The Uniform Manifold Approximation and Projection (UMAP) Method for Dimensionality Reduction

An implementation of the Uniform Manifold Approximation and Projection dimensionality reduction by McInnes et al. (2018) . It also provides means to transform new data and to carry out supervised dimensionality reduction. An implementation of the related LargeVis method of Tang et al. (2016) is also provided. This is a complete re-implementation in R (and C++, via the 'Rcpp' package): no Python installation is required. See the uwot website (<>) for more documentation and examples.


uwot 0.1.3

Bug fixes and minor improvements

  • Fixed an issue where the session would crash if the Annoy nearest neighbor search was unable to find k neighbors for an item.

Known issue

Even with a fix for the bug mentioned above, if the nearest neighbor index file is larger than 2GB in size, Annoy may not be able to read the data back in. This should only occur with very large or high-dimensional datasets. The nearest neighbor search will fail under these conditions. A work-around is to set n_threads = 0, because the index will not be written to disk and re-loaded under these circumstances, at the cost of a longer search time. Alternatively, set the pca parameter to reduce the dimensionality or lower n_trees, both of which will reduce the size of the index on disk. However, either may lower the accuracy of the nearest neighbor results.

uwot 0.1.2

Initial CRAN release.

New features

  • New parameter, tmpdir, which allows the user to specify the temporary directory where nearest neighbor indexes will be written during Annoy nearest neighbor search. The default is base::tempdir(). Only used if n_threads > 1 and nn_method = "annoy".

Bug fixes and minor improvements

  • Fixed an issue with lvish where there was an off-by-one error when calculating input probabilities.

  • Added a safe-guard to lvish to prevent the gaussian precision, beta, becoming overly large when the binary search fails during perplexity calibration.

  • The lvish perplexity calibration uses the log-sum-exp trick to avoid numeric underflow if beta becomes large.

uwot (31 March 2019)

New features

  • New parameter: pcg_rand. If TRUE (the default), then a random number generator from the PCG family is used during the stochastic optimization phase. The old PRNG, a direct translation of an implementation of the Tausworthe "taus88" PRNG used in the Python version of UMAP, can be obtained by setting pcg_rand = FALSE. The new PRNG is slower, but is likely superior in its statistical randomness. This change in behavior will be break backwards compatibility: you will now get slightly different results even with the same seed.
  • New parameter: fast_sgd. If TRUE, then the following combination of parameters are set: n_sgd_threads = "auto", pcg_rand = FALSE and approx_pow = TRUE. These will result in a substantially faster optimization phase, at the cost of being slightly less accurate and results not being exactly repeatable. fast_sgd = FALSE by default but if you are only interested in visualization, then fast_sgd gives perfectly good results. For more generic dimensionality reduction and reproducibility, keep fast_sgd = FALSE.
  • New parameter: init_sdev which specifies how large the standard deviation of each column of the initial coordinates should be. This will scale any input coordinates (including user-provided matrix coordinates). init = "spca" can now be thought of as an alias of init = "pca", init_sdev = 1e-4. This may be too aggressive scaling for some datasets. The typical UMAP spectral initializations tend to result in standard deviations of around 2 to 5, so this might be more appropriate in some cases. If spectral initialization detects multiple components in the affinity graph and falls back to scaled PCA, it uses init_sdev = 1.
  • As a result of adding init_sdev, the init options sspectral, slaplacian and snormlaplacian have been removed (they weren't around for very long anyway). You can get the same behavior by e.g. init = "spectral", init_sdev = 1e-4. init = "spca" is sticking around because I use it a lot.

Bug fixes and minor improvements

  • Spectral initialization (the default) was sometimes generating coordinates that had too large a range, due to an erroneous scale factor that failed to account for negative coordinate values. This could give rise to embeddings with very noticeable outliers distant from the main clusters.
  • Also during spectral initialization, the amount of noise being added had a standard deviation an order of magnitude too large compared to the Python implementation (this probably didn't make any difference though).
  • If requesting a spectral initialization, but multiple disconnected components are present, fall back to init = "spca".
  • Removed dependency on C++ <random> header. This breaks backwards compatibility even if you set pcg_rand = FALSE.
  • metric = "cosine" results were incorrectly using the unmodified Annoy angular distance.
  • Numeric matrix columns can be specified as the target for the categorical metric (fixes

uwot (1 January 2019)

  • Data is now stored column-wise during optimization, which should result in an increase in performance for larger values of n_components (e.g. approximately 50% faster optimization time with MNIST and n_components = 50).
  • New parameter: pca_center, which controls whether to center the data before applying PCA. It would be typical to set this to FALSE if you are applying PCA to binary data (although note you can't use this with setting with metric = "hamming")
  • PCA will now be used when the metric is "manhattan" and "cosine". It's still not applied when using "hamming" (data still needs to be in binary format, not real-valued).
  • If using mixed datatypes, you may override the pca and pca_center parameter values for a given data block by using a list for the value of the metric, with the column ids/names as an unnamed item and the overriding values as named items, e.g. instead of manhattan = 1:100, use manhattan = list(1:100, pca_center = FALSE) to turn off PCA centering for just that block. This functionality exists mainly for the case where you have mixed binary and real-valued data and want to apply PCA to both data types. It's normal to apply centering to real-valued data but not to binary data.

