diff options
Diffstat (limited to 'experiments/run_wikics_paper_setup.py')
| -rw-r--r-- | experiments/run_wikics_paper_setup.py | 14 |
1 files changed, 7 insertions, 7 deletions
diff --git a/experiments/run_wikics_paper_setup.py b/experiments/run_wikics_paper_setup.py index a2bf879..c9525e6 100644 --- a/experiments/run_wikics_paper_setup.py +++ b/experiments/run_wikics_paper_setup.py @@ -1,7 +1,7 @@ #!/usr/bin/env python3 """H15 WikiCS paper-setup depth sweep — Wikipedia academic articles. ~11.7K nodes, avg deg ~4.1, 10-class, undirected. Sparse + few-class -fits GRAFT's regime profile. Test BP vs GRAFT at L ∈ {3,5,10,14,20} × 5 seeds. +fits KAFT's regime profile. Test BP vs KAFT at L ∈ {3,5,10,14,20} × 5 seeds. """ import sys, time import numpy as np @@ -13,7 +13,7 @@ from torch_geometric.nn import GCNConv from torch_geometric.utils import add_self_loops, degree, to_undirected sys.path.insert(0, '/home/yurenh2/graph-grape') -from src.trainers import GraphGrAPETrainer +from src.trainers import KAFTTrainer device = torch.device('cuda:0') # CUDA_VISIBLE_DEVICES=2 maps cuda:0 → physical GPU 2 @@ -88,7 +88,7 @@ def bp_one(L, seed, d, tm, vm, tem, epochs=200, lr=0.01, hidden=64): return bt -def graft_one(L, seed, d, A_hat, A_row, A_row_T, tm, vm, tem, +def kaft_one(L, seed, d, A_hat, A_row, A_row_T, tm, vm, tem, epochs=200, lr=0.01, hidden=64): torch.manual_seed(seed); np.random.seed(seed); torch.cuda.manual_seed_all(seed) data = { @@ -97,7 +97,7 @@ def graft_one(L, seed, d, A_hat, A_row, A_row_T, tm, vm, tem, '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=5e-4, lr_feedback=0.5, num_probes=64, topo_mode='fixed_A', max_topo_power=3, diffusion_alpha=0.5, diffusion_iters=10, @@ -133,13 +133,13 @@ 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_a.append(bp); gf_a.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_a), np.std(bp_a) gf_m, gf_sd = np.mean(gf_a), np.std(gf_a) - 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__': |