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, 3 insertions, 5 deletions

diff --git a/experiments/run_dblp_depth.py b/experiments/run_dblp_depth.py
index d63b94a..f91440a 100644
--- a/experiments/run_dblp_depth.py
+++ b/experiments/run_dblp_depth.py
@@ -11,8 +11,7 @@ import os
 import time
 from torch_geometric.datasets import CitationFull
 from src.data import build_normalized_adj, build_row_normalized_adj, spmm, precompute_traces
-from src.trainers import BPTrainer, DFATrainer, GraphGrAPETrainer
-from benchmark_efficient import GraphGrAPEEfficient
+from src.trainers import BPTrainer, DFATrainer, KAFTTrainer
 
 device = 'cuda:0'
 SEEDS = [0, 1, 2, 3, 4]
@@ -108,7 +107,7 @@ def main():
                 row = {}
                 for mname, cls, extra in [('BP', BPTrainer, {}),
                                            ('DFA', DFATrainer, dict(diffusion_alpha=0.5, diffusion_iters=10)),
-                                           ('GrAPE', GraphGrAPETrainer, grape_extra)]:
+                                           ('GrAPE', KAFTTrainer, grape_extra)]:
                     accs = [train_one(cls, common, extra, s) for s in SEEDS]
                     row[mname] = {'mean': float(np.mean(accs)), 'std': float(np.std(accs))}
                 results[key] = row
@@ -123,7 +122,6 @@ def main():
             common = dict(data=dblp, hidden_dim=64, lr=0.01, weight_decay=5e-4,
                           num_layers=L, residual_alpha=0.0, backbone=bb)
             bp_ms = time_method(BPTrainer, common, {})
-            eff_ms = time_method(GraphGrAPEEfficient, common,
                                  dict(lr_feedback=0.5, num_probes=64, max_topo_power=3,
                                       diff_alpha=0.5, align_every=10))
             key = f"DBLP_eff|{bb}|L={L}"
@@ -146,7 +144,7 @@ def main():
                 key = f"{ds_name}|{bb}|L={L}|lr=0.01"
                 row = {}
                 for mname, cls, extra in [('BP', BPTrainer, {}),
-                                           ('GrAPE', GraphGrAPETrainer, grape_extra)]:
+                                           ('GrAPE', KAFTTrainer, grape_extra)]:
                     accs = [train_one(cls, common, extra, s) for s in SEEDS]
                     row[mname] = {'mean': float(np.mean(accs)), 'std': float(np.std(accs))}
                 results[key] = row