[run] ogbg-molhiv view=gin compute=classic T=0 ns=1 device=cuda:1
python3 rrog/train_ogb_graphprop.py --dataset ogbg-molhiv --view gin --compute classic --T 0 --n_sup 1 --hidden 128 --bs 128 --epochs 100 --eval_every 10 --agg_layers 5 --compute_layers 2 --seed 0 --device cuda:1
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/orion/u/oscarwan/rrog-gnn-runner/.venv/lib/python3.13/site-packages/ogb/graphproppred/dataset_pyg.py:156: UserWarning: The given NumPy array is not writable, and PyTorch does not support non-writable tensors. This means writing to this tensor will result in undefined behavior. You may want to copy the array to protect its data or make it writable before converting it to a tensor. This type of warning will be suppressed for the rest of this program. (Triggered internally at /pytorch/torch/csrc/utils/tensor_numpy.cpp:203.)
  g.y = torch.from_numpy(graph_label[i]).view(1,-1).to(torch.long)
Done!
Saving...
ep10 val_rocauc=0.68958 halt=0.00 train_steps=1.00
ep20 val_rocauc=0.62677 halt=0.00 train_steps=1.00
ep30 val_rocauc=0.75666 halt=0.00 train_steps=1.00
ep40 val_rocauc=0.81675 halt=0.00 train_steps=1.00
ep50 val_rocauc=0.79400 halt=0.00 train_steps=1.00
ep60 val_rocauc=0.77580 halt=0.00 train_steps=1.00
ep70 val_rocauc=0.77900 halt=0.00 train_steps=1.00
ep80 val_rocauc=0.79025 halt=0.00 train_steps=1.00
ep90 val_rocauc=0.78752 halt=0.00 train_steps=1.00
ep100 val_rocauc=0.78382 halt=0.00 train_steps=1.00
[ogbg-molhiv_gin_classic_T0_ns1_h128_e100_s0] best_ep=40 val={'rocauc': 0.816746889084852} test={'rocauc': 0.7723980764402558} adaptive=None steps=None
  wrote /orion/u/oscarwan/rrog-gnn-runner/runs/ogbg-molhiv_gin_classic_T0_ns1_h128_e100_s0.json
[run] ogbg-molhiv view=gin compute=fixed-rrog T=3 ns=3 device=cuda:1
python3 rrog/train_ogb_graphprop.py --dataset ogbg-molhiv --view gin --compute fixed-rrog --T 3 --n_sup 3 --hidden 128 --bs 128 --epochs 100 --eval_every 10 --agg_layers 5 --compute_layers 2 --seed 0 --device cuda:1
ep10 val_rocauc=0.65495 halt=0.00 train_steps=3.00
ep20 val_rocauc=0.70925 halt=0.00 train_steps=3.00
ep30 val_rocauc=0.75238 halt=0.00 train_steps=3.00
ep40 val_rocauc=0.70341 halt=0.00 train_steps=3.00
ep50 val_rocauc=0.73133 halt=0.00 train_steps=3.00
ep60 val_rocauc=0.69975 halt=0.00 train_steps=3.00
ep70 val_rocauc=0.74462 halt=0.00 train_steps=3.00
ep80 val_rocauc=0.73833 halt=0.00 train_steps=3.00
ep90 val_rocauc=0.73695 halt=0.00 train_steps=3.00
ep100 val_rocauc=0.73852 halt=0.00 train_steps=3.00
[ogbg-molhiv_gin_fixed-rrog_T3_ns3_h128_e100_s0] best_ep=30 val={'rocauc': 0.7523760533019792} test={'rocauc': 0.7324378609088626} adaptive=None steps=None
  wrote /orion/u/oscarwan/rrog-gnn-runner/runs/ogbg-molhiv_gin_fixed-rrog_T3_ns3_h128_e100_s0.json
[run] ogbg-molhiv view=gine compute=classic T=0 ns=1 device=cuda:1
python3 rrog/train_ogb_graphprop.py --dataset ogbg-molhiv --view gine --compute classic --T 0 --n_sup 1 --hidden 128 --bs 128 --epochs 100 --eval_every 10 --agg_layers 5 --compute_layers 2 --seed 0 --device cuda:1
ep10 val_rocauc=0.62164 halt=0.00 train_steps=1.00
ep20 val_rocauc=0.74372 halt=0.00 train_steps=1.00
ep30 val_rocauc=0.74063 halt=0.00 train_steps=1.00
ep40 val_rocauc=0.76519 halt=0.00 train_steps=1.00
ep50 val_rocauc=0.78176 halt=0.00 train_steps=1.00
ep60 val_rocauc=0.78597 halt=0.00 train_steps=1.00
ep70 val_rocauc=0.79424 halt=0.00 train_steps=1.00
ep80 val_rocauc=0.78335 halt=0.00 train_steps=1.00
ep90 val_rocauc=0.78834 halt=0.00 train_steps=1.00
ep100 val_rocauc=0.78578 halt=0.00 train_steps=1.00
[ogbg-molhiv_gine_classic_T0_ns1_h128_e100_s0] best_ep=70 val={'rocauc': 0.7942448069762884} test={'rocauc': 0.7445141852874717} adaptive=None steps=None
  wrote /orion/u/oscarwan/rrog-gnn-runner/runs/ogbg-molhiv_gine_classic_T0_ns1_h128_e100_s0.json
[run] ogbg-molhiv view=gine compute=fixed-rrog T=3 ns=3 device=cuda:1
