UWOT

An R implementation of the Uniform Manifold Approximation and Projection (UMAP) method for dimensionality reduction (McInnes and Healy, 2018), that also implements the supervised and metric (out-of-sample) learning extensions to the basic method.

News

September 2018. You can now return the nearest neighbor data via umap_result <- umap(..., ret_nn = TRUE), and it can be re-used by setting umap_newresult <- umap(..., nn_method = umap_result$nn). As the nearest neighbor calculations is likely to be the bottleneck with default settings, this can save a lot of time if you are fiddling with epochs, initialization, output distance functions and so on. It also works with lvish.

Installing

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

# See function man page for help
?umap

Example

iris_umap <- umap(iris, n_neighbors = 50, alpha = 0.5, init = "random")

# 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)

# Use a specific number of threads
mnist_umap <- umap(mnist, n_neighbors = 15, min_dist = 0.001, verbose = TRUE, n_threads = 8)

# Use a different metric
mnist_umap_cosine <- umap(n_neighbors = 15, metric = "cosine", min_dist = 0.001, verbose = TRUE, n_threads = 8)

# Supervised dimension reduction
mnist_umap_s <- umap(n_neighbors = 15, min_dist = 0.001, verbose = TRUE, n_threads = 8, 
                     y = mnist$Label, target_weight = 0.5)
                    
# Add new points to an existing embedding
mnist_train <- head(mnist, 60000)
mnist_test <- tail(mnist, 70000)

# You must set ret_model = TRUE to return extra data we need
# coordinates are in mnist_train_umap$embedding
mnist_train_umap <- umap(mnist_train, verbose = TRUE, ret_model = TRUE)
mnist_test_umap <- umap_transform(mnist_test, mnist_train_umap, verbose = TRUE)

# Save the nearest neighbor data
mnist_nn <- umap(mnist, ret_nn = TRUE)
# coordinates are now in mnist_nn$embedding

# Re-use the nearest neighor data and save a lot of time
mnist_nn_spca <- umap(mnist, nn_method = mnist_nn$nn, init = spca)

# No problem to have ret_nn = TRUE and ret_model = TRUE at the same time

Documentation

Apart from the man pages in R: you may be interested in:

• A description of UMAP using algorithmic terminology similar to t-SNE, rather than the more topological approach of the UMAP publication.
• Examples of the output of UMAP on some datasets, compared to t-SNE.
• How to use UMAP for Supervised and Metric Learning

Implementation Details

For small (N < 4096), exact nearest neighbors are found using the FNN package. Otherwise, approximate nearest neighbors are found using RcppAnnoy.

Coordinate initialization uses RSpectra to do the eigendecomposition of the normalized Laplacian.

The smooth k-nearest neighbor distance and stochastic gradient descent optimization routines are written in C++ (using Rcpp and RcppArmadillo), aping the Python code as closely as possible. It is my first time using Rcpp, so let's assume I did a horrible job.

For the datasets I've tried it with, the results look at least reminiscent of those obtained using the official Python implementation. Below are results for the 70,000 MNIST digits (downloaded using the snedata package). On the left is the result of using the official Python UMAP implementation (via the reticulate package). The right hand image is the result of using uwot.

The project documentation contains some more examples.

Performance

To get a feel for the performance of uwot, here are some timings for processing the MNIST dataset on my not-particularly-beefy laptop, compared with some other packages with their default settings:

The difference in performance between the Python UMAP (powered by the JIT-magic of Numba) and uwot with one thread is due to:

• nearest neighbor search: takes 40 seconds in Python which also has the experimental parallel support in Numba turned on, versus just over 2 minutes in single-threaded uwot. Using 4 threads for the index search part reduces this to 1 minute. This part is the performance bottleneck at the moment. The Python version of UMAP uses pynndescent, a nearest neighbor descent approach, rather than Annoy. Alternative nearest neighbors libraries e.g. kgraph (which is based on the same paper as pynndescent), or HNSW would be interesting to try, but all of the ones I've looked at either don't currently build on Windows or have non-portable compilation flags, so will require some fiddling with.
• the optimization stage: takes 60 seconds in Python (no parallel option here), versus about 66 seconds with uwot with one thread . I think the difference here is due to the pow operations in the gradient. If you like living dangerously, you can try using the fastPrecisePow approximation to the pow function suggested by Martin Ankerl:

