How can we apply the Gower metric to UMAP?

Nobody has claimed this yet.

From my rough work, if we let the custom metric be the Gower metric, the distance matrix for all points in the dataset can be computed for both numerical and categorical data. However, it seems this is simplest when we only use the Gower metric for precomputing the distance matrix for the entire dataset, i.e. with

`umap.UMAP(metric="precomputed").fit_transform(precomputed_distances)`

While it is possible to compute the distance matrix for a dataset beforehand, using metric="precomputed" is inappropriate towards a further transform on the embedding for new data, which is needed for inference, since it doesn't allow for a .transform on the embedding for new data.

I think what I would want is to have a metric which can be plugged into umap.UMAP() such that this metric can handle both numerical and categorical features.

From the examples in the doc, it seems the metric is used for computing the distances between each pair of points separately (i.e. such a metric returns distance(point1, point2)),

**I'm wondering how one could use the Gower distance metric for both fitting against training data and transforming on test data?**

Or is transform for mixed datasets currently still unsupported despite the above?

This is important for me since I'm trying to use UMAP for dimensionality reduction on complex mixed datasets for inference/classification.

Start with the umap.UMAP(metric="precomputed").fit_transform entry point and the documented custom-metric examples. Check how metric evaluation differs between fitting and transforming new data; done would be a documented supported path or a clearly stated limitation for mixed numerical and categorical data.

Written by the indexing model from the issue text.