| |
| """Summarize the prespecified FinGovBench uncertainty-threshold sweep.""" |
|
|
| from __future__ import annotations |
|
|
| import json |
| from pathlib import Path |
|
|
|
|
| ROOT = Path(__file__).resolve().parents[1] |
| SWEEP = ROOT / "results/uq_threshold_sweep" |
|
|
|
|
| def main() -> None: |
| rows = [] |
| for path in sorted(SWEEP.glob("t[0-9][0-9][0-9].json")): |
| threshold = int(path.stem[1:]) / 100 |
| report = json.loads(path.read_text()) |
| for method in ("flat_uq", "sseg"): |
| point = report["methods"][method]["all"]["point"] |
| rows.append( |
| { |
| "threshold": threshold, |
| "method": method, |
| "unsafe_case_release_rate": point["unsafe_case_release_rate"], |
| "safe_release_recall": point["safe_release_recall"], |
| "release_rate": point["release_rate"], |
| "review_fraction": point["review_fraction"], |
| "exact_scope_accuracy": point["exact_scope"], |
| "complete_safe_loop_rate": point["safe_loop"], |
| } |
| ) |
| output = { |
| "benchmark_version": "1.0.0-rc4", |
| "model": "meta-llama/Llama-3.3-70B-Instruct-Turbo", |
| "cases": 3000, |
| "interpretation": ( |
| "The same saved component-risk scores are converted to governance alarms at " |
| "each threshold. This measures whether continuous scores support a tunable " |
| "risk/release frontier. It is not a matched ablation of model probabilities " |
| "because the lexical baseline uses a different detector." |
| ), |
| "rows": rows, |
| } |
| destination = SWEEP / "summary.json" |
| destination.write_text(json.dumps(output, indent=2, sort_keys=True) + "\n") |
| print(destination) |
|
|
|
|
| if __name__ == "__main__": |
| main() |
|
|
