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#!/usr/bin/env python3
"""H7: DFA-GNN depth sweep for Figure 4(a)-style plot.
Runs DFA-GNN at L ∈ {4, 8, 10, 12, 16, 20} × {Cora, CiteSeer, PubMed, DBLP} × 20 seeds.
L=6 data already exists from prior experiments; L=2/3 skipped (CiteSeer L=2 GRAFT soft spot).
Combined with existing BP and GRAFT depth data, produces 3-method depth curves for Figure 4(a).
"""
import torch
import numpy as np
import json
import os
from src.data import load_dataset
from src.trainers import DFAGNNTrainer
from run_dblp_depth import load_dblp
device = 'cuda:0'
SEEDS = list(range(20))
EPOCHS = 200
DEPTHS = [4, 8, 10, 12, 16, 20]
OUT_DIR = 'results/dfagnn_depth_20seeds'
dfagnn_extra = dict(diffusion_alpha=0.5, diffusion_iters=10, max_topo_power=3)
def train_one(data, L, seed):
torch.manual_seed(seed); np.random.seed(seed); torch.cuda.manual_seed_all(seed)
t = DFAGNNTrainer(data=data, hidden_dim=64, lr=0.01, weight_decay=5e-4,
num_layers=L, residual_alpha=0.0, backbone='gcn', **dfagnn_extra)
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 = {}
datasets_cfg = {
'Cora': lambda: load_dataset('Cora', device=device),
'CiteSeer': lambda: load_dataset('CiteSeer', device=device),
'PubMed': lambda: load_dataset('PubMed', device=device),
'DBLP': lambda: load_dblp(),
}
for ds_name, loader in datasets_cfg.items():
data = loader()
for L in DEPTHS:
key = f"{ds_name}_L{L}_DFA-GNN"
if key not in per_seed_data:
per_seed_data[key] = {}
print(f"\n=== {key} (20 seeds) ===", flush=True)
for seed in SEEDS:
sk = str(seed)
if sk in per_seed_data[key]:
print(f" seed {seed}: cached ({per_seed_data[key][sk]*100:.1f}%)", flush=True)
continue
try:
acc = train_one(data, L, seed)
per_seed_data[key][sk] = acc
print(f" seed {seed}: {acc*100:.1f}%", flush=True)
except Exception as e:
print(f" seed {seed}: FAILED - {e}", flush=True)
per_seed_data[key][sk] = 0.0
with open(per_seed_file, 'w') as f:
json.dump(per_seed_data, f, indent=2)
del data; torch.cuda.empty_cache()
# Summary
print(f"\n{'=' * 70}\nDFA-GNN depth sweep summary (20 seeds)\n{'=' * 70}")
results = {}
for ds in datasets_cfg:
print(f"\n{ds}:")
for L in DEPTHS:
key = f"{ds}_L{L}_DFA-GNN"
vals = np.array([per_seed_data[key][str(s)] for s in SEEDS]) * 100
results[key] = {'mean': float(vals.mean()), 'std': float(vals.std()),
'per_seed': vals.tolist()}
print(f" L={L:2d} DFA-GNN {vals.mean():5.1f} ± {vals.std():4.1f}")
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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