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"""Training-based failure diagnosis on LRGB Peptides-struct (real, large, long-range).
The WL partition instrument is vacuous here (graphs ~all distinguishable), so we diagnose
by TRAINING and comparing:
GIN(L) : standard 1-WL backbone at depth L
GIN(L)+RNI : random node features = noise = beyond-1-WL symmetry breaker
GCN(L) : sub-1-WL reference
Reads: deeper helps -> long-range/under-reaching; RNI helps -> a real >1-WL ceiling that
noise breaks; train<<test -> generalization; train high -> compute/optimization ceiling.
Targets z-scored per dim; metric = standardized MAE (lower better). 11 targets.
Run: PYTHONPATH=/home/yurenh2/rrog python3 diag/train_real.py --conv gin --layers 5 --rni 0
"""
import argparse, json, os, time
import numpy as np
import torch
import torch.nn as nn
from torch_geometric.datasets import LRGBDataset
from torch_geometric.loader import DataLoader
from torch_geometric.nn import GINConv, GCNConv, global_mean_pool
ROOT = '/home/yurenh2/rrog/data/lrgb'
OUT = '/home/yurenh2/rrog/runs'
class Net(nn.Module):
def __init__(self, col_sizes, hidden, layers, out_dim, conv='gin', rni=0):
super().__init__()
self.embs = nn.ModuleList([nn.Embedding(int(s), hidden) for s in col_sizes])
self.rni = rni
self.lin_in = nn.Linear(hidden + rni, hidden)
self.convs, self.bns = nn.ModuleList(), nn.ModuleList()
for _ in range(layers):
if conv == 'gin':
mlp = nn.Sequential(nn.Linear(hidden, hidden), nn.ReLU(), nn.Linear(hidden, hidden))
self.convs.append(GINConv(mlp, train_eps=True))
else:
self.convs.append(GCNConv(hidden, hidden))
self.bns.append(nn.BatchNorm1d(hidden))
self.head = nn.Sequential(nn.Linear(hidden, hidden), nn.ReLU(), nn.Linear(hidden, out_dim))
def forward(self, x, edge_index, batch):
h = sum(emb(x[:, i]) for i, emb in enumerate(self.embs))
if self.rni:
h = torch.cat([h, torch.randn(h.size(0), self.rni, device=h.device)], dim=1)
h = self.lin_in(h)
for conv, bn in zip(self.convs, self.bns):
h = bn(conv(h, edge_index)).relu()
return self.head(global_mean_pool(h, batch))
@torch.no_grad()
def mae(model, loader, dev, ymu, ysd):
model.eval(); se = n = 0.0
for b in loader:
b = b.to(dev)
o = model(b.x, b.edge_index, b.batch)
se += (o - b.y).abs().sum().item(); n += b.y.numel()
return se / n
def main():
ap = argparse.ArgumentParser()
ap.add_argument('--conv', choices=['gin', 'gcn'], default='gin')
ap.add_argument('--layers', type=int, default=5)
ap.add_argument('--hidden', type=int, default=128)
ap.add_argument('--rni', type=int, default=0)
ap.add_argument('--epochs', type=int, default=150)
ap.add_argument('--lr', type=float, default=1e-3)
ap.add_argument('--bs', type=int, default=128)
ap.add_argument('--seed', type=int, default=0)
args = ap.parse_args()
torch.manual_seed(args.seed); np.random.seed(args.seed)
dev = 'cuda' if torch.cuda.is_available() else 'cpu'
os.makedirs(OUT, exist_ok=True)
tr = LRGBDataset(root=ROOT, name='Peptides-struct', split='train')
va = LRGBDataset(root=ROOT, name='Peptides-struct', split='val')
te = LRGBDataset(root=ROOT, name='Peptides-struct', split='test')
# per-column embedding sizes + target standardization (train stats)
col_max = None
Ytr = []
for g in tr:
m = g.x.max(0).values
col_max = m if col_max is None else torch.maximum(col_max, m)
Ytr.append(g.y.view(-1))
for ds in (va, te):
for g in ds:
col_max = torch.maximum(col_max, g.x.max(0).values)
col_sizes = (col_max + 2).tolist()
Ytr = torch.stack(Ytr)
ymu, ysd = Ytr.mean(0), Ytr.std(0) + 1e-8
def norm(ds):
out = []
for g in ds:
g = g.clone(); g.y = (g.y.view(1, -1) - ymu) / ysd
out.append(g)
return out
trl = DataLoader(norm(tr), batch_size=args.bs, shuffle=True, drop_last=True)
val = DataLoader(norm(va), batch_size=256)
tel = DataLoader(norm(te), batch_size=256)
trl_eval = DataLoader(norm(tr), batch_size=256)
model = Net(col_sizes, args.hidden, args.layers, out_dim=11, conv=args.conv, rni=args.rni).to(dev)
opt = torch.optim.Adam(model.parameters(), lr=args.lr, weight_decay=1e-5)
sched = torch.optim.lr_scheduler.CosineAnnealingLR(opt, args.epochs)
lossf = nn.L1Loss()
t0 = time.time(); best_val = 9e9; best = {}
for ep in range(args.epochs):
model.train()
for b in trl:
b = b.to(dev); opt.zero_grad()
loss = lossf(model(b.x, b.edge_index, b.batch), b.y)
loss.backward(); torch.nn.utils.clip_grad_norm_(model.parameters(), 1.0); opt.step()
sched.step()
if (ep + 1) % 15 == 0 or ep == args.epochs - 1:
vm = mae(model, val, dev, ymu, ysd)
if vm < best_val:
best_val = vm
best = {'ep': ep + 1, 'train_mae': mae(model, trl_eval, dev, ymu, ysd),
'val_mae': vm, 'test_mae': mae(model, tel, dev, ymu, ysd)}
print(f"ep{ep+1} val_mae={vm:.4f}", flush=True)
tag = f"{args.conv}_L{args.layers}_rni{args.rni}_s{args.seed}"
rep = {'dataset': 'Peptides-struct', 'tag': tag, **vars(args),
'sec': round(time.time() - t0, 1), 'dev': dev, **best}
print(f"[{tag}] train_mae={best.get('train_mae'):.4f} val_mae={best.get('val_mae'):.4f} "
f"test_mae={best.get('test_mae'):.4f} @ep{best.get('ep')} ({rep['sec']}s)")
fn = os.path.join(OUT, f"real_{tag}.json")
with open(fn, 'w') as f:
json.dump(rep, f, indent=2)
print(" wrote", fn)
if __name__ == "__main__":
main()
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