How to handle categorical variables in Parametric UMAP?
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Description
First of all, thank you so much for releasing the Parametric UMAP it works like dream. now I am able to project the unseen data in just a sec and it really helps with my use case.
Currently, I have a mixed dataset and I simply use the one-hot for categorical variables, I have read some old posts before parametric UMAP is available, the approach is to separate the numerical and categorical features into two and one use jaccard/dice distance metrics then combine together. I am wondering if that's still the best way to go with the Parametric UMAP. or if there is a way that we can fit the embedding through umap_loss.
thanks in advance!
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 the Parametric UMAP entry point and the umap_loss mentioned in the issue, then check the existing handling of mixed numerical and categorical data. The issue does not name files or tests; done would require a decided, documented approach for categorical variables in Parametric UMAP.
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