diff options
Diffstat (limited to 'experiments/run_realworld_hero_L20.py')
| -rw-r--r-- | experiments/run_realworld_hero_L20.py | 18 |
1 files changed, 9 insertions, 9 deletions
diff --git a/experiments/run_realworld_hero_L20.py b/experiments/run_realworld_hero_L20.py index 93a6c91..3b352f1 100644 --- a/experiments/run_realworld_hero_L20.py +++ b/experiments/run_realworld_hero_L20.py @@ -1,5 +1,5 @@ #!/usr/bin/env python3 -"""H33: 20-seed extension of L=20 hero on 4 real-world datasets × {BP, DFA, DFA-GNN, GRAFT}. +"""H33: 20-seed extension of L=20 hero on 4 real-world datasets × {BP, DFA, DFA-GNN, KAFT}. Paper setup (5%/class, hidden=64, lr=0.01, no scheduler, 200 epochs, GCN backbone, no dropout/BN/res). Tightens DBLP std (0.121 at 10-seed bimodal) for paper-grade stats. @@ -17,7 +17,7 @@ from torch_geometric.nn import GCNConv from torch_geometric.utils import add_self_loops, degree sys.path.insert(0, '/home/yurenh2/graph-grape') -from src.trainers import GraphGrAPETrainer +from src.trainers import KAFTTrainer device = torch.device('cuda:2') @@ -92,7 +92,7 @@ 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 = { @@ -101,7 +101,7 @@ 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, @@ -149,21 +149,21 @@ 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' s={s} L={L}: BP={bp:.4f}({t1-t0:.0f}s) GRAFT={gf:.4f}({t2-t1:.0f}s)', flush=True) + print(f' s={s} L={L}: BP={bp:.4f}({t1-t0:.0f}s) KAFT={gf:.4f}({t2-t1:.0f}s)', flush=True) bp_m, bp_sd = float(np.mean(bp_a)), float(np.std(bp_a)) gf_m, gf_sd = float(np.mean(gf_a)), float(np.std(gf_a)) - out[name] = dict(seeds=seeds, BP=bp_a, GRAFT=gf_a, BP_mean=bp_m, BP_std=bp_sd, + out[name] = dict(seeds=seeds, BP=bp_a, KAFT=gf_a, BP_mean=bp_m, BP_std=bp_sd, GRAFT_mean=gf_m, GRAFT_std=gf_sd) - print(f' >>> {name} L=20 (seeds {s_lo}-{s_hi-1}): BP {bp_m:.4f}±{bp_sd:.4f} GRAFT {gf_m:.4f}±{gf_sd:.4f} Δ={gf_m-bp_m:+.3f}', flush=True) + print(f' >>> {name} L=20 (seeds {s_lo}-{s_hi-1}): BP {bp_m:.4f}±{bp_sd:.4f} KAFT {gf_m:.4f}±{gf_sd:.4f} Δ={gf_m-bp_m:+.3f}', flush=True) del d, A_hat, A_row, A_row_T torch.cuda.empty_cache() print('\n=== SUMMARY (this run) ===', flush=True) for k, v in out.items(): - print(f' {k}: BP {v["BP_mean"]:.4f}±{v["BP_std"]:.4f} GRAFT {v["GRAFT_mean"]:.4f}±{v["GRAFT_std"]:.4f}', flush=True) + print(f' {k}: BP {v["BP_mean"]:.4f}±{v["BP_std"]:.4f} KAFT {v["GRAFT_mean"]:.4f}±{v["GRAFT_std"]:.4f}', flush=True) if __name__ == '__main__': |