| """Audit omissions including unlabeled units, and propose a supported allowlist. |
| |
| Usage: guide_audit.py TRAIN_JSONL PREDICTIONS_JSONL OUT_JSON |
| Uses the installed B2 thresholds (.38 probability, .5 confidence). A unit must |
| have both label classes in training and validation, at least five examples of |
| each on validation, and at least five omissions at 97% precision with 98% needed-guide retention |
| to qualify. |
| Labels are observational proxies; this does not establish instruction usefulness. |
| """ |
| import collections |
| import json |
| import sys |
| from pathlib import Path |
|
|
|
|
| def audit(train, predictions, threshold=.38, confidence=.5): |
| training = collections.defaultdict(collections.Counter) |
| for row in train: |
| for name, value in row['labels']['guides'].items(): |
| if value is not None: |
| training[name]['positive' if value else 'negative'] += 1 |
| stats = collections.defaultdict(collections.Counter) |
| for row in predictions: |
| for name, value in row['answers'].get('guides', {}).get('probabilities', {}).items(): |
| needed = row['labels']['guides'].get(name) |
| omitted = value < threshold and max(value, 1 - value) >= confidence |
| stats[name]['scored'] += 1 |
| stats[name]['omitted'] += omitted |
| if needed is None: |
| stats[name]['unknown'] += 1 |
| stats[name]['omitted_unknown'] += omitted |
| else: |
| stats[name]['positive' if needed else 'negative'] += 1 |
| stats[name]['omitted_needed' if needed else 'omitted_unneeded'] += omitted |
| allowed = [] |
| for name, s in stats.items(): |
| known_omissions = s['omitted_needed'] + s['omitted_unneeded'] |
| if (training[name]['positive'] > 0 and training[name]['negative'] > 0 |
| and s['positive'] >= 5 and s['negative'] >= 5 and not s['omitted_unknown'] |
| and known_omissions >= 5 and s['omitted_unneeded'] / known_omissions >= .97 |
| and 1 - s['omitted_needed'] / s['positive'] >= .98): |
| allowed.append(name) |
| return {'omittable': sorted(allowed), 'omit_below': threshold, 'confidence_floor': confidence, |
| 'training': {k: dict(v) for k, v in sorted(training.items())}, |
| 'validation': {k: dict(v) for k, v in sorted(stats.items())}, |
| 'limitation': 'Observed tool use supplies guide labels; unlabeled guidance cannot be certified for omission.'} |
|
|
|
|
| def main(): |
| train, predictions, output = sys.argv[1:] |
| def read(path): |
| return [json.loads(s) for s in Path(path).read_text().splitlines() if s.strip()] |
|
|
| training, scored = read(train), read(predictions) |
| candidates = [audit(training, scored, step / 100) for step in range(1, 39)] |
| result = max(candidates, key=lambda r: sum(r['validation'][n]['omitted'] for n in r['omittable'])) |
| if not result['omittable']: |
| result['omit_below'] = 0 |
| result['original_threshold_audit'] = audit(training, scored) |
| result['selection_rule'] = 'Maximize supported omissions on validation; per-unit precision >= .97 and needed retention >= .98.' |
| Path(output).write_text(json.dumps(result, indent=2) + '\n') |
| print(json.dumps(result, indent=2)) |
|
|
|
|
| if __name__ == '__main__': |
| main() |
|
|
