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#!/usr/bin/env python3
"""H33: 20-seed extension of L=20 hero on 4 real-world datasets × {BP, DFA, DFA-GNN, GRAFT}.
Paper setup (5%/class, hidden=64, lr=0.01, no scheduler, 200 epochs, GCN backbone, no dropout/BN/res).
Tightens DBLP std (0.121 at 10-seed bimodal) for paper-grade stats.
Run as: python run_realworld_hero_L20.py [SEED_START SEED_END]
default: 10..19 (extending prior seeds 0..9).
"""
import sys, time
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch_geometric.datasets import CitationFull, Coauthor
from torch_geometric.nn import GCNConv
from torch_geometric.utils import add_self_loops, degree
sys.path.insert(0, '/home/yurenh2/graph-grape')
from src.trainers import GraphGrAPETrainer
device = torch.device('cuda:2')
def build_A_hat(edge_index, N):
edge_index, _ = add_self_loops(edge_index, num_nodes=N)
row, col = edge_index
deg = degree(row, num_nodes=N, dtype=torch.float)
dis = deg.pow(-0.5); dis[dis == float('inf')] = 0
return torch.sparse_coo_tensor(edge_index, dis[row]*dis[col], (N, N)).coalesce()
def build_row_norm(edge_index, N):
ei, _ = add_self_loops(edge_index, num_nodes=N)
row, col = ei
deg = degree(row, num_nodes=N, dtype=torch.float).clamp(min=1)
A_row = torch.sparse_coo_tensor(ei, 1.0/deg[row], (N,N)).coalesce()
A_row_T = torch.sparse_coo_tensor(ei.flip(0), 1.0/deg[col], (N,N)).coalesce()
return A_row, A_row_T
def paper_split(N, y, seed, train_frac=0.05, n_val=500):
g = torch.Generator().manual_seed(seed)
train_mask = torch.zeros(N, dtype=torch.bool)
val_mask = torch.zeros(N, dtype=torch.bool)
test_mask = torch.zeros(N, dtype=torch.bool)
C = int(y.max()) + 1
for c in range(C):
idx = (y == c).nonzero().flatten()
idx = idx[torch.randperm(idx.size(0), generator=g)]
n_tr = max(1, int(round(train_frac * idx.size(0))))
train_mask[idx[:n_tr]] = True
remaining = (~train_mask).nonzero().flatten()
remaining = remaining[torch.randperm(remaining.size(0), generator=g)]
val_mask[remaining[:n_val]] = True
test_mask[remaining[n_val:]] = True
return train_mask, val_mask, test_mask
class GCN(nn.Module):
def __init__(self, in_dim, hidden, out_dim, L):
super().__init__()
self.convs = nn.ModuleList([GCNConv(in_dim if i==0 else hidden,
hidden if i<L-1 else out_dim) for i in range(L)])
def forward(self, x, ei):
for l, c in enumerate(self.convs):
x = c(x, ei)
if l < len(self.convs)-1:
x = F.relu(x)
return x
def bp_one(L, seed, d, tm, vm, tem, epochs=200, lr=0.01, hidden=64):
torch.manual_seed(seed); np.random.seed(seed); torch.cuda.manual_seed_all(seed)
m = GCN(d.x.shape[1], hidden, int(d.y.max())+1, L).to(device)
opt = torch.optim.Adam(m.parameters(), lr=lr, weight_decay=5e-4)
@torch.no_grad()
def ev(mask):
m.eval()
out = m(d.x.float(), d.edge_index)
return (out[mask].argmax(1) == d.y[mask]).float().mean().item()
bv = bt = 0
for ep in range(epochs):
m.train()
out = m(d.x.float(), d.edge_index)
loss = F.cross_entropy(out[tm], d.y[tm])
opt.zero_grad(); loss.backward(); opt.step()
if ep % 5 == 0:
