diff options
Diffstat (limited to 'experiments/run_dblp_depth_scaling.py')
| -rw-r--r-- | experiments/run_dblp_depth_scaling.py | 14 |
1 files changed, 7 insertions, 7 deletions
diff --git a/experiments/run_dblp_depth_scaling.py b/experiments/run_dblp_depth_scaling.py index 4c0bc11..86a5af1 100644 --- a/experiments/run_dblp_depth_scaling.py +++ b/experiments/run_dblp_depth_scaling.py @@ -1,16 +1,16 @@ #!/usr/bin/env python3 """E1: DBLP depth scaling — upgrade depth_stress 3-seed to 20 seeds on DBLP, extend to L={8,12,16,20,24,32}. Goal: confirm (or falsify) the preliminary -finding that GRAFT > ResGCN at L=16 (3-seed: 69.9 vs 63.7) and scales to L=32. +finding that KAFT > ResGCN at L=16 (3-seed: 69.9 vs 63.7) and scales to L=32. -BP vs ResGCN vs GRAFT vs GRAFT+ResGCN, GCN backbone, lr=0.01, 200 epochs.""" +BP vs ResGCN vs KAFT vs KAFT+ResGCN, GCN backbone, lr=0.01, 200 epochs.""" import torch import numpy as np import json import os from scipy import stats as scipy_stats -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, {}), 'ResGCN': (ResGCNTrainer, {}), - 'GRAFT': (GraphGrAPETrainer, grape_extra), - 'GRAFT+ResGCN': (GRAFTResGCN, grape_extra), + 'KAFT': (KAFTTrainer, grape_extra), + 'KAFT+ResGCN': (GRAFTResGCN, grape_extra), } @@ -100,11 +100,11 @@ def main(): 'per_seed': vals.tolist()} print(f" {mname:<15} {vals.mean():5.1f} ± {vals.std():4.1f}") - # GRAFT vs ResGCN (paired) + # KAFT vs ResGCN (paired) g_accs = np.array([per_seed_data[f"DBLP_L{L}_GRAFT"][str(s)] for s in SEEDS]) * 100 r_accs = np.array([per_seed_data[f"DBLP_L{L}_ResGCN"][str(s)] for s in SEEDS]) * 100 t_gr, p_gr = scipy_stats.ttest_rel(g_accs, r_accs) - print(f" GRAFT vs ResGCN: Δ={g_accs.mean() - r_accs.mean():+.1f}, p={p_gr:.4f}") + print(f" KAFT vs ResGCN: Δ={g_accs.mean() - r_accs.mean():+.1f}, p={p_gr:.4f}") with open(os.path.join(OUT_DIR, 'results.json'), 'w') as f: json.dump(results, f, indent=2) |