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, 10 insertions, 10 deletions

diff --git a/experiments/run_shallow_depth.py b/experiments/run_shallow_depth.py
index 68c9138..5f1cc26 100644
--- a/experiments/run_shallow_depth.py
+++ b/experiments/run_shallow_depth.py
@@ -1,8 +1,8 @@
 #!/usr/bin/env python3
 """E2: Shallow depth (L=2,3,4) on 4 datasets. Last exploratory avenue after
-E1 (deep scaling) and E0-extras (more datasets) both failed to extend GRAFT's
-regime. If GRAFT still wins at L=2/3 (standard GNN depth), we can counter
-the reviewer attack 'L=5,6 nobody uses'. If GRAFT matches BP only at L=5,6,
+E1 (deep scaling) and E0-extras (more datasets) both failed to extend KAFT's
+regime. If KAFT still wins at L=2/3 (standard GNN depth), we can counter
+the reviewer attack 'L=5,6 nobody uses'. If KAFT matches BP only at L=5,6,
 paper stays at current scope and we ship."""
 
 import torch
@@ -11,7 +11,7 @@ import json
 import os
 from scipy import stats as scipy_stats
 from src.data import load_dataset
-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, {}),
-    'GRAFT':         (GraphGrAPETrainer, grape_extra),
-    'GRAFT+ResGCN':  (GRAFTResGCN, grape_extra),
+    'KAFT':         (KAFTTrainer, grape_extra),
+    'KAFT+ResGCN':  (GRAFTResGCN, grape_extra),
 }
 
 
@@ -108,10 +108,10 @@ def main():
             t, p = scipy_stats.ttest_rel(gr_accs, bp_accs)
             delta = gr_accs.mean() - bp_accs.mean()
             print(f"  {ds} L={L}: BP {bp_accs.mean():5.1f}±{bp_accs.std():4.1f}  "
-                  f"GRAFT {gr_accs.mean():5.1f}±{gr_accs.std():4.1f}  "
-                  f"GRAFT+ResGCN {stk_accs.mean():5.1f}±{stk_accs.std():4.1f}  "
-                  f"Δ(GRAFT-BP)={delta:+.1f}, p={p:.4f}")
-            for mname, accs in [('BP', bp_accs), ('GRAFT', gr_accs), ('GRAFT+ResGCN', stk_accs)]:
+                  f"KAFT {gr_accs.mean():5.1f}±{gr_accs.std():4.1f}  "
+                  f"KAFT+ResGCN {stk_accs.mean():5.1f}±{stk_accs.std():4.1f}  "
+                  f"Δ(KAFT-BP)={delta:+.1f}, p={p:.4f}")
+            for mname, accs in [('BP', bp_accs), ('KAFT', gr_accs), ('KAFT+ResGCN', stk_accs)]:
                 key = f"{ds}_L{L}_{mname}"
                 results[key] = {'mean': float(accs.mean()), 'std': float(accs.std()),
                                  'per_seed': accs.tolist()}