Update MultiSignalModel API to allow for time-varying ascertainment
Nobody has claimed this yet.
- Dominant language
- Python
- Stars
- 29
- Forks
- 9
- Avg merge
- 9d 9h
- Merged PRs (30d)
- 8
Description
The current Ascertainment implementations assume a scalar ascertainment but time-varying ascertainment specifies a temporal process.
MultiSignalModel computes the shared model-axis length and first_day_dow, but calls each registered AscertainmentModel.sample() without passing either value. This prevents ascertainment models with temporal processes from producing full-axis, calendar-aligned trajectories, despite the AscertainmentModel.sample(**kwargs) contract allowing model-context arguments.
MultiSignalModel should pass n_timepoints and first_day_dow when sampling ascertainment models and include ascertainment components when determining whether obs_start_date is required. Existing scalar ascertainment implementations already accept **kwargs, so this can be backward-compatible.
Contributor guide
No contributing guide indexed for this repository
First steps
- Read the whole issue, then the project's contributing guide.
- Comment on the issue to say you are picking it up — it saves two people doing the same work.
- Fork the repository and make your change on a branch.
- Open a pull request that references the issue number.
Research direction
Start at MultiSignalModel and trace how it computes the shared model-axis length, first_day_dow, and obs_start_date requirements before calling AscertainmentModel.sample(). Check the existing scalar ascertainment implementations and their tests, then verify that temporal models receive n_timepoints and first_day_dow while scalar implementations remain compatible.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- python
- Domain
- backend-api-design
- Issue type
- Feature
- Difficulty
- 3/5
- Estimated time
- 1-2 days
- Activity status
- Active
- Clarity
- Mostly clear
- Newbie friendliness
- 68/100