1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
|
"""H3: Recursive ESAN (subgraph GNN, DS-GNN node-marking bag) on graph 3-coloring.
Per graph, pick S anchor nodes; each view = graph + a 1-hot mark on the anchor. Run the SHARED
recursive GIN on all views, average node-logits over views (DeepSets). Marking breaks node
symmetry -> >1-WL. Self-contained: train (EMA, best solve) + LE (lambda on a marked view,
bucket by aggregate solve) + PTRM (K noisy aggregate forwards); writes color_/le_/ptrm_ JSON
(conv='esan') for diag/aggregate.py.
Run: PYTHONPATH=/home/yurenh2/rrog python3 diag/esan_color.py --grad_mode full --seed 0
"""
import argparse, json, os, time
import numpy as np
import torch
import torch.nn.functional as F
from torch_geometric.data import Data, Batch
from diag.train_color import make_split, featurize, RecGINColor, lyap1, OUT
try:
from sklearn.metrics import roc_auc_score
except Exception:
roc_auc_score = None
S = 4
def dense_A(edge_index, n):
A = torch.zeros(n, n)
if edge_index.shape[1]:
A[edge_index[0], edge_index[1]] = 1.0
return torch.maximum(A, A.t())
def views_of(g, anchors):
out = []
for a in anchors:
mark = torch.zeros(g['n'], 1); mark[a] = 1.0
out.append(Data(x=torch.cat([g['rfeat'], mark], dim=1), edge_index=g['edge_index'], num_nodes=g['n']))
return out
def anchors_for(g, rng):
return rng.choice(g['n'], size=min(S, g['n']), replace=False)
def esan_logits(model, g, dev, anchors, noise=False):
b = Batch.from_data_list(views_of(g, anchors)).to(dev)
out = model(b.x, b.edge_index, b.batch, noise=noise)[-1] # [S*n, k]
return out.view(len(anchors), g['n'], -1).mean(0) # [n, k]
def conf_of(logits, A):
col = logits.argmax(-1)
return int(((col.unsqueeze(0) == col.unsqueeze(1)) & (A > 0)).sum().item() // 2)
@torch.no_grad()
def solve_rate(model, graphs, dev, rng, sample=300):
model.eval(); solved = 0
for g in graphs[:sample]:
solved += int(conf_of(esan_logits(model, g, dev, anchors_for(g, rng)), g['A'].to(dev)) == 0)
return solved / len(graphs[:sample])
def main():
ap = argparse.ArgumentParser()
ap.add_argument('--grad_mode', choices=['full', '1step'], default='full')
ap.add_argument('--epochs', type=int, default=150); ap.add_argument('--M', type=int, default=16)
ap.add_argument('--seed', type=int, default=0); ap.add_argument('--sigma', type=float, default=0.2)
ap.add_argument('--K', type=int, default=16); ap.add_argument('--n_graphs', type=int, default=150)
args = ap.parse_args()
torch.manual_seed(args.seed); np.random.seed(args.seed)
rng = np.random.default_rng(args.seed)
dev = 'cuda' if torch.cuda.is_available() else 'cpu'
tr = featurize(make_split('train', 50, 3, 0.2, 8, 2000, 0), 'none', 16)
te = featurize(make_split('test', 50, 3, 0.2, 8, 500, 100000), 'none', 16)
for g in tr + te:
g['A'] = dense_A(g['edge_index'], g['n'])
in_dim = tr[0]['rfeat'].shape[1] + 1
model = RecGINColor(in_dim, 128, 3, grad_mode=args.grad_mode, conv='gin').to(dev)
opt = torch.optim.Adam(model.parameters(), lr=1e-3, weight_decay=1e-5)
sched = torch.optim.lr_scheduler.CosineAnnealingLR(opt, args.epochs)
ema = {kk: v.detach().clone() for kk, v in model.state_dict().items()}
