| """Score frozen generation/source tasks. CPU only; no model or network access.""" |
| import argparse, collections, json, sys |
| from pathlib import Path |
| ROOT = Path(__file__).resolve().parents[1] |
| sys.path.insert(0, str(ROOT / "scoring")) |
| from common_scoring import score_generation, score_source |
|
|
| def rows(path): |
| return [json.loads(line) for line in Path(path).read_text().splitlines() if line.strip()] |
|
|
| def main(): |
| p = argparse.ArgumentParser() |
| p.add_argument("--suite", required=True, choices=["generation", "source"]) |
| p.add_argument("--predictions", required=True) |
| p.add_argument("--output", required=True) |
| a = p.parse_args() |
| cases = rows(ROOT / "inputs" / (a.suite + ".jsonl")) |
| refs = {x["id"]: x["reference"] for x in rows(ROOT / "references" / (a.suite + ".jsonl"))} |
| key = "unit_id" if a.suite == "generation" else "id" |
| by_id = {x[key]: x for x in cases} |
| predictions = rows(a.predictions) |
| ids = [x["id"] for x in predictions] |
| if len(ids) != len(set(ids)): |
| raise ValueError("duplicate prediction IDs") |
| if set(ids) - by_id.keys(): |
| raise ValueError("unknown prediction IDs") |
| values = [] |
| for pred in predictions: |
| case = by_id[pred["id"]] |
| raw = pred["raw_text"] |
| if not isinstance(raw, str): |
| raise TypeError("raw_text must be a string") |
| eos = pred.get("terminal_eos_text", "") |
| if not isinstance(eos, str): |
| raise TypeError("terminal_eos_text must be a string") |
| if eos and pred.get("terminal_eos_verified") is not True: |
| raise ValueError("verify terminal token ID before specifying terminal_eos_text") |
| score = (score_generation(raw, refs[pred["id"]], case.get("tools", []), eos) |
| if a.suite == "generation" else score_source(case, refs[pred["id"]], raw, eos)) |
| values.append({"id": pred["id"], "kind": case.get("diagnostic_kind", case.get("kind")), "scores": score}) |
| groups = {} |
| metric_names = ({"nonempty","json_valid","reference_top_level_keys_present","exact_json_reference_match", |
| "tool_schema_valid","tool_names_match_reference","tool_arguments_match_reference"} |
| if a.suite == "generation" else |
| {"json_valid","identifiers_match","schema_fields_present","all_nonempty_spans_supported", |
| "numeric_literals_exact","mechanical_grounding_pass","quote_match", |
| "interval_matches_source","exact_reference_position"}) |
| for kind in sorted({x["kind"] for x in values}): |
| group = [x["scores"] for x in values if x["kind"] == kind] |
| groups[kind] = {m: {"passed": sum(s[m] for s in group if type(s.get(m)) is bool), |
| "scored": sum(type(s.get(m)) is bool for s in group)} |
| for m in sorted(metric_names) if any(type(s.get(m)) is bool for s in group)} |
| out = {"suite": a.suite, "expected": len(cases), "submitted": len(values), |
| "complete": len(values) == len(cases), "missing_ids": sorted(by_id.keys() - set(ids)), |
| "scientific_content_review_completed": False, "metrics_by_kind": groups, "items": values} |
| dest = Path(a.output) |
| if dest.exists(): |
| raise FileExistsError("refusing to overwrite scores") |
| dest.write_text(json.dumps(out, ensure_ascii=False, indent=2) + "\n") |
| print(json.dumps({k:v for k,v in out.items() if k not in ["items","missing_ids"]}, ensure_ascii=False)) |
|
|
| if __name__ == "__main__": |
| main() |
|
|
