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[Lane 6 · review] Precision/Recall/F1 — DARC review and @DarcValidated

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Assessment

Difficulty
4/5
Estimated time
3-5 days
Newbie friendliness
30/100
Issue type
Feature
Clarity
Clearly specified
Activity status
Quiet
Tech stack
kotlin, python, scikit-learn
Domain
machine-learning, testing

Research direction

Start with the repository’s “Contributing → DARC: advanced contribution workflow → Criteria for setting @DarcValidated,” then inspect the three metric partials, their tests, and the shipped code on develop. Run the documented examples and the reference fixture check, verify citations and the stated contract, and post pass/fail findings on #1222. If all checks pass, open a PR annotating the three compute() overrides; otherwise send specific failures to their sub-issues.

Written by the indexing model from the issue text.

Description

assessment size:s skill:review sub-issue

Sub-issue of #1222 (Precision, Recall and F1Score metrics).

Lane: 6 · Review (DARC sign-off)
Skill needed: a second pair of eyes with ML-metrics and Kotlin literacy. Must not be the implementer of #1226–#1229 or the author of #1234.
Size: s (~2 h)
Blocked by: #1229 and #1234 merged.

What to do

Walk all four DARC phases against the shipped code, per Contributing → DARC: advanced
contribution workflow → Criteria for setting @DarcValidated:

  1. D — Document. The three partials have non-empty math / intuition / examples /
    references tags; none are placeholders.
  2. A — Assess. Run every documented example against develop; shapes, dtypes and
    numbers must match the prose. Spot-check the zero-division and macro-vs-micro claims
    against the tests. Cross-check the reference fixture (pred [2,0,1,2], target [2,1,1,0]) against a one-off sklearn.metrics.precision_recall_fscore_support run.
  3. R — Research. Click every citation in the partials; each must still resolve and
    support the specific claim it backs.
  4. C — Code. The implementation matches the documented contract (averaging modes,
    zero-division, dim / threshold semantics, single accumulator per metric).
  5. If all four pass, open a PR adding
    @DarcValidated(by = "First Last <[email protected]>", on = "YYYY-MM-DD") to the
    compute() override of Precision, Recall and F1Score. If any fails, send the
    specific item back to its sub-issue — don't annotate.

Acceptance

  • Review findings posted on #1222 (pass, or the list sent back)
  • @DarcValidated PR merged, or a clear statement of what's blocking it
  • #1222 can be closed (all lanes done, ground-truth explicitly deferred to SKEEP-005 / #1223)

Notes

Precedent to flag, not to solve: @DarcValidated targets FUNCTION and today the KSP
OperatorDocProcessor only reads it off TensorOps functions — so the badge will be
recorded in source but will not render on any generated page for a Metric. Mention
that in the PR so whoever owns the doc pipeline can decide whether Metric
implementations become a second processed category. That is a follow-up, not a blocker.

Dominant language
Kotlin
Stars
52
Forks
15
Avg merge
1d 4h
Merged PRs (30d)
37

Getting set up

First steps

  1. Read the whole issue, then the project's contributing guide.
  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.

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