| """Score one LegalReasonBench matter from a finished Harbor trial. |
| |
| The matter reward is points-weighted across its assignments: the sum of signed |
| points earned (penalties subtract) over the sum of available positive points, |
| clamped to [0, 1]. Harbor's own multi_step_reward_strategy = "mean" averages the |
| per-assignment rewards instead and is diagnostic only. |
| |
| Usage: python score_matter.py <harbor trial dir> |
| """ |
| import json |
| import sys |
| from pathlib import Path |
|
|
|
|
| def score(trial_dir: Path) -> dict: |
| earned = positive = 0.0 |
| steps = [] |
| |
| |
| for details in sorted(trial_dir.rglob("reward-details.json")): |
| correctness = json.loads(details.read_text()).get("correctness") |
| aggregation = correctness.get("aggregation") if isinstance(correctness, dict) else None |
| if not isinstance(aggregation, dict) or "earned" not in aggregation or "positive_total" not in aggregation: |
| continue |
| earned += float(aggregation["earned"]) |
| positive += float(aggregation["positive_total"]) |
| steps.append({ |
| "file": str(details.relative_to(trial_dir)), |
| "assignment_reward": correctness.get("score"), |
| "earned": aggregation["earned"], |
| "positive_total": aggregation["positive_total"], |
| }) |
| if not steps: |
| raise SystemExit(f"no assignment reward-details.json under {trial_dir}") |
| overall = max(0.0, min(1.0, earned / positive)) if positive > 0 else 0.0 |
| return {"matter_reward": overall, "earned": earned, "positive_total": positive, "assignments": steps} |
|
|
|
|
| if __name__ == "__main__": |
| print(json.dumps(score(Path(sys.argv[1])), indent=2)) |
|
|
