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, 11 insertions, 11 deletions

diff --git a/experiments/run_grad_reach_20seeds.py b/experiments/run_grad_reach_20seeds.py
index b5ad53b..b9ac8b9 100644
--- a/experiments/run_grad_reach_20seeds.py
+++ b/experiments/run_grad_reach_20seeds.py
@@ -12,7 +12,7 @@ import json
 import os
 from scipy import stats as scipy_stats
 from src.data import load_dataset, spmm
-from src.trainers import BPTrainer, GraphGrAPETrainer
+from src.trainers import BPTrainer, KAFTTrainer
 
 device = 'cuda:0'
 ALL_SEEDS = list(range(20))
@@ -31,7 +31,7 @@ def measure_one(data, L, backbone, seed):
     torch.manual_seed(seed); np.random.seed(seed); torch.cuda.manual_seed_all(seed)
     bp = BPTrainer(**common)
     torch.manual_seed(seed); np.random.seed(seed); torch.cuda.manual_seed_all(seed)
-    gr = GraphGrAPETrainer(**common, **grape_extra)
+    gr = KAFTTrainer(**common, **grape_extra)
     gr.align_mode = 'chain_norm'
 
     for _ in range(EPOCHS):
@@ -46,10 +46,10 @@ def measure_one(data, L, backbone, seed):
     loss.backward()
     bp_norms = [bp.weights[l].grad.norm().item() for l in range(L)]
 
-    # GRAFT feedback norms
+    # KAFT feedback norms
     Z_gr, inter = gr.forward()
     E0, E_bar = gr._output_error(Z_gr)
-    graft_norms = []
+    kaft_norms = []
     for l in range(L - 1):
         power = min(L - l, gr.max_topo_power)
         topo_E = E_bar
@@ -57,13 +57,13 @@ def measure_one(data, L, backbone, seed):
             topo_E = spmm(A, topo_E)
         fb = topo_E @ gr.Rs[l]
         relu_gate = (inter['Zs'][l].detach() > 0).float()
-        graft_norms.append((relu_gate * fb).norm().item())
+        kaft_norms.append((relu_gate * fb).norm().item())
 
     bp_acc = bp.evaluate('test_mask')
     gr_acc = gr.evaluate('test_mask')
 
     del bp, gr; torch.cuda.empty_cache()
-    return bp_norms, graft_norms, bp_acc, gr_acc
+    return bp_norms, kaft_norms, bp_acc, gr_acc
 
 
 def main():
@@ -101,10 +101,10 @@ def main():
 
             bn, gn, ba, ga = measure_one(data, L, backbone, seed)
             per_seed_data[key][seed_key] = {
-                'bp_norms': bn, 'graft_norms': gn,
+                'bp_norms': bn, 'kaft_norms': gn,
                 'bp_acc': ba, 'gr_acc': ga
             }
-            print(f"  seed {seed}: BP {ba*100:.1f}% GRAFT {ga*100:.1f}%", flush=True)
+            print(f"  seed {seed}: BP {ba*100:.1f}% KAFT {ga*100:.1f}%", flush=True)
 
             # Save incrementally
             with open(old_per_seed_file, 'w') as f:
@@ -121,7 +121,7 @@ def main():
         t_stat, p_val = scipy_stats.ttest_rel(gr_accs, bp_accs)
 
         avg_bp_norms = np.mean([sd[str(s)]['bp_norms'] for s in ALL_SEEDS], axis=0)
-        avg_gr_norms = np.mean([sd[str(s)]['graft_norms'] for s in ALL_SEEDS], axis=0)
+        avg_gr_norms = np.mean([sd[str(s)]['kaft_norms'] for s in ALL_SEEDS], axis=0)
 
         results[key] = {
             'bp_acc_mean': float(bp_accs.mean()),
@@ -142,10 +142,10 @@ def main():
         sig = '***' if p_val < 0.001 else ('**' if p_val < 0.01 else ('*' if p_val < 0.05 else 'ns'))
         print(f"\n  {key}:")
         print(f"    BP:    {bp_accs.mean():.1f} ± {bp_accs.std():.1f}%")
-        print(f"    GRAFT: {gr_accs.mean():.1f} ± {gr_accs.std():.1f}%")
+        print(f"    KAFT: {gr_accs.mean():.1f} ± {gr_accs.std():.1f}%")
         print(f"    Δ:     {(gr_accs-bp_accs).mean():+.1f} ± {(gr_accs-bp_accs).std():.1f}%  t={t_stat:.2f}  p={p_val:.4f} {sig}")
         print(f"    BP norm L0: {avg_bp_norms[0]:.6f}")
-        print(f"    GRAFT norm L0: {avg_gr_norms[0]:.4f}")
+        print(f"    KAFT norm L0: {avg_gr_norms[0]:.4f}")
 
     with open(os.path.join(OUT_DIR, 'results.json'), 'w') as f:
         json.dump(results, f, indent=2)