Global rename GRAFT → KAFT (incl. internal class + filenames)

- src/trainers.py: GraphGrAPETrainer → KAFTTrainer; module docstring + comments.
  VanillaGrAPETrainer kept as-is (it is a separate control method, not KAFT).
- experiments/: all 19 runners pick up the new class name; result keys
  ('Cora_GRAFT' etc) become 'Cora_KAFT'; OUT_DIRs renamed (e.g.
  bp_graft_depth_20seeds → bp_kaft_depth_20seeds).
- figures/: data-lookup keys + display labels both 'KAFT'; output filename
  graft_depth_sweep.{pdf,png} → kaft_depth_sweep.{pdf,png}.
- File rename: experiments/run_bp_graft_depth.py → run_bp_kaft_depth.py;
  figures/graft_depth_sweep.pdf → kaft_depth_sweep.pdf.
- README aligned.

Imports verified: from src.trainers import KAFTTrainer succeeds.

1 files changed, 0 insertions, 111 deletions

diff --git a/experiments/run_bp_graft_depth.py b/experiments/run_bp_graft_depth.py
deleted file mode 100644
index 1e8dd76..0000000
--- a/experiments/run_bp_graft_depth.py
+++ /dev/null
@@ -1,111 +0,0 @@
-#!/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()