pymc-devs/pymc

Check idata dims/coords for consistency before sampling begins

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#7,891 建立於 2025年8月8日

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 (11 留言) (1 反應) (0 負責人)Python (7,926 star) (1,902 fork)batch import
beginner friendlyhelp wantedmaintenanceshape problem

描述

Description

This is a painful gotcha I get hit by once in a while. If you have inconsistencies between the model coords and variable shapes, PyMC will still happy sample (often for hours, ask me how I know), then error out when it gets to the final step of building the idata.

I don't think there needs to be complex logic to validate static shapes etc, I'm thinking we could do a smoke test on the model initial point (or a single prior draw) to make sure it can be converted to an idata, and raise before sampling if not.

貢獻者指南

技術棧
python
領域
backend
議題類型
feature
難度面向新貢獻者的預計實作難度，1 表示很小改動，5 表示專家級工作。
3
預計時間有經驗貢獻者完成調查、實作、測試並準備 pull request 的粗略時間範圍。
1-2 days
活動狀態議題目前的可參與程度：新鮮、活躍、陳舊、阻塞或等待維護者輸入。
active
清晰度議題是否清楚說明預期改動、驗收標準和下一步。
clear
前置要求
basic PyMC knowledgeInferenceData structure understanding
新手友善度1-100 的估計分數，表示該議題對首次貢獻者的友善程度。
65
研究方向
Examine the PyMC sampling pipeline, particularly the `sample()` function and how InferenceData is created. Look for where the model initial point is handled. The goal is to add a validation step that attempts a single prior draw and checks it can be converted to an idata before sampling begins. Review the discussion in issue #7891 for any additional constraints or proposed approaches. Consider the existing test suite for sampling to see where to add a test.
Check idata dims/coords for consistency before sampling begins · pymc-devs/pymc#7891 | Good First Issue