Bug fixes and minor improvements

  • Fixed bug that affected umap_transform, where negative sampling was over the size of the test data (should be the training data).
  • Some other performance improvements (around 10% faster for the optimization stage with MNIST).
  • When verbose = TRUE, log the Annoy recall accuracy, which may help tune values of n_trees and search_k.

uwot (December 23 2018)

New features

  • New parameter: n_sgd_threads, which controls the number of threads used in the stochastic gradient descent. By default this is now single-threaded and should result in reproducible results when using set.seed. To get back the old, less consistent, but faster settings, set n_sgd_threads = "auto".
  • API change for consistency with Python UMAP:
    • alpha is now learning_rate.
    • gamma is now repulsion_strength.
  • Default spectral initialization now looks for disconnected components and initializes them separately (also applies to laplacian and normlaplacian).
  • New init options: sspectral, snormlaplacian and slaplacian. These are like spectral, normlaplacian, laplacian respectively, but scaled so that each dimension has a standard deviation of 1e-4. This is like the difference between the pca and spca options.

Bug fixes and minor improvements

  • Hamming distance support (was actually using Euclidean distance).
  • Smooth knn/perplexity calibration results had a small dependency on the number of threads used.
  • Anomalously long spectral initialization times should now be reduced.
  • Internal changes and fixes thanks to a code review by Aaron Lun (

uwot (December 9 2018)

New features

  • New parameter pca: set this to a positive integer to reduce matrix of data frames to that number of columns using PCA. Only works if metric = "euclidean". If you have > 100 columns, this can substantially improve the speed of the nearest neighbor search. t-SNE implementations often set this value to 50.

Bug fixes and minor improvements

  • Laplacian Eigenmap initialization convergence failure is now correctly detected.
  • C++ code was over-writing data passed from R as a function argument.

uwot (December 5 2018)

New features

  • Highly experimental mixed data type support for metric: instead of specifying a single metric name (e.g. metric = "euclidean"), you can pass a list, where the name of each item is the metric to use and the value is a vector of the names of the columns to use with that metric, e.g. metric = list("euclidean" = c("A1", "A2"), "cosine" = c("B1", "B2", "B3")) treats columns A1 and A2 as one block, using the Euclidean distance to find nearest neighbors, whereas B1, B2 and B3 are treated as a second block, using the cosine distance.
  • Factor columns can also be used in the metric, using the metric name categorical.
  • y may now be a data frame or matrix if multiple target data is available.
  • New parameter target_metric, to specify the distance metric to use with numerical y. This has the same capabilities as metric.
  • Multiple external nearest neighbor data sources are now supported. Instead of passing a list of two matrices, pass a list of lists, one for each external metric.
  • More details on mixed data types can be found at
  • Compatibility with older versions of RcppParallel (contributed by sirusb).
  • scale = "Z" To Z-scale each column of input (synonym for scale = TRUE or scale = "scale").
  • New scaling option, scale = "colrange" to scale columns in the range (0, 1).

uwot (November 4 2018)

New features

  • Hamming distance is now supported, due to upgrade to RcppAnnoy 0.0.11.

uwot (October 21 2018)

New features

  • For supervised UMAP with numeric y, you may pass nearest neighbor data directly, in the same format as that supported by X-related nearest neighbor data. This may be useful if you don't want to use Euclidean distances for the y data, or if you have missing data (and have a way to assign nearest neighbors for those cases, obviously). See the Nearest Neighbor Data Format section for details.

uwot (September 22 2018)

New features

  • New parameter ret_nn: when TRUE returns nearest neighbor matrices as a nn list: indices in item idx and distances in item dist. Embedded coordinates are in embedding. Both ret_nn and ret_model can be TRUE, and should not cause any compatibility issues with supervised embeddings.
  • nn_method can now take precomputed nearest neighbor data. Must be a list of two matrices: idx, containing integer indexes, and dist containing distances. By no coincidence, this is the format return by ret_nn.

Bug fixes and minor improvements

  • Embedding to n_components = 1 was broken (
  • User-supplied matrices to init parameter were being modified, in defiance of basic R pass-by-copy semantics.

uwot (August 14 2018)

Bug fixes and minor improvements

  • metric = "cosine" is working again for n_threads greater than 0 (


New features

  • August 5 2018. You can now use an existing embedding to add new points via umap_transform. See the example section below.

  • August 1 2018. Numerical vectors are now supported for supervised dimension reduction.

  • July 31 2018. (Very) initial support for supervised dimension reduction: categorical data only at the moment. Pass in a factor vector (use NA for unknown labels) as the y parameter and edges with bad (or unknown) labels are down-weighted, hopefully leading to better separation of classes. This works remarkably well for the Fashion MNIST dataset.

  • July 22 2018. You can now use the cosine and Manhattan distances with the Annoy nearest neighbor search, via metric = "cosine" and metric = "manhattan", respectively. Hamming distance is not supported because RcppAnnoy doesn't yet support it.

Reference manual

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0.1.3 by James Melville, a month ago

Report a bug at

Browse source code at

Authors: James Melville [aut, cre]

Documentation:   PDF Manual  

GPL-3 license

Imports Rcpp, methods, FNN, RSpectra, RcppAnnoy, RcppParallel, irlba

Depends on Matrix

Suggests testthat, covr

Linking to Rcpp, RcppProgress, RcppParallel, RcppAnnoy, dqrng

System requirements: GNU make

See at CRAN