transform() speed for online outlier detection
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Description
Hi there!
I am having very good performances with UMAP on my dataset for outlier detection (Isolation forest after UMAP projection).
However, when I am trying to use only `transform()` on 1 sample after a `fit()` on 500 samples, it takes longer on the single sample setting than on the full data (500).
I saw another issue mentioning the fact that UMAP will just take as much time on the full dataset than one sample, but then it shouldn't be longer on 5000 samples, right? Well, the `transform()` on 5000 samples is in fact much slower than 1 or 500 samples, so I do not fully understand how things work between fit and transform for UMAP.
Could anybody help me on this? I am trying to do some online outlier detection.
Best,
Timon
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 reproducing the reported transform timings after fitting on 500 samples, comparing inputs of 1, 500, and 5000 samples. Use the timing results to trace the relevant UMAP transform path; done means explaining or correcting the unexpected scaling behavior.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- python
- Domain
- machine-learning, performance
- Issue type
- Bug
- Difficulty
- 4/5
- Estimated time
- 3-5 days
- Activity status
- Stale
- Clarity
- Needs clarification
- Newbie friendliness
- 20/100