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

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まだ誰も着手していません。

評価

難易度
4/5
見積もり時間
3〜5日
初心者へのやさしさ
30/100
issue の種類
機能追加
明瞭さ
明確に書かれている
活発さ
静か
技術スタック
kotlin, python, scikit-learn
領域
machine-learning, testing

調査の方向性

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.

索引モデルが issue の本文から書いたものです。

説明

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.

主要言語
Kotlin
スター
52
フォーク
15
平均マージ
1日 8時間
マージ済み PR（30日）
37

環境構築

はじめの一歩

  1. issue を最後まで読み、次にプロジェクトのコントリビューションガイドを読みます。
  2. 着手することを issue にコメントします — 二人が同じ作業をするのを防げます。
  3. リポジトリをフォークし、ブランチを切って変更します。
  4. issue 番号を参照したプルリクエストを送ります。

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