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Evaluate an agent (trained or baseline) against held-out tasks.
Held-out evaluation tasks (last 5 of 25):
multimodal_caption_speak_024
multimodal_full_pipeline_025
code_to_speech_020
doc_quick_summary_015
audio_sentiment_005
Usage:
# Baseline heuristic
python scripts/evaluate.py --agent heuristic
# Trained checkpoint
python scripts/evaluate.py --agent trained --model-path ./outputs/phase4
# LLM via OpenAI API (zero-shot)
OPENAI_API_KEY=... python scripts/evaluate.py --agent llm --model gpt-4o-mini
"""
import argparse
import asyncio
import json
import os
import sys
from pathlib import Path
from typing import Any, Dict, List
ROOT = Path(__file__).resolve().parent.parent
sys.path.insert(0, str(ROOT))
HOLDOUT_TASKS = [
"multimodal_caption_speak_024",
"multimodal_full_pipeline_025",
"code_to_speech_020",
"doc_quick_summary_015",
"audio_sentiment_005",
]
async def evaluate_agent(agent_name: str, env_url: str, n_episodes: int = 1) -> Dict[str, Any]:
"""Run agent on each held-out task, collect grades."""
from spaces_pipeline_env import SpacesPipelineEnv
if agent_name == "heuristic":
from inference import HeuristicAgent
agent = HeuristicAgent()
elif agent_name == "llm":
from inference import LLMAgent
agent = LLMAgent(
api_key=os.getenv("OPENAI_API_KEY"),
base_url=os.getenv("API_BASE_URL", "https://router.huggingface.co/v1"),
model=os.getenv("MODEL_NAME", "Qwen/Qwen2.5-72B-Instruct"),
)
elif agent_name == "trained":
# Stub: would load PEFT adapter
print("Trained agent loader not implemented in this build")
sys.exit(1)
else:
print(f"Unknown agent: {agent_name}")
sys.exit(1)
env = SpacesPipelineEnv(base_url=env_url)
await env.connect()
results: List[Dict[str, Any]] = []
try:
for task_id in HOLDOUT_TASKS:
for ep in range(n_episodes):
print(f"\n=== {task_id} (ep {ep+1}/{n_episodes}) ===")
result = await env.reset(task=task_id, seed=42 + ep)
obs = result.observation
agent.reset(task_id)
while not result.done:
action = agent.act(obs)
if action is None:
break
result = await env.step(action)
obs = result.observation
grade = obs.grade_score or 0.0
details = obs.grade_details or {}
print(f" Grade: {grade:.3f} | Components: {details.get('components', {})}")
results.append({
"task_id": task_id,
"episode": ep,
"grade": grade,
"details": details,
})
finally:
await env.close()
avg_grade = sum(r["grade"] for r in results) / len(results) if results else 0.0
pass_rate = sum(1 for r in results if r["grade"] >= 0.5) / len(results) if results else 0.0
print(f"\n=== Summary ===")
print(f" Average grade: {avg_grade:.3f}")
print(f" Pass rate (>=0.5): {pass_rate:.1%}")
return {"avg_grade": avg_grade, "pass_rate": pass_rate, "results": results}
async def main():
parser = argparse.ArgumentParser()
parser.add_argument("--agent", default="heuristic", choices=["heuristic", "llm", "trained"])
parser.add_argument("--env-url", default="http://localhost:8000")
parser.add_argument("--episodes", type=int, default=1)
parser.add_argument("--output", help="Save results to JSON")
args = parser.parse_args()
results = await evaluate_agent(args.agent, args.env_url, args.episodes)
if args.output:
with open(args.output, "w") as f:
json.dump(results, f, indent=2, default=str)
print(f"Saved to {args.output}")
if __name__ == "__main__":
asyncio.run(main())
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