| """Sensitivity check: do the headline findings survive excluding confidence-imputed trials? |
| |
| A response with a parseable answer but no stated CONF receives an imputed neutral confidence |
| of 50 and is flagged conf_stated=0 in the trials file. This script recomputes, on the subset |
| of trials WITH stated confidence only: (a) the accuracy-gated calibration-error separation, |
| and (b) the confidence-inversion contrast (accuracy at maximal stated confidence versus below) |
| for the three poorly calibrated models. Offline; standard library only. |
| python3 sensitivity.py |
| """ |
| import json, math, random, pathlib |
| from collections import defaultdict |
|
|
| HERE = pathlib.Path(__file__).resolve().parent |
| trials = json.load(open(HERE / ".." / "data" / "tcb_v03_trials.json")) |
| LABEL_FIXES = {"g7": "Yes"} |
| for r in trials: |
| if r["id"] in LABEL_FIXES: |
| r["truth"] = LABEL_FIXES[r["id"]] |
| if r.get("answer") is not None: |
| r["correct"] = int(r["answer"] == r["truth"]) |
|
|
| def ece(rows, nb=5): |
| bins = defaultdict(list) |
| for r in rows: |
| p = r["conf"] / 100; b = min(nb - 1, int(p * nb)); bins[b].append(r) |
| n = len(rows); e = 0.0 |
| for rs in bins.values(): |
| conf = sum(x["conf"] / 100 for x in rs) / len(rs) |
| acc = sum(x["correct"] for x in rs) / len(rs) |
| e += len(rs) / n * abs(conf - acc) |
| return e |
|
|
| def boot_ci(rows, fn, B=2000, seed=0): |
| rng = random.Random(seed); vals = [] |
| for _ in range(B): |
| s = [rows[rng.randrange(len(rows))] for _ in rows] |
| try: vals.append(fn(s)) |
| except Exception: pass |
| vals.sort(); return (vals[int(.025 * len(vals))], vals[int(.975 * len(vals))]) |
|
|
| by = defaultdict(list) |
| n_imputed = 0 |
| for r in trials: |
| if r.get("adherent"): |
| if not r.get("conf_stated", 1): |
| n_imputed += 1 |
| continue |
| by[r["model"]].append(r) |
| print(f"excluded {n_imputed} confidence-imputed trials; re-analysis on stated-confidence rows only\n") |
|
|
| |
| import collections |
| full = collections.defaultdict(list) |
| for r in trials: |
| if r.get("adherent"): |
| full[r["model"]].append(r) |
| print(f"{'model':22s} {'n_imp':>5s} ece(all) ece(stated-only) delta") |
| for m, rs in full.items(): |
| stated = by[m] |
| if not stated: continue |
| e_all, e_st = ece(rs), ece(stated) |
| print(f"{m:22s} {len(rs)-len(stated):5d} {e_all:.4f} {e_st:.4f} {e_st-e_all:+.4f}") |
|
|
