db1b1f6f9f
- check/rules.py（规则集 2026.11-r1，不调模型）：大小写金额、分项合计、超最高限价、 多个报价、别的项目名称、没填的地方、落款日期、投标有效期、附加条件、响应表里的未响应； 每条挂投标原文，有依据的挂招标侧出处；缺限价、截止时间、有效期要求时写进覆盖范围 - 文字层的句子带上单元格框，表格按单元格上沿分行 - 按 Fable 审读意见收紧致命级：认不准的降为重要或提示 - 评测：score.py 评规则问题；P07 注入记录改为空白处前后原文，剔除注入到人员表的 D10 - 上传页的检查范围文案同步 Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
310 lines
13 KiB
Python
310 lines
13 KiB
Python
"""查标评分：对照响应清单给一次离线检查打分（evals/check/GUIDELINES.md §7）。
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cd server && ../.venv/bin/python ../evals/check/score.py <投标文件> [--run base] [--include-unreviewed]
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- 清单项 → 标注单元：清单项的招标侧句子和单元的句子有重叠就对上（和读标评分同一口径）；
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- 按单元去重：同一要求在清单里有几条，只要一条报出就算召回，几条都报不算误报；
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- 问题类型按类型族比（D01 / D02 / D09 / D11 / D13 → unresponded，D03 / D07 → suspected_unmet，……）；
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证明材料类（D02 / D11 / D13）报成「可能在扫描件里，请人工核对」也算标出来了（C10），另外单独计数；
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- 只算复核过的问题（决策 E1）；--include-unreviewed 时也算没复核的，报告里标明；
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- 读标没抽到的问题单列「读标漏抽」：不算查标漏报，但计入端到端召回；
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- 对不上任何标注单元的问题单列「无法对照」。
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输出：evals/out/check/<投标文件>/runs/<运行>/score.json、score.md。
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"""
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from __future__ import annotations
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import argparse
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import json
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import pathlib
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import sys
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from collections import Counter, defaultdict
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from typing import Any
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EVALS = pathlib.Path(__file__).resolve().parents[1]
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sys.path.insert(0, str(EVALS))
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import gold # noqa: E402
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OUT = EVALS / "out" / "check"
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FAMILY = {
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"D01": {"unresponded"},
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"D02": {"unresponded", "manual_review"},
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"D09": {"unresponded"},
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"D11": {"unresponded", "manual_review"},
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"D13": {"unresponded", "manual_review"},
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"D16": {"unresponded"},
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"D03": {"suspected_unmet"},
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"D07": {"suspected_unmet"},
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"D08": {"suspected_unmet"},
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"D05": {"negative_deviation", "suspected_unmet"},
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"D06": {"negative_deviation"},
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"D10": {"negative_deviation"},
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"D12": {"qualification"},
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"D14": {"qualification"},
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"D15": {"qualification", "unresponded"},
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}
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FATAL_CATS = {"substantive", "qualification"}
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RULE_CODES = {
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"P01": "PRICE.OVER_CEILING_TOTAL",
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"P02": "PRICE.OVER_CEILING_LOT",
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"P03": "PRICE.OVER_CEILING_ITEM",
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"P04": "PRICE.CAPITAL_MISMATCH",
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"P05": "PRICE.SUM_MISMATCH",
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"P06": "FORM.FOREIGN_PROJECT",
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"P07": "FORM.BLANK_FIELD",
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"P08": "FORM.DATE_AFTER_DEADLINE",
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"P09": "FORM.VALIDITY_SHORT",
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"P10": "FORM.CONDITION_ADDED",
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"P11": "FORM.MISSING_DOC",
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"P12": "PRICE.MULTIPLE_QUOTES",
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"P13": "PRICE.ITEM_MISSING",
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}
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def squash(t: str) -> str:
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return "".join(t.split())
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def score_rules(manifest: dict[str, Any], res: dict[str, Any]) -> dict[str, Any]:
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"""规则问题（P 类）：规则编号对上、投标侧出处里有注入的那段原文才算查出；没对上任何注入问题的规则问题算误报。
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D10（符合性条件响应表）按表级问题记在 rule_defects 里，它由响应匹配查（negative_deviation），这里一起对照。"""
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hits = list(res.get("rules") or [])
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used: set[int] = set()
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found: Counter[str] = Counter()
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total: Counter[str] = Counter()
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missed: list[str] = []
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for rd in manifest.get("rule_defects") or []:
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kind, snip = rd["type"], squash(rd.get("t") or "")
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total[kind] += 1
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if kind == "D10":
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pool = [
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(-1, {"evidence": it["evidence"]})
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for it in res["items"]
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if it["finding"] and it["finding"]["type"] == "negative_deviation"
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] + [(i, h) for i, h in enumerate(hits) if h["code"] == "FORM.RESPONSE_NEGATIVE"]
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else:
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pool = [(i, h) for i, h in enumerate(hits) if h["code"] == RULE_CODES.get(kind)]
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key = snip[:12]
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cell = squash(rd.get("variant") or "") if kind == "D10" else ""
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ok = next(
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(
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i
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for i, h in pool
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if any(
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(key and (key in squash(e["text"]) or squash(e["text"]) in snip))
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or (cell and squash(e["text"]) == cell)
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for e in h["evidence"]
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)
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),
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None,
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)
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if ok is None:
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missed.append(f"{rd['id']}（{rd.get('variant', '')}）：{rd.get('t', '')[:40]}")
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continue