python3 rrog/train_ogb_graphprop.py --dataset ogbg-molhiv --view gine --compute fixed-rrog --T 3 --n_sup 3 --hidden 128 --bs 128 --epochs 100 --eval_every 10 --agg_layers 5 --compute_layers 2 --seed 0 --device cuda:1
ep10 val_rocauc=0.61178 halt=0.00 train_steps=3.00
ep20 val_rocauc=0.71290 halt=0.00 train_steps=3.00
ep30 val_rocauc=0.72785 halt=0.00 train_steps=3.00
ep40 val_rocauc=0.75746 halt=0.00 train_steps=3.00
ep50 val_rocauc=0.71552 halt=0.00 train_steps=3.00
ep60 val_rocauc=0.73246 halt=0.00 train_steps=3.00
ep70 val_rocauc=0.73966 halt=0.00 train_steps=3.00
ep80 val_rocauc=0.74506 halt=0.00 train_steps=3.00
ep90 val_rocauc=0.73921 halt=0.00 train_steps=3.00
ep100 val_rocauc=0.73342 halt=0.00 train_steps=3.00
[ogbg-molhiv_gine_fixed-rrog_T3_ns3_h128_e100_s0] best_ep=40 val={'rocauc': 0.7574588477366255} test={'rocauc': 0.7400818864790746} adaptive=None steps=None
  wrote /orion/u/oscarwan/rrog-gnn-runner/runs/ogbg-molhiv_gine_fixed-rrog_T3_ns3_h128_e100_s0.json
[run] ogbg-molhiv view=gcn compute=classic T=0 ns=1 device=cuda:1
python3 rrog/train_ogb_graphprop.py --dataset ogbg-molhiv --view gcn --compute classic --T 0 --n_sup 1 --hidden 128 --bs 128 --epochs 100 --eval_every 10 --agg_layers 5 --compute_layers 2 --seed 0 --device cuda:1
ep10 val_rocauc=0.65811 halt=0.00 train_steps=1.00
ep20 val_rocauc=0.74430 halt=0.00 train_steps=1.00
ep30 val_rocauc=0.77543 halt=0.00 train_steps=1.00
ep40 val_rocauc=0.73750 halt=0.00 train_steps=1.00
ep50 val_rocauc=0.75570 halt=0.00 train_steps=1.00
ep60 val_rocauc=0.75804 halt=0.00 train_steps=1.00
ep70 val_rocauc=0.74526 halt=0.00 train_steps=1.00
ep80 val_rocauc=0.72804 halt=0.00 train_steps=1.00
ep90 val_rocauc=0.74118 halt=0.00 train_steps=1.00
ep100 val_rocauc=0.73306 halt=0.00 train_steps=1.00
[ogbg-molhiv_gcn_classic_T0_ns1_h128_e100_s0] best_ep=30 val={'rocauc': 0.7754323437193807} test={'rocauc': 0.719778288495336} adaptive=None steps=None
  wrote /orion/u/oscarwan/rrog-gnn-runner/runs/ogbg-molhiv_gcn_classic_T0_ns1_h128_e100_s0.json
[run] ogbg-molhiv view=gcn compute=fixed-rrog T=3 ns=3 device=cuda:1
python3 rrog/train_ogb_graphprop.py --dataset ogbg-molhiv --view gcn --compute fixed-rrog --T 3 --n_sup 3 --hidden 128 --bs 128 --epochs 100 --eval_every 10 --agg_layers 5 --compute_layers 2 --seed 0 --device cuda:1
ep10 val_rocauc=0.63622 halt=0.00 train_steps=3.00
ep20 val_rocauc=0.69664 halt=0.00 train_steps=3.00
ep30 val_rocauc=0.74131 halt=0.00 train_steps=3.00
ep40 val_rocauc=0.74547 halt=0.00 train_steps=3.00
ep50 val_rocauc=0.73229 halt=0.00 train_steps=3.00
ep60 val_rocauc=0.73274 halt=0.00 train_steps=3.00
ep70 val_rocauc=0.73576 halt=0.00 train_steps=3.00
ep80 val_rocauc=0.73819 halt=0.00 train_steps=3.00
ep90 val_rocauc=0.74222 halt=0.00 train_steps=3.00
ep100 val_rocauc=0.73546 halt=0.00 train_steps=3.00
[ogbg-molhiv_gcn_fixed-rrog_T3_ns3_h128_e100_s0] best_ep=40 val={'rocauc': 0.7454683519498334} test={'rocauc': 0.7482879159504818} adaptive=None steps=None
  wrote /orion/u/oscarwan/rrog-gnn-runner/runs/ogbg-molhiv_gcn_fixed-rrog_T3_ns3_h128_e100_s0.json
[run] ogbg-molhiv view=graphsage compute=classic T=0 ns=1 device=cuda:1
python3 rrog/train_ogb_graphprop.py --dataset ogbg-molhiv --view graphsage --compute classic --T 0 --n_sup 1 --hidden 128 --bs 128 --epochs 100 --eval_every 10 --agg_layers 5 --compute_layers 2 --seed 0 --device cuda:1
ep10 val_rocauc=0.71550 halt=0.00 train_steps=1.00
ep20 val_rocauc=0.76542 halt=0.00 train_steps=1.00
ep30 val_rocauc=0.71584 halt=0.00 train_steps=1.00
ep40 val_rocauc=0.79686 halt=0.00 train_steps=1.00
ep50 val_rocauc=0.72350 halt=0.00 train_steps=1.00
ep60 val_rocauc=0.76958 halt=0.00 train_steps=1.00
ep70 val_rocauc=0.74210 halt=0.00 train_steps=1.00
ep80 val_rocauc=0.72790 halt=0.00 train_steps=1.00
ep90 val_rocauc=0.74689 halt=0.00 train_steps=1.00
