Release Notes

Some notes on new features in various releases

What’s new in 0.5

ParametricUMAP learns embeddings with neural networks.
AlignedUMAP can align multiple embeddings using relations between datasets.
DensMAP can preserve local density information in embeddings.
UMAP now depends on PyNNDescent, but has faster more parallel performance as a result.
UMAP now supports an update method to add new data and retrain.
Various performance improvements and bug fixes.
Additional plotting support, including text searching in interactive plots.
Support for “maximal distances” in neighbor graphs.

Inverse transform method. Generate points in the original space corresponding to points in embedded space. (Thanks to Joseph Courtney)
Different embedding spaces. Support for embedding to a variety of different spaces other than Euclidean. (Thanks to Joseph Courtney)
New metrics, including Hellinger distance for sparse count data.
New discrete/label metrics, including hierarchical categories, counts, ordinal data, and string edit distance.
Support for parallelism in neighbor search and layout optimization. (Thanks to Tom White)
Support for alternative methods to handling duplicated data samples. (Thanks to John Healy)
New plotting methods for fast and easy plots.
Initial support for dataframe embedding – still experimental, but worth trying.
Support for transform methods with sparse data.
Multithreading support when no random seed is set.

Supervised and semi-supervised dimension reduction. Support for using labels or partial labels for dimension reduction.
Transform method. Support for adding new unseen points to an existing embedding.
Performance improvements.