Precomputed for forward + Non-precomputed metric for inverse_transform
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Description
Hello,
Thank you for this awesome library :)
I would like to use a custom metric with UMAP. It's quite fast to compute this metric in a batched way, however it's very slow to just pass it to UMAP which evaluates it at lots of points pairwise unbatched-ly.
I realise I can compute the distance matrix upfront and pass it to UMAP to use, however I would like to be able to use the inverse_transform, which requires a non-precomputed metric.
Is there some way to use the precomputed metric for the forward transform and then change some property to make UMAP use a custom metric for the inverse_transform?
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 reviewing UMAP's forward-transform and inverse_transform entry points, focusing on how each selects and uses the metric. Done means the forward path can use a precomputed metric while inverse_transform can use a non-precomputed custom metric, with the supported usage documented and covered by tests.
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
- 25/100