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#!/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")
parser.add_argument(
"--caption",
default="results/figs/figureS_dynamic_stability_caption.md")
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},
"caption": args.caption,
"caption_sha256": sha256(args.caption),
"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()
|
