godofecht / godofecht/flow-scikit
[Parity #478] Track dtype, memory layout, preprocessing and conversion work explicitly
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- Dominant language
- Python
- Stars
- 2
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- Avg merge
- 4h 33m
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Description
Parent: #478.
Flow/sklearn timings must expose representation differences rather than accidentally rewarding one side for cheaper inputs. Record source dtype, estimator internal dtype, row/column-major layout, contiguity/alignment, preprocessing/scaling, label encoding and every timed/untimed conversion or copy.
Where sklearn internally promotes or copies, distinguish API-realistic end-to-end timing from kernel-only equivalent-representation timing. Where Flow uses f32 and sklearn f64, make that a visible semantic/performance dimension with numerical-error evidence.
Acceptance: canonical and scaled artifacts contain representation/preprocessing metadata; hidden untimed conversion bytes = 0; equivalent-representation microbenchmarks exist for disputed gaps; Pages can distinguish end-to-end API wins from dtype/layout advantages.
Contributor guide
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First steps
- Read the whole issue, then the project's contributing guide.
- Comment on the issue to say you are picking it up — it saves two people doing the same work.
- Fork the repository and make your change on a branch.
- Open a pull request that references the issue number.
Research direction
Start with parent #478, then trace canonical and scaled artifact generation, timing paths, and Pages output. Inventory where representation and preprocessing metadata, conversion accounting, equivalent-representation microbenchmarks, and numerical-error evidence are handled. Done means the acceptance criteria are visible in artifacts and reports without hidden conversion bytes.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- python, scikit-learn
- Domain
- data, machine-learning, performance
- Issue type
- Feature
- Difficulty
- 5/5
- Estimated time
- Over a week
- Activity status
- Active
- Clarity
- Mostly clear
- Newbie friendliness
- 38/100