# Set approx_pow = TRUE to use the approximation
mnist_umap <- umap(mnist, n_neighbors = 15, min_dist = 0.001, approx_pow = TRUE, verbose = TRUE)

For what I think seem like typical values of b (between 0.7 and 0.9) and the squared distance (0-1000), I found the maximum relative error was about 0.06. However, I haven't done much testing, beyond looking to see that the MNIST results are not obviously worsened. Results in the table above with approx_pow = TRUE do show a worthwhile improvement.

I would welcome any further suggestions on improvements (particularly speeding up the optimization loop). However, it's certainly fast enough for my needs.

Memory Usage

By the deeply unscientific method of me looking at how much memory the R session was taking up according to the Task Manager, processing MNIST with four threads saw the memory usage increase by nearly 1 GB at some points. There are some manual calls to gc() after some stages to avoid holding onto unused memory for longer than usual. The larger the value of n_neighbors, the more memory you can expect to take up (see, for example, the discussion of the lvish function below).

Multi-threading support

RcppParallel is used for the nearest neighbor index search, the smooth knn/perplexity calibration, and the optimization, which is the same approach that LargeVis takes.

You can (and should) adjust the number of threads via the n_threads parameter; for now, the default is half of whatever RcppParallel thinks should be the default. I have also exposed the grain_size parameter. If a thread would process less than grain_size number of items, then no multithreading is carried out. Set n_threads = 0 to use the previous non-threaded search; with n_threads = 1, you get the new multi-threaded code but with only one thread.

I've not experienced any problems with using multiple threads for a little while, but if you have any problems with crashing sessions, please file an issue.

Limitations and Other Issues

• Only Euclidean, cosine, and Manhattan distances are supported for finding nearest neighbors from data frame and dense matrix input. But if you can calculate a distance matrix for your data, you can pass it in as dist object. For larger distance matrices, you can pass in a sparseMatrix (from the Matrix package). Neither approach is supremely efficient at the moment. Proper sparse matrix support is limited by the nearest neighbor search routine: Annoy is intended for dense vectors. Adding a library for sparse nearest neighbor search would be a good extension.
• I haven't tried this on anything much larger than MNIST and Fashion MNIST (so at least around 100,000 rows with 500-1,000 columns works fine). Bear in mind that Annoy itself says it works best with dimensions < 100, but still works "surprisingly well" up to 1000.
• The spectral initialization default for umap (and the Laplacian eigenmap initialization, init = "laplacian") can sometimes run into problems. If it fails to converge it will fall back to random initialization, but on occasion I've seen it take an extremely long time (a couple of hours) to converge. If initialization is taking more than a few minutes, I suggest stopping the calculation and using the scaled PCA (init = "spca") instead.
• For supervised dimensionality reduction using a numeric vector, only the Euclidean distance is supported for building the target graph.
• R CMD check currently reports the following note: GNU make is a SystemRequirements., which is expected and due to using RcppParallel. On Linux, it sometimes notes that the libs sub-directory is over 1 MB. I am unsure if this is anything to worry about.

Other Methods

Some other dimensionality reduction methods are also available in uwot:

t-UMAP

If you choose the UMAP curve parameters to be a = 1 and b = 1, you get back the Cauchy distribution used in t-Distributed Stochastic Neighbor Embedding and LargeVis. This also happens to significantly simplify the gradient leading to a noticeable speed-up: for MNIST, I saw the optimization time drop from 66 seconds to 18 seconds. The trade off is that you will see larger, more spread-out clusters than with the typical UMAP settings (they're still more compact than you see in t-SNE, however). To try t-UMAP, use the tumap function:

mnist_tumap <- tumap(mnist, n_neighbors = 15, verbose = TRUE)

Note that using umap(a = 1, b = 1) doesn't use the simplified gradient, so you won't see any speed-up that way.

lvish: a LargeVis-ish method.

