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#!/usr/bin/env python3
"""E0a+E0c+E0e: hero-table coverage expansion. Adds 3 datasets (PubMed stack,
Coauthor-Physics, Coauthor-CS) × 5 methods (BP, DFA, DFA-GNN, KAFT,
KAFT+ResGCN) × 20 seeds, all GCN L=6. Goal: 6-row hero table of homophilous
citation/coauthor graphs where KAFT or KAFT+ResGCN is best per row."""
import torch
import numpy as np
import json
import os
from scipy import stats as scipy_stats
from src.data import load_dataset, spmm
from src.trainers import BPTrainer, DFATrainer, DFAGNNTrainer, KAFTTrainer
from run_deep_baselines import ResGCNTrainer
from run_combo_20seeds import GRAFTResGCN
from run_large_graph_scout import load_and_check
import torch.nn.functional as F
device = 'cuda:0'
SEEDS = list(range(20))
EPOCHS = 200
OUT_DIR = 'results/hero_extras_20seeds'
grape_extra = dict(diffusion_alpha=0.5, diffusion_iters=10,
lr_feedback=0.5, num_probes=64, topo_mode='fixed_A')
dfagnn_extra = dict(diffusion_alpha=0.5, diffusion_iters=10, max_topo_power=3)
# DFA-GNN + ResGCN wrapper (from run_dfagnn_resgcn.py but inlined for module independence)
class DFAGNNResGCN(DFAGNNTrainer):
def forward(self):
X = self.data['X']
H = X
H0 = None
Hs, Zs = [], []
for l in range(self.num_layers):
Z = self._graph_conv(H, self.weights[l], l)
Zs.append(Z)
if l < self.num_layers - 1:
H_new = F.relu(Z)
if H_new.size(1) == H.size(1):
H = H + H_new
else:
H = H_new
Hs.append(H)
if l == 0:
H0 = H
else:
return Z, {'Hs': Hs, 'Zs': Zs, 'H0': H0}
return Z, {'Hs': Hs, 'Zs': Zs, 'H0': H0}
METHODS = {
'BP': (BPTrainer, {}),
'DFA': (DFATrainer, dfagnn_extra),
'DFA-GNN': (DFAGNNTrainer, dfagnn_extra),
'KAFT': (KAFTTrainer, grape_extra),
'KAFT+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 load_dataset_hero(name):
"""Return data dict in the same format as load_dataset, for any hero-list dataset."""
if name == 'PubMed':
return load_dataset('PubMed', device=device)
# Coauthor-* uses load_and_check which returns (stats, data) tuple
stats, data = load_and_check(name)
if data is None:
raise RuntimeError(f"Failed to load {name}")
return data
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 = {}
# Order from fastest (PubMed ~19K) to slower (Physics ~34K, CS ~18K)
DATASETS = ['PubMed', 'Coauthor-CS', 'Coauthor-Physics']
for ds_name in DATASETS:
print(f"\n{'=' * 70}\n{ds_name} (GCN L=6, 20 seeds, 5 methods)\n{'=' * 70}", flush=True)
data = load_dataset_hero(ds_name)
common = dict(data=data, hidden_dim=64, lr=0.01, weight_decay=5e-4,
num_layers=6, residual_alpha=0.0, backbone='gcn')
for mname, (cls, extra) in METHODS.items():
key = f"{ds_name}_{mname}"
if key not in per_seed_data:
per_seed_data[key] = {}
print(f"\n--- {key} ---", 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)
del data; torch.cuda.empty_cache()
# Summary
print(f"\n{'=' * 70}\nHero-extras summary (20 seeds, GCN L=6)\n{'=' * 70}")
results = {}
for ds in DATASETS:
print(f"\n{ds}:")
method_means = {}
for mname in METHODS:
key = f"{ds}_{mname}"
vals = np.array([per_seed_data[key].get(str(s), 0.0) 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:<16} {vals.mean():5.1f} ± {vals.std():4.1f}")
# Flag best method
best_method = max(method_means.keys(), key=lambda k: method_means[k][0])
print(f" >>> Best: {best_method} ({method_means[best_method][0]:.1f}%)")
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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