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#!/usr/bin/env python3
"""
CitationFull-DBLP experiment + depth sweep L=2-6 补数据.
"""
import torch
import torch.nn.functional as F
import numpy as np
import json
import os
import time
from torch_geometric.datasets import CitationFull
from src.data import build_normalized_adj, build_row_normalized_adj, spmm, precompute_traces
from src.trainers import BPTrainer, DFATrainer, KAFTTrainer
device = 'cuda:0'
SEEDS = [0, 1, 2, 3, 4]
EPOCHS = 200
OUT_DIR = 'results/dblp_depth'
def load_dblp():
ds = CitationFull(root='./data', name='DBLP')
data = ds[0]
N, C = data.num_nodes, ds.num_classes
# Random split
rng = torch.Generator().manual_seed(42)
train_mask = torch.zeros(N, dtype=torch.bool)
val_mask = torch.zeros(N, dtype=torch.bool)
test_mask = torch.zeros(N, dtype=torch.bool)
for c in range(C):
idx = (data.y == c).nonzero(as_tuple=True)[0]
perm = torch.randperm(len(idx), generator=rng)
n_tr = max(1, int(0.05 * len(idx))) # 5% train (like Planetoid)
n_va = max(1, int(0.1 * len(idx)))
train_mask[idx[perm[:n_tr]]] = True
val_mask[idx[perm[n_tr:n_tr + n_va]]] = True
test_mask[idx[perm[n_tr + n_va:]]] = True
A_hat = build_normalized_adj(data.edge_index, N)
A_row, A_row_T = build_row_normalized_adj(data.edge_index, N)
traces = {k: torch.tensor(0.0) for k in range(5)} # skip expensive trace computation
return {
'X': data.x.to(device), 'y': data.y.to(device),
'A_hat': A_hat.to(device), 'A_row': A_row.to(device), 'A_row_T': A_row_T.to(device),
'train_mask': train_mask.to(device), 'val_mask': val_mask.to(device),
'test_mask': test_mask.to(device),
'num_nodes': N, 'num_features': data.x.shape[1], 'num_classes': C,
'traces': {k: v.to(device) for k, v in traces.items()},
}
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, te = t.evaluate('val_mask'), t.evaluate('test_mask')
if v > bv: bv, bt = v, te
del t; torch.cuda.empty_cache()
return bt
def time_method(cls, common, extra, n_warmup=10, n_steps=200):
torch.manual_seed(0)
t = cls(**common, **extra)
if hasattr(t, 'align_mode'):
t.align_mode = 'chain_norm'
for _ in range(n_warmup):
t.train_step()
torch.cuda.synchronize()
times = []
for _ in range(n_steps):
torch.cuda.synchronize(); t0 = time.perf_counter()
t.train_step()
torch.cuda.synchronize(); times.append(time.perf_counter() - t0)
del t; torch.cuda.empty_cache()
return float(np.median(times) * 1000)
def main():
os.makedirs(OUT_DIR, exist_ok=True)
results = {}
grape_extra = dict(diffusion_alpha=0.5, diffusion_iters=10,
lr_feedback=0.5, num_probes=64, topo_mode='fixed_A')
# ======== Part 1: DBLP full sweep ========
print("=" * 60)
print("Part 1: CitationFull-DBLP")
print("=" * 60)
dblp = load_dblp()
print(f"DBLP: N={dblp['num_nodes']}, F={dblp['num_features']}, C={dblp['num_classes']}, "
f"train={dblp['train_mask'].sum().item()}", flush=True)
for bb in ['gcn', 'sage', 'gin', 'appnp']:
for L in [5, 6]:
for lr in [0.001, 0.005, 0.01]:
common = dict(data=dblp, hidden_dim=64, lr=lr, weight_decay=5e-4,
num_layers=L, residual_alpha=0.0, backbone=bb)
key = f"DBLP|{bb}|L={L}|lr={lr}"
row = {}
for mname, cls, extra in [('BP', BPTrainer, {}),
('DFA', DFATrainer, dict(diffusion_alpha=0.5, diffusion_iters=10)),
('GrAPE', KAFTTrainer, grape_extra)]:
accs = [train_one(cls, common, extra, s) for s in SEEDS]
row[mname] = {'mean': float(np.mean(accs)), 'std': float(np.std(accs))}
results[key] = row
bp, dfa, gr = row['BP']['mean']*100, row['DFA']['mean']*100, row['GrAPE']['mean']*100
print(f" {bb:>6} L={L} lr={lr:.3f} | BP {bp:.1f} DFA {dfa:.1f} GrAPE {gr:.1f} | "
f"Δ(BP) {gr-bp:+.1f} Δ(DFA) {gr-dfa:+.1f}", flush=True)
# DBLP efficiency
print("\nDBLP Efficiency:")
for bb in ['gcn', 'sage', 'gin', 'appnp']:
for L in [5, 6]:
common = dict(data=dblp, hidden_dim=64, lr=0.01, weight_decay=5e-4,
num_layers=L, residual_alpha=0.0, backbone=bb)
bp_ms = time_method(BPTrainer, common, {})
dict(lr_feedback=0.5, num_probes=64, max_topo_power=3,
diff_alpha=0.5, align_every=10))
key = f"DBLP_eff|{bb}|L={L}"
results[key] = {'BP_ms': bp_ms, 'GrAPE_Eff_ms': eff_ms, 'speedup': bp_ms / eff_ms}
print(f" {bb:>6} L={L} | BP {bp_ms:.2f}ms GrAPE-Eff {eff_ms:.2f}ms | "
f"speedup {bp_ms/eff_ms:.2f}x", flush=True)
# ======== Part 2: Depth sweep L=2-4 补数据 (Planetoid × GCN/SAGE/APPNP) ========
print("\n" + "=" * 60)
print("Part 2: Depth sweep L=2,3,4 补数据")
print("=" * 60)
from src.data import load_dataset
for ds_name in ['Cora', 'CiteSeer', 'PubMed']:
data = load_dataset(ds_name, device=device)
for bb in ['gcn', 'sage', 'appnp']:
for L in [2, 3, 4]:
common = dict(data=data, hidden_dim=64, lr=0.01, weight_decay=5e-4,
num_layers=L, residual_alpha=0.0, backbone=bb)
key = f"{ds_name}|{bb}|L={L}|lr=0.01"
row = {}
for mname, cls, extra in [('BP', BPTrainer, {}),
('GrAPE', KAFTTrainer, grape_extra)]:
accs = [train_one(cls, common, extra, s) for s in SEEDS]
row[mname] = {'mean': float(np.mean(accs)), 'std': float(np.std(accs))}
results[key] = row
bp, gr = row['BP']['mean']*100, row['GrAPE']['mean']*100
print(f" {ds_name:>10} {bb:>6} L={L} | BP {bp:.1f} GrAPE {gr:.1f} | Δ {gr-bp:+.1f}", flush=True)
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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