$ show YingfanWang/PaCMAP
PaCMAP is a production-ready Python library for dimensionality reduction and manifold learning, implementing the Pairwise Controlled Manifold Approximation Projection algorithm. The core package (source/pacmap/) provides a scikit-learn compatible API with BaseEstimator inheritance, enabling seamless integration into ML pipelines. The library supports multiple KNN backends (FAISS, Annoy, Voyager) through a unified abstraction layer, with heavy use of Numba JIT compilation for performance-critical operations like distance calculation and gradient updates. Research components (experiments/, evaluation/, data/) support algorithm validation against benchmarks like t-SNE and UMAP, but the primary deliverable is the reusable library itself. The codebase demonstrates strong engineering practices with comprehensive pytest coverage, model persistence mechanisms, and deterministic random state management across Numba functions.
The LLM identified 3 files worth highlighting for reuse:
Core implementation of PaCMAP and LocalMAP algorithms with Numba JIT-compiled performance-critical loops. Demonstrates sophisticated optimization strategy using multi-stage optimization (Neighbor, Mid-Near, Further pairs) with adaptive weights. The backend abstraction pattern cleanly separates KNN search logic from optimization logic, allowing FAISS, Annoy, and Voyager backends to be swapped without modifying core algorithm code. This is a genuinely reusable component that could be extracted for other manifold learning projects.
Comprehensive test suite covering KNN backends, distance metrics, determinism, and transform functionality. Tests verify that different backends produce consistent results within acceptable tolerance, and that random_state management works correctly across Numba functions. The test fixtures in test/fixtures/datasets.json provide reproducible test data. This demonstrates professional engineering practices for scientific computing libraries where numerical precision and reproducibility are critical.
Benchmarking infrastructure for comparing PaCMAP against t-SNE, UMAP, TriMAP, and LargeVis. Implements standardized evaluation metrics (KNN accuracy, SVM accuracy, CTE, RTE) that could be reused for evaluating other dimensionality reduction algorithms. The experimental design pattern with configurable parameters and consistent evaluation methodology demonstrates research-grade engineering practices that could be adapted for other algorithm comparisons.