Skip to contents

uwot

An R implementation of the Uniform Manifold Approximation and Projection (UMAP) method for dimensionality reduction of McInnes et al. (2018). Also included are the supervised and metric (out-of-sample) learning extensions to the basic method. Translated from the Python implementation.

News

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.

Installing

From CRAN

From github

uwot makes use of C++ code which must be compiled. You may have to carry out a few extra steps before being able to build this package:

Windows: install Rtools and ensure C:\Rtools\bin is on your path.

Mac OS X: using a custom ~/.R/Makevars may cause linking errors. This sort of thing is a potential problem on all platforms but seems to bite Mac owners more. The R for Mac OS X FAQ may be helpful here to work out what you can get away with. To be on the safe side, I would advise building uwot without a custom Makevars.

install.packages("devtools")
devtools::install_github("jlmelville/uwot")

Example 

library(uwot)

# umap2 is a version of the umap() function with better defaults
iris_umap <- umap2(iris)

# but you can still use the umap function (which most of the existing 
# documentation does)
iris_umap <- umap(iris)

# Load mnist from somewhere, e.g.
# devtools::install_github("jlmelville/snedata")
# mnist <- snedata::download_mnist()

mnist_umap <- umap(mnist, n_neighbors = 15, min_dist = 0.001, verbose = TRUE)
plot(
  mnist_umap,
  cex = 0.1,
  col = grDevices::rainbow(n = length(levels(mnist$Label)))[as.integer(mnist$Label)] |>
    grDevices::adjustcolor(alpha.f = 0.1),
  main = "R uwot::umap",
  xlab = "",
  ylab = ""
)

# I recommend the following optional packages
# for faster or more flexible nearest neighbor search:
install.packages(c("RcppHNSW", "rnndescent"))
# for faster spectral initialization
install.packages("RSpectra")

# Installing RcppHNSW will allow the use of the usually faster HNSW method:
mnist_umap_hnsw <- umap(mnist, n_neighbors = 15, min_dist = 0.001, 
                        nn_method = "hnsw")
# nndescent is also available
mnist_umap_nnd <- umap(mnist, n_neighbors = 15, min_dist = 0.001, 
                       nn_method = "nndescent")
# umap2 will choose HNSW by default if available
mnist_umap2 <- umap2(mnist)
MNIST UMAP
MNIST UMAP

Documentation

https://jlmelville.github.io/uwot/. For more examples see the get started doc. There are plenty of articles describing various aspects of the package.

License

GPLv3 or later.

Citation

If you want to cite the use of uwot, then use the output of running citation("uwot") (you can do this with any R package).

See Also