PairwiseDistancesReductions have been introduced as a hierarchy of Cython classes to implement back-ends of some scikit-learn algorithms.

Pieces of work include:

• ENH Introduce PairwiseDistancesReduction and PairwiseDistancesArgKmin (feature branch) #22134
• ENH Introduce PairwiseDistancesRadiusNeighborhood #22320
• FEA Add support for float32 on PairwiseDistancesReduction using Tempita #23865
• Support sparse-dense data-sets pairs:
  • FEA CSR support for all DistanceMetric #23604
  • FEA Fused sparse-dense support for PairwiseDistancesReduction  #23585
  • MAINT Introduce MiddleTermComputer, an abstraction generalizing GEMMTermComputer #24807 by @Vincent-Maladiere
  • EHN Optimized CSR-CSR support for Euclidean specializations of PairwiseDistancesReductions #24556 by @Vincent-Maladiere
  • ENH Add the fused CSR dense case for Euclidean Specializations #25044
• MAINT Adapt scheduling of PairwiseDistancesRadiusNeighborhood #22829
• DOC Fix docstrings and comments for PairwiseDistancesReduction #23978
• MAINT Adapt PairwiseDistancesReduction heuristic for strategy="auto" #24043
• Document and communicate about the changes of behaviour regarding n_jobs:
  • DOC Update "Parallelism, resource management, and configuration" section #24997
  • Add a comment in impacted user-facing PairwiseDistancesReductions-backed API's doc-strings regarding the non-use of n_jobs
• Dedicated ASV benchmark suite: scikit-learn/pairwise-distances-reductions-asv-suite

Note that this needs not be personal work, I would be really glad having others help on this subject, proposing changes and implementations! 🙂