import argparse import json import re from collections import defaultdict from pathlib import Path def read_jsonl(path): with Path(path).open(encoding="utf-8") as handle: return [json.loads(line) for line in handle if line.strip()] def tokens(text): return re.findall(r"[a-z0-9çğıöşü]+", str(text).lower()) def token_f1(prediction, reference): pred = tokens(prediction) ref = tokens(reference) if not pred or not ref: return float(pred == ref) pred_counts = defaultdict(int) ref_counts = defaultdict(int) for token in pred: pred_counts[token] += 1 for token in ref: ref_counts[token] += 1 overlap = sum(min(count, ref_counts[token]) for token, count in pred_counts.items()) if overlap == 0: return 0.0 precision = overlap / len(pred) recall = overlap / len(ref) return 2 * precision * recall / (precision + recall) def section(text, name, following): end = "|".join(re.escape(item) for item in following) pattern = rf"{re.escape(name)}\s*:\s*(.*?)(?=(?:{end})\s*:|$)" match = re.search(pattern, text, flags=re.IGNORECASE | re.DOTALL) return match.group(1).strip() if match else "" def score_one(response, reference): rationale = section(response, "Gerekçe", ["Karar", "Öneriler"]) decision = section(response, "Karar", ["Öneriler"]) recommendations = section(response, "Öneriler", []) expected_recommendations = " ".join(reference["expected_recommendations"]) format_score = sum( bool(re.search(rf"{heading}\s*:", response, re.IGNORECASE)) for heading in ("Gerekçe", "Karar", "Öneriler") ) / 3 return { "rationale_f1": token_f1(rationale, reference["reasoning_summary"]), "decision_f1": token_f1(decision, reference["expected_decision"]), "recommendations_f1": token_f1(recommendations, expected_recommendations), "format_score": format_score, } def main(): parser = argparse.ArgumentParser() parser.add_argument("--predictions", required=True) parser.add_argument("--reference", default="benchmark/reference.jsonl") parser.add_argument("--questions", default="benchmark/questions.jsonl") parser.add_argument("--output", required=True) args = parser.parse_args() predictions = {row["id"]: row for row in read_jsonl(args.predictions)} references = {row["id"]: row for row in read_jsonl(args.reference)} questions = {row["id"]: row for row in read_jsonl(args.questions)} details = [] for uid, reference in references.items(): prediction = predictions.get(uid, {}) response = prediction.get("response", "") metrics = score_one(response, reference) total = 100 * ( 0.30 * metrics["rationale_f1"] + 0.40 * metrics["decision_f1"] + 0.20 * metrics["recommendations_f1"] + 0.10 * metrics["format_score"] ) details.append({ "id": uid, "category": questions[uid]["category"], "difficulty": questions[uid]["difficulty"], "manual": questions[uid]["manual"], "score": total, **metrics, "error": prediction.get("error") or ("missing_prediction" if uid not in predictions else None), }) by_category = defaultdict(list) for row in details: by_category[row["category"]].append(row["score"]) report = { "num_questions": len(details), "num_predictions": len(predictions), "overall_score": sum(row["score"] for row in details) / len(details), "manual_subset_score": sum(row["score"] for row in details if row["manual"]) / sum(row["manual"] for row in details), "category_scores": { key: sum(values) / len(values) for key, values in sorted(by_category.items()) }, "errors": sum(row["error"] is not None for row in details), "details": details, } output = Path(args.output) output.parent.mkdir(parents=True, exist_ok=True) output.write_text(json.dumps(report, ensure_ascii=False, indent=2), encoding="utf-8") print(json.dumps({key: value for key, value in report.items() if key != "details"}, ensure_ascii=False, indent=2)) if __name__ == "__main__": main()