Add a seed parameter for repeatability about umap HOT 7 CLOSED

Well that was a lot more work than I intended. For the record (since others may face this, and so I will remember in the future) the issue is that numba very cleverly swaps out np.random calls for something lower level (to avoid roundtrips back to python I presume), and this does (may?) not play nice with setting a random seed for numpy. Once I worked out what the issue was and rewrote everything to deal with that the issue resolved itself nicely and we get something repeatable. I believe setting random_state now works and should provide consistent embeddings (with a consistent random state).

from umap.

I agree, that would make a lot of sense. Ideally it isn't too hard, but has some quirks given how I am currently handling random number generation. It is certainly on my list of things to do (which is unfortunately long).

from umap.

It might be a good idea to publish a roadmap, community will be able to contribute!

from umap.

Sounds like a good plan -- any suggestions for where and how best to do that?

from umap.

Well, an issue on github will do. People will add comments, and you will be able to update the issue after each release.

from umap.

Basic random seed support is now in place via the random_seed parameter which takes an int. Ideally things would work a little differently as per standard sklearn with a random_state that supports more input types (e.g. numpy random states) but that will take a little thought as to the best way to do that.

Edit: and it doesn't actually achieve the desired result :-( Not sure why though. It should provide slightly more consistency though.

from umap.

Okay, that helps more. I have a nagging feeling there will be more minor things like the eigenvector solver to track down if I want to truly eliminate variability.

from umap.