"""BayesLoop 与 SevenBatch 的配对消融（离线，自建模拟器）。 同一场景、同一误差场下配对比较两种策略，输出逐场景行与聚合统计： all_cleared / correct_absence / anomalies / 虚拟时间 / 检测数 / 距离 / 实机计算墙钟。基准配置 persistent_search=False（比较"首次证明找全"的 虚拟时间口径；persistent 复扫到限时的行为与策略质量无关）。 Run: uv run python scripts/benchmark_q3_bayes.py --seeds 0,1,2,3,4,5 """ from __future__ import annotations import argparse from dataclasses import asdict, replace import hashlib import json import math from pathlib import Path from statistics import mean, median from time import perf_counter from mcm26b.config import ProblemConfig from mcm26b.planner.bayes_loop import BayesLoop from mcm26b.planner.seven import SevenBatch from mcm26b.protocol.client import SimulatorClient from mcm26b.harness import LocalSimTransport, LocalSimulator from referee import Source from mcm26b.harness import random_omni_scene ROOT = Path(__file__).resolve().parents[1] VARIANTS = {"seven_batch": SevenBatch, "bayes": BayesLoop} def run_case(name, sources, error_seed, config, out): scene_path = out / "scenes" / f"{name}.json" scene_path.parent.mkdir(parents=True, exist_ok=True) scene_path.write_text(json.dumps( {"sources": [asdict(s) for s in sources], "error_seed": error_seed}, indent=2), encoding="utf-8") rows = [] for label, cls in VARIANTS.items(): sim = LocalSimulator(sources, robot_id="Q3-BAYES", config=config, error_seed=error_seed) client = SimulatorClient("Q3-BAYES", config=config, transport=LocalSimTransport(sim)) loop = cls(client, config) start = perf_counter() summary = loop.run() elapsed = perf_counter() - start actions = client.log.action_events() log_path = out / "actions" / f"{name}-{label}.jsonl" log_path.parent.mkdir(parents=True, exist_ok=True) with log_path.open("w", encoding="utf-8") as handle: for event in actions: handle.write(json.dumps(event, ensure_ascii=False) + "\n") truth = {s.channel for s in sources} absent = set(range(1, 21)) - truth # 缺席证明有效性：PF 缺席以幸存集为空为据，无源频道缺席即为正确 correct_absence = set(summary["absent_channels"]) <= absent accounted = summary.get("accounted_virtual_time_s") or summary["virtual_time_s"] row = {"case": name, "variant": label, "source_count": len(sources), "all_cleared": sim.cleared == truth, "completed": summary["completed"], "correct_absence": correct_absence, "normal_exit": summary["exit_reason"] == "user_exit", "virtual_time_s": summary["virtual_time_s"], "accounted_virtual_time_s": accounted, "measures": summary["measures"], "clears": summary["clears"], "failed_clears": summary["clears"] - len(sim.cleared), "distance_m": summary["move_distance_m"], "skipped_measures": summary.get("skipped_measures", 0), "no_shrink_measures": summary.get("no_shrink_measures", 0), "compute_wall_s": elapsed, "anomalies": summary["anomalies"], "stop_reason": summary["stop_reason"], "actions_sha256": hashlib.sha256(log_path.read_bytes()).hexdigest()} rows.append(row) print(f"{name} {label}: cleared={row['all_cleared']} " f"T={row['virtual_time_s']:.0f} M={row['measures']} " f"D={row['distance_m']:.0f} wall={elapsed:.1f}s", flush=True) return rows def main(): parser = argparse.ArgumentParser(description=__doc__) parser.add_argument("--seeds", default="0,1,2,3,4,5") parser.add_argument("--counts", default="10,16") parser.add_argument("--out", type=Path, default=ROOT / "output/q3/bayes-ablation") parser.add_argument("--bearing-margin-deg", type=float, default=0.005) parser.add_argument("--bayes-json", type=Path, default=None, help="BayesConfig 覆盖参数 JSON（如 tune_q3_bayes 的" " best_params.json）；缺省用库默认配置") args = parser.parse_args() args.out.mkdir(parents=True, exist_ok=True) base = ProblemConfig() bayes_params = {} if args.bayes_json is not None: bayes_params = json.loads(args.bayes_json.read_text(encoding="utf-8")) if "best_params" in bayes_params: bayes_params = bayes_params["best_params"] base = replace(base, bayes=replace(base.bayes, **bayes_params) if bayes_params else base.bayes, search=replace(base.search, bearing_rounding_margin_deg=args.bearing_margin_deg, persistent_search=False)) if bayes_params: print("bayes 覆盖参数:", json.dumps(bayes_params, ensure_ascii=False)) config = base rows = [] seeds = [int(s) for s in args.seeds.split(",")] counts = [int(c) for c in args.counts.split(",")] for count in counts: for seed in seeds: rows.extend(run_case(f"random-{count}-seed{seed}", random_omni_scene(seed, count, config=config), 20260910 + seed, config, args.out)) # 圆域边界源（r_eff=1000，扇区接缝处）：最刁钻的发现与定位场景 edge = [Source(c + 1, 1800 * math.cos(math.pi / 6 + 2 * math.pi * c / count), 1800 * math.sin(math.pi / 6 + 2 * math.pi * c / count), 1000) for c in range(count)] rows.extend(run_case(f"boundary-{count}", edge, 20260910, config, args.out)) aggregates = {} for label in VARIANTS: part = [r for r in rows if r["variant"] == label] baseline = {r["case"]: r for r in rows if r["variant"] == "seven_batch"} savings = [1 - r["virtual_time_s"] / baseline[r["case"]]["virtual_time_s"] for r in part] valid = sum(r["all_cleared"] and r["completed"] and r["correct_absence"] and r["normal_exit"] and not r["anomalies"] for r in part) aggregates[label] = { "runs": len(part), "all_cleared_runs": sum(r["all_cleared"] for r in part), "valid_runs": valid, "mean_virtual_s": mean(r["virtual_time_s"] for r in part), "median_virtual_s": median(r["virtual_time_s"] for r in part), "max_virtual_s": max(r["virtual_time_s"] for r in part), "mean_measures": mean(r["measures"] for r in part), "mean_distance_m": mean(r["distance_m"] for r in part), "mean_skipped_measures": mean(r["skipped_measures"] for r in part), "mean_compute_wall_s": mean(r["compute_wall_s"] for r in part), "mean_paired_time_reduction": mean(savings), "min_paired_time_reduction": min(savings), "max_paired_time_reduction": max(savings), "failed_clears": sum(r["failed_clears"] for r in part), } result = {"source": "local_simulator_only", "seeds": args.seeds, "counts": args.counts, "config": asdict(config), "aggregates": aggregates, "runs": rows, "limitations": "自建模拟器合成数据；persistent 关闭以对齐找全口径。"} (args.out / "benchmark.json").write_text( json.dumps(result, ensure_ascii=False, indent=2), encoding="utf-8") print(json.dumps(aggregates, ensure_ascii=False, indent=2)) ok = all(a["valid_runs"] == a["runs"] for a in aggregates.values()) \ and aggregates["bayes"]["mean_paired_time_reduction"] > 0 return 0 if ok else 1 if __name__ == "__main__": raise SystemExit(main())