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#!/usr/bin/env python3
"""Plot the audited B1 activity-bias result without rerunning analysis."""
import argparse
import json
from pathlib import Path
import matplotlib.pyplot as plt
import numpy as np
ROOT = Path(__file__).resolve().parents[1]
DEFAULT_GATE = ROOT / "results" / "contrastive_bias" / "b1_gate.json"
DEFAULT_OUT = ROOT / "results" / "contrastive_bias" / "b1_summary"
def main():
parser = argparse.ArgumentParser()
parser.add_argument("--gate", type=Path, default=DEFAULT_GATE)
parser.add_argument("--out", type=Path, default=DEFAULT_OUT)
args = parser.parse_args()
with open(args.gate, encoding="utf-8") as handle:
report = json.load(handle)
rows = report["activity_table"]
ratios = np.arange(len(rows))
labels = [f"{row['ratio']:g}×" for row in rows]
clean = report["clean_final_validation_accuracy"]
plt.rcParams.update({
"font.family": "DejaVu Sans", "font.size": 9,
"axes.spines.top": False, "axes.spines.right": False,
})
figure, axes = plt.subplots(1, 2, figsize=(7.2, 2.65))
ax = axes[0]
ax.axhline(clean, color="#6b747b", linestyle="--", linewidth=1.2,
label=f"clean DP: {clean:.2f}")
ax.plot(ratios, [row["raw"] for row in rows], "X-", color="#c23b3b",
linewidth=1.5, markersize=7, label="raw (nonfinite at epoch 1)")
ax.plot(ratios, [row["innovation"] for row in rows], "o-",
color="#0878a8", linewidth=1.8, markersize=5, label="innovation")
ax.plot(ratios, [row["oracle"] for row in rows], "s-",
color="#263640", linewidth=1.5, markersize=4.5, label="oracle")
ax.set_xticks(ratios, labels)
ax.set_ylim(0, 78)
ax.set_xlabel("activity-dependent bias / clean teaching RMS")
ax.set_ylabel("final validation accuracy (%)")
ax.set_title("a Task result", loc="left", fontweight="bold")
ax.legend(frameon=False, fontsize=7.5, loc="lower right")
ax = axes[1]
width = 0.23
ax.bar(ratios - width, [1, 1, 1], width, color="#c23b3b", label="raw")
ax.bar(ratios, [20, 20, 20], width, color="#0878a8", label="innovation")
ax.bar(ratios + width, [20, 20, 20], width, color="#263640", label="oracle")
ax.set_xticks(ratios, labels)
ax.set_ylim(0, 22)
ax.set_yticks((0, 5, 10, 15, 20))
ax.set_xlabel("activity-dependent bias / clean teaching RMS")
ax.set_ylabel("epochs completed before nonfinite loss")
ax.set_title("b Stability", loc="left", fontweight="bold")
ax.legend(frameon=False, fontsize=7.5, loc="lower right")
ax.text(
0.02, 0.95,
"innovation residual bias ≤ 8.96×10⁻⁸\npredictor label observations = 0",
transform=ax.transAxes, va="top", fontsize=7.5,
)
figure.suptitle(
"Dual Prop under neuron-specific activity bias",
x=0.08, ha="left", fontsize=12, fontweight="bold",
)
figure.tight_layout(rect=(0, 0, 1, 0.94))
args.out.parent.mkdir(parents=True, exist_ok=True)
figure.savefig(args.out.with_suffix(".pdf"), bbox_inches="tight")
figure.savefig(args.out.with_suffix(".png"), dpi=220, bbox_inches="tight")
plt.close(figure)
if __name__ == "__main__":
main()
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