figure: visualize dynamic innovation stability repair

1 files changed, 220 insertions, 0 deletions

diff --git a/experiments/plot_dynamic_stability.py b/experiments/plot_dynamic_stability.py
new file mode 100755
index 0000000..3c99260
--- /dev/null
+++ b/experiments/plot_dynamic_stability.py
@@ -0,0 +1,220 @@
+#!/usr/bin/env python3
+"""Render the audited dynamic neutral-projection stability figure."""
+import argparse
+import hashlib
+import json
+import math
+import os
+
+import matplotlib
+matplotlib.use("Agg")
+import matplotlib.pyplot as plt
+
+
+PDF_METADATA = {
+    "Creator": "SDIL audited figure pipeline",
+    "Producer": "Matplotlib",
+    "CreationDate": None,
+    "ModDate": None,
+}
+
+
+def sha256(path):
+    digest = hashlib.sha256()
+    with open(path, "rb") as handle:
+        for chunk in iter(lambda: handle.read(1024 * 1024), b""):
+            digest.update(chunk)
+    return digest.hexdigest()
+
+
+def read(path):
+    with open(path) as handle:
+        return json.load(handle)
+
+
+def main():
+    parser = argparse.ArgumentParser()
+    parser.add_argument(
+        "--dynamic_train", default="results/kp_dynamic_projection/dynamic.json")
+    parser.add_argument(
+        "--dynamic_train_gate", default="results/kp_dynamic_projection_gate.json")
+    parser.add_argument(
+        "--fixed_lesion",
+        default=("results/kp_innovation_stability/"
+                 "closed_form_frozen_innovation.json"))
+    parser.add_argument(
+        "--dynamic_short",
+        default="results/kp_dynamic_projection_short/dynamic.json")
+    parser.add_argument(
+        "--dynamic_short_gate",
+        default="results/kp_dynamic_projection_short_gate.json")
+    parser.add_argument(
+        "--mt1_gate", default="results/kp_innovation_short_gate.json")
+    parser.add_argument(
+        "--kp_gate", default="results/kp_short_gate.json")
+    parser.add_argument(
+        "--out", default="results/figs/figureS_dynamic_stability")
+    args = parser.parse_args()
+
+    dynamic = read(args.dynamic_train)
+    dynamic_gate = read(args.dynamic_train_gate)
+    lesion = read(args.fixed_lesion)
+    short = read(args.dynamic_short)
+    short_gate = read(args.dynamic_short_gate)
+    mt1 = read(args.mt1_gate)
+    kp = read(args.kp_gate)
+    if (dynamic_gate.get("protocol") !=
+            "kp_dynamic_neutral_projection_training_prefix_v1"
+            or dynamic_gate.get("status") != "passed"):
+        raise ValueError("dynamic training gate did not pass")
+    if (short_gate.get("protocol") !=
+            "kp_dynamic_neutral_projection_short_v1"
+            or short_gate.get("status") != "passed"):
+        raise ValueError("dynamic short gate did not pass")
+    if (mt1.get("protocol") != "kp_mixed_traffic_short_v1"
+            or mt1.get("status") != "failed"):
+        raise ValueError("MT-1 comparator is not the frozen failure")
+    if kp.get("protocol") != "kolen_pollack_short_v1" or kp.get(
+            "status") != "passed":
+        raise ValueError("clean KP comparator is not frozen")
+    if dynamic_gate["source_commit"] != dynamic["provenance"]["git_commit"]:
+        raise ValueError("D1 record/gate provenance mismatch")
+    if short_gate["metrics"]["source_commit"] != short[
+            "provenance"]["git_commit"]:
+        raise ValueError("D2 record/gate provenance mismatch")
+    if (lesion["predictor_mode"] != "closed_form"
+            or lesion["predictor_every"] != 0
+            or lesion.get("stability_margin", 0.0) != 0.0
+            or lesion["provenance"]["git_tracked_dirty"]):
+        raise ValueError("fixed-predictor diagnostic drift")
+    if (len(dynamic["trajectory"]) != 352
+            or len(lesion["trajectory"]) != 352):
+        raise ValueError("training-prefix trajectory drift")
+
+    steps = list(range(1, 353))
+    dynamic_loss = [float(row["batch_loss"])
+                    for row in dynamic["trajectory"]]
+    lesion_loss = [float(row["batch_loss"])
+                   for row in lesion["trajectory"]]
+    pre_ratio = [float(row["neutral_projection"][
+        "pre_projection_traffic_rms_ratio"])
+        for row in dynamic["trajectory"]]
+    post_ratio = [float(row["neutral_projection"][
+        "post_projection_traffic_rms_ratio"])
+        for row in dynamic["trajectory"]]
+    numbers = dynamic_loss + lesion_loss + pre_ratio + post_ratio
+    if not all(math.isfinite(value) and value >= 0 for value in numbers):
+        raise ValueError("nonfinite plotting trajectory")
+
+    accuracies = [
+        100 * float(mt1["metrics"]["accuracy"]["raw"]),
+        100 * float(mt1["metrics"]["accuracy"]["matched"]),
+        100 * float(mt1["metrics"]["accuracy"]["innovation"]),
+        100 * float(kp["metrics"]["accuracy"]),
