"""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")

# Per-model ECE with vs without imputed-confidence rows (stability check).
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}")