one dimensional data: Should an error be raised?
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Description
I noticed that you can feed a dataframe with only one feature to `UMAP.fit` and it gives you a two dimensional embedding back.
I know that the intended use case for UMAP is high-dimensions, but to be error prove, would it be good to require 3 and more features in case a two dimensional representation is requested?
If I read the [How UMAP works](https://umap-learn.readthedocs.io/en/latest/how_umap_works.html) section I guess this behaviour is possible due to the graph based algorithm.
Contributor guide
First steps
- Read the whole issue, then the project's contributing guide.
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- Open a pull request that references the issue number.
Research direction
Start by reading the UMAP.fit entry point and the linked “How UMAP works” documentation to understand the one-feature, two-dimensional case. Determine whether this input should be rejected or remain supported, then define the expected behavior and corresponding validation coverage before proposing a change.
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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