diff options
Diffstat (limited to 'experiments/run_grad_reach_20seeds.py')
| -rw-r--r-- | experiments/run_grad_reach_20seeds.py | 22 |
1 files changed, 11 insertions, 11 deletions
diff --git a/experiments/run_grad_reach_20seeds.py b/experiments/run_grad_reach_20seeds.py index b5ad53b..b9ac8b9 100644 --- a/experiments/run_grad_reach_20seeds.py +++ b/experiments/run_grad_reach_20seeds.py @@ -12,7 +12,7 @@ import json import os from scipy import stats as scipy_stats from src.data import load_dataset, spmm -from src.trainers import BPTrainer, GraphGrAPETrainer +from src.trainers import BPTrainer, KAFTTrainer device = 'cuda:0' ALL_SEEDS = list(range(20)) @@ -31,7 +31,7 @@ def measure_one(data, L, backbone, seed): torch.manual_seed(seed); np.random.seed(seed); torch.cuda.manual_seed_all(seed) bp = BPTrainer(**common) torch.manual_seed(seed); np.random.seed(seed); torch.cuda.manual_seed_all(seed) - gr = GraphGrAPETrainer(**common, **grape_extra) + gr = KAFTTrainer(**common, **grape_extra) gr.align_mode = 'chain_norm' for _ in range(EPOCHS): @@ -46,10 +46,10 @@ def measure_one(data, L, backbone, seed): loss.backward() bp_norms = [bp.weights[l].grad.norm().item() for l in range(L)] - # GRAFT feedback norms + # KAFT feedback norms Z_gr, inter = gr.forward() E0, E_bar = gr._output_error(Z_gr) - graft_norms = [] + kaft_norms = [] for l in range(L - 1): power = min(L - l, gr.max_topo_power) topo_E = E_bar @@ -57,13 +57,13 @@ def measure_one(data, L, backbone, seed): topo_E = spmm(A, topo_E) fb = topo_E @ gr.Rs[l] relu_gate = (inter['Zs'][l].detach() > 0).float() - graft_norms.append((relu_gate * fb).norm().item()) + kaft_norms.append((relu_gate * fb).norm().item()) bp_acc = bp.evaluate('test_mask') gr_acc = gr.evaluate('test_mask') del bp, gr; torch.cuda.empty_cache() - return bp_norms, graft_norms, bp_acc, gr_acc + return bp_norms, kaft_norms, bp_acc, gr_acc def main(): @@ -101,10 +101,10 @@ def main(): bn, gn, ba, ga = measure_one(data, L, backbone, seed) per_seed_data[key][seed_key] = { - 'bp_norms': bn, 'graft_norms': gn, + 'bp_norms': bn, 'kaft_norms': gn, 'bp_acc': ba, 'gr_acc': ga } - print(f" seed {seed}: BP {ba*100:.1f}% GRAFT {ga*100:.1f}%", flush=True) + print(f" seed {seed}: BP {ba*100:.1f}% KAFT {ga*100:.1f}%", flush=True) # Save incrementally with open(old_per_seed_file, 'w') as f: @@ -121,7 +121,7 @@ def main(): t_stat, p_val = scipy_stats.ttest_rel(gr_accs, bp_accs) avg_bp_norms = np.mean([sd[str(s)]['bp_norms'] for s in ALL_SEEDS], axis=0) - avg_gr_norms = np.mean([sd[str(s)]['graft_norms'] for s in ALL_SEEDS], axis=0) + avg_gr_norms = np.mean([sd[str(s)]['kaft_norms'] for s in ALL_SEEDS], axis=0) results[key] = { 'bp_acc_mean': float(bp_accs.mean()), @@ -142,10 +142,10 @@ def main(): sig = '***' if p_val < 0.001 else ('**' if p_val < 0.01 else ('*' if p_val < 0.05 else 'ns')) print(f"\n {key}:") print(f" BP: {bp_accs.mean():.1f} ± {bp_accs.std():.1f}%") - print(f" GRAFT: {gr_accs.mean():.1f} ± {gr_accs.std():.1f}%") + print(f" KAFT: {gr_accs.mean():.1f} ± {gr_accs.std():.1f}%") print(f" Δ: {(gr_accs-bp_accs).mean():+.1f} ± {(gr_accs-bp_accs).std():.1f}% t={t_stat:.2f} p={p_val:.4f} {sig}") print(f" BP norm L0: {avg_bp_norms[0]:.6f}") - print(f" GRAFT norm L0: {avg_gr_norms[0]:.4f}") + print(f" KAFT norm L0: {avg_gr_norms[0]:.4f}") with open(os.path.join(OUT_DIR, 'results.json'), 'w') as f: json.dump(results, f, indent=2) |