What's New • uwot

A place for me to put the old less-structured updates that I post at the top of the README.md file for the package. When they aren’t new any more, they will get moved here. You should look at the Changelog for fuller details.

April 21 2024 As ordained by prophecy, version 0.2.2 of uwot has been released to CRAN. RSpectra is back as a main dependency and I thought I had worked out a clever scheme to avoid the failures seen in some installations with the irlba/Matrix interactions. This releases fixes the problem on all the systems I have access to (including GitHub Actions CI) but some CRAN checks remain failing. How embarrassing. That said, if you have had issues, it’s possible this new release will help you too.

April 18 2024 Version 0.2.1 of uwot has been released to CRAN. Some features to be aware of: RcppHNSW and rnndescent are now supported as optional dependencies. If you install and load them, you can use them as an alternative to RcppAnnoy in the nearest neighbor search and should be faster. Also, a new umap2 function has been added, with updated defaults compared to umap. Please see the updated and new articles on HNSW, rnndescent, working with sparse data and umap2. I consider this worthy of moving from 0.1.x to 0.2.x, but in the interests of full disclosure, on-going irlba problems has caused a CRAN check failure, so we might be onto 0.2.2 sooner than I’d like.

November 26 2023 Happy 1 million CRAN downloads to uwot. To celebrate (actually it’s just a coincidence) I have reorganized the horror-show that was the sprawling README into actual articles, via the fantastic pkgdown package. Find it at https://jlmelville.github.io/uwot/.

June 28 2023 Version 0.1.16 has been released to CRAN. This is a very minor tweak to 0.1.15 to further support the new release of RcppAnnoy.

June 26 2023 Version 0.1.15 has been released to CRAN. This is to support a new release of RcppAnnoy, but there are also some bug fixes and other minor improvements. There are some new functions: optimize_graph_layout will carry out the UMAP optimization step on a sparse similiarity matrix, e.g. the output of similarity_graph. simplicial_set_union and simplicial_set_intersect provide ways to merge different views of the same data into one sparse similiarity matrix. As usual, NEWS.md has all the details.

August 22 2022 Just when you least expected it, version 0.1.14 has been released to CRAN (the NEWS file on CRAN calls it 0.1.13.9000 because I forgot to update that file, but let’s keep that amongst ourselves). This release includes a bug fix for umap_transform when you use external nearest neighbors and new function similarity_graph, to support extracting just the high dimensional fuzzy simplicial set.

August 16 2022 Version 0.1.13 has been released to CRAN (0.1.12 was a failed submission). Among other things you can now pass your own nearest neighbors data in sparse matrix form. Also there is an option to reproduce relative cluster density by approximating the densMAP method. See the NEWS page for more.

December 12 2021 Version 0.1.11 has been released to CRAN. It is now possible to get reproducible results (for a given value of set.seed) when running the optimization step with multiple threads (n_sgd_threads greater than 1). You may need to increase n_epochs to get similar levels of convergence. To run in this mode, set batch = TRUE. Thanks to Aaron Lun who came up with the design for this and also implemented it in his umappp C++ library. See NEWS.md for other changes.

December 15 2020 Version 0.1.10 has been released to CRAN. This is mainly to maintain compatibility with RcppAnnoy, but also a small change was made to avoid it grinding away pointlessly in the presence of NA values, based on an observation by David McGaughey on Twitter (which I can no longer link to).

November 15 2020 Version 0.1.9 has been released to CRAN. The main addition is support for the Pearson correlation. Also, a slight license change from GPL-3 to GPL-3 or later.

August 1 2020 New metric supported: Pearson correlation (with metric = "correlation"). This should give similar results to the Python UMAP (and sklearn) implementation of the correlation metric.

March 16 2020 A new version (0.1.8) is on CRAN. This is a minor release in terms of features, but you can now export the UMAP graph (https://github.com/jlmelville/uwot/issues/47), and there are some bug fixes for: loading Annoy indexes (https://github.com/jlmelville/uwot/issues/31), reproducibility across platforms (https://github.com/jlmelville/uwot/issues/46) and we no longer use RcppParallel for the multi-threading support, which should lead to fewer installation problems.

March 4 2020 I had to cancel my submission of version 0.1.7 to CRAN because of a broken example in a library using uwot. In the mean time I have switched to using std::thread rather than tinythread++.

March 1 2020 Version 0.1.6 was rejected from CRAN due to undefined behavior issues that originate from RcppAnnoy and RcppParallel. I am hopeful that the Annoy behavior is fixed and a suitable version of RcppAnnoy will be released onto CRAN eventually. The RcppParallel issues originate with the use of tbb and seems much harder to deal with. As there is no way to use RcppParallel without tbb yet, I am temporarily replacing the use of RcppParallel with just a subset of the code needed to run parallel for loops with the tinythread++ library.

December 4 2019 Version 0.1.5 released on CRAN. This fixes a couple of crash bugs, including one where the R API was being called from inside a thread. This may have been causing the issues seen by users of monocle and seurat.

September 23 2019 Version 0.1.4 released on CRAN. This ensures compatibility with RcppAnnoy 0.0.13 when using load_uwot.

April 6 2019. uwot is now on CRAN. Also, some minor-to-horrible bugs in the lvish perplexity routine have been fixed.

For visualization purposes, it seems reasonable to use the old PRNG (pcg_rand = FALSE), along with multiple threads during SGD (n_sgd_threads = "auto"), and the UMAP gradient approximation (approx_pow = TRUE), which combined will show a very noticeable speed up during optimization. I have added a new parameter, fast_sgd, which if set to TRUE, sets these options for you.