ep100 val_rocauc=0.74191 halt=0.00 train_steps=1.00
[ogbg-molhiv_graphsage_classic_T0_ns1_h128_e100_s0] best_ep=40 val={'rocauc': 0.7968596903782088} test={'rocauc': 0.7625118291199134} adaptive=None steps=None
  wrote /orion/u/oscarwan/rrog-gnn-runner/runs/ogbg-molhiv_graphsage_classic_T0_ns1_h128_e100_s0.json
[run] ogbg-molhiv view=graphsage compute=fixed-rrog T=3 ns=3 device=cuda:1
python3 rrog/train_ogb_graphprop.py --dataset ogbg-molhiv --view graphsage --compute fixed-rrog --T 3 --n_sup 3 --hidden 128 --bs 128 --epochs 100 --eval_every 10 --agg_layers 5 --compute_layers 2 --seed 0 --device cuda:1
ep10 val_rocauc=0.66879 halt=0.00 train_steps=3.00
ep20 val_rocauc=0.75868 halt=0.00 train_steps=3.00
ep30 val_rocauc=0.73014 halt=0.00 train_steps=3.00
ep40 val_rocauc=0.74615 halt=0.00 train_steps=3.00
ep50 val_rocauc=0.74824 halt=0.00 train_steps=3.00
ep60 val_rocauc=0.73541 halt=0.00 train_steps=3.00
ep70 val_rocauc=0.74158 halt=0.00 train_steps=3.00
ep80 val_rocauc=0.74460 halt=0.00 train_steps=3.00
ep90 val_rocauc=0.74456 halt=0.00 train_steps=3.00
ep100 val_rocauc=0.73640 halt=0.00 train_steps=3.00
[ogbg-molhiv_graphsage_fixed-rrog_T3_ns3_h128_e100_s0] best_ep=20 val={'rocauc': 0.7586759626690183} test={'rocauc': 0.7641070704339598} adaptive=None steps=None
  wrote /orion/u/oscarwan/rrog-gnn-runner/runs/ogbg-molhiv_graphsage_fixed-rrog_T3_ns3_h128_e100_s0.json
[run] ogbg-molhiv view=gatv2 compute=classic T=0 ns=1 device=cuda:1
python3 rrog/train_ogb_graphprop.py --dataset ogbg-molhiv --view gatv2 --compute classic --T 0 --n_sup 1 --hidden 128 --bs 128 --epochs 100 --eval_every 10 --agg_layers 5 --compute_layers 2 --seed 0 --device cuda:1
ep10 val_rocauc=0.71585 halt=0.00 train_steps=1.00
ep20 val_rocauc=0.78346 halt=0.00 train_steps=1.00
ep30 val_rocauc=0.75100 halt=0.00 train_steps=1.00
ep40 val_rocauc=0.77132 halt=0.00 train_steps=1.00
ep50 val_rocauc=0.76468 halt=0.00 train_steps=1.00
ep60 val_rocauc=0.73519 halt=0.00 train_steps=1.00
ep70 val_rocauc=0.74196 halt=0.00 train_steps=1.00
ep80 val_rocauc=0.75164 halt=0.00 train_steps=1.00
ep90 val_rocauc=0.74230 halt=0.00 train_steps=1.00
ep100 val_rocauc=0.74250 halt=0.00 train_steps=1.00
[ogbg-molhiv_gatv2_classic_T0_ns1_h128_e100_s0] best_ep=20 val={'rocauc': 0.7834576474622771} test={'rocauc': 0.75625253481141} adaptive=None steps=None
  wrote /orion/u/oscarwan/rrog-gnn-runner/runs/ogbg-molhiv_gatv2_classic_T0_ns1_h128_e100_s0.json
[run] ogbg-molhiv view=gatv2 compute=fixed-rrog T=3 ns=3 device=cuda:1
python3 rrog/train_ogb_graphprop.py --dataset ogbg-molhiv --view gatv2 --compute fixed-rrog --T 3 --n_sup 3 --hidden 128 --bs 128 --epochs 100 --eval_every 10 --agg_layers 5 --compute_layers 2 --seed 0 --device cuda:1
ep10 val_rocauc=0.71208 halt=0.00 train_steps=3.00
ep20 val_rocauc=0.78355 halt=0.00 train_steps=3.00
ep30 val_rocauc=0.73986 halt=0.00 train_steps=3.00
ep40 val_rocauc=0.77098 halt=0.00 train_steps=3.00
ep50 val_rocauc=0.74696 halt=0.00 train_steps=3.00
ep60 val_rocauc=0.74733 halt=0.00 train_steps=3.00
ep70 val_rocauc=0.71626 halt=0.00 train_steps=3.00
ep80 val_rocauc=0.70159 halt=0.00 train_steps=3.00
ep90 val_rocauc=0.69616 halt=0.00 train_steps=3.00
ep100 val_rocauc=0.69278 halt=0.00 train_steps=3.00
[ogbg-molhiv_gatv2_fixed-rrog_T3_ns3_h128_e100_s0] best_ep=20 val={'rocauc': 0.7835464432686655} test={'rocauc': 0.746617354525966} adaptive=None steps=None
  wrote /orion/u/oscarwan/rrog-gnn-runner/runs/ogbg-molhiv_gatv2_fixed-rrog_T3_ns3_h128_e100_s0.json
[run] ogbg-molhiv view=graphconv compute=classic T=0 ns=1 device=cuda:1
python3 rrog/train_ogb_graphprop.py --dataset ogbg-molhiv --view graphconv --compute classic --T 0 --n_sup 1 --hidden 128 --bs 128 --epochs 100 --eval_every 10 --agg_layers 5 --compute_layers 2 --seed 0 --device cuda:1
ep10 val_rocauc=0.70572 halt=0.00 train_steps=1.00