t0 = time.time(); best = -1; best_state = None
for ep in range(args.epochs):
model.train(); order = rng.permutation(len(tr))
for s0 in range(0, len(order) - args.M, args.M):
sel = order[s0:s0 + args.M]
views, As = [], []
for i in sel:
g = tr[i]; views += views_of(g, anchors_for(g, rng)); As.append(g['A'])
b = Batch.from_data_list(views).to(dev)
opt.zero_grad()
logits = model(b.x, b.edge_index, b.batch, noise=False)[-1].view(args.M, S, 50, 3).mean(1)
Ab = torch.stack(As).to(dev)
p = F.softmax(logits, -1)
loss = (torch.einsum('bik,bjk->bij', p, p) * Ab).sum() / (Ab.sum() + 1e-9)
loss.backward(); torch.nn.utils.clip_grad_norm_(model.parameters(), 1.0); opt.step()
with torch.no_grad():
for kk, v in model.state_dict().items():
ema[kk].mul_(0.999).add_(v.detach(), alpha=0.001) if torch.is_floating_point(v) else ema[kk].copy_(v)
sched.step()
if (ep + 1) % 20 == 0 or ep == args.epochs - 1:
bk = {kk: v.detach().clone() for kk, v in model.state_dict().items()}
model.load_state_dict(ema); sr = solve_rate(model, te, dev, rng)
if sr > best:
best = sr; best_state = {kk: ema[kk].detach().cpu().clone() for kk in ema}
model.load_state_dict(bk)
print(f"ep{ep+1} solve={sr:.3f}", flush=True)
model.load_state_dict({kk: best_state[kk].to(dev) for kk in best_state}); model.eval()
lams, fails = [], []; passk = lamsel = rand = 0; Lr, Sr = [], []
nstep = model.n_sup * model.T
for gi, g in enumerate(te[:args.n_graphs]):
anc = anchors_for(g, rng)
mark = torch.zeros(g['n'], 1); mark[anc[0]] = 1.0
xin = torch.cat([g['rfeat'], mark], dim=1).to(dev); ei = g['edge_index'].to(dev)
lam0 = lyap1(model, xin, ei, nstep, dev, seed=gi)
c0 = conf_of(esan_logits(model, g, dev, anc), g['A'].to(dev))
lams.append(lam0); fails.append(int(c0 > 0))
confs, rl = [], []
for j in range(args.K):
confs.append(conf_of(esan_logits(model, g, dev, anc, noise=True), g['A'].to(dev)))
rl.append(lyap1(model, xin, ei, nstep, dev, seed=1000 * gi + j))
confs, rl = np.array(confs), np.array(rl); sv = confs == 0
passk += int(sv.any()); lamsel += int(sv[rl.argmin()]); rand += int(sv[0])
Lr += rl.tolist(); Sr += sv.tolist()
lams, fails = np.array(lams), np.array(fails); s, f = lams[fails == 0], lams[fails == 1]
auc = (roc_auc_score(fails, lams) if roc_auc_score and len(s) and len(f) else float('nan'))
n = len(te[:args.n_graphs])
print(f"[esan/{args.grad_mode}] solve={best:.3f} LE AUROC={auc:.3f} mean_lam={lams.mean():+.3f} "
f"passK={passk/n:.3f} lamsel={lamsel/n:.3f} ({time.time()-t0:.0f}s)")
base = f"esan_{args.grad_mode}_none_n50_k3_p0.2_T3_ns3_s{args.seed}"
com = {'conv': 'esan', 'pe': 'none', 'grad_mode': args.grad_mode, 'contract': False,
'seed': args.seed, 'arch': 'rrog_once_agg_node_compute'}
json.dump({**com, 'solve_rate': best}, open(os.path.join(OUT, f"color_{base}.json"), 'w'))
json.dump({**com, 'auroc': float(auc), 'mean_lam': float(lams.mean())},
open(os.path.join(OUT, f"le_color_{base}.json"), 'w'))
json.dump({**com, 'det': 1 - float(fails.mean()),
'sigmas': {'0.2': {'passk': passk / n, 'lamsel': lamsel / n, 'random': rand / n}}},
open(os.path.join(OUT, f"ptrm_color_{base}.json"), 'w'))
print(" wrote", base)
if __name__ == "__main__":
main()
|
