[FEA] KmeansPlusPlus Initialization Should Be Weighted
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- Cuda
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Description
When sample weights are passed, the init sample chosen by KmeansPlusPlus should be weighted. Currently it is uniformly random. So if the user supplies weights, those are not taken into account in the init sample. Corresponding line in scikit-learn where the probabilities are scaled:
https://github.com/scikit-learn/scikit-learn/blob/ceeffc5d6e3494d4973472e7690ec8bce2f83b4e/sklearn/cluster/_kmeans.py#L263
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 cuVS's KmeansPlusPlus initialization implementation and compare its initial-sample selection with the linked scikit-learn code, which scales probabilities using sample weights. Confirm how sample weights enter the clustering path, then verify that weighted inputs affect the initial sample selection while unweighted inputs retain current behavior.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- scikit-learn
- Domain
- machine-learning
- Issue type
- Feature
- Difficulty
- 4/5
- Estimated time
- 3-5 days
- Activity status
- Stale
- Clarity
- Mostly clear
- Newbie friendliness
- 42/100