| """Score only complete independent human labels; never fill missing labels.""" |
| from pathlib import Path |
| import argparse,csv,json,collections |
| FIELDS=['representable','decomposition_correct','ambiguity'] |
| def read(p): |
| with Path(p).open(newline='') as f:rows=list(csv.DictReader(f)) |
| assert len({r['id'] for r in rows})==len(rows),'duplicate ids' |
| return {r['id']:r for r in rows} |
| def main(): |
| a=argparse.ArgumentParser();a.add_argument('--a',required=True);a.add_argument('--b',required=True);a.add_argument('--key',required=True);a.add_argument('--adjudicated');a.add_argument('--output',required=True);p=a.parse_args() |
| aa,bb=read(p.a),read(p.b);key={r['id']:r for r in json.loads(Path(p.key).read_text())} |
| assert aa.keys()==bb.keys()==key.keys(),'unaligned forms' |
| missing=[(i,f) for i in key for f in FIELDS if aa[i][f] not in ['0','1'] or bb[i][f] not in ['0','1']] |
| if missing: |
| report=dict(status='INCOMPLETE',items=len(key),missing_or_unresolved_fields=len(missing),reason='Human 0/1 labels required; no statistics estimated.') |
| else: |
| agreement={} |
| for field in FIELDS: |
| x=[int(aa[i][field]) for i in key];y=[int(bb[i][field]) for i in key];n=len(x);po=sum(a==b for a,b in zip(x,y))/n;px=sum(x)/n;py=sum(y)/n;pe=px*py+(1-px)*(1-py) |
| agreement[field]=dict(raw_agreement=po,kappa=(po-pe)/(1-pe) if pe<1 else None,scope='unweighted stratified audit sample') |
| disagreements=[i for i in key if any(aa[i][f]!=bb[i][f] for f in FIELDS)] |
| report=dict(status='AWAITING_ADJUDICATION',agreement=agreement,disagreement_ids=disagreements) |
| if p.adjudicated: |
| gold=read(p.adjudicated);assert gold.keys()==key.keys() |
| assert all(gold[i][f] in ['0','1'] for i in key for f in FIELDS),'unresolved adjudication' |
| counts=collections.defaultdict(float) |
| for i,k in key.items(): |
| pred=bool(k['accepted']);truth=gold[i]['representable']=='1';weight=k['weight'];counts[('T' if pred==truth else 'F')+('P' if pred else 'N')]+=weight |
| tp,fp,tn,fn=[counts[k] for k in ['TP','FP','TN','FN']] |
| ratio=lambda a,b:a/b if b else None |
| report.update(status='COMPLETE',weighted_counts=dict(counts),precision=ratio(tp,tp+fp),recall=ratio(tp,tp+fn),false_acceptance_rate=ratio(fp,fp+tn),false_rejection_rate=ratio(fn,tp+fn),target='semantic representability under documented grammar',decomposition_precision_among_accepted=ratio(sum(key[i]['weight'] for i in key if key[i]['accepted'] and gold[i]['decomposition_correct']=='1'),sum(key[i]['weight'] for i in key if key[i]['accepted']))) |
| Path(p.output).write_text(json.dumps(report,indent=2));print(json.dumps(report,indent=2)) |
| if __name__=='__main__':main() |
|
|
