godofecht / godofecht/flow-scikit
[Perf #478] KernelRidge RBF fit: eliminate the ~9.7x training gap
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- Python
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Description
Parent: #478
Canonical v2 Diabetes is the largest current hole: Flow fit 57.95 ms vs sklearn 5.998 ms. Score parity is verified, so this is a pure implementation/performance problem.
Profile RBF Gram construction, symmetry use, distance evaluation, solver choice, factorization, copies/layout and allocation. Determine whether sklearn is using a materially better LAPACK/BLAS path or whether Flow is performing redundant O(n^2)/O(n^3) work. Compare kernel and solve phases independently.
Acceptance: separate Gram/build/solve profiles; count kernel evaluations and matrix copies; use symmetry where legal; benchmark n=100..10k where feasible; retain verified R2 parity; fit >=0.95x first, target >=1.05x.
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First steps
- Read the whole issue, then the project's contributing guide.
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Research direction
Start at the KernelRidge RBF fit path and profile Gram construction and solve phases independently against sklearn. Measure kernel evaluations, matrix copies, symmetry use, factorization, and allocation across n=100..10k where feasible; done means the profiles are separated, R2 parity is retained, and performance reaches at least 0.95x, with 1.05x as the target.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- python
- Domain
- machine-learning, performance
- Issue type
- Refactor
- Difficulty
- 5/5
- Estimated time
- Over a week
- Activity status
- Active
- Clarity
- Mostly clear
- Newbie friendliness
- 35/100