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#!/usr/bin/env python3
"""Task 7016bd94 Part 1: ResGCN vs KAFT, 20 seeds, paired t-tests."""
import torch
import numpy as np
import json
import os
from scipy import stats as scipy_stats
from src.data import load_dataset
from src.trainers import BPTrainer, KAFTTrainer
from run_deep_baselines import ResGCNTrainer
from run_dblp_depth import load_dblp
device = 'cuda:0'
SEEDS = list(range(20))
EPOCHS = 200
OUT_DIR = 'results/resgcn_20seeds'
grape_extra = dict(diffusion_alpha=0.5, diffusion_iters=10,
lr_feedback=0.5, num_probes=64, topo_mode='fixed_A')
def train_one(cls, common, extra, seed):
torch.manual_seed(seed); np.random.seed(seed); torch.cuda.manual_seed_all(seed)
t = cls(**common, **extra)
if hasattr(t, 'align_mode'):
t.align_mode = 'chain_norm'
bv, bt = 0, 0
for ep in range(EPOCHS):
t.train_step()
if ep % 5 == 0:
v = t.evaluate('val_mask')
te = t.evaluate('test_mask')
if v > bv: bv, bt = v, te
del t; torch.cuda.empty_cache()
return bt
def main():
os.makedirs(OUT_DIR, exist_ok=True)
per_seed_file = os.path.join(OUT_DIR, 'per_seed_data.json')
if os.path.exists(per_seed_file):
with open(per_seed_file) as f:
per_seed_data = json.load(f)
else:
per_seed_data = {}
METHODS = {
'BP': (BPTrainer, {}),
'ResGCN': (ResGCNTrainer, {}),
'KAFT': (KAFTTrainer, grape_extra),
}
datasets_cfg = {
'Cora': lambda: load_dataset('Cora', device=device),
'CiteSeer': lambda: load_dataset('CiteSeer', device=device),
'DBLP': lambda: load_dblp(),
}
results = {}
for ds_name, loader in datasets_cfg.items():
data = loader()
common = dict(data=data, hidden_dim=64, lr=0.01, weight_decay=5e-4,
num_layers=6, residual_alpha=0.0, backbone='gcn')
for mname, (cls, extra) in METHODS.items():
key = f"{ds_name}_{mname}"
print(f"\n=== {key} (20 seeds) ===", flush=True)
if key not in per_seed_data:
per_seed_data[key] = {}
for seed in SEEDS:
sk = str(seed)
if sk in per_seed_data[key]:
print(f" seed {seed}: cached", flush=True)
continue
acc = train_one(cls, common, extra, seed)
per_seed_data[key][sk] = acc
print(f" seed {seed}: {acc*100:.1f}%", flush=True)
with open(per_seed_file, 'w') as f:
json.dump(per_seed_data, f, indent=2)
accs = np.array([per_seed_data[key][str(s)] for s in SEEDS]) * 100
results[key] = {
'mean': float(accs.mean()), 'std': float(accs.std()),
'accs': accs.tolist(),
}
print(f" {mname}: {accs.mean():.1f} ± {accs.std():.1f}%")
del data; torch.cuda.empty_cache()
# Paired t-tests: KAFT vs ResGCN
print("\n" + "=" * 70)
print("Paired t-tests: KAFT vs ResGCN (20 seeds)")
print("-" * 70)
for ds in ['Cora', 'CiteSeer', 'DBLP']:
bp_accs = np.array(results[f"{ds}_BP"]['accs'])
res_accs = np.array(results[f"{ds}_ResGCN"]['accs'])
gr_accs = np.array(results[f"{ds}_GRAFT"]['accs'])
# KAFT vs ResGCN
t_stat, p_val = scipy_stats.ttest_rel(gr_accs, res_accs)
delta = gr_accs.mean() - res_accs.mean()
sig = '***' if p_val < 0.001 else ('**' if p_val < 0.01 else ('*' if p_val < 0.05 else 'ns'))
results[f"{ds}_GRAFT_vs_ResGCN"] = {
'delta': float(delta), 't_stat': float(t_stat),
'p_value': float(p_val), 'significant': bool(p_val < 0.05),
}
# KAFT vs BP
t2, p2 = scipy_stats.ttest_rel(gr_accs, bp_accs)
d2 = gr_accs.mean() - bp_accs.mean()
sig2 = '***' if p2 < 0.001 else ('**' if p2 < 0.01 else ('*' if p2 < 0.05 else 'ns'))
results[f"{ds}_GRAFT_vs_BP"] = {
'delta': float(d2), 't_stat': float(t2),
'p_value': float(p2), 'significant': bool(p2 < 0.05),
}
# ResGCN vs BP
t3, p3 = scipy_stats.ttest_rel(res_accs, bp_accs)
d3 = res_accs.mean() - bp_accs.mean()
results[f"{ds}_ResGCN_vs_BP"] = {
'delta': float(d3), 't_stat': float(t3),
'p_value': float(p3), 'significant': bool(p3 < 0.05),
}
print(f"\n{ds}:")
print(f" BP: {bp_accs.mean():.1f} ± {bp_accs.std():.1f}")
print(f" ResGCN: {res_accs.mean():.1f} ± {res_accs.std():.1f}")
print(f" KAFT: {gr_accs.mean():.1f} ± {gr_accs.std():.1f}")
print(f" KAFT vs ResGCN: Δ{delta:+.1f}% p={p_val:.6f} {sig}")
print(f" KAFT vs BP: Δ{d2:+.1f}% p={p2:.6f} {sig2}")
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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