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, 7 insertions, 7 deletions

diff --git a/experiments/run_dblp_depth_scaling.py b/experiments/run_dblp_depth_scaling.py
index 4c0bc11..86a5af1 100644
--- a/experiments/run_dblp_depth_scaling.py
+++ b/experiments/run_dblp_depth_scaling.py
@@ -1,16 +1,16 @@
 #!/usr/bin/env python3
 """E1: DBLP depth scaling — upgrade depth_stress 3-seed to 20 seeds on DBLP,
 extend to L={8,12,16,20,24,32}. Goal: confirm (or falsify) the preliminary
-finding that GRAFT > ResGCN at L=16 (3-seed: 69.9 vs 63.7) and scales to L=32.
+finding that KAFT > ResGCN at L=16 (3-seed: 69.9 vs 63.7) and scales to L=32.
 
-BP vs ResGCN vs GRAFT vs GRAFT+ResGCN, GCN backbone, lr=0.01, 200 epochs."""
+BP vs ResGCN vs KAFT vs KAFT+ResGCN, GCN backbone, lr=0.01, 200 epochs."""
 
 import torch
 import numpy as np
 import json
 import os
 from scipy import stats as scipy_stats
-from src.trainers import BPTrainer, GraphGrAPETrainer
+from src.trainers import BPTrainer, KAFTTrainer
 from run_deep_baselines import ResGCNTrainer
 from run_combo_20seeds import GRAFTResGCN
 from run_dblp_depth import load_dblp
@@ -27,8 +27,8 @@ grape_extra = dict(diffusion_alpha=0.5, diffusion_iters=10,
 METHODS = {
     'BP':            (BPTrainer, {}),
     'ResGCN':        (ResGCNTrainer, {}),
-    'GRAFT':         (GraphGrAPETrainer, grape_extra),
-    'GRAFT+ResGCN':  (GRAFTResGCN, grape_extra),
+    'KAFT':         (KAFTTrainer, grape_extra),
+    'KAFT+ResGCN':  (GRAFTResGCN, grape_extra),
 }
 
 
@@ -100,11 +100,11 @@ def main():
                             'per_seed': vals.tolist()}
             print(f"  {mname:<15} {vals.mean():5.1f} ± {vals.std():4.1f}")
 
-        # GRAFT vs ResGCN (paired)
+        # KAFT vs ResGCN (paired)
         g_accs = np.array([per_seed_data[f"DBLP_L{L}_GRAFT"][str(s)] for s in SEEDS]) * 100
         r_accs = np.array([per_seed_data[f"DBLP_L{L}_ResGCN"][str(s)] for s in SEEDS]) * 100
         t_gr, p_gr = scipy_stats.ttest_rel(g_accs, r_accs)
-        print(f"  GRAFT vs ResGCN:  Δ={g_accs.mean() - r_accs.mean():+.1f},  p={p_gr:.4f}")
+        print(f"  KAFT vs ResGCN:  Δ={g_accs.mean() - r_accs.mean():+.1f},  p={p_gr:.4f}")
 
     with open(os.path.join(OUT_DIR, 'results.json'), 'w') as f:
         json.dump(results, f, indent=2)