Partial fit / Incremental UMAP
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Description
Hi @lmcinnes,
There has been quite an exciting about UMAP in scRNA-Seq area recently. However, fitting larger data-sets might be an issue depending on amount of available memory. I would love to see a 'partial_fit' method (akin to one available in scikit's incremental PCA) in UMAP, so that data could be lazily loaded and fitted. I do realize that this might be non-trivial, if not theoretically infeasible, but your recent implementation of 'transform' method got my hopes high. It would be nice to have your thoughts on this.
Thanks.
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 reading UMAP's existing transform implementation and the behavior of scikit-learn's IncrementalPCA. Investigate whether a partial_fit method can support lazily loaded, larger datasets without violating UMAP's constraints; the work is done when the feasibility and expected behavior are established and implemented if practical.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- python
- Domain
- machine-learning
- Issue type
- Feature
- Difficulty
- 5/5
- Estimated time
- Over a week
- Activity status
- Stale
- Clarity
- Needs clarification
- Newbie friendliness
- 20/100