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found[kind] += 1
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if ok >= 0:
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used.add(ok)
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# 响应表规则报的「负偏离」对上了响应问题（D05 / D06）的注入位置：算那个单元查出（rule_units），不算规则误报
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rule_units: set[str] = set()
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for i, h in enumerate(hits):
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if h["code"] != "FORM.RESPONSE_NEGATIVE":
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continue
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texts = [squash(e["text"]) for e in h["evidence"]]
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for uid, u in manifest["units"].items():
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if u.get("expect") != "defect" or "negative_deviation" not in FAMILY.get(u.get("defect", ""), set()):
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continue
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if any(
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(w := squash(x.get("t") or "")[:12]) and any(w in t or (t and t in w) for t in texts)
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for x in u.get("where") or []
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):
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rule_units.add(uid)
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used.add(i)
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fp = [h for i, h in enumerate(hits) if i not in used]
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return {"found": found, "total": total, "missed": missed, "fp": fp, "n": len(hits), "units": rule_units}
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def pct(a: int, b: int) -> str:
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return f"{a}/{b}（{a / b:.0%}）" if b else "—"
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def main() -> None:
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ap = argparse.ArgumentParser()
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ap.add_argument("bid")
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ap.add_argument("--run", default="base")
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ap.add_argument("--include-unreviewed", action="store_true")
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a = ap.parse_args()
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d = OUT / a.bid
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manifest = json.loads((d / "manifest.json").read_text())
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res = json.loads((d / "runs" / a.run / "results.json").read_text())
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units, _ = gold.units(manifest["tender"])
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sid_units: dict[str, set[str]] = defaultdict(set)
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for u in units:
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for s in u.sids:
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sid_units[s].add(u.id)
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# 标注集里同一条要求有时记了两个单元（两个类别各一条，或表格一行拆成几条）：句子有重叠的单元合成一组，按组计
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parent = {u.id: u.id for u in units}
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def find(x: str) -> str:
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while parent[x] != x:
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x = parent[x]
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return x
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for ids in sid_units.values():
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first, *rest = sorted(ids)
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for o in rest:
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parent[find(o)] = find(first)
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group = {u.id: find(u.id) for u in units}
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rs = score_rules(manifest, res)
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per_unit: dict[str, list[dict[str, Any]]] = defaultdict(list)
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unmapped: list[dict[str, Any]] = []
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for it in res["items"]:
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hit = {u for s in it["sentences"] for u in sid_units.get(s, ())}
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for u in hit:
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per_unit[u].append(it)
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if not hit and it["finding"]:
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unmapped.append(it)
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rec: Counter[str] = Counter()
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rec_total: Counter[str] = Counter()
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sev_ok = sev_n = 0
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misses: list[str] = []
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extraction_miss: list[str] = []
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fp: list[str] = []
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fp_by_trap: Counter[str] = Counter()
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ok_units = trap_units = 0
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other_findings = 0
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seen_groups: set[str] = set()
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for uid, e in sorted(manifest["units"].items(), key=lambda kv: kv[1].get("expect") != "defect"):
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g = group.get(uid, uid)
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if g in seen_groups:
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continue # 同组的单元只算一次（挂了问题的那个优先）
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seen_groups.add(g)
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items = [it for x in manifest["units"] if group.get(x, x) == g for it in per_unit.get(x, [])]
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found = [it for it in items if it["finding"]]
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if e["expect"] == "defect":
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if not e.get("reviewed") and not a.include_unreviewed:
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continue
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keys = [e["defect"], f"粒度:{e['granularity']}", "带标志词" if e.get("worded") else "不带标志词"]
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fatal = e["cat"] in FATAL_CATS
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if fatal:
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keys.append("致命级")
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for k in keys:
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rec_total[k] += 1
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rec_total["端到端"] += 1
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if not items:
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extraction_miss.append(f"{uid}（{e['defect']}，{e['cat']}）")
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continue
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rec_total["查标"] += 1
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good = [it for it in found if it["finding"]["type"] in FAMILY.get(e["defect"], set())]
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if not good and any(x in rs["units"] for x in manifest["units"] if group.get(x, x) == g):
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good = [{"finding": {"type": "negative_deviation", "severity": "important"}}]
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rec["其中由响应表规则查出"] += 1
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if good:
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for k in [*keys, "端到端", "查标"]:
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rec[k] += 1
