godofecht / godofecht/flow-scikit

[Parity #478] Track dtype, memory layout, preprocessing and conversion work explicitly

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Dominant language
Python
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2
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0
Avg merge
4h 33m
Merged PRs (30d)
9

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.

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First steps

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  2. Comment on the issue to say you are picking it up — it saves two people doing the same work.
  3. Fork the repository and make your change on a branch.
  4. 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

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