experiments: freeze context-vectorizer C2 recovery

2 files changed, 201 insertions, 0 deletions

diff --git a/experiments/analyze_c2_context_validation.py b/experiments/analyze_c2_context_validation.py
new file mode 100644
index 0000000..c1ba9b3
--- /dev/null
+++ b/experiments/analyze_c2_context_validation.py
@@ -0,0 +1,151 @@
+"""Audit the independent C2 context-conditioned 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_context_val_v1_"
+METHODS = ("bp", "fa", "dfa", "sdil")
+DEPTHS = (1, 4)
+TASK_SEEDS = (3, 4, 5)
+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_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}
+    method = args["mode"]
+    expected_vectorizer = "context_gated" if method == "sdil" else "linear"
+    expected_eta_a = 0.01 if method == "sdil" else 0.02
+    if args.get("vectorizer_mode") != expected_vectorizer:
+        mismatches["vectorizer_mode"] = (args.get("vectorizer_mode"), expected_vectorizer)
+    if args.get("eta_A") != expected_eta_a:
+        mismatches["eta_A"] = (args.get("eta_A"), expected_eta_a)
+    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 not math.isfinite(row["final"].get("val_loss", math.nan)):
+        raise RuntimeError(f"nonfinite final validation loss: {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}")
+    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)} task_seeds={list(TASK_SEEDS)}")
+    print("| method | depth | validation (%) | depth gain | lesion drop |")
+    print("|:---|---:|---:|---:|---:|")
+    accs = {}
+    gains = {}
+    for method in METHODS:
+        for depth in DEPTHS:
+            accs[(method, depth)] = [
+                100 * rows[(method, depth, task_seed, model_seed)]["final"]["val_acc"]
+                for task_seed in TASK_SEEDS for model_seed in MODEL_SEEDS]
+        gains[method] = [deep - shallow for shallow, deep in
+                         zip(accs[(method, 1)], accs[(method, 4)])]
+        for depth in DEPTHS:
+            mean, sd = mean_sd(accs[(method, depth)])
+            gain_text = "--" if depth == 1 else f"{statistics.mean(gains[method]):+.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} | {mean:.3f} +/- {sd:.3f} | "
+                  f"{gain_text} | {lesion_text} |")
+
+    bp_gain = statistics.mean(gains["bp"])
+    sdil_gain = statistics.mean(gains["sdil"])
+    recovery = sdil_gain / bp_gain if bp_gain > 0 else -math.inf
+    competitor_recoveries = {
+        method: statistics.mean(gains[method]) / bp_gain for method in ("fa", "dfa")
+    }
+    strongest_recovery = max(competitor_recoveries.values())
+    strongest_deep = max(statistics.mean(accs[(method, 4)]) for method in ("fa", "dfa"))
+    deep_advantage = statistics.mean(accs[("sdil", 4)]) - strongest_deep
+    positive_gains = sum(value > 0 for value in gains["sdil"])
+    lesion_drops = [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(lesion_drops)
+    lesion_positive = sum(value > 0 for value in lesion_drops)
+    comparator_ok = strongest_recovery <= 0.5 or deep_advantage >= 2.0
+    passed = (bp_gain >= 5.0 and recovery >= 0.7 and positive_gains >= 10
+              and comparator_ok and lesion_mean >= 2.0 and lesion_positive >= 10)
+
+    print(f"BP gain={bp_gain:+.3f}; context-SDIL gain={sdil_gain:+.3f}; "
+          f"recovery={100 * recovery:.1f}%; positive gains={positive_gains}/15")
+    print(f"competitor recoveries={competitor_recoveries}; strongest={100 * strongest_recovery:.1f}%")
+    print(f"context-SDIL d4 advantage over strongest endpoint={deep_advantage:+.3f}")
+    print(f"context-SDIL lesion mean={lesion_mean:+.3f}; positive={lesion_positive}/15")
+    print(f"C2 context validation gate: {'PASS' if passed else 'FAIL'}")
+    if not passed:
+        raise SystemExit(1)
+
+
+if __name__ == "__main__":
+    main()
diff --git a/experiments/c2_context_validation.sh b/experiments/c2_context_validation.sh
new file mode 100755
index 0000000..ace1064
--- /dev/null
+++ b/experiments/c2_context_validation.sh
@@ -0,0 +1,50 @@
+#!/usr/bin/env bash
+# Independent C2 validation of bounded context-conditioned apical feedback.
+# Usage: c2_context_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 3 4 5; do
+  for model_seed in $MODEL_SEEDS; do
+    for mode in bp fa dfa sdil; do
+      for depth in 1 4; do
+        tag="c2_context_val_v1_tent_l2_w8_${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 [ "$depth" -eq 4 ]; then
+          lesion=0.3333333333333333
+        fi
+        vectorizer=linear
+        eta_a=0.02
+        if [ "$mode" = sdil ]; then
+          vectorizer=context_gated
+          eta_a=0.01
+        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 "$lesion" \
+          --vectorizer_mode "$vectorizer" \
+          --epochs 80 --batch_size 256 --eta 0.03 --momentum 0.9 \
+          --eta_A "$eta_a" --eta_P 0.002 \
+          --pert_sigma 0.01 --pert_every 4 --pert_ndirs 1 \
+          --pert_mode simultaneous \
+          --traffic_mode none --nuis_rho 0 \
+          --use_residual 1 --learn_A 1 --learn_P 1 --p_neutral 1 \
+          --task_train_examples 10000 --task_test_examples 5000 \
+          --task_levels 2 --task_n_in 1 --task_seed "$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