#!/usr/bin/env python3 """Class-wise metrics and confidence-resolution analysis (camera-ready additions). Derives, offline from the shipped per-trial data: 1. Per-model confusion matrices (yes = positive class), per-class precision / recall / F1, macro-F1, and balanced accuracy under the validated protocol, plus pooled rows for the validated and naive protocols -> writes ../tables/tcb_classwise.tex (Table: class-wise decomposition). 2. Confidence-resolution statistics: accuracy on trials with stated confidence below 100 versus exactly 100 (pooled and per model), and the pooled AUROC of stated confidence for predicting correctness. 3. Spearman rank correlation between panel accuracy and panel ECE. Stdlib only; no endpoint, no network. """ import json import pathlib import statistics HERE = pathlib.Path(__file__).resolve().parent DATA = HERE.parent / "data" TABLES = HERE.parent / "tables" # Table 1 display order and short names. ORDER = [ ("glm-5.1-fp8", "glm"), ("deepseek-v4-pro", "deepseek"), ("gemma-4-31b", "gemma"), ("gptoss-120b", "gptoss"), ("leanstral-2603", "leanstral"), ("qwen3-omni-30b-a3b", "qwen-omni"), ("qwen3.6-35b-a3b-fp8", "qwen3.6-35b"), ("qwen3.6-27b-fp8", "qwen3.6-27b"), ("qwen3.5-9b", "qwen3.5-9b"), ] def confusion(trials): tp = fn = fp = tn = 0 for t in trials: a = t.get("answer") if a not in ("Yes", "No"): continue if t["truth"] == "Yes": tp += a == "Yes" fn += a == "No" else: fp += a == "Yes" tn += a == "No" return tp, fn, fp, tn def classwise(tp, fn, fp, tn): p_yes = tp / (tp + fp) if tp + fp else float("nan") r_yes = tp / (tp + fn) if tp + fn else float("nan") f_yes = 2 * p_yes * r_yes / (p_yes + r_yes) if p_yes + r_yes else 0.0 p_no = tn / (tn + fn) if tn + fn else float("nan") r_no = tn / (tn + fp) if tn + fp else float("nan") f_no = 2 * p_no * r_no / (p_no + r_no) if p_no + r_no else 0.0 return dict(p_yes=p_yes, r_yes=r_yes, f_yes=f_yes, p_no=p_no, r_no=r_no, f_no=f_no, macro_f1=(f_yes + f_no) / 2, bal_acc=(r_yes + r_no) / 2) def spearman(x, y): def rank(v): s = sorted(v) return [(sum(1 for u in s if u < a) + (sum(1 for u in s if u == a) + 1) / 2) for a in v] rx, ry = rank(x), rank(y) mx, my = statistics.mean(rx), statistics.mean(ry) num = sum((a - mx) * (b - my) for a, b in zip(rx, ry)) den = (sum((a - mx) ** 2 for a in rx) * sum((b - my) ** 2 for b in ry)) ** 0.5 return num / den def main(): trials = json.load(open(DATA / "tcb_v03_trials.json")) naive = json.load(open(DATA / "tcb_v03_trials_naive.json")) lines = [ "\\setlength{\\tabcolsep}{3.0pt}", "\\begin{tabular}{lrrrrrrrrrrrr}", "\\toprule", " & \\multicolumn{4}{c}{Confusion} & \\multicolumn{3}{c}{Yes class}" " & \\multicolumn{3}{c}{No class} & & \\\\", "\\cmidrule(lr){2-5}\\cmidrule(lr){6-8}\\cmidrule(lr){9-11}", "Model & TP & FN & FP & TN & Prec & Rec & F1 & Prec & Rec & F1" " & Macro-F1 & Bal.\\ acc. \\\\", "\\midrule", ] pooled_tp = pooled_fn = pooled_fp = pooled_tn = 0 for model, short in ORDER: tp, fn, fp, tn = confusion([t for t in trials if t["model"] == model]) pooled_tp += tp; pooled_fn += fn; pooled_fp += fp; pooled_tn += tn m = classwise(tp, fn, fp, tn) lines.append( f"{short} & {tp} & {fn} & {fp} & {tn}" f" & {m['p_yes']:.3f} & {m['r_yes']:.3f} & {m['f_yes']:.3f}" f" & {m['p_no']:.3f} & {m['r_no']:.3f} & {m['f_no']:.3f}" f" & {m['macro_f1']:.3f} & {m['bal_acc']:.3f} \\\\") lines.append("\\midrule") for label, (tp, fn, fp, tn) in ( ("Pooled (validated)", (pooled_tp, pooled_fn, pooled_fp, pooled_tn)), ("Pooled (naive)", confusion(naive))): m = classwise(tp, fn, fp, tn) lines.append( f"{label} & {tp} & {fn} & {fp} & {tn}" f" & {m['p_yes']:.3f} & {m['r_yes']:.3f} & {m['f_yes']:.3f}" f" & {m['p_no']:.3f} & {m['r_no']:.3f} & {m['f_no']:.3f}" f" & {m['macro_f1']:.3f} & {m['bal_acc']:.3f} \\\\") lines += ["\\bottomrule", "\\end{tabular}"] out = TABLES / "tcb_classwise.tex" out.write_text("\n".join(lines) + "\n") print(f"wrote {out}") # Confidence resolution: does below-ceiling stated confidence predict error? stated = [t for t in trials if t.get("conf_stated") and t.get("answer") in ("Yes", "No")] at100 = [t for t in stated if t["conf"] == 100] below = [t for t in stated if t["conf"] < 100] acc = lambda ts: sum(t["correct"] for t in ts) / len(ts) print(f"stated-confidence trials: {len(stated)}" f" (of {sum(1 for t in trials if t.get('answer') in ('Yes','No'))} parseable)") print(f"conf = 100: n={len(at100)} acc={acc(at100):.3f}") print(f"conf < 100: n={len(below)} acc={acc(below):.3f}") for model, short in ORDER: lo = [t for t in below if t["model"] == model] hi = [t for t in at100 if t["model"] == model] if len(lo) >= 10: print(f" {short}: conf<100 n={len(lo)} acc={acc(lo):.3f}" f" vs conf=100 acc={acc(hi):.3f}") correct = [t["conf"] for t in stated if t["correct"]] wrong = [t["conf"] for t in stated if not t["correct"]] wins = sum(1 for a in correct for b in wrong if a > b) ties = sum(1 for a in correct for b in wrong if a == b) auroc = (wins + 0.5 * ties) / (len(correct) * len(wrong)) print(f"pooled AUROC(stated conf -> correct) = {auroc:.3f}" f" (n_correct={len(correct)}, n_incorrect={len(wrong)})") # Accuracy vs ECE rank correlation across the panel (Table 1 values are # re-derivable via analyze_panel.py; recomputed here from the trials). accs, eces = [], [] for model, short in ORDER: ts = [t for t in trials if t["model"] == model and t.get("answer") in ("Yes", "No")] accs.append(acc(ts)) confs = [(t["conf"] if t.get("conf_stated") else 50) / 100 for t in ts] bins = [[] for _ in range(5)] for c, t in zip(confs, ts): bins[min(4, int(c * 5))].append((c, t["correct"])) ece = sum(len(b) / len(ts) * abs(statistics.mean(c for c, _ in b) - statistics.mean(k for _, k in b)) for b in bins if b) eces.append(ece) print(f"Spearman(accuracy, ECE) across the panel: {spearman(accs, eces):.3f}") if __name__ == "__main__": main()