1 files changed, 111 insertions, 0 deletions
diff --git a/experiments/run_bp_graft_depth.py b/experiments/run_bp_graft_depth.py
new file mode 100644
index 0000000..1e8dd76
--- /dev/null
+++ b/experiments/run_bp_graft_depth.py
@@ -0,0 +1,111 @@
+#!/usr/bin/env python3
+"""H9: BP + GRAFT depth sweep on Cora/CiteSeer/PubMed.
+
+E1 already did DBLP L={8,12,16,20,24,32}. This fills the gap for Cora/CiteSeer/PubMed
+at L={8,10,12,16,20} so we can plot Figure 4(a)-style depth curves on 4 datasets.
+
+BP + GRAFT only (GRAFT+ResGCN not needed for this figure — that's stacking table).
+"""
+
+import torch
+import numpy as np
+import json
+import os
+from src.data import load_dataset
+from src.trainers import BPTrainer, GraphGrAPETrainer
+
+device = 'cuda:0'
+SEEDS = list(range(20))
+EPOCHS = 200
+DEPTHS = [8, 10, 12, 16, 20]
+OUT_DIR = 'results/bp_graft_depth_20seeds'
+
+grape_extra = dict(diffusion_alpha=0.5, diffusion_iters=10,
+                   lr_feedback=0.5, num_probes=64, topo_mode='fixed_A')
+
+METHODS = {
+    'BP':    (BPTrainer, {}),
+    'GRAFT': (GraphGrAPETrainer, 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 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 = {}
+
+    datasets_cfg = {
+        'Cora': lambda: load_dataset('Cora', device=device),
+        'CiteSeer': lambda: load_dataset('CiteSeer', device=device),
+        'PubMed': lambda: load_dataset('PubMed', device=device),
+    }
+
+    for ds_name, loader in datasets_cfg.items():
+        data = loader()
+        for L in DEPTHS:
+            common = dict(data=data, hidden_dim=64, lr=0.01, weight_decay=5e-4,
+                          num_layers=L, residual_alpha=0.0, backbone='gcn')
+
+            for mname, (cls, extra) in METHODS.items():
+                key = f"{ds_name}_L{L}_{mname}"
+                if key not in per_seed_data:
+                    per_seed_data[key] = {}
+
+                print(f"\n=== {key} (20 seeds) ===", 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}\nBP/GRAFT depth sweep summary\n{'=' * 70}")
+    results = {}
+    for ds in datasets_cfg:
+        print(f"\n{ds}:")
+        for L in DEPTHS:
+            for m in METHODS:
+                key = f"{ds}_L{L}_{m}"
+                vals = np.array([per_seed_data[key][str(s)] for s in SEEDS]) * 100
+                results[key] = {'mean': float(vals.mean()), 'std': float(vals.std()),
+                                 'per_seed': vals.tolist()}
+                print(f"  L={L:2d} {m:<6} {vals.mean():5.1f} ± {vals.std():4.1f}")
+
+    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()