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, 9 insertions, 9 deletions

diff --git a/experiments/run_realworld_hero_L20.py b/experiments/run_realworld_hero_L20.py
index 93a6c91..3b352f1 100644
--- a/experiments/run_realworld_hero_L20.py
+++ b/experiments/run_realworld_hero_L20.py
@@ -1,5 +1,5 @@
 #!/usr/bin/env python3
-"""H33: 20-seed extension of L=20 hero on 4 real-world datasets × {BP, DFA, DFA-GNN, GRAFT}.
+"""H33: 20-seed extension of L=20 hero on 4 real-world datasets × {BP, DFA, DFA-GNN, KAFT}.
 Paper setup (5%/class, hidden=64, lr=0.01, no scheduler, 200 epochs, GCN backbone, no dropout/BN/res).
 
 Tightens DBLP std (0.121 at 10-seed bimodal) for paper-grade stats.
@@ -17,7 +17,7 @@ from torch_geometric.nn import GCNConv
 from torch_geometric.utils import add_self_loops, degree
 
 sys.path.insert(0, '/home/yurenh2/graph-grape')
-from src.trainers import GraphGrAPETrainer
+from src.trainers import KAFTTrainer
 
 device = torch.device('cuda:2')
 
@@ -92,7 +92,7 @@ def bp_one(L, seed, d, tm, vm, tem, epochs=200, lr=0.01, hidden=64):
     return bt
 
 
-def graft_one(L, seed, d, A_hat, A_row, A_row_T, tm, vm, tem,
+def kaft_one(L, seed, d, A_hat, A_row, A_row_T, tm, vm, tem,
               epochs=200, lr=0.01, hidden=64):
     torch.manual_seed(seed); np.random.seed(seed); torch.cuda.manual_seed_all(seed)
     data = {
@@ -101,7 +101,7 @@ def graft_one(L, seed, d, A_hat, A_row, A_row_T, tm, vm, tem,
         'num_features': d.x.shape[1], 'num_classes': int(d.y.max())+1,
         'num_nodes': d.num_nodes, 'traces': {},
     }
-    trainer = GraphGrAPETrainer(
+    trainer = KAFTTrainer(
         data=data, hidden_dim=hidden, lr=lr, weight_decay=5e-4,
         lr_feedback=0.5, num_probes=64, topo_mode='fixed_A', max_topo_power=3,
         diffusion_alpha=0.5, diffusion_iters=10,
@@ -149,21 +149,21 @@ def main():
             t0 = time.time()
             bp = bp_one(L, s, d, tm, vm, tem)
             t1 = time.time()
-            gf = graft_one(L, s, d, A_hat, A_row, A_row_T, tm, vm, tem)
+            gf = kaft_one(L, s, d, A_hat, A_row, A_row_T, tm, vm, tem)
             t2 = time.time()
             bp_a.append(bp); gf_a.append(gf)
-            print(f'  s={s} L={L}: BP={bp:.4f}({t1-t0:.0f}s)  GRAFT={gf:.4f}({t2-t1:.0f}s)', flush=True)
+            print(f'  s={s} L={L}: BP={bp:.4f}({t1-t0:.0f}s)  KAFT={gf:.4f}({t2-t1:.0f}s)', flush=True)
         bp_m, bp_sd = float(np.mean(bp_a)), float(np.std(bp_a))
         gf_m, gf_sd = float(np.mean(gf_a)), float(np.std(gf_a))
-        out[name] = dict(seeds=seeds, BP=bp_a, GRAFT=gf_a, BP_mean=bp_m, BP_std=bp_sd,
+        out[name] = dict(seeds=seeds, BP=bp_a, KAFT=gf_a, BP_mean=bp_m, BP_std=bp_sd,
                         GRAFT_mean=gf_m, GRAFT_std=gf_sd)
-        print(f'  >>> {name} L=20 (seeds {s_lo}-{s_hi-1}): BP {bp_m:.4f}±{bp_sd:.4f}  GRAFT {gf_m:.4f}±{gf_sd:.4f}  Δ={gf_m-bp_m:+.3f}', flush=True)
+        print(f'  >>> {name} L=20 (seeds {s_lo}-{s_hi-1}): BP {bp_m:.4f}±{bp_sd:.4f}  KAFT {gf_m:.4f}±{gf_sd:.4f}  Δ={gf_m-bp_m:+.3f}', flush=True)
         del d, A_hat, A_row, A_row_T
         torch.cuda.empty_cache()
 
     print('\n=== SUMMARY (this run) ===', flush=True)
     for k, v in out.items():
-        print(f'  {k}: BP {v["BP_mean"]:.4f}±{v["BP_std"]:.4f}  GRAFT {v["GRAFT_mean"]:.4f}±{v["GRAFT_std"]:.4f}', flush=True)
+        print(f'  {k}: BP {v["BP_mean"]:.4f}±{v["BP_std"]:.4f}  KAFT {v["GRAFT_mean"]:.4f}±{v["GRAFT_std"]:.4f}', flush=True)
 
 
 if __name__ == '__main__':