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e9ca231 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 | """Benchmark MedFlow-OpenEnv with a few lightweight policies.
Usage:
python scripts/benchmark_medflow.py --episodes 3
Outputs:
- outputs/benchmarks/benchmark_summary.json
- outputs/benchmarks/benchmark_summary.md
The benchmark compares a greedy policy against a random policy across all
defined tasks and records the environment's final score and episode stats.
"""
from __future__ import annotations
import argparse
import json
import random
import sys
from pathlib import Path
from statistics import mean
from typing import Dict, Iterable, List
ROOT = Path(__file__).resolve().parents[1]
if str(ROOT) not in sys.path:
sys.path.insert(0, str(ROOT))
from app.env import HospitalQueueEnvironment
from app.models import HospitalAction
from app.tasks import ALL_TASKS
def _choose_greedy_action(obs) -> HospitalAction:
waiting = list(obs.waiting_patients)
doctors = list(obs.doctors)
free_docs = [d for d in doctors if not d.get("busy", False)]
if waiting and free_docs:
priority_rank = {"emergency": 0, "urgent": 1, "normal": 2}
patient = sorted(
waiting,
key=lambda p: (
priority_rank.get(p.get("priority", "normal"), 3),
-int(p.get("severity_score", 0)),
-int(p.get("wait_minutes", 0)),
int(p.get("id", 0)),
),
)[0]
required = patient.get("required_specialization")
matching = [d for d in free_docs if d.get("specialization") == required]
general = [d for d in free_docs if d.get("specialization") == "General"]
doctor = (matching or general or free_docs)[0]
return HospitalAction(action_type="assign", patient_id=int(patient["id"]), doctor_id=int(doctor["id"]))
if waiting:
patient = waiting[0]
return HospitalAction(action_type="prioritize", patient_id=int(patient["id"]))
for d in doctors:
if (not d.get("busy", False)) and d.get("current_patient_id") is not None:
return HospitalAction(action_type="discharge", patient_id=int(d["current_patient_id"]))
return HospitalAction(action_type="wait")
def _choose_random_action(obs) -> HospitalAction:
waiting = list(obs.waiting_patients)
doctors = list(obs.doctors)
free_docs = [d for d in doctors if not d.get("busy", False)]
if waiting and free_docs:
patient = random.choice(waiting)
doctor = random.choice(free_docs)
return HospitalAction(action_type="assign", patient_id=int(patient["id"]), doctor_id=int(doctor["id"]))
if waiting:
return HospitalAction(action_type="prioritize", patient_id=int(random.choice(waiting)["id"]))
for d in doctors:
if d.get("current_patient_id") is not None:
return HospitalAction(action_type="discharge", patient_id=int(d["current_patient_id"]))
return HospitalAction(action_type="wait")
def run_episode(task_id: str, policy: str, seed: int = 42) -> Dict[str, object]:
random.seed(seed)
env = HospitalQueueEnvironment()
obs = env.reset(task_id=task_id, seed=seed)
total_reward = 0.0
steps = 0
while not obs.done and steps < ALL_TASKS[task_id].max_steps:
if policy == "greedy":
action = _choose_greedy_action(obs)
elif policy == "random":
action = _choose_random_action(obs)
else:
raise ValueError(f"Unknown policy: {policy}")
obs = env.step(action)
total_reward += float(obs.reward or 0.0)
steps += 1
state = env.state
return {
"task_id": task_id,
"policy": policy,
"final_score": float(state.final_score or 0.0),
"total_reward": round(total_reward, 3),
"steps": steps,
"total_seen": state.total_patients_seen,
"total_arrived": state.total_patients_arrived,
"avg_wait_all": float(state.avg_wait_all),
"critical_deaths": len(state.emergency_response_times),
"submitted": bool(state.submitted),
}
def _write_markdown(rows: List[Dict[str, object]], out_path: Path) -> None:
lines = [
"# MedFlow OpenEnv Benchmark Summary",
"",
"| task_id | policy | final_score | total_reward | steps | seen/arrived | avg_wait_all |",
"| --- | --- | ---: | ---: | ---: | ---: | ---: |",
]
for row in rows:
lines.append(
f"| {row['task_id']} | {row['policy']} | {row['final_score']:.3f} | {row['total_reward']:.3f} | {row['steps']} | {row['total_seen']}/{row['total_arrived']} | {row['avg_wait_all']:.2f} |"
)
out_path.write_text("\n".join(lines), encoding="utf-8")
def _summarize(rows: List[Dict[str, object]]) -> Dict[str, object]:
by_policy: Dict[str, List[Dict[str, object]]] = {}
for row in rows:
by_policy.setdefault(str(row["policy"]), []).append(row)
summary: Dict[str, object] = {"policies": {}, "rows": rows}
for policy, policy_rows in by_policy.items():
summary["policies"][policy] = {
"avg_final_score": round(mean(float(r["final_score"]) for r in policy_rows), 3),
"avg_total_reward": round(mean(float(r["total_reward"]) for r in policy_rows), 3),
"avg_steps": round(mean(int(r["steps"]) for r in policy_rows), 1),
"success_rate": round(sum(1 for r in policy_rows if float(r["final_score"]) >= 0.10) / len(policy_rows), 3),
}
return summary
def main(episodes: int, outdir: Path, seed: int) -> None:
outdir.mkdir(parents=True, exist_ok=True)
random.seed(seed)
rows: List[Dict[str, object]] = []
for task_id in ALL_TASKS.keys():
for policy in ("greedy", "random"):
for episode in range(episodes):
row = run_episode(task_id=task_id, policy=policy, seed=seed + episode)
rows.append(row)
print(
f"{task_id} | {policy} | score={row['final_score']:.3f} | reward={row['total_reward']:.3f} | steps={row['steps']}"
)
summary = _summarize(rows)
summary_path = outdir / "benchmark_summary.json"
markdown_path = outdir / "benchmark_summary.md"
summary_path.write_text(json.dumps(summary, indent=2), encoding="utf-8")
_write_markdown(rows, markdown_path)
print(f"Saved JSON summary to {summary_path}")
print(f"Saved markdown summary to {markdown_path}")
for policy, stats in summary["policies"].items():
print(
f"{policy}: avg_final_score={stats['avg_final_score']:.3f}, avg_total_reward={stats['avg_total_reward']:.3f}, success_rate={stats['success_rate']:.3f}"
)
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument("--episodes", type=int, default=3)
parser.add_argument("--seed", type=int, default=42)
parser.add_argument("--outdir", type=Path, default=Path("outputs/benchmarks"))
args = parser.parse_args()
main(episodes=args.episodes, outdir=args.outdir, seed=args.seed) |