ep20 val_rocauc=0.75834 halt=0.00 train_steps=1.00
ep30 val_rocauc=0.75592 halt=0.00 train_steps=1.00
ep40 val_rocauc=0.71993 halt=0.00 train_steps=1.00
ep50 val_rocauc=0.74917 halt=0.00 train_steps=1.00
ep60 val_rocauc=0.76022 halt=0.00 train_steps=1.00
ep70 val_rocauc=0.76575 halt=0.00 train_steps=1.00
ep80 val_rocauc=0.77066 halt=0.00 train_steps=1.00
ep90 val_rocauc=0.76111 halt=0.00 train_steps=1.00
ep100 val_rocauc=0.76138 halt=0.00 train_steps=1.00
[ogbg-molhiv_graphconv_classic_T0_ns1_h128_e100_s0] best_ep=80 val={'rocauc': 0.7706618655692731} test={'rocauc': 0.7108113327796983} adaptive=None steps=None
  wrote /orion/u/oscarwan/rrog-gnn-runner/runs/ogbg-molhiv_graphconv_classic_T0_ns1_h128_e100_s0.json
[run] ogbg-molhiv view=graphconv compute=fixed-rrog T=3 ns=3 device=cuda:1
python3 rrog/train_ogb_graphprop.py --dataset ogbg-molhiv --view graphconv --compute fixed-rrog --T 3 --n_sup 3 --hidden 128 --bs 128 --epochs 100 --eval_every 10 --agg_layers 5 --compute_layers 2 --seed 0 --device cuda:1
ep10 val_rocauc=0.69385 halt=0.00 train_steps=3.00
ep20 val_rocauc=0.74585 halt=0.00 train_steps=3.00
ep30 val_rocauc=0.70566 halt=0.00 train_steps=3.00
ep40 val_rocauc=0.76126 halt=0.00 train_steps=3.00
ep50 val_rocauc=0.74274 halt=0.00 train_steps=3.00
ep60 val_rocauc=0.73851 halt=0.00 train_steps=3.00
ep70 val_rocauc=0.75489 halt=0.00 train_steps=3.00
ep80 val_rocauc=0.77125 halt=0.00 train_steps=3.00
ep90 val_rocauc=0.76654 halt=0.00 train_steps=3.00
ep100 val_rocauc=0.76644 halt=0.00 train_steps=3.00
[ogbg-molhiv_graphconv_fixed-rrog_T3_ns3_h128_e100_s0] best_ep=80 val={'rocauc': 0.7712528169704094} test={'rocauc': 0.6979180748952278} adaptive=None steps=None
  wrote /orion/u/oscarwan/rrog-gnn-runner/runs/ogbg-molhiv_graphconv_fixed-rrog_T3_ns3_h128_e100_s0.json
[run] ogbg-molhiv view=transformer compute=classic T=0 ns=1 device=cuda:1
python3 rrog/train_ogb_graphprop.py --dataset ogbg-molhiv --view transformer --compute classic --T 0 --n_sup 1 --hidden 128 --bs 128 --epochs 100 --eval_every 10 --agg_layers 5 --compute_layers 2 --seed 0 --device cuda:1
ep10 val_rocauc=0.73727 halt=0.00 train_steps=1.00
ep20 val_rocauc=0.75895 halt=0.00 train_steps=1.00
ep30 val_rocauc=0.76173 halt=0.00 train_steps=1.00
ep40 val_rocauc=0.72264 halt=0.00 train_steps=1.00
ep50 val_rocauc=0.74231 halt=0.00 train_steps=1.00
ep60 val_rocauc=0.73453 halt=0.00 train_steps=1.00
ep70 val_rocauc=0.73009 halt=0.00 train_steps=1.00
ep80 val_rocauc=0.72118 halt=0.00 train_steps=1.00
ep90 val_rocauc=0.71814 halt=0.00 train_steps=1.00
ep100 val_rocauc=0.72113 halt=0.00 train_steps=1.00
[ogbg-molhiv_transformer_classic_T0_ns1_h128_e100_s0] best_ep=30 val={'rocauc': 0.7617332941407015} test={'rocauc': 0.7547248884682979} adaptive=None steps=None
  wrote /orion/u/oscarwan/rrog-gnn-runner/runs/ogbg-molhiv_transformer_classic_T0_ns1_h128_e100_s0.json
[run] ogbg-molhiv view=transformer compute=fixed-rrog T=3 ns=3 device=cuda:1
python3 rrog/train_ogb_graphprop.py --dataset ogbg-molhiv --view transformer --compute fixed-rrog --T 3 --n_sup 3 --hidden 128 --bs 128 --epochs 100 --eval_every 10 --agg_layers 5 --compute_layers 2 --seed 0 --device cuda:1
ep10 val_rocauc=0.71907 halt=0.00 train_steps=3.00
ep20 val_rocauc=0.77500 halt=0.00 train_steps=3.00
ep30 val_rocauc=0.75579 halt=0.00 train_steps=3.00
ep40 val_rocauc=0.80881 halt=0.00 train_steps=3.00
ep50 val_rocauc=0.75874 halt=0.00 train_steps=3.00
ep60 val_rocauc=0.75197 halt=0.00 train_steps=3.00
ep70 val_rocauc=0.70705 halt=0.00 train_steps=3.00
ep80 val_rocauc=0.71709 halt=0.00 train_steps=3.00
ep90 val_rocauc=0.71606 halt=0.00 train_steps=3.00
ep100 val_rocauc=0.71290 halt=0.00 train_steps=3.00
[ogbg-molhiv_transformer_fixed-rrog_T3_ns3_h128_e100_s0] best_ep=40 val={'rocauc': 0.8088103811483441} test={'rocauc': 0.7413024585256572} adaptive=None steps=None
