"""评测的汇总与统计（细案 01 做法 11、12；评测-D10）。所有汇总都在后端算好，界面直接显示（界面层不放逻辑：同一个数不能 有两套算法）。纯计算，输入是一次评测的逐题结果（EvalResult 的列，result_row 的形状）。 - 准确率和可追溯准确率：分母是能判的题（通过 + 不通过）；不能评分、跳过的单列，既不算对也不算错，没跑的（停止、中断、 到了上限、止损没开评）另列 not_run，出错的另列 errors。都带 Wilson 95% 区间和样本数——题集小、模型有随机性，差两三题就可能被读成「变好了」「变差了」（评测-D10：只展示、不卡门）。 - 作答路径、出具档位（声明了出具的按 formal / degraded / withheld，没声明的单列「探索问答」）、复核结论（verdict × severity） 和自动重来的次数、复核用时超过问数据页等待（25 秒）的题数。 - 结论裁判（打开了才有）：supported / partial / contradicted / insufficient / 未判的句数；文字要点的判断单列。 - look：每题平均几次（分模型和系统），用满这一轮上限的比例；主模型（助手）调用次数。 - 耗时：建图、运行（墙钟、执行）、复核、整题的 P50 / P90。 - token 和成本按角色：助手 = 建图 + 挑表 + 复核；写作 = 答案运行里的模型调用（按模型分）；裁判；期望探针不调模型，为 0。 - 报告引用率：numbers_cited / numbers，另给 uncited_numbers、unresolved、unknown_entities。 - 逐题 E1：答案的查询和期望的取数等价的比例、差异方面的分布（score.sql_match，口径账本同一个比对函数）。 - 重复跑 k 次的题：每题的通过率。 McNemar（精确、双侧）给 A/B 的配对表用（compare.py）：只看「只 A 对」「只 B 对」的那几题，二项分布 p=0.5 的双侧概率。 """ from __future__ import annotations import math from collections import Counter from collections.abc import Iterable, Mapping, Sequence from typing import Any #: 95% 区间的 z Z95 = 1.959963984540054 #: 问数据页等复核最多这么久（毫秒，同 answer.REVIEW_PAGE_WAIT_S） REVIEW_PAGE_WAIT_MS = 25_000 TIERS = ("formal", "degraded", "withheld", "explore") TIMING_KEYS = ("build_ms", "run_wall_ms", "run_active_ms", "review_ms", "total_ms") def wilson(passed: int, n: int, z: float = Z95) -> tuple[float, float] | None: """Wilson 区间（缺省 95%）。n 为 0 时没有区间。""" if n <= 0: return None p = passed / n denom = 1 + z * z / n centre = (p + z * z / (2 * n)) / denom half = z * math.sqrt(p * (1 - p) / n + z * z / (4 * n * n)) / denom return round(max(0.0, centre - half), 4), round(min(1.0, centre + half), 4) def mcnemar_exact(only_a: int, only_b: int) -> float: """精确 McNemar 双侧 p 值：不一致的那几题里，「只 A 对」服从 Binomial(b + c, 0.5)。没有不一致的题时为 1。""" n = only_a + only_b if n == 0: return 1.0 k = min(only_a, only_b) tail = sum(math.comb(n, i) for i in range(k + 1)) / (2 ** n) return round(min(1.0, 2 * tail), 6) def percentile(values: Sequence[float], q: float) -> float | None: """最近秩法的百分位（P50、P90）。没有值时为 None。""" data = sorted(v for v in values if v is not None) if not data: return None rank = max(1, math.ceil(q / 100 * len(data))) return data[min(rank, len(data)) - 1] def rate(part: int, whole: int) -> dict[str, Any]: """{passed, n, rate, ci}：比例和它的 Wilson 区间。""" return {"passed": part, "n": whole, "rate": round(part / whole, 4) if whole else None, "ci": wilson(part, whole)} def _usage_total(rows: Iterable[Mapping[str, Any] | None]) -> dict[str, Any]: from app.eval.answer import add_usage out = add_usage(None, None) for row in rows: out = add_usage(out, row) return out def summarize(results: Sequence[Mapping[str, Any]], *, judge_on: bool = False) -> dict[str, Any]: """一次评测（一臂）的总表。results 是逐题结果（result_row 的形状），只算已经结束的那些。""" from app.eval.score import SCORER_VERSION done = [r for r in results if r.get("status") in ("ok", "skipped", "failed", "cancelled", "partial")] #: 评完了的（status=ok）；没跑的（停止、中断、到了上限、止损没开评）单列成 not_run，出错的单列成 errors（同 runner._counts） evaluated = [r for r in done if r.get("status") == "ok"] verdicts = Counter(r.get("verdict") or "none" for r in done) graded = [r for r in done if r.get("verdict") in ("pass", "fail")] passed = [r for r in graded if r.get("verdict") == "pass"] traced = [r for r in passed if r.get("traced")] outcomes = Counter(s.get("outcome") for r in done for s in r.get("scores") or [] if s.get("required") and s.get("kind") != "text") text = Counter(s.get("outcome") for r in done for s in r.get("scores") or [] if s.get("kind") == "text") answered = [r for r in done if r.get("path")] tiers = Counter((r.get("tier") or "explore") for r in answered if r.get("path") == "ran") reviews = [r.get("review") or {} for r in answered if r.get("review")] looks = [r.get("looks") or {} for r in answered] timings = {k: [float((r.get("timings") or {}).get(k)) for r in answered if (r.get("timings") or {}).get(k) is not None] for k in TIMING_KEYS} usage = {role: _usage_total((r.get("usage") or {}).get(role) for r in done) for role in ("assistant", "writer", "judge")} cost = round(sum(u["cost_usd"] for u in usage.values()), 6) unpriced = sum(int(u.get("unpriced_calls") or 0) for u in usage.values()) stats = [((r.get("answer_stats") or {})) for r in answered] numbers = sum(int(s.get("numbers") or 0) for s in stats) cited = sum(int(s.get("numbers_cited") or 0) for s in stats) e1 = [r.get("sql_match") for r in done if isinstance(r.get("sql_match"), Mapping) and (r["sql_match"].get("status") in ("proven", "related", "none"))] aspects = Counter(a for m in e1 if m.get("status") == "related" for a in m.get("aspects") or []) claims = Counter() for r in done: for key, n in (((r.get("judge") or {}).get("claims") or {}).get("counts") or {}).items(): claims[key] += int(n or 0) by_case: dict[str, list[Mapping[str, Any]]] = {} for r in graded: by_case.setdefault(str(r.get("case_id")), []).append(r) repeated = {cid: rate(sum(1 for r in rows if r.get("verdict") == "pass"), len(rows)) for cid, rows in by_case.items() if len(rows) > 1} return { "scorer_version": SCORER_VERSION, "items": len(results), "done": len(done), "cases": len({r.get("case_id") for r in results}), "verdicts": {k: verdicts.get(k, 0) for k in ("pass", "fail", "ungradable", "skipped")}, "not_run": sum(1 for r in done if r.get("status") in ("cancelled", "skipped")), "errors": sum(1 for r in done if r.get("status") == "failed"), "accuracy": rate(len(passed), len(graded)), "traced_accuracy": rate(len(traced), len(graded)), "outcomes": dict(outcomes), "text": dict(text), "paths": dict(Counter(r.get("path") for r in answered)), "tiers": {k: tiers.get(k, 0) for k in TIERS}, "review": { "verdicts": dict(Counter(str(v.get("verdict") or "ok") for v in reviews)), "severity": dict(Counter(str(v.get("severity")) for v in reviews if v.get("severity"))), "retries": sum(1 for r in answered if int(r.get("attempts") or 0) > 0), "slow": sum(1 for r in answered if ((r.get("timings") or {}).get("review_ms") or 0) > REVIEW_PAGE_WAIT_MS), }, "judge": {"on": judge_on, "claims": dict(claims) if judge_on else None}, "looks": { "model": sum(int(x.get("model") or 0) for x in looks), "system": sum(int(x.get("system") or 0) for x in looks), "per_case": round(sum(int(x.get("model") or 0) + int(x.get("system") or 0) for x in looks) / len(looks), 2) if looks else None, "limit_hit": rate(sum(1 for x in looks if x.get("limit_hit")), len(looks))["rate"] if looks else None, }, "calls": {"assistant": int(usage["assistant"].get("calls") or 0), "per_case": round(int(usage["assistant"].get("calls") or 0) / len(answered), 2) if answered else None}, "timings": {k: {"p50": percentile(v, 50), "p90": percentile(v, 90), "n": len(v)} for k, v in timings.items()}, "usage": {**usage, "gold": {"cost_usd": 0.0, "calls": 0}}, "cost_usd": cost, "unpriced_calls": unpriced, # 每题平均花费（下一次开跑前的估算按它）：只按评完了的题平均，没开评的不摊。有估不出金额的调用时 cost_usd 只是估得出 # 的那部分：一分都没估出来（模型都不在价格目录里）的为 None，不写成 0（像不花钱）；估出了一部分的照给，unpriced_calls # 说明它只是下限 "per_case_cost_usd": round(cost / len(evaluated), 6) if evaluated and (cost > 0 or not unpriced) else None, "citations": {"numbers": numbers, "numbers_cited": cited, "uncited_numbers": sum(int(s.get("uncited_numbers") or 0) for s in stats), "unresolved": sum(int(s.get("unresolved") or 0) for s in stats), "unknown_entities": sum(int(s.get("unknown_entities") or 0) for s in stats), "rate": round(cited / numbers, 4) if numbers else None}, "e1": {**rate(sum(1 for m in e1 if m.get("status") == "proven"), len(e1)), "aspects": dict(aspects)}, "pass_rates": repeated, } __all__ = ["REVIEW_PAGE_WAIT_MS", "TIERS", "Z95", "mcnemar_exact", "percentile", "rate", "summarize", "wilson"]