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if all(it["finding"]["type"] == "manual_review" for it in good):
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rec["其中只是扫描件待人工核对"] += 1
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if fatal:
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sev_n += 1
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sev_ok += any(it["finding"]["severity"] == "fatal" for it in good)
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else:
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got = ", ".join(sorted({it["verdict"] for it in items}))
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misses.append(f"{uid}（{e['defect']} {e.get('variant', '')}）→ 查标结论：{got}")
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elif e["expect"] == "ok":
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ok_units += 1
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if e.get("trap"):
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trap_units += 1
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if found:
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fp.append(f"{uid}（{e['cat']}，{e['mode']}）：{found[0]['finding']['title'][:40]}")
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if e.get("trap"):
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fp_by_trap[e["trap"]] += 1
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elif found:
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other_findings += 1
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findings = [it for it in res["items"] if it["finding"]]
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lines = [
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f"# 查标评分：{a.bid} · 运行 {a.run}",
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"",
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f"- 投标文件：{manifest['kind']}，风格 {manifest['style']}，{manifest['split']}；清单 {len(res['items'])} 条，报出问题 {len(findings)} 个",
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f"- 金标准：{'含未复核的问题（试跑口径）' if a.include_unreviewed else '只算复核过的问题'}；复核方式见决策 E1",
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f"- 模型：{res['summary']['usage']}",
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"",
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"## 召回",
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"",
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f"- 查标召回（读标抽到的要求上）：{pct(rec['查标'], rec_total['查标'])}",
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f"- 端到端召回（含读标漏抽）：{pct(rec['端到端'], rec_total['端到端'])}",
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f"- 致命级：{pct(rec['致命级'], rec_total['致命级'])}；其中级别也判成致命的 {pct(sev_ok, sev_n)}",
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"",
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"| 分类 | 召回 |",
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"|---|---|",
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*[f"| {k} | {pct(rec[k], rec_total[k])} |" for k in sorted(rec_total) if k not in ("端到端", "查标", "致命级")],
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"",
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"## 误报",
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"",
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f"- 规矩响应的要求 {ok_units} 条，报了问题的 {len(fp)} 条（每 100 条 {100 * len(fp) / ok_units:.1f}）"
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if ok_units
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else "- 没有规矩响应的要求",
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f"- 干扰项 {trap_units} 条，误报 {dict(fp_by_trap)}",
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f"- 要求本来不需要响应或无法判断（na / unknown）却报了问题：{other_findings}",
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f"- 无法对照（对不上任何标注单元）的问题：{len(unmapped)}",
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"",
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f"## 漏报（{len(misses)}）",
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"",
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*[f"- {x}" for x in misses],
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"",
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f"## 读标漏抽（{len(extraction_miss)}）",
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"",
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*[f"- {x}" for x in extraction_miss],
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"",
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f"## 误报明细（{len(fp)}）",
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"",
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*[f"- {x}" for x in fp[:80]],
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*(["- ……"] if len(fp) > 80 else []),
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"",
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]
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lines += [
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f"## 规则问题：注入 {sum(rs['total'].values())} 个，查出 {sum(rs['found'].values())} 个；规则报出 {rs['n']} 个，误报 {len(rs['fp'])} 个",
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"",
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"| 问题 | 查出 |",
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"|---|---|",
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*[f"| {k} | {pct(rs['found'][k], rs['total'][k])} |" for k in sorted(rs["total"])],
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"",
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*[f"- 漏：{x}" for x in rs["missed"]],
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*[
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f"- 误报：{h['code']} {h['title']}：{h['evidence'][0]['text'][:50] if h['evidence'] else ''}"
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for h in rs["fp"]
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],
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"",
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"### 规则检查没覆盖的",
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"",
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*[f"- {x}" for x in (res.get("rules_coverage") or {}).get("not_checked", [])],
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"",
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]
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out = d / "runs" / a.run
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(out / "score.md").write_text("\n".join(lines))
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summary = {
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"recall": {k: [rec[k], rec_total[k]] for k in rec_total},
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"fp": len(fp),
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"ok_units": ok_units,
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"fp_by_trap": dict(fp_by_trap),
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"unmapped": len(unmapped),
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"other_findings": other_findings,
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"extraction_miss": len(extraction_miss),
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"include_unreviewed": a.include_unreviewed,
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"rules": {
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"found": dict(rs["found"]),
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"total": dict(rs["total"]),
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"fp": len(rs["fp"]),
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"fp_by_code": dict(Counter(h["code"] for h in rs["fp"])),
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"n": rs["n"],
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},
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}
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(out / "score.json").write_text(json.dumps(summary, ensure_ascii=False, indent=1))
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print("\n".join(lines[:20]))
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if __name__ == "__main__":
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main()
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