As UMAP's implementation is similar to LargeVis in some respects, this package also offers a LargeVis-like method, lvish:

# perplexity, init and n_epoch values shown are the defaults
# use perplexity instead of n_neighbors to control local neighborhood size
mnist_lv <- lvish(mnist, perplexity = 50, init = "lvrand", n_epochs = 5000, 
                  verbose = TRUE)
# Make hilarious Lembas bread joke

Although lvish is like the real LargeVis in terms of the input weights, output weight function and gradient, and so should give results that resemble the real thing, note that:

• Like the real LargeVis, matrix input data is normalized by centering each column and then the entire matrix is scaled by dividing by the maximum absolute value. This differs from umap, where no scaling is carried out. Scaling can be controlled by the scale parameter.
• Nearest neighbor results are not refined via the neighbor expansion method. The search_k parameter is twice as large than Annoy's default to compensate.
• The other nearest neighbor index parameter, n_trees, is not dynamically chosen based on data set size. In LargeVis, it ranges between 10 (for N < 100,000) and 100 (for N > 5,000,000). The lvish default of 50 would cover datasets up to N = 5,000,000, and combined with the default search_k, seems suitable for the datasets I've looked at.
• Negative edges are generated by uniform sampling of vertexes rather than their degree ^ 0.75.
• The default number of epochs is dataset-dependent, to generate the same number of edge samples that would be used by the default settings of the reference LargeVis implementation. This normally results in a substantially longer run time than for umap. You may be able to get away with fewer epochs, and using the UMAP initialization of init = "spectral", rather than the default Gaussian random initialization (init = "lvrand") can help.

The left-hand image below is the result of running the official LargeVis implementation on MNIST. The image on the right is that from running lvish with its default settings (apart from setting n_threads = 8). Given they were both initialized from different random configurations, there's no reason to believe they would be identical, but they look pretty similar:

Because the default number of neighbors is 3 times the perplexity, and the default perplexity = 50, the nearest neighbor search needs to find 150 nearest neighbors per data point, an order of magnitude larger than the UMAP defaults. This leads to a less sparse input graph and hence more edges to sample. Combined with the increased number of epochs, expect lvish to be slower than umap: with default single-threaded settings, it took about 20 minutes to embed the MNIST data under the same circumstances as described in the "Performance" section. With n_threads = 4, it took 7 minutes. In addition, storing those extra edges requires a lot more memory than the umap defaults: my R session increased by around 3.2 GB, versus 1 GB for umap.

As an alternative to the usual Gaussian input weight function, you can use the k-nearest neighbor graph itself, by setting kernel = "knn". This will give each edge between neighbors a uniform weight equal to 1/perplexity, which leads to each row's probability distribution having the target perplexity. This matrix will then be symmetrized in the usual way. The advantage of this is that the number of neighbors is reduced to the same as the perplexity (indeed, the n_neighbors parameter is ignored with this setting), and leads to less memory usage and a faster runtime. You can also get away with setting the perplexity to a much lower value than usual with this kernel (e.g. perplexity = 15) and get closer to UMAP's performance. If you use the default LargeVis random initialization, you will still need more epochs than UMAP, but you can still expect to see a big improvement. Something like the following works for MNIST:

mnist_lv <- lvish(mnist, kernel = "knn", perplexity = 15, n_epochs = 1500,
                  init = "lvrand", verbose = TRUE)

License

GPLv3 or later.

See Also

• The UMAP reference implementation and publication.
• There is now a UMAP package on CRAN (see also its github repo). Another R package is https://github.com/ropenscilabs/umapr.
• uwot uses the RcppProgress package to show a text-based progress bar when verbose = TRUE.
• My somewhat convoluted method to ensure the C++ random numbers are repeatable makes use of a (not convoluted) get_seed function suggested in a blog post by Rory Nolan.