Bump umap-learn from 0.4.6 to 0.5.1

[dependabot-preview]

Bumps umap-learn from 0.4.6 to 0.5.1.

Release notes

Sourced from umap-learn's releases.

0.5.1

A few minor fixes, including a bug fix against the upcoming numba 0.53

Major release 0.5

Adds support for ParametricUMAP, DensMAP, AlignedUMAP and more.

Changelog

Sourced from umap-learn's changelog.

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.

What's new in 0.4

• 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.

What's new in 0.3

• 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.

What's new in 0.2

• A new layout algorithm that handles large datasets (more) correctly.
• Performance improvements.

Commits

• See full diff in compare view

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