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"""Compile ABCDEF Step 3 final analysis."""
import json, os
import numpy as np
import matplotlib
matplotlib.use("Agg")
import matplotlib.pyplot as plt
ROOT = "/home/yurenh2/rrm/research/flossing"
OUT = f"{ROOT}/plots_step3_final"
os.makedirs(OUT, exist_ok=True)
runs = {
"A: baseline α=0 from 18228": ("step3_A_baseline_18228.json", "C0", "-"),
"B: CF α=10 λ*=-0.15 from 18228": ("step3_B_rf_18228.json", "C3", "-"),
"C: CF α=10 λ*=-0.05 from 26040": ("step3_C_rf_26040.json", "C2", "-"),
"D: CF α=10 λ*=0 from 26040": ("step3_D_rf_26040_lstar0.json", "C4", "-"),
"E: CF α=10 λ*=0 from 18228": ("step3_E_rf_18228_lstar0.json", "C1", "-"),
"F: extended D (1500 step)": ("step3_F_rf_26040_lstar0_1500.json", "C2", "--"),
}
fig, axes = plt.subplots(2, 2, figsize=(15, 9))
summary = []
for label, (fn, color, ls) in runs.items():
d = json.loads(open(f"{ROOT}/{fn}").read())
key = label.split(":")[0]
eval_steps = [e["step"] for e in d["evals"]]
eval_accs = [e["acc"] for e in d["evals"]]
steps = [r["step"] for r in d["steps"]]
sup = np.array([r["sup_loss"] for r in d["steps"]])
lyap_mean = np.array([r["lyap1_mean"] for r in d["steps"]])
frac = np.array([r["frac_above_star"] for r in d["steps"]])
def smooth(x, w=20):
if len(x) < w: return x
return np.convolve(x, np.ones(w)/w, mode="same")
axes[0,0].plot(eval_steps, eval_accs, f"{color}{ls}", marker="o", label=label, lw=1.5, alpha=0.85)
axes[0,1].plot(steps, smooth(sup), f"{color}{ls}", label=key, alpha=0.7)
axes[1,0].plot(steps, smooth(lyap_mean), f"{color}{ls}", label=key, alpha=0.7)
axes[1,1].plot(steps, smooth(frac), f"{color}{ls}", label=key, alpha=0.7)
summary.append({
"key": key, "label": label,
"init_acc": d["initial_acc"],
"final_acc": d["final_acc"],
"delta": d["final_acc"] - d["initial_acc"],
"final_lyap_mean": d["steps"][-1]["lyap1_mean"],
"final_frac_above": d["steps"][-1]["frac_above_star"],
"n_steps": len(d["steps"]),
})
axes[0,0].set_title("Test exact accuracy vs training step")
axes[0,0].set_xlabel("step"); axes[0,0].set_ylabel("exact_acc"); axes[0,0].legend(fontsize=8, loc="best"); axes[0,0].grid(alpha=0.3)
axes[0,1].set_title("Supervised loss (smoothed)")
axes[0,1].set_xlabel("step"); axes[0,1].set_ylabel("sup_loss"); axes[0,1].legend(fontsize=8); axes[0,1].grid(alpha=0.3)
axes[1,0].set_title(r"$\lambda_{joint,1}$ mean trajectory (smoothed)")
axes[1,0].axhline(0, color="k", ls=":", lw=0.6, alpha=0.6)
axes[1,0].set_xlabel("step"); axes[1,0].set_ylabel(r"$\lambda_{1,joint}$"); axes[1,0].legend(fontsize=8); axes[1,0].grid(alpha=0.3)
axes[1,1].set_title(r"Fraction of batch with $\lambda > \lambda^*$ (smoothed)")
axes[1,1].set_xlabel("step"); axes[1,1].set_ylabel("frac > λ*"); axes[1,1].legend(fontsize=8); axes[1,1].grid(alpha=0.3)
fig.suptitle("Step 3 — Contractive Flossing (CF) as training-time regularizer on HRM Sudoku-Extreme-1k", fontsize=11)
fig.tight_layout()
fig.savefig(f"{OUT}/abcdef_full.png", dpi=130)
plt.close()
# Bar chart of final Δ acc
fig, ax = plt.subplots(1, 1, figsize=(9, 5))
keys = [s["key"] for s in summary]
deltas = [s["delta"]*100 for s in summary]
colors = ["C0", "C3", "C2", "C4", "C1", "C2"]
bars = ax.bar(keys, deltas, color=colors)
ax.axhline(0, color="k", lw=0.6)
for bar, delta in zip(bars, deltas):
ax.text(bar.get_x() + bar.get_width()/2, bar.get_height() + (0.3 if delta>0 else -1),
f"{delta:+.1f}%", ha="center", fontsize=9, fontweight="bold")
ax.set_ylabel("Δ test exact_accuracy (pp)")
ax.set_title("CF intervention: final accuracy change vs baseline")
ax.grid(alpha=0.3, axis="y")
fig.tight_layout()
fig.savefig(f"{OUT}/abcdef_deltas.png", dpi=130)
plt.close()
print(f"{'key':>3} {'init':>7} {'final':>7} {'Δ':>7} {'n_steps':>8} {'final_λ':>9} {'frac>λ*':>9}")
for s in summary:
print(f" {s['key']:>3} {s['init_acc']:>7.3f} {s['final_acc']:>7.3f} {s['delta']*100:>6.1f}% {s['n_steps']:>8} "
f"{s['final_lyap_mean']:>+9.3f} {s['final_frac_above']:>9.2f}")
print(f"\nplots → {OUT}/")
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