This is the core implementation of UMAP (Uniform Manifold Approximation and Projection), a scikit-learn-compatible library for non-linear dimensionality reduction. The repository provides a well-structured modular architecture with core classes UMAP, ParametricUMAP (TensorFlow-based), and AlignedUMAP for jointly embedding multiple related datasets. It features a rich set of numba-accelerated distance functions, sparse matrix support, spectral initialization, and a sophisticated optimization engine. Although primarily a library, the repo includes extensive Jupyter notebooks and documentation demonstrating research workflows (parametric UMAP with reconstruction, hyperparameter animation, global loss tuning). The codebase is actively maintained, with ongoing work to remove deprecated patterns such as fastmath, but retains some technical debt (unresolved FIXMEs, sparse test coverage in some areas, and dependency on older scikit-learn APIs). Overall, it is a high-quality, reusable library with clear separation of concerns, making it suitable for integration into data science pipelines and for study of manifold learning techniques.

PyNNDescent is a Python library for fast approximate nearest neighbor search, implementing the NN-descent algorithm. The core pipeline builds a k-nearest neighbor graph via random projection tree initialization followed by iterative graph refinement, with all hot loops JIT-compiled using Numba with explicit types and fastmath. The public API follows scikit-learn conventions: NNDescent constructor for training, .query() for approximate nearest neighbor queries, .neighbor_graph for extracting the training-set graph, and .update() for incremental insertion. It supports dense and sparse data, multiple distance metrics, and parallel execution via Numba's prange. The library is a dependency of UMAP and OpenTSNE, which consume the training-set neighbor graph for manifold learning and density-based clustering.