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.
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.
install.packages("uwot")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")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)
https://jlmelville.github.io/uwot/. For more examples see the get started doc. There are plenty of articles describing various aspects of the package.
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).
- The UMAP reference implementation and publication.
- The UMAP R package
(see also its github repo), predates
uwot's arrival on CRAN. - Another R package is umapr, but it is no longer being maintained.
- umappp is a full C++ implementation, and yaumap provides an R wrapper. The batch implementation in umappp are the basis for uwot's attempt at the same.
uwotuses the RcppProgress package to show a text-based progress bar whenverbose = TRUE.
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