Comments (4)
Hi Maarten
That seems odd. To answer the question about synchronizing the parameters - the object 'umap.defaults' has all the arguments and values. The argument names match with those in umap-learn, so you can construct a python command setting the arguments one by one. But it sounds you have done that already.
The python group released a new version of umap-learn
recently. I haven't checked yet how well the R package interfaces with that. It's a to-do item and I'll update if necessary. Curious, what version are you using?
(You closed this now - did you manage to resolve it?)
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Thanks for your message! I made an embarrassing mistake in transferring the raw data over from R to python: the sample annotation got shuffled during a data merging step and as such samples were not correctly labeled in python's UMAP plot. I finally figured out that this was the problem after having painstakingly checked all other steps (was assuming my rusty python skills were the source of the problem). The UMAPs looks highly similar now, just rotated about 90 degrees from each other.
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To quantitate the semblance between the two implementations on my data, I combined the 4 coordinate axes (2 from each UMAP) into one matrix and did a PCA on this matrix. I found the first two PCs to describe over 99% of all variance, consistent with rotational symmetry.
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Glad it worked out. Good luck with your analysis
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Related Issues (20)
- predict() on a umap object with n_components=1 gets two errors -- Looks like missing drop=F HOT 2
- Failed creating initial embedding; using random embedding instead HOT 3
- Intel MKL FATAL ERROR HOT 3
- Add support for umap-learn 0.5 HOT 4
- Sparse Matrix support HOT 4
- is there any spark version implementations? HOT 2
- missing value where TRUE/FALSE needed HOT 3
- Problem with using custom metric HOT 2
- umap() produces matrix instead of S3 object HOT 2
- method = "python" does not work HOT 1
- when random_state is set automatically in config, it is not sufficient for reproducibility HOT 1
- Citing the package HOT 1
- Type error in optimize_embedding HOT 3
- transforming new data to an embedding HOT 3
- Error with n_components=1 HOT 3
- Number of threads HOT 3
- Allow for supervised/semi-supervised dimension reduction with labels HOT 1
- min_dist not updating with Python backend HOT 3
- predict() generates different predictions if called with multiple points at once versus called with each point individually HOT 7
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