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#!/usr/bin/env python3
"""E1: DBLP depth scaling — upgrade depth_stress 3-seed to 20 seeds on DBLP,
extend to L={8,12,16,20,24,32}. Goal: confirm (or falsify) the preliminary
finding that GRAFT > ResGCN at L=16 (3-seed: 69.9 vs 63.7) and scales to L=32.
BP vs ResGCN vs GRAFT vs GRAFT+ResGCN, GCN backbone, lr=0.01, 200 epochs."""
import torch
import numpy as np
import json
import os
from scipy import stats as scipy_stats
from src.trainers import BPTrainer, GraphGrAPETrainer
from run_deep_baselines import ResGCNTrainer
from run_combo_20seeds import GRAFTResGCN
from run_dblp_depth import load_dblp
device = 'cuda:0' # selected via CUDA_VISIBLE_DEVICES
SEEDS = list(range(20))
EPOCHS = 200
DEPTHS = [8, 12, 16, 20, 24, 32]
OUT_DIR = 'results/dblp_depth_scaling_20seeds'
grape_extra = dict(diffusion_alpha=0.5, diffusion_iters=10,
lr_feedback=0.5, num_probes=64, topo_mode='fixed_A')
METHODS = {
'BP': (BPTrainer, {}),
'ResGCN': (ResGCNTrainer, {}),
'GRAFT': (GraphGrAPETrainer, grape_extra),
'GRAFT+ResGCN': (GRAFTResGCN, 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 = {}
data = load_dblp()
for L in DEPTHS:
print(f"\n{'=' * 70}\nDepth L={L}\n{'=' * 70}", flush=True)
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"DBLP_L{L}_{mname}"
if key not in per_seed_data:
per_seed_data[key] = {}
print(f"\n--- {key} ({len(SEEDS)} 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(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)
# Summary
print(f"\n{'=' * 70}\nDBLP depth scaling summary\n{'=' * 70}")
results = {}
for L in DEPTHS:
print(f"\nL={L}:")
method_means = {}
for mname in METHODS:
key = f"DBLP_L{L}_{mname}"
vals = np.array([per_seed_data[key][str(s)] for s in SEEDS]) * 100
method_means[mname] = (vals.mean(), vals.std())
results[key] = {'mean': float(vals.mean()), 'std': float(vals.std()),
'per_seed': vals.tolist()}
print(f" {mname:<15} {vals.mean():5.1f} ± {vals.std():4.1f}")
# GRAFT vs ResGCN (paired)
g_accs = np.array([per_seed_data[f"DBLP_L{L}_GRAFT"][str(s)] for s in SEEDS]) * 100
r_accs = np.array([per_seed_data[f"DBLP_L{L}_ResGCN"][str(s)] for s in SEEDS]) * 100
t_gr, p_gr = scipy_stats.ttest_rel(g_accs, r_accs)
print(f" GRAFT vs ResGCN: Δ={g_accs.mean() - r_accs.mean():+.1f}, p={p_gr:.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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