#!/usr/bin/env python """Do internal and external stability move together? (protocol 12, 13.2) python src/joint.py --transport raw Three levels, because they can disagree and each answers a different question: 1. ACROSS MODELS Does a model with higher BCS also have higher ISS? n = number of evaluated models, so this is suggestive at best -- it is the weakest of the three. 2. WITHIN MODEL, ACROSS FACTS Are the SAME facts unstable behaviourally and internally? This is the real test. A model can sit at the group mean while the two measures disagree completely fact by fact, and only this level would reveal it. 3. BY BEHAVIOUR GROUP Protocol 13.2: mean ISS for Stable Correct vs Stable Wrong vs Stable Abstention vs Unstable. This is the table the paper needs, and it also separates the two ways a fact can be "stable": stably right and stably wrong should both show high ISS if internal state drives behaviour. Protocol 12 lists cells such as "BCS high / ISS low" -- consistent answers reached through inconsistent internal states. Reporting only a single pooled correlation would hide exactly those cases, so the per-model breakdown is printed even when the pooled number looks tidy. """ import os, sys, glob, json, argparse, collections import numpy as np from scipy.stats import spearmanr, pearsonr sys.path.insert(0, os.path.dirname(os.path.abspath(__file__))) import mcommon as mc GROUPS = ["Stable Correct", "Stable Wrong", "Stable Abstention", "Stable Unresolved", "Unstable"] def load_pairs(model, transport, mode): ip = mc.out("metrics", "iss", f"{model}.{transport}.{mode}.per_fact.jsonl") bp = mc.out("metrics", "behavioral", f"{model}.{mode}.per_fact.jsonl") if not (os.path.exists(ip) and os.path.exists(bp)): return None iss = {r["fact_id"]: r for r in mc.read_jsonl(ip)} beh = {r["fact_id"]: r for r in mc.read_jsonl(bp)} common = sorted(set(iss) & set(beh)) if len(common) < 20: return None return [{"fact_id": f, "relation": iss[f]["relation"], "iss": iss[f]["iss"], "iss_late": iss[f]["iss_late"], "bcs": beh[f]["bcs"], "bes": beh[f]["bes"], "group": beh[f]["behavior_group"]} for f in common] def main(): ap = argparse.ArgumentParser() ap.add_argument("--transport", choices=["raw", "jlens"], default="raw") ap.add_argument("--coverage", choices=["complete_family", "full_set"], default=None) args = ap.parse_args() mode = args.coverage or mc.cfg()["headline_coverage"] names = [m["name"] for m in mc.models_cfg()["evaluated_models"]] per_model, model_level = {}, [] for m in names: rows = load_pairs(m, args.transport, mode) if rows is None: continue per_model[m] = rows isum = json.load(open(mc.out("metrics", "iss", f"{m}.{args.transport}.{mode}.summary.json"))) bsum = json.load(open(mc.out("metrics", "behavioral", f"{m}.{mode}.summary.json"))) kp = mc.out("metrics", "kts", f"{m}.{args.transport}.{mode}.summary.json") ksum = json.load(open(kp)) if os.path.exists(kp) else {} model_level.append({ "model": m, "family": mc.model_entry(m).get("family"), "params_b": mc.model_entry(m).get("params_b"), "tuning": mc.model_entry(m).get("tuning"), "iss": isum["iss"], "bcs": bsum["bcs"], "bes": bsum["bes"], "kts": ksum.get("kts"), "kts_id": ksum.get("kts_id"), "unstable_rate": bsum["unstable_rate"], "stable_correct_rate": bsum["stable_correct_rate"], "n_joined": len(rows)}) if not model_level: raise SystemExit("no model has BOTH ISS and BCS/BES yet") out = {"transport": args.transport, "coverage_mode": mode, "n_models": len(model_level), "model_level": model_level} # ---- level 1: across models def corr(a, b): a, b = np.asarray(a, float), np.asarray(b, float) ok = np.isfinite(a) & np.isfinite(b) if ok.sum() < 3: return None return {"spearman": float(spearmanr(a[ok], b[ok]).statistic), "pearson": float(pearsonr(a[ok], b[ok]).statistic), "n": int(ok.sum())} across = {} for x in ("bcs", "bes", "unstable_rate", "stable_correct_rate"): for y in ("iss", "kts", "kts_id"): c = corr([r[x] for r in model_level], [r[y] for r in model_level]) if c: across[f"{x}__{y}"] = c out["across_models"] = across # ---- level 2: within model, across facts within, pooled = {}, [] for m, rows in per_model.items(): within[m] = { "n_facts": len(rows), "iss_vs_bcs": corr([r["iss"] for r in rows], [r["bcs"] for r in rows]), "iss_vs_bes": corr([r["iss"] for r in rows], [r["bes"] for r in rows]), } # Standardise inside each model before pooling, otherwise the pooled # correlation would mostly reflect between-model level differences # rather than the within-model fact-by-fact association we are after. for key in ("iss", "bcs"): v = np.array([r[key] for r in rows], float) sd = v.std() or 1.0 for r, z in zip(rows, (v - v.mean()) / sd): r[f"z_{key}"] = float(z) pooled += rows out["within_model"] = within out["pooled_within_model"] = corr([r["z_iss"] for r in pooled], [r["z_bcs"] for r in pooled]) # ---- level 3: behaviour groups (protocol 13.2) by_group = collections.defaultdict(list) for m, rows in per_model.items(): for r in rows: by_group[r["group"]].append(r["iss"]) out["iss_by_behavior_group"] = { g: {"n": len(by_group[g]), "iss_mean": float(np.mean(by_group[g])), "iss_sd": float(np.std(by_group[g]))} for g in GROUPS if by_group[g]} out["iss_by_behavior_group_per_model"] = { m: {g: float(np.mean([r["iss"] for r in rows if r["group"] == g])) for g in GROUPS if any(r["group"] == g for r in rows)} for m, rows in per_model.items()} mc.write_json(mc.out("metrics", f"joint_internal_external.{args.transport}.{mode}.json"), out) # ------------------------------------------------------------ report print(f"\n=== 1. ACROSS MODELS (n={len(model_level)}) ===") print(f"{'pair':34s} {'Spearman':>9s} {'Pearson':>9s}") for k, v in across.items(): print(f"{k:34s} {v['spearman']:>9.3f} {v['pearson']:>9.3f}") print(f"\n=== 2. WITHIN MODEL, ACROSS FACTS ===") print(f"{'model':30s} {'n':>5s} {'ISSvBCS':>8s} {'ISSvBES':>8s}") for m, v in within.items(): a = v["iss_vs_bcs"]["spearman"] if v["iss_vs_bcs"] else float("nan") b = v["iss_vs_bes"]["spearman"] if v["iss_vs_bes"] else float("nan") print(f"{m:30s} {v['n_facts']:>5d} {a:>8.3f} {b:>8.3f}") p = out["pooled_within_model"] if p: print(f"{'POOLED (z-scored per model)':30s} {p['n']:>5d} {p['spearman']:>8.3f}") print(f"\n=== 3. ISS BY BEHAVIOUR GROUP (protocol 13.2) ===") print(f"{'group':22s} {'facts':>7s} {'ISS mean':>9s} {'sd':>7s}") for g, v in out["iss_by_behavior_group"].items(): print(f"{g:22s} {v['n']:>7d} {v['iss_mean']:>9.3f} {v['iss_sd']:>7.3f}") print() if __name__ == "__main__": main()