"""MA-Bench scorer. Standalone: python score.py rubric_items.jsonl verdicts.jsonl verdicts.jsonl: one line per graded brief, {"case_id": "C1", "brief_id": "...", "failed_items": ["S1-01-n", ...]} where failed_items are the rubric item_ids the judge marked as NOT satisfying pass_condition (the judge returns failures only; every unlisted item counts as passed). For D and CD items the pass condition asserts the mistake is present, so an unlisted (passed) negative item incurs its negative weight. Prints per-brief unweighted pass rate and severity-weighted score, then per-case means. """ import json import sys import collections import statistics as st def load_items(path): by = collections.defaultdict(dict) for line in open(path): it = json.loads(line) by[it["case_id"]][it["item_id"]] = it return by def score(items, failed): pos = [i for i in items.values() if i["weight"] > 0] neg = [i for i in items.values() if i["weight"] < 0] earned = sum(i["weight"] for i in pos if i["item_id"] not in failed) incurred = sum( i["weight"] for i in neg if i["item_id"] not in failed ) # mistake present correct = sum(1 for i in pos if i["item_id"] not in failed) + sum( 1 for i in neg if i["item_id"] in failed ) return { "pass_rate": correct / len(items), "weighted": (earned + incurred) / sum(i["weight"] for i in pos), "detrimental_present": sum(1 for i in neg if i["item_id"] not in failed), } def main(): items = load_items(sys.argv[1]) per_case = collections.defaultdict(list) for line in open(sys.argv[2]): v = json.loads(line) s = score(items[v["case_id"]], set(v["failed_items"])) per_case[v["case_id"]].append(s) print( f"{v['case_id']}\t{v.get('brief_id', '')}\tpass={s['pass_rate']:.3f}\tweighted={s['weighted']:.3f}\tdetrimental={s['detrimental_present']}" ) for c, ss in sorted(per_case.items()): print( f"# {c}\tn={len(ss)}\tpass={st.mean(x['pass_rate'] for x in ss):.3f}\tweighted={st.mean(x['weighted'] for x in ss):.3f}" ) if __name__ == "__main__": main()