statsmodels / statsmodels/statsmodels
use RandomState for random numbers
Open
Nobody has claimed this yet.
design
FAQ
- Dominant language
- Python
- Stars
- 11.6k
- Forks
- 3.6k
- Avg merge
- 7h 37m
- Merged PRs (30d)
- 96
Description
We should follow sklearn's example and use RandomState for any random variable or random process generation.
makes it easier to initialize with seed and will work better with parallel processing.
Contributor guide
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
Search the Python codebase for random-number and random-process generation, then identify which call sites need a consistent RandomState entry point. Review how seeding and parallel processing are currently handled; done means the affected generation paths can be initialized reproducibly and work correctly with parallel execution.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- python
- Domain
- data
- Issue type
- Refactor
- Difficulty
- 5/5
- Estimated time
- Over a week
- Activity status
- Stale
- Clarity
- Needs clarification
- Newbie friendliness
- 25/100