  wrote /orion/u/oscarwan/rrog-gnn-runner/runs/ogbg-molhiv_transformer_fixed-rrog_T3_ns3_h128_e100_s0.json
[run] ogbg-molhiv view=pna compute=classic T=0 ns=1 device=cuda:1
python3 rrog/train_ogb_graphprop.py --dataset ogbg-molhiv --view pna --compute classic --T 0 --n_sup 1 --hidden 128 --bs 128 --epochs 100 --eval_every 10 --agg_layers 5 --compute_layers 2 --seed 0 --device cuda:1
/orion/u/oscarwan/rrog-gnn-runner/.venv/lib/python3.13/site-packages/torch_geometric/utils/_scatter.py:91: UserWarning: The usage of `scatter(reduce='min')` can be accelerated via the 'torch-scatter' package, but it was not found
  warnings.warn(
/orion/u/oscarwan/rrog-gnn-runner/.venv/lib/python3.13/site-packages/torch_geometric/utils/_scatter.py:91: UserWarning: The usage of `scatter(reduce='max')` can be accelerated via the 'torch-scatter' package, but it was not found
  warnings.warn(
ep10 val_rocauc=0.74671 halt=0.00 train_steps=1.00
/orion/u/oscarwan/rrog-gnn-runner/.venv/lib/python3.13/site-packages/torch_geometric/utils/_scatter.py:91: UserWarning: The usage of `scatter(reduce='min')` can be accelerated via the 'torch-scatter' package, but it was not found
  warnings.warn(
/orion/u/oscarwan/rrog-gnn-runner/.venv/lib/python3.13/site-packages/torch_geometric/utils/_scatter.py:91: UserWarning: The usage of `scatter(reduce='max')` can be accelerated via the 'torch-scatter' package, but it was not found
  warnings.warn(
ep20 val_rocauc=0.72354 halt=0.00 train_steps=1.00
/orion/u/oscarwan/rrog-gnn-runner/.venv/lib/python3.13/site-packages/torch_geometric/utils/_scatter.py:91: UserWarning: The usage of `scatter(reduce='min')` can be accelerated via the 'torch-scatter' package, but it was not found
  warnings.warn(
/orion/u/oscarwan/rrog-gnn-runner/.venv/lib/python3.13/site-packages/torch_geometric/utils/_scatter.py:91: UserWarning: The usage of `scatter(reduce='max')` can be accelerated via the 'torch-scatter' package, but it was not found
  warnings.warn(
ep30 val_rocauc=0.74514 halt=0.00 train_steps=1.00
/orion/u/oscarwan/rrog-gnn-runner/.venv/lib/python3.13/site-packages/torch_geometric/utils/_scatter.py:91: UserWarning: The usage of `scatter(reduce='min')` can be accelerated via the 'torch-scatter' package, but it was not found
  warnings.warn(
/orion/u/oscarwan/rrog-gnn-runner/.venv/lib/python3.13/site-packages/torch_geometric/utils/_scatter.py:91: UserWarning: The usage of `scatter(reduce='max')` can be accelerated via the 'torch-scatter' package, but it was not found
  warnings.warn(
ep40 val_rocauc=0.68222 halt=0.00 train_steps=1.00
/orion/u/oscarwan/rrog-gnn-runner/.venv/lib/python3.13/site-packages/torch_geometric/utils/_scatter.py:91: UserWarning: The usage of `scatter(reduce='min')` can be accelerated via the 'torch-scatter' package, but it was not found
  warnings.warn(
/orion/u/oscarwan/rrog-gnn-runner/.venv/lib/python3.13/site-packages/torch_geometric/utils/_scatter.py:91: UserWarning: The usage of `scatter(reduce='max')` can be accelerated via the 'torch-scatter' package, but it was not found
  warnings.warn(
ep50 val_rocauc=0.73145 halt=0.00 train_steps=1.00
/orion/u/oscarwan/rrog-gnn-runner/.venv/lib/python3.13/site-packages/torch_geometric/utils/_scatter.py:91: UserWarning: The usage of `scatter(reduce='min')` can be accelerated via the 'torch-scatter' package, but it was not found
  warnings.warn(
/orion/u/oscarwan/rrog-gnn-runner/.venv/lib/python3.13/site-packages/torch_geometric/utils/_scatter.py:91: UserWarning: The usage of `scatter(reduce='max')` can be accelerated via the 'torch-scatter' package, but it was not found
  warnings.warn(
ep60 val_rocauc=0.77795 halt=0.00 train_steps=1.00
/orion/u/oscarwan/rrog-gnn-runner/.venv/lib/python3.13/site-packages/torch_geometric/utils/_scatter.py:91: UserWarning: The usage of `scatter(reduce='min')` can be accelerated via the 'torch-scatter' package, but it was not found