+        100 * float(short_gate["metrics"]["accuracy"]),
+    ]
+    labels = ["Raw", "Norm-\nmatched", "Frozen\ninnovation",
+              "Clean\nKP", "Dynamic\ninnovation"]
+
+    plt.rcParams.update({
+        "font.family": "DejaVu Sans",
+        "font.size": 10,
+        "axes.titleweight": "bold",
+        "axes.spines.top": False,
+        "axes.spines.right": False,
+    })
+    blue = "#0072B2"
+    red = "#C44E52"
+    orange = "#E69F00"
+    gray = "#777777"
+    fig, axes = plt.subplots(1, 3, figsize=(13.2, 4.15),
+                             gridspec_kw={"width_ratios": [1.2, 1.05, 1.05]})
+
+    left, middle, right = axes
+    left.semilogy(steps, lesion_loss, color=red, linewidth=1.8,
+                  label="Frozen predictor (controller lesion)")
+    left.semilogy(steps, dynamic_loss, color=blue, linewidth=2.1,
+                  label="Dynamic neutral projection")
+    left.axhline(10, color="#BBBBBB", linestyle="--", linewidth=0.9)
+    left.set_xlim(1, 352)
+    left.set_xlabel("Training minibatch")
+    left.set_ylabel("Cross-entropy (log scale)")
+    left.set_title("a   Stability is a trajectory property", loc="left")
+    left.grid(axis="y", which="major", color="#E1E1E1", linewidth=0.7)
+    left.legend(frameon=False, fontsize=8.5, loc="upper left")
+    left.text(345, lesion_loss[-1], f"  {lesion_loss[-1]:.1e}",
+              color=red, fontsize=8.5, va="center")
+
+    middle.semilogy(steps, pre_ratio, color=orange, linewidth=1.9,
+                    label="Before fast projection")
+    middle.semilogy(steps, post_ratio, color=blue, linewidth=2.1,
+                    label="After fast projection")
+    middle.fill_between(steps, post_ratio, pre_ratio, color=blue, alpha=0.08)
+    middle.set_xlim(1, 352)
+    middle.set_ylim(1e-9, 2e-2)
+    middle.set_xlabel("Training minibatch")
+    middle.set_ylabel("Neutral residual / traffic RMS")
+    middle.set_title("b   Local coupling is continually removed", loc="left")
+    middle.grid(axis="y", which="major", color="#E1E1E1", linewidth=0.7)
+    middle.legend(frameon=False, fontsize=8.5, loc="upper left")
+    middle.annotate(
+        f"worst after: {max(post_ratio):.1e}",
+        xy=(steps[-1], post_ratio[-1]), xytext=(180, 2.5e-7),
+        arrowprops={"arrowstyle": "-", "color": blue, "linewidth": 1},
+        color=blue, fontsize=8.5)
+
+    colors = [gray, orange, red, "#9A9A9A", blue]
+    bars = right.bar(range(len(accuracies)), accuracies, color=colors,
+                     width=0.72)
+    right.set_xticks(range(len(labels)), labels)
+    right.tick_params(axis="x", labelsize=8.5)
+    right.set_ylim(0, 100)
+    right.set_ylabel("Validation accuracy (%)")
+    right.set_title("c   The stable signal restores learning", loc="left")
+    right.grid(axis="y", color="#E1E1E1", linewidth=0.7)
+    for bar, value in zip(bars, accuracies):
+        right.text(bar.get_x() + bar.get_width() / 2, value + 2.0,
+                   f"{value:.2f}", ha="center", va="bottom",
+                   fontsize=8.5, fontweight="bold")
+    right.text(
+        0.98, 0.53,
+        "20 epochs · seed 0\n1.326× BP MACs\n0 task-loss queries",
+        transform=right.transAxes, ha="right", va="center", fontsize=8.5,
+        bbox={"boxstyle": "round,pad=0.4", "facecolor": "#E8F2F8",
+              "edgecolor": "none"})
+
+    fig.suptitle(
+        "Dynamic somato-dendritic innovation turns a multiplicative failure into a stable local update",
+        y=1.02, fontsize=13.2, fontweight="bold")
+    fig.tight_layout(w_pad=2.4)
+    os.makedirs(os.path.dirname(os.path.abspath(args.out)), exist_ok=True)
+    png = args.out + ".png"
+    pdf = args.out + ".pdf"
+    fig.savefig(png, dpi=240, bbox_inches="tight", facecolor="white")
+    fig.savefig(pdf, bbox_inches="tight", metadata=PDF_METADATA,
+                facecolor="white")
+    plt.close(fig)
+
+    sources = [args.dynamic_train, args.dynamic_train_gate, args.fixed_lesion,
+               args.dynamic_short, args.dynamic_short_gate, args.mt1_gate,
+               args.kp_gate]
+    output = {
+        "script": os.path.relpath(__file__),
+        "script_sha256": sha256(__file__),
+        "sources": {path: sha256(path) for path in sources},
+        "png": png,
+        "png_sha256": sha256(png),
+        "pdf": pdf,
+        "pdf_sha256": sha256(pdf),
+        "fixed_predictor_curve_is_pre_grid_diagnostic": True,
+    }
+    manifest = args.out + "_manifest.json"
+    output["manifest"] = manifest
+    with open(manifest, "w") as handle:
+        json.dump(output, handle, indent=2, sort_keys=True)
+        handle.write("\n")
+    print(json.dumps(output, indent=2, sort_keys=True))
+
+
+if __name__ == "__main__":
+    main()