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"""Generate final toy LQ experiment plots from v2 results across 3 seeds."""
import os
import json
import numpy as np
import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt
output_dir = 'report'
os.makedirs(output_dir, exist_ok=True)
# Load all v2 results with term_grad_weight=1.0, fm=0.0
seeds = [42, 123, 456]
all_data = []
for seed in seeds:
path = f'results/toy_lq_frozen/toy_lq_v2_seed{seed}_lam0.1_sig0.1_tgw1.0_fm0.0.json'
if os.path.exists(path):
with open(path) as f:
all_data.append(json.load(f))
if not all_data:
print("No results found!")
exit()
# Also load v1 baseline (no term_grad) for comparison
v1_path = 'results/toy_lq/toy_lq_seed42.json'
v1_data = None
if os.path.exists(v1_path):
with open(v1_path) as f:
v1_data = json.load(f)
# Aggregate final per-layer results across seeds
methods = ['dfa', 'state', 'credit']
colors = {'dfa': '#2196F3', 'state': '#FF9800', 'credit': '#4CAF50'}
labels = {'dfa': 'DFA', 'state': 'State Bridge', 'credit': 'Credit Bridge'}
# Per-layer costate cosine
fig, axes = plt.subplots(1, 3, figsize=(18, 5))
for ax, metric, title, ylabel in zip(
axes,
['costate_cos', 'rho', 'nudge'],
['Exact Costate Cosine', 'Perturbation Correlation (ρ)', 'Nudging Test'],
['Cosine Similarity', 'Pearson Correlation', 'Loss Change (negative=good)']
):
for method in methods:
key = f'{method}_{metric}'
values_per_seed = []
for data in all_data:
pl = data['final_per_layer']
if key in pl:
values_per_seed.append(pl[key])
if values_per_seed:
arr = np.array(values_per_seed)
mean = arr.mean(axis=0)
std = arr.std(axis=0)
layers = np.arange(len(mean))
ax.plot(layers, mean, 'o-', color=colors[method], label=labels[method], markersize=5)
ax.fill_between(layers, mean - std, mean + std, alpha=0.15, color=colors[method])
ax.set_xlabel('Layer', fontsize=12)
ax.set_ylabel(ylabel, fontsize=12)
ax.set_title(title, fontsize=13)
ax.legend(fontsize=11)
ax.grid(True, alpha=0.3)
if metric == 'costate_cos':
ax.set_ylim(-0.15, 1.05)
elif metric == 'rho':
ax.axhline(y=0, color='gray', linestyle='--', alpha=0.5)
elif metric == 'nudge':
ax.axhline(y=0, color='gray', linestyle='--', alpha=0.5)
fig.suptitle('Toy LQ Sanity Check: Per-Layer Diagnostics (3 seeds)', fontsize=14, y=1.02)
fig.tight_layout()
fig.savefig(os.path.join(output_dir, 'toy_per_layer_diagnostics.png'), dpi=150, bbox_inches='tight')
plt.close(fig)
print("Saved toy_per_layer_diagnostics.png")
# Training curves
fig, axes = plt.subplots(1, 3, figsize=(18, 5))
metric_keys = [
('costate_cos', 'Avg Costate Cosine', 'Cosine Similarity'),
('rho', 'Avg Perturbation ρ', 'Pearson Correlation'),
('nudge', 'Avg Nudging', 'Loss Change'),
]
for ax, (metric, title, ylabel) in zip(axes, metric_keys):
for method in methods:
key = f'{method}_{metric}'
all_curves = []
for data in all_data:
log = data['log']
full_key = f'{method}_costate_cos' if metric == 'costate_cos' else f'{method}_{metric}'
if full_key in log:
all_curves.append(np.array(log[full_key]))
if all_curves:
# All should have same length, use shortest
min_len = min(len(c) for c in all_curves)
