tzt
6e5294353f
- scripts/sense_report.py：collect 数据 -> 夜间标签推导 -> 一致率/档位分布/ T1 决策精度（conformal 覆盖）/T1 真值占比/混淆矩阵 -> CSV+Markdown 报告入 research/v2_experiments/；晋升门四条自动判定 （一致率>=85% + 覆盖>=1-α-2% + 标签>=min_labels + T1 占比 40-55%） - 测试 +2（90% 一致率合成集走完整管道含混淆矩阵与晋升结论 / 标签不足时正确拒绝 live），全量 412 passed
159 lines
6.5 KiB
Python
159 lines
6.5 KiB
Python
"""E-G1 shadow 报告（T-G8）：一致率 / 档位分布 / conformal 覆盖率 / 成本延迟 delta。
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数据源：sense.sqlite3 tier_observations（collect/shadow 期积累的观察）。
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输出：CSV + Markdown 报告入 research/v2_experiments/（E-G1_sense_report.*）。
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晋升门核对（§6）：一致率 >=85%、覆盖 <= α+2%、T1 占比 40–55%、标签 >= min_labels。
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用法：
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python scripts/sense_report.py --db data/sense.sqlite3
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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 sys
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import time
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from pathlib import Path
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ROOT = Path(__file__).resolve().parent.parent
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sys.path.insert(0, str(ROOT))
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if hasattr(sys.stdout, "reconfigure"):
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sys.stdout.reconfigure(encoding="utf-8")
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sys.stderr.reconfigure(encoding="utf-8")
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from gateway.sense.labeler import derive_true_tiers # noqa: E402
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from gateway.sense.store import SenseStore # noqa: E402
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TIERS = ("T1", "T2", "T3")
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def collect(store, min_labels: int, alpha: float, now: float) -> dict:
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"""从观察表推导标签并聚合报告数据。"""
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derived = derive_true_tiers(store, now=now)
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rows = store.labeled_rows(limit=500000)
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n = len(rows)
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by_true = {t: 0 for t in TIERS}
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by_decided = {t: 0 for t in TIERS}
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agree = 0
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# conformal 覆盖：判 T1 且真 T1 的比例（P(true==T1|判T1) 反向即误判率）
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t1_pred = t1_pred_correct = 0
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t3_pred = t3_pred_correct = 0
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confusion = {d: {t: 0 for t in TIERS} for d in TIERS}
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for r in rows:
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decided = r["decided_tier"]
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true = r["true_tier"]
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if decided in by_decided:
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by_decided[decided] += 1
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if true in by_true:
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by_true[true] += 1
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if decided in confusion and true in confusion.get(decided, {}):
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confusion[decided][true] += 1
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if decided == true:
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agree += 1
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if decided == "T1":
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t1_pred += 1
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t1_pred_correct += 1 if true == "T1" else 0
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if decided == "T3":
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t3_pred += 1
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t3_pred_correct += 1 if true == "T3" else 0
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agreement = agree / n if n else 0.0
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t1_precision = t1_pred_correct / t1_pred if t1_pred else 1.0
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t3_precision = t3_pred_correct / t3_pred if t3_pred else 1.0
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# T1 占比（真值口径，§12 总验收 40–55%）
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t1_share = by_true["T1"] / n if n else 0.0
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return {
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"n": n, "derived_now": derived, "agreement": agreement,
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"by_true": by_true, "by_decided": by_decided,
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"t1_precision": t1_precision, "t3_precision": t3_precision,
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"confusion": confusion, "t1_share": t1_share,
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"min_labels": min_labels, "alpha": alpha,
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"gates": {
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"agreement_ok": agreement >= 0.85,
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"coverage_ok": t1_precision >= 1.0 - alpha - 0.02,
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"labels_ok": n >= min_labels,
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"t1_share_ok": 0.40 <= t1_share <= 0.55,
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},
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}
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def write_report(data: dict, out_dir: Path, since_label: str) -> Path:
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out_dir.mkdir(parents=True, exist_ok=True)
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csv_path = out_dir / "E-G1_sense_report.csv"
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lines = ["metric,value"]
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lines.append(f"observations,{data['n']}")
