CRAN release 0.1.5

Bug fixes and minor improvements

• The R API was being accessed from inside multi-threaded code to seed the (non-R) random number generators. Probably this was causing users in downstream projects (seurat and monocle) to experience strange RcppParallel-related crashes. Thanks to aldojongejan for reporting this (#39).
• Passing a floating point value smaller than one to n_threads caused a crash. This was particularly insidious if running with a system with only one default thread available as the default n_threads becomes 0.5. Now n_threads (and n_sgd_threads) are rounded to the nearest integer.
• Initialization of supervised UMAP should now be faster (#34). Contributed by Aaron Lun.

CRAN release 0.1.4

This release is to ensure the save/load capabilities of uwot continue to work with a forthcoming release of RcppAnnoy.

Bug fixes and minor improvements

• Fixed incorrect loading of Annoy indexes to be compatible with newer versions of RcppAnnoy (#31). My thanks to Dirk Eddelbuettel and Erik Bernhardsson for aid in identifying the problem.
• Fix for ERROR: there is already an InterruptableProgressMonitor instance defined.
• If verbose = TRUE, the a, b curve parameters are now logged.

v0.1.3

Moments after getting uwot on CRAN, I found a horrible bug, necessitating this patch release.

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.

v0.1.2

uwot 0.1.2 (April 6 2019)

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.

v0.0.0.9010

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 #20).

v0.0.0.9009

New features

• 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.

v0.0.0.9008

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 intialization times should now be reduced.
• Internal changes and fixes thanks to a code review by @LTLA.

v0.0.0.9007

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.

v0.0.0.9006

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 https://github.com/jlmelville/uwot#mixed-data-types.
• 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).

v0.0.0.9005

New features

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