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

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评估

难度
4/5
预计耗时
3-5 天
新手友好度
30/100
Issue 类型
功能
描述清晰度
描述清楚
活跃度
冷清
技术栈
kotlin, python, scikit-learn
领域
machine-learning, testing

调研方向

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.

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描述

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.

主要语言
Kotlin
星标
52
派生
15
平均合并
1 天 4 小时
30 天内合并 PR
37

环境准备

  • 没有 Dockerfile 或 Docker Compose 文件
  • 没有 Pull Request 模板
  • 阅读贡献指南

从这里开始

  1. 先读完整个 Issue，再读项目的贡献指南。
  2. 在 Issue 下留言说明你要接手 —— 这能避免两个人做同样的事。
  3. Fork 仓库，在一个分支上完成修改。
  4. 提交 Pull Request，并在描述里引用这个 Issue 编号。

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