| """Minimal exact-answer demo, not the paper's complete biomedical alias judge.""" |
| import argparse, collections, gzip, json, unicodedata |
| from pathlib import Path |
|
|
| def records(path): |
| opener=gzip.open if str(path).endswith('.gz') else open |
| with opener(path,'rt',encoding='utf-8') as f: |
| for line in f: |
| if line.strip():yield json.loads(line) |
|
|
| def norm(value): |
| return ' '.join(unicodedata.normalize('NFKC',str(value)).casefold().split()) |
|
|
| def evaluate(qa,predictions,aliases=None): |
| gold={} |
| for row in qa: |
| qid=row['qa_id'] |
| if qid in gold:raise ValueError('Duplicate QA ID') |
| gold[qid]=row |
| counts=collections.Counter(total_qa=len(gold));seen=set() |
| for row in predictions: |
| qid=row['qa_id'] |
| if qid in seen:raise ValueError('Duplicate prediction ID; use one condition per file') |
| if qid not in gold:raise ValueError('Unknown prediction QA ID') |
| seen.add(qid) |
| if row.get('error') or row.get('api_error'): |
| counts['api_errors']+=1;continue |
| prediction=row.get('answer') |
| if isinstance(prediction,list) and len(prediction)==1:prediction=prediction[0] |
| if not isinstance(prediction,str) or not prediction.strip(): |
| counts['invalid_predictions']+=1;continue |
| target=gold[qid].get('short_answer',gold[qid].get('answer')) |
| if isinstance(target,list) and len(target)==1:target=target[0] |
| if not isinstance(target,str):raise ValueError('Demo requires a single string answer') |
| accepted=[target]+(aliases or {}).get(qid,[]) |
| counts['scored']+=1 |
| counts['correct']+=int(norm(prediction) in {norm(x) for x in accepted}) |
| counts['missing_predictions']=len(gold)-len(seen) |
| return dict(counts,accuracy_pct=100*counts['correct']/counts['scored'] if counts['scored'] else None, |
| scoring='NFKC + casefold + whitespace exact match; optional explicit QA aliases', |
| error_policy='exclude API errors and invalid/missing predictions; report counts') |
|
|
| def main(): |
| p=argparse.ArgumentParser(description=__doc__) |
| p.add_argument('--qa',required=True,type=Path) |
| p.add_argument('--predictions',required=True,type=Path) |
| p.add_argument('--aliases',type=Path,help='JSON object: QA ID -> list of accepted answer strings') |
| a=p.parse_args() |
| aliases=json.loads(a.aliases.read_text(encoding='utf-8')) if a.aliases else None |
| print(json.dumps(evaluate(records(a.qa),records(a.predictions),aliases),indent=2)) |
|
|
| if __name__=='__main__':main() |
|
|
