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#!/usr/bin/env python3
"""Gradient reach with 20 seeds (0-19) for statistical significance.
Extends 5-seed results. Loads existing seeds 0-4 data if available,
runs seeds 5-19, then combines for final statistics.
"""
import torch
import torch.nn.functional as F
import numpy as np
import json
import os
from scipy import stats as scipy_stats
from src.data import load_dataset, spmm
from src.trainers import BPTrainer, GraphGrAPETrainer
device = 'cuda:0'
ALL_SEEDS = list(range(20))
EPOCHS = 100
OUT_DIR = 'results/gradient_reach_20seeds'
OLD_FILE = 'results/gradient_reach_5seeds/results.json'
def measure_one(data, L, backbone, seed):
A = data['A_hat']
common = dict(data=data, hidden_dim=64, lr=0.01, weight_decay=5e-4,
num_layers=L, residual_alpha=0.0, backbone=backbone)
grape_extra = dict(diffusion_alpha=0.5, diffusion_iters=10,
lr_feedback=0.5, num_probes=64, topo_mode='fixed_A')
torch.manual_seed(seed); np.random.seed(seed); torch.cuda.manual_seed_all(seed)
bp = BPTrainer(**common)
torch.manual_seed(seed); np.random.seed(seed); torch.cuda.manual_seed_all(seed)
gr = GraphGrAPETrainer(**common, **grape_extra)
gr.align_mode = 'chain_norm'
for _ in range(EPOCHS):
bp.train_step()
gr.train_step()
# BP gradients
bp.optimizer.zero_grad()
Z_bp, _ = bp.forward()
mask = data['train_mask']
loss = F.cross_entropy(Z_bp[mask], data['y'][mask])
loss.backward()
bp_norms = [bp.weights[l].grad.norm().item() for l in range(L)]
# GRAFT feedback norms
Z_gr, inter = gr.forward()
E0, E_bar = gr._output_error(Z_gr)
graft_norms = []
for l in range(L - 1):
power = min(L - l, gr.max_topo_power)
topo_E = E_bar
for _ in range(power):
topo_E = spmm(A, topo_E)
fb = topo_E @ gr.Rs[l]
relu_gate = (inter['Zs'][l].detach() > 0).float()
graft_norms.append((relu_gate * fb).norm().item())
bp_acc = bp.evaluate('test_mask')
gr_acc = gr.evaluate('test_mask')
del bp, gr; torch.cuda.empty_cache()
return bp_norms, graft_norms, bp_acc, gr_acc
def main():
os.makedirs(OUT_DIR, exist_ok=True)
data = load_dataset('Cora', device=device)
# Load existing per-seed data if available
old_per_seed_file = os.path.join(OUT_DIR, 'per_seed_data.json')
if os.path.exists(old_per_seed_file):
with open(old_per_seed_file) as f:
per_seed_data = json.load(f)
print(f"Loaded existing per-seed data from {old_per_seed_file}")
else:
per_seed_data = {}
configs = [
('gcn', 6),
('gcn', 10),
('appnp', 6),
('appnp', 10),
]
for backbone, L in configs:
key = f"{backbone}_L{L}"
print(f"\n=== {backbone.upper()} L={L} (20 seeds) ===", flush=True)
if key not in per_seed_data:
per_seed_data[key] = {}
for seed in ALL_SEEDS:
seed_key = str(seed)
if seed_key in per_seed_data[key]:
print(f" seed {seed}: already done, skipping", flush=True)
continue
bn, gn, ba, ga = measure_one(data, L, backbone, seed)
per_seed_data[key][seed_key] = {
'bp_norms': bn, 'graft_norms': gn,
'bp_acc': ba, 'gr_acc': ga
}
print(f" seed {seed}: BP {ba*100:.1f}% GRAFT {ga*100:.1f}%", flush=True)
# Save incrementally
with open(old_per_seed_file, 'w') as f:
json.dump(per_seed_data, f, indent=2)
# Aggregate results
results = {}
for backbone, L in configs:
key = f"{backbone}_L{L}"
sd = per_seed_data[key]
bp_accs = np.array([sd[str(s)]['bp_acc'] for s in ALL_SEEDS]) * 100
gr_accs = np.array([sd[str(s)]['gr_acc'] for s in ALL_SEEDS]) * 100
t_stat, p_val = scipy_stats.ttest_rel(gr_accs, bp_accs)
avg_bp_norms = np.mean([sd[str(s)]['bp_norms'] for s in ALL_SEEDS], axis=0)
avg_gr_norms = np.mean([sd[str(s)]['graft_norms'] for s in ALL_SEEDS], axis=0)
results[key] = {
'bp_acc_mean': float(bp_accs.mean()),
'bp_acc_std': float(bp_accs.std()),
'gr_acc_mean': float(gr_accs.mean()),
'gr_acc_std': float(gr_accs.std()),
'delta_mean': float((gr_accs - bp_accs).mean()),
'delta_std': float((gr_accs - bp_accs).std()),
't_stat': float(t_stat),
'p_value': float(p_val),
'n_seeds': 20,
'avg_bp_norms': avg_bp_norms.tolist(),
'avg_gr_norms': avg_gr_norms.tolist(),
'bp_accs': bp_accs.tolist(),
'gr_accs': gr_accs.tolist(),
}
sig = '***' if p_val < 0.001 else ('**' if p_val < 0.01 else ('*' if p_val < 0.05 else 'ns'))
print(f"\n {key}:")
print(f" BP: {bp_accs.mean():.1f} ± {bp_accs.std():.1f}%")
print(f" GRAFT: {gr_accs.mean():.1f} ± {gr_accs.std():.1f}%")
print(f" Δ: {(gr_accs-bp_accs).mean():+.1f} ± {(gr_accs-bp_accs).std():.1f}% t={t_stat:.2f} p={p_val:.4f} {sig}")
print(f" BP norm L0: {avg_bp_norms[0]:.6f}")
print(f" GRAFT norm L0: {avg_gr_norms[0]:.4f}")
with open(os.path.join(OUT_DIR, 'results.json'), 'w') as f:
json.dump(results, f, indent=2)
print(f"\nSaved to {OUT_DIR}/results.json")
if __name__ == '__main__':
main()
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