scikit-learn / scikit-learn/scikit-learn
Automatically force_all_finite=False in non-initial warm_start runs
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- Dominant language
- Python
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Description
For estimators that have a warm_start option, we generally expect that the same data is passed in for each call to fit as was done in the first. I think we can save a little on runtime by using check_array with force_all_finite=False in the case that warm_start=True and the model has already been fitted.
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
Start by locating estimators that support warm_start and the check_array calls used during fit. Trace the initial and subsequent fit paths, then add focused tests showing the intended finite-value checking behavior for repeated warm-start fits and confirming existing initial-fit behavior remains unchanged.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- python
- Domain
- machine-learning, performance
- Issue type
- Feature
- Difficulty
- 4/5
- Estimated time
- 3-5 days
- Activity status
- Stale
- Clarity
- Mostly clear
- Newbie friendliness
- 35/100