  warnings.warn(
/orion/u/oscarwan/rrog-gnn-runner/.venv/lib/python3.13/site-packages/torch_geometric/utils/_scatter.py:91: UserWarning: The usage of `scatter(reduce='max')` can be accelerated via the 'torch-scatter' package, but it was not found
  warnings.warn(
ep70 val_rocauc=0.75368 halt=0.00 train_steps=1.00
/orion/u/oscarwan/rrog-gnn-runner/.venv/lib/python3.13/site-packages/torch_geometric/utils/_scatter.py:91: UserWarning: The usage of `scatter(reduce='min')` can be accelerated via the 'torch-scatter' package, but it was not found
  warnings.warn(
/orion/u/oscarwan/rrog-gnn-runner/.venv/lib/python3.13/site-packages/torch_geometric/utils/_scatter.py:91: UserWarning: The usage of `scatter(reduce='max')` can be accelerated via the 'torch-scatter' package, but it was not found
  warnings.warn(
ep80 val_rocauc=0.70352 halt=0.00 train_steps=1.00
/orion/u/oscarwan/rrog-gnn-runner/.venv/lib/python3.13/site-packages/torch_geometric/utils/_scatter.py:91: UserWarning: The usage of `scatter(reduce='min')` can be accelerated via the 'torch-scatter' package, but it was not found
  warnings.warn(
/orion/u/oscarwan/rrog-gnn-runner/.venv/lib/python3.13/site-packages/torch_geometric/utils/_scatter.py:91: UserWarning: The usage of `scatter(reduce='max')` can be accelerated via the 'torch-scatter' package, but it was not found
  warnings.warn(
ep90 val_rocauc=0.72530 halt=0.00 train_steps=1.00
/orion/u/oscarwan/rrog-gnn-runner/.venv/lib/python3.13/site-packages/torch_geometric/utils/_scatter.py:91: UserWarning: The usage of `scatter(reduce='min')` can be accelerated via the 'torch-scatter' package, but it was not found
  warnings.warn(
/orion/u/oscarwan/rrog-gnn-runner/.venv/lib/python3.13/site-packages/torch_geometric/utils/_scatter.py:91: UserWarning: The usage of `scatter(reduce='max')` can be accelerated via the 'torch-scatter' package, but it was not found
  warnings.warn(
ep100 val_rocauc=0.72001 halt=0.00 train_steps=1.00
[ogbg-molhiv_pna_classic_T0_ns1_h128_e100_s0] best_ep=60 val={'rocauc': 0.7779523074661963} test={'rocauc': 0.7647926765677203} adaptive=None steps=None
  wrote /orion/u/oscarwan/rrog-gnn-runner/runs/ogbg-molhiv_pna_classic_T0_ns1_h128_e100_s0.json
[run] ogbg-molhiv view=pna compute=fixed-rrog T=3 ns=3 device=cuda:1
python3 rrog/train_ogb_graphprop.py --dataset ogbg-molhiv --view pna --compute fixed-rrog --T 3 --n_sup 3 --hidden 128 --bs 128 --epochs 100 --eval_every 10 --agg_layers 5 --compute_layers 2 --seed 0 --device cuda:1
/orion/u/oscarwan/rrog-gnn-runner/.venv/lib/python3.13/site-packages/torch_geometric/utils/_scatter.py:91: UserWarning: The usage of `scatter(reduce='min')` can be accelerated via the 'torch-scatter' package, but it was not found
  warnings.warn(
/orion/u/oscarwan/rrog-gnn-runner/.venv/lib/python3.13/site-packages/torch_geometric/utils/_scatter.py:91: UserWarning: The usage of `scatter(reduce='max')` can be accelerated via the 'torch-scatter' package, but it was not found
  warnings.warn(
ep10 val_rocauc=0.59545 halt=0.00 train_steps=3.00
/orion/u/oscarwan/rrog-gnn-runner/.venv/lib/python3.13/site-packages/torch_geometric/utils/_scatter.py:91: UserWarning: The usage of `scatter(reduce='min')` can be accelerated via the 'torch-scatter' package, but it was not found
  warnings.warn(
/orion/u/oscarwan/rrog-gnn-runner/.venv/lib/python3.13/site-packages/torch_geometric/utils/_scatter.py:91: UserWarning: The usage of `scatter(reduce='max')` can be accelerated via the 'torch-scatter' package, but it was not found
  warnings.warn(
ep20 val_rocauc=0.66872 halt=0.00 train_steps=3.00
/orion/u/oscarwan/rrog-gnn-runner/.venv/lib/python3.13/site-packages/torch_geometric/utils/_scatter.py:91: UserWarning: The usage of `scatter(reduce='min')` can be accelerated via the 'torch-scatter' package, but it was not found
  warnings.warn(