v = ev(vm)
if v > bv: bv, bt = v, ev(tem)
return bt
def graft_one(L, seed, d, A_hat, A_row, A_row_T, tm, vm, tem,
epochs=200, lr=0.01, hidden=64):
torch.manual_seed(seed); np.random.seed(seed); torch.cuda.manual_seed_all(seed)
data = {
'X': d.x.float(), 'A_hat': A_hat, 'A_row': A_row, 'A_row_T': A_row_T,
'y': d.y, 'train_mask': tm, 'val_mask': vm, 'test_mask': tem,
'num_features': d.x.shape[1], 'num_classes': int(d.y.max())+1,
'num_nodes': d.num_nodes, 'traces': {},
}
trainer = GraphGrAPETrainer(
data=data, hidden_dim=hidden, lr=lr, weight_decay=5e-4,
lr_feedback=0.5, num_probes=64, topo_mode='fixed_A', max_topo_power=3,
diffusion_alpha=0.5, diffusion_iters=10,
num_layers=L, residual_alpha=0.0, backbone='gcn',
use_batchnorm=False, dropout=0.0,
)
trainer.align_mode = 'chain_norm'
bv = bt = 0
for ep in range(epochs):
trainer.train_step()
if ep % 5 == 0:
v = trainer.evaluate('val_mask')
if v > bv: bv, bt = v, trainer.evaluate('test_mask')
return bt
DATASETS = [
('CFull-CiteSeer', lambda: CitationFull(root='/home/yurenh2/graph-grape/data/CFull', name='CiteSeer')[0]),
('CFull-DBLP', lambda: CitationFull(root='/home/yurenh2/graph-grape/data/CFull', name='DBLP')[0]),
('CFull-PubMed', lambda: CitationFull(root='/home/yurenh2/graph-grape/data/CFull', name='PubMed')[0]),
('Coauthor-Physics', lambda: Coauthor(root='/home/yurenh2/graph-grape/data/Coauthor', name='Physics')[0]),
]
def main():
s_lo = int(sys.argv[1]) if len(sys.argv) > 1 else 10
s_hi = int(sys.argv[2]) if len(sys.argv) > 2 else 20
seeds = list(range(s_lo, s_hi))
L = 20
print(f'>>> Hero L=20 extension: seeds={seeds}', flush=True)
out = {}
for name, loader in DATASETS:
print(f'\n=== {name} ===', flush=True)
d = loader().to(device)
N = d.num_nodes
A_hat = build_A_hat(d.edge_index, N)
A_row, A_row_T = build_row_norm(d.edge_index, N)
print(f' N={N}, deg={d.edge_index.shape[1]/N:.1f}, C={int(d.y.max())+1}', flush=True)
bp_a, gf_a = [], []
for s in seeds:
tm, vm, tem = paper_split(N, d.y.cpu(), s)
tm = tm.to(device); vm = vm.to(device); tem = tem.to(device)
t0 = time.time()
bp = bp_one(L, s, d, tm, vm, tem)
t1 = time.time()
gf = graft_one(L, s, d, A_hat, A_row, A_row_T, tm, vm, tem)
t2 = time.time()
bp_a.append(bp); gf_a.append(gf)
print(f' s={s} L={L}: BP={bp:.4f}({t1-t0:.0f}s) GRAFT={gf:.4f}({t2-t1:.0f}s)', flush=True)
bp_m, bp_sd = float(np.mean(bp_a)), float(np.std(bp_a))
gf_m, gf_sd = float(np.mean(gf_a)), float(np.std(gf_a))
out[name] = dict(seeds=seeds, BP=bp_a, GRAFT=gf_a, BP_mean=bp_m, BP_std=bp_sd,
GRAFT_mean=gf_m, GRAFT_std=gf_sd)
print(f' >>> {name} L=20 (seeds {s_lo}-{s_hi-1}): BP {bp_m:.4f}±{bp_sd:.4f} GRAFT {gf_m:.4f}±{gf_sd:.4f} Δ={gf_m-bp_m:+.3f}', flush=True)
del d, A_hat, A_row, A_row_T
torch.cuda.empty_cache()
print('\n=== SUMMARY (this run) ===', flush=True)
for k, v in out.items():
print(f' {k}: BP {v["BP_mean"]:.4f}±{v["BP_std"]:.4f} GRAFT {v["GRAFT_mean"]:.4f}±{v["GRAFT_std"]:.4f}', flush=True)
if __name__ == '__main__':
main()
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