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#!/usr/bin/env python3
"""Ablation study with 20 seeds: BP → DFA → DFA-GNN → VanillaGrAPE → GRAFT."""
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, DFATrainer, DFAGNNTrainer, VanillaGrAPETrainer, GraphGrAPETrainer
device = 'cuda:0'
SEEDS = list(range(20))
EPOCHS = 200
OUT_DIR = 'results/ablation_20seeds'
METHODS = {
'BP': (BPTrainer, {}),
'DFA': (DFATrainer, {}),
'DFA-GNN': (DFAGNNTrainer, {'topo_mode': 'fixed_A'}),
'VanillaGrAPE': (VanillaGrAPETrainer, {
'diffusion_alpha': 0.5, 'diffusion_iters': 10,
'lr_feedback': 0.5, 'num_probes': 64, 'topo_mode': 'fixed_A'
}),
'GRAFT': (GraphGrAPETrainer, {
'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 = {}
results = {}
for ds_name in ['Cora', 'CiteSeer', 'PubMed']:
data = load_dataset(ds_name, device=device)
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 between adjacent methods
print("\n=== Paired t-tests (adjacent methods) ===")
method_names = list(METHODS.keys())
for ds in ['Cora', 'CiteSeer', 'PubMed']:
print(f"\n{ds}:")
for i in range(len(method_names) - 1):
m1, m2 = method_names[i], method_names[i+1]
a1 = np.array(results[f"{ds}_{m1}"]['accs'])
a2 = np.array(results[f"{ds}_{m2}"]['accs'])
t_stat, p_val = scipy_stats.ttest_rel(a2, a1)
sig = '***' if p_val < 0.001 else ('**' if p_val < 0.01 else ('*' if p_val < 0.05 else 'ns'))
delta = a2.mean() - a1.mean()
results[f"{ds}_{m1}_vs_{m2}"] = {
'delta': float(delta), 't_stat': float(t_stat), 'p_value': float(p_val)
}
print(f" {m1} → {m2}: Δ{delta:+.1f}% p={p_val:.4f} {sig}")
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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