1 files changed, 126 insertions, 0 deletions
diff --git a/experiments/analyze_kp_short.py b/experiments/analyze_kp_short.py
new file mode 100755
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+++ b/experiments/analyze_kp_short.py
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+#!/usr/bin/env python3
+"""Audit and gate the frozen KP-1 ResNet-20 development record."""
+import argparse
+import json
+import math
+import os
+
+
+SPLIT_HASH = "8328b206a97c420e49e54e3eca4abe3274c4756b084355784ea3fb8059e4515b"
+BP_EPOCH20_ACCURACY = 0.8102
+BP_EPOCH20_MACS = 109_487_808_000_000
+
+
+def main():
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--input", default="results/kp_short/kp.json")
+    parser.add_argument(
+        "--bp", default="results/oral_a_dev/bp_reference_primary.json")
+    parser.add_argument("--out", default="results/kp_short_gate.json")
+    args = parser.parse_args()
+    with open(args.input) as handle:
+        record = json.load(handle)
+    run_args = record["args"]
+    expected = {
+        "mode": "kp", "depth": 20, "width": 16, "seed": 0,
+        "loader_seed": 0, "batch_size": 128, "epochs": 20,
+        "train_limit": 0, "val_examples": 5000, "split_seed": 2027,
+        "eval_split": "validation", "eval_every": 0,
+        "augment_train": 1, "lr": 0.1, "output_lr": 0.1,
+        "lr_schedule": "step", "lr_milestones": "100,150",
+        "lr_gamma": 0.1, "warmup_epochs": 0, "momentum": 0.9,
+        "weight_decay": 1e-4, "normalization": "batchnorm",
+        "a_scale": 1.0, "alignment_probe": 32,
+    }
+    for key, value in expected.items():
+        if run_args.get(key) != value:
+            raise ValueError(f"KP-1 {key} drift")
+    if record["provenance"]["git_tracked_dirty"]:
+        raise ValueError("tracked-dirty KP-1 result")
+    if record["split"]["validation_index_sha256"] != SPLIT_HASH:
+        raise ValueError("KP-1 split drift")
+    protocol = record["evaluation_protocol"]
+    if protocol["test_evaluations"] or protocol["test_used_for_selection"]:
+        raise ValueError("KP-1 touched test")
+    if record.get("calibration_metric_space") != (
+            "reciprocal_local_activity_products"):
+        raise ValueError("KP-1 metric-space drift")
+
+    with open(args.bp) as handle:
+        bp = json.load(handle)
+    bp_epoch20 = [row for row in bp["epochs"] if row["epoch"] == 20]
+    if (len(bp_epoch20) != 1
+            or float(bp_epoch20[0]["eval_accuracy"]) != BP_EPOCH20_ACCURACY):
+        raise ValueError("matched BP epoch-20 endpoint drift")
+    if int(bp["work"]["total_macs_estimate"]) // 10 != BP_EPOCH20_MACS:
+        raise ValueError("matched BP epoch-20 MAC reference drift")
+
+    epoch_tracking = [row.get("feedback_tracking") for row in record["epochs"]]
+    if len(epoch_tracking) != 20 or any(value is None for value in epoch_tracking):
+        raise ValueError("KP-1 tracking trajectory is incomplete")
+    trajectory_values = []
+    for row, tracking in zip(record["epochs"], epoch_tracking):
+        trajectory_values.extend([
+            float(row["train_loss"]),
+            float(tracking["mean_feedback_forward_cosine"]),
+            float(tracking["mean_feedback_forward_relative_error"]),
+            float(tracking["min_feedback_forward_cosine"]),
+            float(tracking["max_feedback_forward_relative_error"]),
+        ])
+    diagnostics = record["diagnostics"]
+    accuracy = float(record["final"]["accuracy"])
+    loss = float(record["final"]["loss"])
+    early = float(diagnostics["early_third_mean"])
+    final_cosine = float(diagnostics["mean_feedback_forward_cosine"])
+    late_cosine = sum(float(value["mean_feedback_forward_cosine"])
+                      for value in epoch_tracking[10:]) / 10
+    total_macs = int(record["work"]["total_macs_estimate"])
+    queries = int(record["work"]["logical_batch_loss_queries"])
+    finite = (bool(record["final"]["finite"])
+              and all(math.isfinite(value) for value in
+                      trajectory_values + [accuracy, loss, early, final_cosine]))
+    checks = {
+        "record_and_trajectory_finite": finite,
+        "accuracy_at_least_0.70": accuracy >= 0.70,
+        "within_15_points_of_bp_epoch20": (
+            accuracy >= BP_EPOCH20_ACCURACY - 0.15),
+        "early_alignment_at_least_0.50": early >= 0.50,
+        "final_feedback_cosine_at_least_0.80": final_cosine >= 0.80,
+        "epoch11_to20_feedback_cosine_at_least_0.70": late_cosine >= 0.70,
+        "zero_task_loss_queries": queries == 0,
+        "macs_at_most_1.40x_bp": total_macs <= 1.40 * BP_EPOCH20_MACS,
+    }
+    status = "passed" if all(checks.values()) else "failed"
+    metrics = {
+        "accuracy": accuracy, "loss": loss,
+        "bp_epoch20_accuracy": BP_EPOCH20_ACCURACY,
+        "early_third_alignment": early,
+        "final_mean_feedback_forward_cosine": final_cosine,
+        "epoch11_to20_mean_feedback_forward_cosine": late_cosine,
+        "final_mean_feedback_forward_relative_error": float(
+            diagnostics["mean_feedback_forward_relative_error"]),
+        "total_macs": total_macs, "bp_epoch20_total_macs": BP_EPOCH20_MACS,
+        "mac_ratio_to_bp": total_macs / BP_EPOCH20_MACS,
+        "logical_batch_loss_queries": queries,
+        "peak_memory_allocated_bytes": int(
+            record["hardware"]["peak_memory_allocated_bytes"]),
+        "wall_s": float(record["timing"]["total_timed_wall_s"]),
+        "source_commit": record["provenance"]["git_commit"],
+    }
+    output = {
+        "protocol": "kolen_pollack_short_v1", "status": status,
+        "checks": checks, "metrics": metrics,
+        "full_validation_opened": status == "passed",
+        "confirmation_test_seeds_touched": False,
+        "review_score_before": 5, "review_score_after": 5,
+        "score_change_rule": "inherited KP baseline cannot raise score",
+    }
+    os.makedirs(os.path.dirname(os.path.abspath(args.out)), exist_ok=True)
+    with open(args.out, "w") as handle:
+        json.dump(output, handle, indent=2, sort_keys=True)
+        handle.write("\n")
+    print(json.dumps(output, indent=2))
+
+
+if __name__ == "__main__":
+    main()