nf-core / nf-core/deepmodeloptim
[nf-tests] Assure reproducibility
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Description
random sampling
There are many random sampling methods, including random.sample, and other low level within library sampling.
Setting random.seed(0) at the very beginning of a script won't work.
set operations
Sets are unordered, consequently everything handled with sets are not gonna follow a certain order, and this is not controllable.
However, set operations are very efficient.
Alternatives?
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
No files, tests, or entry points are named. Begin by identifying every random-sampling and set-operation path covered by nf-tests, then compare reproducibility alternatives for each. Done means the project has a documented, agreed approach that produces repeatable results without removing the efficiency benefits of set operations.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- python
- Domain
- data-engineering, testing-qa
- Issue type
- Feature
- Difficulty
- 5/5
- Estimated time
- Over a week
- Activity status
- Stale
- Clarity
- Needs clarification
- Newbie friendliness
- 25/100