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"""Per-sample λ_1 histogram: success vs failure on TRM checkpoints."""
import numpy as np
import matplotlib
matplotlib.use("Agg")
import matplotlib.pyplot as plt
def roc_auc_score(y_true, y_score):
"""Simple AUROC: pair-counting."""
pos = y_score[y_true == 1]
neg = y_score[y_true == 0]
if len(pos) == 0 or len(neg) == 0:
return float("nan")
# Probability that pos > neg (with 0.5 for ties)
return float(np.mean(pos[:, None] > neg[None, :]) +
0.5 * np.mean(pos[:, None] == neg[None, :]))
ROOT = "/home/yurenh2/rrm/research/flossing"
CKPTS = [
(26041, 5000),
(78123, 15000),
(130205, 25000),
(182287, 35000),
(234369, 45000),
]
fig, axes = plt.subplots(2, 3, figsize=(16, 8), sharex=True)
axes = axes.flatten()
bins = np.linspace(-0.2, 0.3, 60)
for ax, (step, epoch) in zip(axes, CKPTS):
d = np.load(f"{ROOT}/diag_trm_singleGPU_step{step}_512.npz")
lam1 = d["lyap_spec"][:, 0]
succ = d["exact_correct"] > 0.5
# AUROC: predict fail (label=1) from λ_1
fail_label = (~succ).astype(int)
auroc = roc_auc_score(fail_label, lam1) if fail_label.sum() > 0 and (1 - fail_label).sum() > 0 else float("nan")
ax.hist(lam1[succ], bins=bins, alpha=0.55, color="C2", density=False,
label=f"succ (n={succ.sum()}, μ={lam1[succ].mean():+.3f})")
ax.hist(lam1[~succ], bins=bins, alpha=0.55, color="C3", density=False,
label=f"fail (n={(~succ).sum()}, μ={lam1[~succ].mean():+.3f})")
ax.axvline(0, color="k", lw=0.5, ls=":")
ax.set_title(f"epoch {epoch:>5} | acc={succ.mean():.3f} AUROC={auroc:.3f}")
ax.set_xlabel(r"$\lambda_1$")
ax.set_ylabel("count")
ax.legend(fontsize=8, loc="upper left")
ax.grid(alpha=0.3)
# Hide last subplot
axes[-1].axis("off")
fig.suptitle("TRM per-sample $\\lambda_1$ distribution: success (green) vs failure (red)\n"
"across training checkpoints. As acc rises, both distributions drift right; gap stays large.",
fontsize=11)
fig.tight_layout()
out = f"{ROOT}/plots_trm_lyap_hist.png"
fig.savefig(out, dpi=130)
print(f"→ {out}")
# Print AUROC summary
print(f"\n{'epoch':>6} {'acc':>7} {'AUROC':>7}")
for step, epoch in CKPTS:
d = np.load(f"{ROOT}/diag_trm_singleGPU_step{step}_512.npz")
lam1 = d["lyap_spec"][:, 0]
succ = d["exact_correct"] > 0.5
fail_label = (~succ).astype(int)
auroc = roc_auc_score(fail_label, lam1) if fail_label.sum() > 0 else float("nan")
print(f"{epoch:>6} {succ.mean():>7.3f} {auroc:>7.3f}")
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