Drop VanillaGrAPE — control method not exposed in releaseHEADmain

- src/trainers.py: removed class VanillaGrAPETrainer (~30 lines) and
  cleaned the module-level methods-compared docstring.
- experiments/run_ablation_20seeds.py: dropped VanillaGrAPE row from the
  4-method ablation grid; sweep is now BP → DFA → DFA-GNN → KAFT.

Smoke test: BPTrainer / DFATrainer / DFAGNNTrainer / KAFTTrainer all train
cleanly at GCN L=4, Cora, 50 epochs (test acc 77.3 / 76.6 / 78.4 / 79.0).

2 files changed, 6 insertions, 44 deletions

diff --git a/experiments/run_ablation_20seeds.py b/experiments/run_ablation_20seeds.py
index d2bd434..e90917d 100644
--- a/experiments/run_ablation_20seeds.py
+++ b/experiments/run_ablation_20seeds.py
@@ -1,5 +1,5 @@
 #!/usr/bin/env python3
-"""Ablation study with 20 seeds: BP → DFA → DFA-GNN → VanillaGrAPE → KAFT."""
+"""Ablation study with 20 seeds: BP → DFA → DFA-GNN → KAFT."""
 
 import torch
 import numpy as np
@@ -7,7 +7,7 @@ import json
 import os
 from scipy import stats as scipy_stats
 from src.data import load_dataset
-from src.trainers import BPTrainer, DFATrainer, DFAGNNTrainer, VanillaGrAPETrainer, KAFTTrainer
+from src.trainers import BPTrainer, DFATrainer, DFAGNNTrainer, KAFTTrainer
 
 device = 'cuda:0'
 SEEDS = list(range(20))
@@ -18,10 +18,6 @@ METHODS = {
     'BP': (BPTrainer, {}),
     'DFA': (DFATrainer, {}),
     'DFA-GNN': (DFAGNNTrainer, {'topo_mode': 'fixed_A'}),
-    'VanillaGrAPE': (VanillaGrAPETrainer, {
-        'diffusion_alpha': 0.5, 'diffusion_iters': 10,
-        'lr_feedback': 0.5, 'num_probes': 64, 'topo_mode': 'fixed_A'
-    }),
     'KAFT': (KAFTTrainer, {
         'diffusion_alpha': 0.5, 'diffusion_iters': 10,
         'lr_feedback': 0.5, 'num_probes': 64, 'topo_mode': 'fixed_A'
diff --git a/src/trainers.py b/src/trainers.py
index 7589fc9..9da6a6a 100644
--- a/src/trainers.py
+++ b/src/trainers.py
@@ -3,11 +3,10 @@ Training methods for KAFT experiments.
 Generalized to L-layer residual GCN.
 
 Methods compared:
-  BP           — Standard backprop GCN
-  DFA          — Fixed random R, P=I
-  DFA-GNN      — Fixed random R, P=Â^{L-l}
-  VanillaGrAPE — Aligned R (per layer), P=I
-  KAFT   — Aligned R (per layer) + topology P=Â^{L-l}
+  BP       — Standard backprop GCN
+  DFA      — Fixed random R, P=I
+  DFA-GNN  — Fixed random R, P=Â^{L-l}
+  KAFT     — Learned R (per layer) + topology P=Â^{min(L-1-l, K)}
 """
 
 import torch
@@ -575,39 +574,6 @@ class DFAGNNTrainer(_FeedbackTrainerBase):
 
 
 # ---------------------------------------------------------------------------
-# Vanilla GrAPE Trainer
-# ---------------------------------------------------------------------------
-
-class VanillaGrAPETrainer(_FeedbackTrainerBase):
-    """Aligned R per layer, no topology (P=I)."""
-
-    def __init__(self, data, hidden_dim, lr, weight_decay,
-                 lr_feedback=0.5, num_probes=64,
-                 diffusion_alpha=0.5, diffusion_iters=10,
-                 num_layers=2, residual_alpha=0.0, backbone='gcn', **_kw):
-        super().__init__(data, hidden_dim, lr, weight_decay,
-                         diffusion_alpha, diffusion_iters,
-                         num_layers, residual_alpha, backbone,
-                         _kw.get('use_batchnorm', False), _kw.get('dropout', 0.0))
-        self.lr_fb = lr_feedback
-        self.num_probes = num_probes
-        # One R per hidden layer
-        self.Rs = [torch.randn(self.d_out, hidden_dim, device=self.device) * 0.01
-                   for _ in range(num_layers - 1)]
-
-    def _alignment_step(self, inter):
-        metrics = {}
-        for l in range(self.num_layers - 1):
-            cos = _align_R_layer(self, l)
-            metrics[f'cos_feat_L{l}'] = cos
-        metrics['cos_feat'] = sum(metrics.values()) / len(metrics)
-        return metrics
-
-    def _compute_hidden_feedback(self, l, inter, E_bar):
-        return E_bar @ self.Rs[l]
-
-
-# ---------------------------------------------------------------------------
 # KAFT Trainer
 # ---------------------------------------------------------------------------