/orion/u/oscarwan/rrog-gnn-runner/.venv/lib/python3.13/site-packages/torch_geometric/utils/_scatter.py:91: UserWarning: The usage of `scatter(reduce='max')` can be accelerated via the 'torch-scatter' package, but it was not found
  warnings.warn(
ep30 val_rocauc=0.63794 halt=0.00 train_steps=3.00
/orion/u/oscarwan/rrog-gnn-runner/.venv/lib/python3.13/site-packages/torch_geometric/utils/_scatter.py:91: UserWarning: The usage of `scatter(reduce='min')` can be accelerated via the 'torch-scatter' package, but it was not found
  warnings.warn(
/orion/u/oscarwan/rrog-gnn-runner/.venv/lib/python3.13/site-packages/torch_geometric/utils/_scatter.py:91: UserWarning: The usage of `scatter(reduce='max')` can be accelerated via the 'torch-scatter' package, but it was not found
  warnings.warn(
ep40 val_rocauc=0.65636 halt=0.00 train_steps=3.00
/orion/u/oscarwan/rrog-gnn-runner/.venv/lib/python3.13/site-packages/torch_geometric/utils/_scatter.py:91: UserWarning: The usage of `scatter(reduce='min')` can be accelerated via the 'torch-scatter' package, but it was not found
  warnings.warn(
/orion/u/oscarwan/rrog-gnn-runner/.venv/lib/python3.13/site-packages/torch_geometric/utils/_scatter.py:91: UserWarning: The usage of `scatter(reduce='max')` can be accelerated via the 'torch-scatter' package, but it was not found
  warnings.warn(
ep50 val_rocauc=0.75063 halt=0.00 train_steps=3.00
/orion/u/oscarwan/rrog-gnn-runner/.venv/lib/python3.13/site-packages/torch_geometric/utils/_scatter.py:91: UserWarning: The usage of `scatter(reduce='min')` can be accelerated via the 'torch-scatter' package, but it was not found
  warnings.warn(
/orion/u/oscarwan/rrog-gnn-runner/.venv/lib/python3.13/site-packages/torch_geometric/utils/_scatter.py:91: UserWarning: The usage of `scatter(reduce='max')` can be accelerated via the 'torch-scatter' package, but it was not found
  warnings.warn(
ep60 val_rocauc=0.78904 halt=0.00 train_steps=3.00
/orion/u/oscarwan/rrog-gnn-runner/.venv/lib/python3.13/site-packages/torch_geometric/utils/_scatter.py:91: UserWarning: The usage of `scatter(reduce='min')` can be accelerated via the 'torch-scatter' package, but it was not found
  warnings.warn(
/orion/u/oscarwan/rrog-gnn-runner/.venv/lib/python3.13/site-packages/torch_geometric/utils/_scatter.py:91: UserWarning: The usage of `scatter(reduce='max')` can be accelerated via the 'torch-scatter' package, but it was not found
  warnings.warn(
ep70 val_rocauc=0.76138 halt=0.00 train_steps=3.00
/orion/u/oscarwan/rrog-gnn-runner/.venv/lib/python3.13/site-packages/torch_geometric/utils/_scatter.py:91: UserWarning: The usage of `scatter(reduce='min')` can be accelerated via the 'torch-scatter' package, but it was not found
  warnings.warn(
/orion/u/oscarwan/rrog-gnn-runner/.venv/lib/python3.13/site-packages/torch_geometric/utils/_scatter.py:91: UserWarning: The usage of `scatter(reduce='max')` can be accelerated via the 'torch-scatter' package, but it was not found
  warnings.warn(
ep80 val_rocauc=0.77449 halt=0.00 train_steps=3.00
/orion/u/oscarwan/rrog-gnn-runner/.venv/lib/python3.13/site-packages/torch_geometric/utils/_scatter.py:91: UserWarning: The usage of `scatter(reduce='min')` can be accelerated via the 'torch-scatter' package, but it was not found
  warnings.warn(
/orion/u/oscarwan/rrog-gnn-runner/.venv/lib/python3.13/site-packages/torch_geometric/utils/_scatter.py:91: UserWarning: The usage of `scatter(reduce='max')` can be accelerated via the 'torch-scatter' package, but it was not found
  warnings.warn(
ep90 val_rocauc=0.76722 halt=0.00 train_steps=3.00
/orion/u/oscarwan/rrog-gnn-runner/.venv/lib/python3.13/site-packages/torch_geometric/utils/_scatter.py:91: UserWarning: The usage of `scatter(reduce='min')` can be accelerated via the 'torch-scatter' package, but it was not found
  warnings.warn(
