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#!/usr/bin/env python3
"""H11: Fill depth sweep at L=14 and L=18 to densify Fig 4(a).
3 methods (BP / DFA-GNN / GRAFT) × 4 datasets × 2 depths × 20 seeds = 480 runs.
"""
import torch
import numpy as np
import json
import os
from src.data import load_dataset
from src.trainers import BPTrainer, DFAGNNTrainer, GraphGrAPETrainer
from run_dblp_depth import load_dblp
device = 'cuda:0'
SEEDS = list(range(20))
EPOCHS = 200
DEPTHS = [14, 18]
OUT_DIR = 'results/depth_extras_20seeds'
grape_extra = dict(diffusion_alpha=0.5, diffusion_iters=10,
lr_feedback=0.5, num_probes=64, topo_mode='fixed_A')
dfagnn_extra = dict(diffusion_alpha=0.5, diffusion_iters=10, max_topo_power=3)
METHODS = {
'BP': (BPTrainer, {}),
'DFA-GNN': (DFAGNNTrainer, dfagnn_extra),
'GRAFT': (GraphGrAPETrainer, grape_extra),
}
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 = {}
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:
common = dict(data=data, hidden_dim=64, lr=0.01, weight_decay=5e-4,
num_layers=L, residual_alpha=0.0, backbone='gcn')
for mname, (cls, extra) in METHODS.items():
key = f"{ds_name}_L{L}_{mname}"
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]:
continue
try:
acc = train_one(cls, common, extra, 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()
print(f"\nDone. Saved to {per_seed_file}")
if __name__ == '__main__':
main()
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