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lines.append(f"derived_now,{data['derived_now']}")
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lines.append(f"agreement,{data['agreement']:.4f}")
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lines.append(f"t1_precision,{data['t1_precision']:.4f}")
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lines.append(f"t3_precision,{data['t3_precision']:.4f}")
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lines.append(f"t1_share,{data['t1_share']:.4f}")
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for t in TIERS:
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lines.append(f"by_true_{t},{data['by_true'][t]}")
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lines.append(f"by_decided_{t},{data['by_decided'][t]}")
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csv_path.write_text("\n".join(lines), encoding="utf-8")
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g = data["gates"]
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md = out_dir / "E-G1_sense_report.md"
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gates_pass = all(g.values())
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md_lines = [
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"# E-G1 语义分级 shadow 报告",
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"",
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f"- 生成时间：{time.strftime('%Y-%m-%d %H:%M:%S')}（{since_label}）",
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f"- 观察总数：{data['n']}（本次夜间推导回填 {data['derived_now']} 条）",
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f"- shadow 一致率：**{data['agreement']*100:.1f}%**（门：≥85% "
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f"{'✅' if g['agreement_ok'] else '❌'}）",
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f"- T1 决策精度（conformal 覆盖）：**{data['t1_precision']*100:.1f}%**"
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f"（门：≥{(1-data['alpha']-0.02)*100:.0f}% "
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f"{'✅' if g['coverage_ok'] else '❌'}）",
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f"- T1 真值占比：**{data['t1_share']*100:.1f}%**（目标区间 40–55% "
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f"{'✅' if g['t1_share_ok'] else '❌'}）",
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f"- 标签量：{data['n']} / min_labels {data['min_labels']} "
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f"{'✅' if g['labels_ok'] else '❌'}",
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"",
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"## 档位分布（真值 / 决策）",
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"",
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"| 档 | 真值 | 决策 |",
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"|---|---|---|",
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]
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for t in TIERS:
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md_lines.append(f"| {t} | {data['by_true'][t]} | {data['by_decided'][t]} |")
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md_lines += ["", "## 混淆矩阵（行=决策，列=真值）", "",
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"| 决策\\真值 | T1 | T2 | T3 |", "|---|---|---|---|"]
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for d in TIERS:
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row = data["confusion"].get(d, {})
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md_lines.append(f"| {d} | {row.get('T1',0)} | {row.get('T2',0)} "
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f"| {row.get('T3',0)} |")
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md_lines += ["", f"## 晋升结论：{'**可申请 live**' if gates_pass else '**继续 collect/shadow**'}",
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""]
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md.write_text("\n".join(md_lines), encoding="utf-8")
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return md
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def main() -> int:
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ap = argparse.ArgumentParser(description="E-G1 shadow 报告（T-G8）")
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ap.add_argument("--db", default="data/sense.sqlite3")
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ap.add_argument("--out", default="research/v2_experiments")
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ap.add_argument("--alpha", type=float, default=0.05)
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ap.add_argument("--min-labels", type=int, default=500)
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args = ap.parse_args()
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if not Path(args.db).exists():
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print(f"[report] 无观察库 {args.db}——先以 collect 模式积累观察。")
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return 1
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store = SenseStore.init_db(args.db)
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data = collect(store, args.min_labels, args.alpha, time.time())
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md = write_report(data, Path(args.out), time.strftime("%Y%m%d-%H%M"))
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print(f"[report] n={data['n']} 一致率={data['agreement']*100:.1f}% "
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f"T1 精度={data['t1_precision']*100:.1f}% T1 占比={data['t1_share']*100:.1f}%")
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print(f"[report] 报告: {md}")
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gates = data["gates"]
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print(f"[report] 晋升门：一致率{'✅' if gates['agreement_ok'] else '❌'} "
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f"覆盖{'✅' if gates['coverage_ok'] else '❌'} "
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f"标签{'✅' if gates['labels_ok'] else '❌'} "
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f"T1占比{'✅' if gates['t1_share_ok'] else '❌'}")
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return 0
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if __name__ == "__main__":
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sys.exit(main())
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