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https://github.com/jlmelville/uwot
An R package implementing the UMAP dimensionality reduction method.
https://github.com/jlmelville/uwot
dimensionality-reduction r umap
Last synced: 6 days ago
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An R package implementing the UMAP dimensionality reduction method.
- Host: GitHub
- URL: https://github.com/jlmelville/uwot
- Owner: jlmelville
- License: gpl-3.0
- Created: 2018-06-12T14:35:45.000Z (over 6 years ago)
- Default Branch: master
- Last Pushed: 2024-09-07T16:03:10.000Z (about 2 months ago)
- Last Synced: 2024-10-29T20:25:21.731Z (7 days ago)
- Topics: dimensionality-reduction, r, umap
- Language: R
- Homepage: https://jlmelville.github.io/uwot/
- Size: 60.1 MB
- Stars: 318
- Watchers: 21
- Forks: 31
- Open Issues: 22
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# uwot
[](https://github.com/jlmelville/uwot/actions)
[](https://ci.appveyor.com/project/jlmelville/uwot)
[](https://app.codecov.io/github/jlmelville/uwot?branch=master)
[](https://cran.r-project.org/package=uwot)
[](https://cran.r-project.org/package=uwot)
[](https://www.r-pkg.org/pkg/uwot)
[](https://www.r-pkg.org/pkg/uwot)
[](https://github.com/jlmelville/uwot)
An R implementation of the
[Uniform Manifold Approximation and Projection (UMAP)](https://arxiv.org/abs/1802.03426)
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](https://github.com/lmcinnes/umap).
## News
*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](https://cran.r-project.org/package=rnndescent) and
[rnndescent](https://cran.r-project.org/package=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](https://jlmelville.github.io/uwot/articles/hnsw-umap.html),
[rnndescent](https://jlmelville.github.io/uwot/articles/rnndescent-umap.html),
[working with sparse data](https://jlmelville.github.io/uwot/articles/sparse-data-example.html)
and [umap2](https://jlmelville.github.io/uwot/articles/umap2.html). 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](https://github.com/jlmelville/uwot/issues/115) has caused a
CRAN check failure, so we might be onto 0.2.2 sooner than I'd like.
## Installing
### From CRAN
```R
install.packages("uwot")
```
### 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](https://cran.r-project.org/bin/windows/Rtools/) and ensure
`C:\Rtools\bin` is on your path.
**Mac OS X**: using a custom `~/.R/Makevars`
[may cause linking errors](https://github.com/jlmelville/uwot/issues/1).
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](https://cran.r-project.org/bin/macosx/RMacOSX-FAQ.html#Installation-of-source-packages)
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`.
```R
install.packages("devtools")
devtools::install_github("jlmelville/uwot")
```
## Example
```R
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"))
library(RcppHNSW)
library(rnndescent)
# 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)
```
## Documentation
. For more examples see the
[get started](https://jlmelville.github.io/uwot/articles/uwot.html) doc.
There are plenty of [articles](https://jlmelville.github.io/uwot/articles/index.html)
describing various aspects of the package.
## License
[GPLv3 or later](https://www.gnu.org/licenses/gpl-3.0.txt).
## 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
* The [UMAP reference implementation](https://github.com/lmcinnes/umap) and
[publication](https://arxiv.org/abs/1802.03426).
* The [UMAP R package](https://cran.r-project.org/package=umap)
(see also its [github repo](https://github.com/tkonopka/umap)), predates
`uwot`'s arrival on CRAN.
* Another R package is [umapr](https://github.com/ropensci-archive/umapr), but
it is no longer being maintained.
* [umappp](https://github.com/LTLA/umappp) is a full C++ implementation, and
[yaumap](https://github.com/LTLA/yaumap) provides an R wrapper. The batch
implementation in umappp are the basis for uwot's attempt at the same.
* `uwot` uses the [RcppProgress](https://cran.r-project.org/package=RcppProgress)
package to show a text-based progress bar when `verbose = TRUE`.