly the issue explains the expected change, acceptance criteria, and next step.
Needs investigation
Prerequisites
basic understanding of PyMCfamiliarity with distribution docstrings
Newbie friendlinessA 1-100 score estimating how approachable this issue is for first-time contributors.
40