"""Audit the frozen 120-run C2 local-rule validation panel.""" import glob import json import math import os import statistics ROOT = os.path.join(os.path.dirname(os.path.dirname(os.path.abspath(__file__))), "results") PREFIX = "c2_local_val_v1_" METHODS = ("bp", "fa", "dfa", "sdil") DEPTHS = (1, 4) TASK_SEEDS = (0, 1, 2) MODEL_SEEDS = (0, 1, 2, 3, 4) def audit_row(path, row): args = row["args"] required = { "dataset": "tentmap", "width": 8, "act": "relu", "residual": 1, "epochs": 80, "batch_size": 256, "eta": 0.03, "momentum": 0.9, "eta_A": 0.02, "eta_P": 0.002, "pert_sigma": 0.01, "pert_every": 4, "pert_ndirs": 1, "pert_mode": "simultaneous", "traffic_mode": "none", "nuis_rho": 0.0, "use_residual": 1, "learn_A": 1, "learn_P": 1, "p_neutral": 1, "task_train_examples": 10000, "task_test_examples": 5000, "task_levels": 2, "task_n_in": 1, "val_examples": 2000, "split_seed": 2027, "eval_split": "validation", "eval_every": 0, "diagnostics": "alignment", "diagnostics_schedule": "final", "probe_bs": 512, } mismatches = {key: (args.get(key), expected) for key, expected in required.items() if args.get(key) != expected} expected_lesion = 1.0 / 3.0 if args["depth"] == 4 else 0.0 if abs(args.get("residual_lesion_fraction", 0.0) - expected_lesion) > 1e-12: mismatches["residual_lesion_fraction"] = ( args.get("residual_lesion_fraction"), expected_lesion) if mismatches: raise RuntimeError(f"protocol mismatch {path}: {mismatches}") if row["final"].get("eval_split") != "validation": raise RuntimeError(f"test-contaminated validation row: {path}") if any("eval_acc" in step or "cos_r_negg" in step for step in row.get("steps", [])): raise RuntimeError(f"intermediate held-out metric/diagnostic: {path}") split = row.get("split", {}) if (not split.get("split_from_training_only") or split.get("validation_examples") != 2000 or split.get("evaluation_split") != "validation"): raise RuntimeError(f"invalid validation split {path}: {split}") protocol = row.get("diagnostic_protocol", {}) if protocol != {"probe_source": "training_prefix", "probe_examples": 512, "schedule": "final"}: raise RuntimeError(f"diagnostic protocol mismatch {path}: {protocol}") if row.get("provenance", {}).get("git_dirty") is not False: raise RuntimeError(f"dirty or unknown source provenance: {path}") if args["depth"] == 4: lesion = row["final"].get("residual_lesion") if not lesion or lesion.get("lesioned_layers") != [3]: raise RuntimeError(f"incorrect d4 lesion: {path}") def mean_sd(values): return statistics.mean(values), statistics.stdev(values) def main(): paths = sorted(glob.glob(os.path.join(ROOT, PREFIX + "*.json"))) rows = {} commits = set() split_hashes = {} for path in paths: with open(path) as handle: row = json.load(handle) audit_row(path, row) args = row["args"] key = (args["mode"], args["depth"], args["task_seed"], args["seed"]) if key in rows: raise RuntimeError(f"duplicate row: {key}") rows[key] = row commits.add(row["provenance"]["git_commit"]) split_hashes.setdefault(args["task_seed"], set()).add( row["split"]["validation_index_sha256"]) expected = {(method, depth, task_seed, model_seed) for method in METHODS for depth in DEPTHS for task_seed in TASK_SEEDS for model_seed in MODEL_SEEDS} if set(rows) != expected or len(commits) != 1: raise RuntimeError(f"incomplete/mixed panel: rows={len(rows)}, " f"missing={expected - set(rows)}, extra={set(rows) - expected}, " f"commits={commits}") if any(len(hashes) != 1 for hashes in split_hashes.values()): raise RuntimeError(f"methods did not share splits within tasks: {split_hashes}") print(f"commit={next(iter(commits))} rows={len(rows)}") print("| method | depth | validation (%) | depth gain (points) | lesion drop (points) |") print("|:---|---:|---:|---:|---:|") method_accs = {} method_gains = {} for method in METHODS: for depth in DEPTHS: values = [100 * rows[(method, depth, task_seed, model_seed)]["final"]["val_acc"] for task_seed in TASK_SEEDS for model_seed in MODEL_SEEDS] method_accs[(method, depth)] = values gains = [deep - shallow for shallow, deep in zip( method_accs[(method, 1)], method_accs[(method, 4)])] method_gains[method] = gains for depth in DEPTHS: acc_mean, acc_sd = mean_sd(method_accs[(method, depth)]) gain_text = "--" if depth == 1 else f"{statistics.mean(gains):+.3f}" if depth == 1: lesion_text = "--" else: drops = [100 * rows[(method, 4, task_seed, model_seed)]["final"] ["residual_lesion"]["lesion_acc_drop"] for task_seed in TASK_SEEDS for model_seed in MODEL_SEEDS] lesion_text = f"{statistics.mean(drops):+.3f}" print(f"| {method} | {depth} | {acc_mean:.3f} +/- {acc_sd:.3f} | " f"{gain_text} | {lesion_text} |") bp_gain = statistics.mean(method_gains["bp"]) sdil_gain = statistics.mean(method_gains["sdil"]) recovery = sdil_gain / bp_gain if bp_gain > 0 else -math.inf competitor_recoveries = { method: statistics.mean(method_gains[method]) / bp_gain for method in ("fa", "dfa") } strongest_recovery = max(competitor_recoveries.values()) strongest_deep = max(statistics.mean(method_accs[(method, 4)]) for method in ("fa", "dfa")) sdil_deep_advantage = statistics.mean(method_accs[("sdil", 4)]) - strongest_deep sdil_lesions = [100 * rows[("sdil", 4, task_seed, model_seed)]["final"] ["residual_lesion"]["lesion_acc_drop"] for task_seed in TASK_SEEDS for model_seed in MODEL_SEEDS] lesion_mean = statistics.mean(sdil_lesions) lesion_positive = sum(value > 0 for value in sdil_lesions) comparator_ok = strongest_recovery <= 0.5 or sdil_deep_advantage >= 2.0 passed = (bp_gain >= 5.0 and recovery >= 0.7 and comparator_ok and lesion_mean >= 2.0 and lesion_positive >= 10) print(f"BP mean gain={bp_gain:+.3f}; SDIL mean gain={sdil_gain:+.3f}; " f"recovery={100 * recovery:.1f}%") print(f"competitor recoveries={competitor_recoveries}; strongest={100 * strongest_recovery:.1f}%") print(f"SDIL d4 advantage over strongest FA/DFA endpoint={sdil_deep_advantage:+.3f} points") print(f"SDIL d4 lesion mean={lesion_mean:+.3f}; positive={lesion_positive}/15") print(f"C2 local validation gate: {'PASS' if passed else 'FAIL'}") if not passed: raise SystemExit(1) if __name__ == "__main__": main()