| """Assemble the Hugging Face dataset folder for Five Desks v2 and v3 (same image; v3 trains a subset).""" |
|
|
| import json |
| import shutil |
| import sys |
| from pathlib import Path |
|
|
| ROOT = Path(__file__).resolve().parents[1] |
| sys.path.insert(0, str(ROOT)) |
| sys.path.insert(0, str(ROOT / "scripts")) |
|
|
| from diligence.environment import TASK_IDS, DiligenceEnvironment, build |
| from selection_v3 import SELECTED |
|
|
| TIP = "put your working in the action" |
| |
| RUNS = {"a": None, "b": lambda t: not t.startswith("math-reasoning"), "reasoning2": None} |
| out = ROOT / "dataset" |
| shutil.rmtree(out, ignore_errors=True) |
| (out / "calibration").mkdir(parents=True) |
| calib = {} |
| for run, keep in RUNS.items(): |
| for line in open(ROOT / f"reports/calibration-v2/{run}/episodes.jsonl"): |
| ep = json.loads(line) |
| if keep and not keep(ep["task_id"]): |
| continue |
| hinted = TIP in ep["turns"][0]["obs"] |
| calib.setdefault(ep["task_id"], []).append((ep["reward"], hinted)) |
| shutil.copy(ROOT / f"reports/calibration-v2/{run}/summary.json", out / f"calibration/v2_{run}_summary.json") |
| shutil.copy(ROOT / f"reports/calibration-v2/{run}/episodes.jsonl", out / f"calibration/v2_{run}_episodes.jsonl") |
|
|
|
|
| def mean_of(pairs): |
| return round(sum(r for r, _ in pairs) / len(pairs), 3) if pairs else None |
|
|
| with open(out / "tasks.jsonl", "w") as f: |
| for task_id in TASK_IDS: |
| task = build(task_id, 0) |
| prompt = DiligenceEnvironment().reset(task_id=task_id, seed=0).output |
| f.write(json.dumps({ |
| "task_id": task_id, |
| "family": task.family, |
| "domain": task.domain, |
| "level": int(task_id[-1]), |
| "submitted_for_training": task_id in SELECTED, |
| "qwen3_8_27b_mean_reward": mean_of(calib.get(task_id, [])), |
| "qwen3_8_27b_mean_with_hint": mean_of([p for p in calib.get(task_id, []) if p[1]]), |
| "qwen3_8_27b_mean_without_hint": mean_of([p for p in calib.get(task_id, []) if not p[1]]), |
| "qwen3_8_27b_rewards": [r for r, _ in calib.get(task_id, [])], |
| "example_seed": 0, |
| "example_prompt": prompt, |
| "example_files": json.dumps(task.files), |
| "example_solution": json.dumps(task.oracle()), |
| "example_caveat_missed": json.dumps(task.naive()), |
| "max_tool_calls": task.max_steps, |
| }) + "\n") |
|
|
| shutil.copy(ROOT / "reports/proxy/FINDINGS.md", out / "calibration/stand_in_test_findings.md") |
| transfer = out / "calibration/transfer" |
| for item in sorted((ROOT / "reports/transfer").iterdir()): |
| if item.is_dir() and (item / "summary.json").exists(): |
| (transfer / item.name).mkdir(parents=True) |
| for name in ["summary.json", "episodes.jsonl"]: |
| if (item / name).exists(): |
| shutil.copy(item / name, transfer / item.name / name) |
| elif item.suffix == ".txt": |
| transfer.mkdir(parents=True, exist_ok=True) |
| shutil.copy(item, transfer / item.name) |
| src = out / "source" |
| for item in ["diligence", "server", "scripts", "tests", "proxy_eval", "five_desks", "openenv.yaml", |
| "requirements.in", "requirements.lock", ".dockerignore"]: |
| path = ROOT / item |
| if path.is_dir(): |
| shutil.copytree(path, src / item, ignore=shutil.ignore_patterns("__pycache__", "*.pyc")) |
| else: |
| shutil.copy(path, src / item) |
| shutil.copy(ROOT / "card_v3.md", out / "README.md") |
| print("dataset at", out, sorted(p.name for p in out.iterdir())) |
|
|
