diff options
| -rw-r--r-- | experiments/analyze_c2_local_validation.py | 149 | ||||
| -rwxr-xr-x | experiments/c2_local_validation.sh | 43 |
2 files changed, 192 insertions, 0 deletions
diff --git a/experiments/analyze_c2_local_validation.py b/experiments/analyze_c2_local_validation.py new file mode 100644 index 0000000..bd45116 --- /dev/null +++ b/experiments/analyze_c2_local_validation.py @@ -0,0 +1,149 @@ +"""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() diff --git a/experiments/c2_local_validation.sh b/experiments/c2_local_validation.sh new file mode 100755 index 0000000..e670ff3 --- /dev/null +++ b/experiments/c2_local_validation.sh @@ -0,0 +1,43 @@ +#!/usr/bin/env bash +# Frozen C2 local-rule validation panel after the BP useful-depth screen passed. +# Usage: c2_local_validation.sh <gpu> "<model seeds>" +set -eu + +cd "$(dirname "$0")/.." +GPU="${1:?GPU index required}" +MODEL_SEEDS="${2:-0 1 2 3 4}" +PYTHON="${PYTHON:-/home/yurenh2/miniconda3/envs/ep_pascal/bin/python3}" + +for task_seed in 0 1 2; do + for model_seed in $MODEL_SEEDS; do + for mode in bp fa dfa sdil; do + for depth in 1 4; do + tag="c2_local_val_v1_tent_l2_w8_${mode}_d${depth}_t${task_seed}_s${model_seed}" + out="results/${tag}.json" + if [ -s "$out" ]; then + echo "[$tag] exists; skipping" + continue + fi + lesion=0 + if [ "$depth" -eq 4 ]; then + lesion=0.3333333333333333 + fi + CUDA_VISIBLE_DEVICES="$GPU" "$PYTHON" experiments/run.py \ + --mode "$mode" --dataset tentmap --device cuda \ + --depth "$depth" --width 8 --act relu --residual 1 \ + --residual_lesion_fraction "$lesion" \ + --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 \ + --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 --task_seed "$task_seed" \ + --val_examples 2000 --split_seed 2027 --eval_split validation --eval_every 0 \ + --diagnostics alignment --diagnostics_schedule final --probe_bs 512 \ + --seed "$model_seed" --log_every 100000 --tag "$tag" + done + done + done +done |