$ show lmcinnes/umap
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.
The LLM identified 3 files worth highlighting for reuse:
Self-contained collection of ~30 distance functions (Euclidean, cosine, correlation, Hellinger, Levenshtein, etc.) with analytical gradients, all compiled via @numba.njit. Each function is a standalone callable that can be reused in any project requiring fast pairwise distance computation, not just UMAP. The module demonstrates expert use of numba for numerical performance and is decoupled from the rest of the codebase.
Provides numba-accelerated implementations of distance metrics (e.g., Euclidean, cosine, correlation) for scipy.sparse matrices (CSR). Each function handles sparse dot products and row norms efficiently, and can be extracted for use in any sparse distance computation pipeline. The module is isolated (depends only on numba and scipy.sparse) and showcases techniques for avoiding dense conversion.
Contains miscellaneous but reusable helper functions: fast_knn_indices (using njt), csr_unique (dedup sparse rows), disconnected_vertces (detect graph isolates). These solve common data engineering problems (duplicate removal, graph consistency) and are implemented with sparse matrix awareness. They import no internal modules beyond numba, numpy, and scipy, making them easy to extract for other projects.