From ba6ead6d7a41b7ed78bb228181b7262d0c75d2eb Mon Sep 17 00:00:00 2001
From: YurenHao0426 <blackhao0426@gmail.com>
Date: Mon, 4 May 2026 23:10:10 -0500
Subject: =?UTF-8?q?Global=20rename=20GRAFT=20=E2=86=92=20KAFT=20(incl.=20i?=
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- 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.
---
 experiments/run_cs_full.py | 14 +++++++-------
 1 file changed, 7 insertions(+), 7 deletions(-)

(limited to 'experiments/run_cs_full.py')

diff --git a/experiments/run_cs_full.py b/experiments/run_cs_full.py
index d66fc59..d170e13 100644
--- a/experiments/run_cs_full.py
+++ b/experiments/run_cs_full.py
@@ -1,6 +1,6 @@
 #!/usr/bin/env python3
 """H19 CitationFull-CiteSeer (4.2K, deg 2.5, 6-class) — same regime as Planetoid CiteSeer.
-Quick BP + GRAFT depth sweep to confirm/extend the 'GRAFT wins on real sparse citation' story."""
+Quick BP + KAFT depth sweep to confirm/extend the 'KAFT wins on real sparse citation' story."""
 
 import torch, sys, numpy as np, time
 import torch.nn as nn, torch.nn.functional as F
@@ -9,7 +9,7 @@ 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
 
 DATA_ROOT = '/home/yurenh2/graph-grape/data/CFull'
 device = torch.device('cuda:0')
@@ -90,7 +90,7 @@ def bp_one(L, seed, d, train_mask, val_mask, test_mask, epochs=200, lr=5e-3, hid
     return bt
 
 
-def graft_one(L, seed, d, A_hat, A_row, A_row_T, train_mask, val_mask, test_mask,
+def kaft_one(L, seed, d, A_hat, A_row, A_row_T, train_mask, val_mask, test_mask,
               epochs=200, lr=5e-3, hidden=128):
     torch.manual_seed(seed); np.random.seed(seed); torch.cuda.manual_seed_all(seed)
     data = {
@@ -99,7 +99,7 @@ def graft_one(L, seed, d, A_hat, A_row, A_row_T, train_mask, val_mask, test_mask
         '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=0.0,
         lr_feedback=0.5, num_probes=64, topo_mode='fixed_A', max_topo_power=3,
         diffusion_alpha=0.5, diffusion_iters=10,
@@ -133,14 +133,14 @@ 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_accs.append(bp); gf_accs.append(gf)
-            print(f'  L={L} s={s}: BP={bp:.4f}({t1-t0:.0f}s) GRAFT={gf:.4f}({t2-t1:.0f}s)', flush=True)
+            print(f'  L={L} s={s}: BP={bp:.4f}({t1-t0:.0f}s) KAFT={gf:.4f}({t2-t1:.0f}s)', flush=True)
         bp_m, bp_sd = np.mean(bp_accs), np.std(bp_accs)
         gf_m, gf_sd = np.mean(gf_accs), np.std(gf_accs)
         bp_res[L] = (bp_m, bp_sd); gf_res[L] = (gf_m, gf_sd)
-        print(f'>>> L={L}: BP {bp_m:.4f}±{bp_sd:.4f}  GRAFT {gf_m:.4f}±{gf_sd:.4f}  Δ={gf_m-bp_m:+.3f}', flush=True)
+        print(f'>>> L={L}: BP {bp_m:.4f}±{bp_sd:.4f}  KAFT {gf_m:.4f}±{gf_sd:.4f}  Δ={gf_m-bp_m:+.3f}', flush=True)
 
 
 if __name__ == '__main__':
-- 
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