arr = np.array([c[:min_len] for c in all_curves])
steps = np.array(all_data[0]['log']['steps'][:min_len])
mean = arr.mean(axis=0)
std = arr.std(axis=0)
ax.plot(steps, mean, '-', color=colors[method], label=labels[method])
ax.fill_between(steps, mean - std, mean + std, alpha=0.15, color=colors[method])
ax.set_xlabel('Training Step', fontsize=12)
ax.set_ylabel(ylabel, fontsize=12)
ax.set_title(title, fontsize=13)
ax.legend(fontsize=11)
ax.grid(True, alpha=0.3)
fig.suptitle('Toy LQ: Training Curves (3 seeds)', fontsize=14, y=1.02)
fig.tight_layout()
fig.savefig(os.path.join(output_dir, 'toy_training_curves.png'), dpi=150, bbox_inches='tight')
plt.close(fig)
print("Saved toy_training_curves.png")
# Compare v1 (no term grad) vs v2 (with term grad) for credit bridge
if v1_data:
fig, ax = plt.subplots(1, 1, figsize=(10, 6))
# v1 credit bridge (no term grad matching)
v1_log = v1_data['log']
ax.plot(v1_log['steps'], v1_log['credit_costate_cos'],
'--', color='red', label='Credit Bridge (w/o terminal grad)', alpha=0.8)
# v2 credit bridge (with term grad)
v2_log = all_data[0]['log'] # seed 42
ax.plot(v2_log['steps'], v2_log['credit_costate_cos'],
'-', color='green', label='Credit Bridge (w/ terminal grad)')
# State bridge for reference
ax.plot(v2_log['steps'], v2_log['state_costate_cos'],
'-', color='orange', label='State Bridge')
ax.set_xlabel('Training Step', fontsize=12)
ax.set_ylabel('Avg Costate Cosine', fontsize=12)
ax.set_title('Effect of Terminal Gradient Matching', fontsize=13)
ax.legend(fontsize=11)
ax.grid(True, alpha=0.3)
ax.set_ylim(-0.1, 1.05)
fig.tight_layout()
fig.savefig(os.path.join(output_dir, 'toy_term_grad_effect.png'), dpi=150)
plt.close(fig)
print("Saved toy_term_grad_effect.png")
# Bridge residual (from v1 which has it)
if v1_data and v1_data['log'].get('bridge_residual'):
fig, ax = plt.subplots(1, 1, figsize=(10, 6))
ax.plot(v1_data['log']['steps'], v1_data['log']['bridge_residual'], '-', color='green')
ax.set_xlabel('Training Step', fontsize=12)
ax.set_ylabel('Bridge Residual', fontsize=12)
ax.set_title('Credit Bridge: Bridge Residual Over Training', fontsize=13)
ax.grid(True, alpha=0.3)
fig.tight_layout()
fig.savefig(os.path.join(output_dir, 'toy_bridge_residual.png'), dpi=150)
plt.close(fig)
print("Saved toy_bridge_residual.png")
# Print summary table
print("\n" + "="*80)
print("TOY LQ FINAL RESULTS (3 seeds, 8000 steps)")
print("="*80)
for method in methods:
cos_vals = []
rho_vals = []
nudge_vals = []
for data in all_data:
pl = data['final_per_layer']
cos_vals.append(np.mean(pl[f'{method}_costate_cos']))
rho_vals.append(np.mean(pl[f'{method}_rho']))
nudge_vals.append(np.mean(pl[f'{method}_nudge']))
cos_mean, cos_std = np.mean(cos_vals), np.std(cos_vals)
rho_mean, rho_std = np.mean(rho_vals), np.std(rho_vals)
nudge_mean, nudge_std = np.mean(nudge_vals), np.std(nudge_vals)
print(f"{labels[method]:<20} Cosine: {cos_mean:.4f}±{cos_std:.4f} "
f"ρ: {rho_mean:.4f}±{rho_std:.4f} "
f"Nudge: {nudge_mean:.4f}±{nudge_std:.4f}")
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