experiments: freeze feedback-first C2 screen

1 files changed, 136 insertions, 0 deletions

diff --git a/experiments/analyze_c2_feedback_warmup_pilot.py b/experiments/analyze_c2_feedback_warmup_pilot.py
new file mode 100644
index 0000000..baca786
--- /dev/null
+++ b/experiments/analyze_c2_feedback_warmup_pilot.py
@@ -0,0 +1,136 @@
+"""Audit and select the frozen feedback-first C2 development screen."""
+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_awarmdev_v1_"
+WARMUP_STEPS = (0, 100, 400)
+DEPTHS = (1, 4)
+MODEL_SEEDS = (0, 1, 2)
+
+
+def audit_row(path, row):
+    args = row["args"]
+    required = {
+        "mode": "sdil", "dataset": "tentmap", "width": 8,
+        "act": "relu", "residual": 1, "vectorizer_mode": "context_gated",
+        "epochs": 80, "batch_size": 256, "eta": 0.03, "momentum": 0.9,
+        "eta_A": 0.01, "eta_P": 0.002, "pert_sigma": 0.01,
+        "pert_every": 4, "pert_ndirs": 1, "pert_mode": "simultaneous",
+        "a_warmup_ndirs": 4, "a_warmup_mode": "layerwise",
+        "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, "task_seed": 0,
+        "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}
+    if args.get("a_warmup_steps") not in WARMUP_STEPS:
+        mismatches["a_warmup_steps"] = (args.get("a_warmup_steps"), WARMUP_STEPS)
+    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 development 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}")
+    if row.get("provenance", {}).get("git_dirty") is not False:
+        raise RuntimeError(f"dirty or unknown source provenance: {path}")
+    expected_warmup_events = args["a_warmup_steps"]
+    if row.get("cost", {}).get("feedback_warmup_perturbation_events") != expected_warmup_events:
+        raise RuntimeError(f"warmup cost mismatch {path}: {row.get('cost')}")
+    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 = set()
+    for path in paths:
+        with open(path) as handle:
+            row = json.load(handle)
+        audit_row(path, row)
+        args = row["args"]
+        key = (args["a_warmup_steps"], args["depth"], args["seed"])
+        if key in rows:
+            raise RuntimeError(f"duplicate row: {key}")
+        rows[key] = row
+        commits.add(row["provenance"]["git_commit"])
+        split_hashes.add(row["split"]["validation_index_sha256"])
+    expected = {(warmup, depth, seed) for warmup in WARMUP_STEPS
+                for depth in DEPTHS for seed in MODEL_SEEDS}
+    if set(rows) != expected or len(commits) != 1 or len(split_hashes) != 1:
+        raise RuntimeError(f"incomplete/mixed pilot: rows={len(rows)}, "
+                           f"missing={expected - set(rows)}, extra={set(rows) - expected}, "
+                           f"commits={commits}, split_hashes={split_hashes}")
+
+    summaries = {}
+    print(f"commit={next(iter(commits))} rows={len(rows)} task_seed=0")
+    print("| A-only steps | depth | validation (%) | depth gain | d4 lesion | d4 work/ordinary |")
+    print("|---:|---:|---:|---:|---:|---:|")
+    for warmup in WARMUP_STEPS:
+        accs = {depth: [100 * rows[(warmup, depth, seed)]["final"]["val_acc"]
+                        for seed in MODEL_SEEDS] for depth in DEPTHS}
+        gains = [deep - shallow for shallow, deep in zip(accs[1], accs[4])]
+        nonfinite = [seed for depth in DEPTHS for seed in MODEL_SEEDS
+                     if not math.isfinite(rows[(warmup, depth, seed)]["final"]
+                                          .get("val_loss", math.nan))]
+        lesions = [100 * rows[(warmup, 4, seed)]["final"]["residual_lesion"]
+                   ["lesion_acc_drop"] for seed in MODEL_SEEDS]
+        work_ratios = [rows[(warmup, 4, seed)]["cost"]
+                       ["training_forward_equivalent_examples"]
+                       / rows[(warmup, 4, seed)]["cost"]
+                       ["ordinary_training_forward_examples"] for seed in MODEL_SEEDS]
+        eligible = (not nonfinite and statistics.mean(accs[4]) >= 90.0
+                    and sum(value > 0 for value in gains) >= 2
+                    and statistics.mean(lesions) >= 2.0
+                    and sum(value > 0 for value in lesions) >= 2)
+        summaries[warmup] = {"d4_mean": statistics.mean(accs[4]), "eligible": eligible}
+        for depth in DEPTHS:
+            mean, sd = mean_sd(accs[depth])
+            gain_text = "--" if depth == 1 else f"{statistics.mean(gains):+.3f}"
+            lesion_text = "--" if depth == 1 else f"{statistics.mean(lesions):+.3f}"
+            work_text = "--" if depth == 1 else f"{statistics.mean(work_ratios):.1f}x"
+            print(f"| {warmup} | {depth} | {mean:.3f} +/- {sd:.3f} | "
+                  f"{gain_text} | {lesion_text} | {work_text} |")
+        print(f"steps={warmup} positive gains={sum(value > 0 for value in gains)}/3; "
+              f"positive lesions={sum(value > 0 for value in lesions)}/3; "
+              f"nonfinite={nonfinite}; eligible={eligible}")
+
+    eligible = [warmup for warmup in WARMUP_STEPS if summaries[warmup]["eligible"]]
+    if not eligible:
+        print("C2 feedback-first development: NO ELIGIBLE PREFIX")
+        raise SystemExit(1)
+    best_mean = max(summaries[warmup]["d4_mean"] for warmup in eligible)
+    selected = min(warmup for warmup in eligible
+                   if summaries[warmup]["d4_mean"] >= best_mean - 1.0)
+    print(f"C2 feedback-first development selection: A-only steps={selected} "
+          f"(best d4={best_mean:.3f}%, selected d4={summaries[selected]['d4_mean']:.3f}%)")
+
+
+if __name__ == "__main__":
+    main()