/orion/u/oscarwan/rrog-gnn-runner/.venv/lib/python3.13/site-packages/torch_geometric/utils/_scatter.py:91: UserWarning: The usage of `scatter(reduce='max')` can be accelerated via the 'torch-scatter' package, but it was not found
  warnings.warn(
ep100 val_rocauc=0.76642 halt=0.00 train_steps=3.00
[ogbg-molhiv_pna_fixed-rrog_T3_ns3_h128_e100_s0] best_ep=60 val={'rocauc': 0.7890395355673133} test={'rocauc': 0.7629792000618011} adaptive=None steps=None
  wrote /orion/u/oscarwan/rrog-gnn-runner/runs/ogbg-molhiv_pna_fixed-rrog_T3_ns3_h128_e100_s0.json
[run] ogbg-molhiv view=gen compute=classic T=0 ns=1 device=cuda:1
python3 rrog/train_ogb_graphprop.py --dataset ogbg-molhiv --view gen --compute classic --T 0 --n_sup 1 --hidden 128 --bs 128 --epochs 100 --eval_every 10 --agg_layers 5 --compute_layers 2 --seed 0 --device cuda:1
ep10 val_rocauc=0.69984 halt=0.00 train_steps=1.00
ep20 val_rocauc=0.70881 halt=0.00 train_steps=1.00
ep30 val_rocauc=0.75302 halt=0.00 train_steps=1.00
ep40 val_rocauc=0.72936 halt=0.00 train_steps=1.00
ep50 val_rocauc=0.70265 halt=0.00 train_steps=1.00
ep60 val_rocauc=0.70368 halt=0.00 train_steps=1.00
ep70 val_rocauc=0.70119 halt=0.00 train_steps=1.00
ep80 val_rocauc=0.70259 halt=0.00 train_steps=1.00
ep90 val_rocauc=0.68337 halt=0.00 train_steps=1.00
ep100 val_rocauc=0.68908 halt=0.00 train_steps=1.00
[ogbg-molhiv_gen_classic_T0_ns1_h128_e100_s0] best_ep=30 val={'rocauc': 0.7530159954928474} test={'rocauc': 0.7313524788041483} adaptive=None steps=None
  wrote /orion/u/oscarwan/rrog-gnn-runner/runs/ogbg-molhiv_gen_classic_T0_ns1_h128_e100_s0.json
[run] ogbg-molhiv view=gen compute=fixed-rrog T=3 ns=3 device=cuda:1
python3 rrog/train_ogb_graphprop.py --dataset ogbg-molhiv --view gen --compute fixed-rrog --T 3 --n_sup 3 --hidden 128 --bs 128 --epochs 100 --eval_every 10 --agg_layers 5 --compute_layers 2 --seed 0 --device cuda:1
ep10 val_rocauc=0.69494 halt=0.00 train_steps=3.00
ep20 val_rocauc=0.72712 halt=0.00 train_steps=3.00
ep30 val_rocauc=0.73977 halt=0.00 train_steps=3.00
ep40 val_rocauc=0.77045 halt=0.00 train_steps=3.00
ep50 val_rocauc=0.76458 halt=0.00 train_steps=3.00
ep60 val_rocauc=0.81280 halt=0.00 train_steps=3.00
ep70 val_rocauc=0.75125 halt=0.00 train_steps=3.00
ep80 val_rocauc=0.75873 halt=0.00 train_steps=3.00
ep90 val_rocauc=0.75440 halt=0.00 train_steps=3.00
ep100 val_rocauc=0.73884 halt=0.00 train_steps=3.00
[ogbg-molhiv_gen_fixed-rrog_T3_ns3_h128_e100_s0] best_ep=60 val={'rocauc': 0.8127970066627475} test={'rocauc': 0.7631375654222755} adaptive=None steps=None
  wrote /orion/u/oscarwan/rrog-gnn-runner/runs/ogbg-molhiv_gen_fixed-rrog_T3_ns3_h128_e100_s0.json
[run] ogbg-molhiv view=film compute=classic T=0 ns=1 device=cuda:1
python3 rrog/train_ogb_graphprop.py --dataset ogbg-molhiv --view film --compute classic --T 0 --n_sup 1 --hidden 128 --bs 128 --epochs 100 --eval_every 10 --agg_layers 5 --compute_layers 2 --seed 0 --device cuda:1
ep10 val_rocauc=0.74848 halt=0.00 train_steps=1.00
ep20 val_rocauc=0.79215 halt=0.00 train_steps=1.00
ep30 val_rocauc=0.78752 halt=0.00 train_steps=1.00
ep40 val_rocauc=0.77785 halt=0.00 train_steps=1.00
ep50 val_rocauc=0.78076 halt=0.00 train_steps=1.00
ep60 val_rocauc=0.75260 halt=0.00 train_steps=1.00
ep70 val_rocauc=0.77654 halt=0.00 train_steps=1.00
ep80 val_rocauc=0.75306 halt=0.00 train_steps=1.00
ep90 val_rocauc=0.74877 halt=0.00 train_steps=1.00
ep100 val_rocauc=0.74891 halt=0.00 train_steps=1.00
[ogbg-molhiv_film_classic_T0_ns1_h128_e100_s0] best_ep=20 val={'rocauc': 0.7921535126396236} test={'rocauc': 0.7842136773595473} adaptive=None steps=None
  wrote /orion/u/oscarwan/rrog-gnn-runner/runs/ogbg-molhiv_film_classic_T0_ns1_h128_e100_s0.json
[run] ogbg-molhiv view=film compute=fixed-rrog T=3 ns=3 device=cuda:1
python3 rrog/train_ogb_graphprop.py --dataset ogbg-molhiv --view film --compute fixed-rrog --T 3 --n_sup 3 --hidden 128 --bs 128 --epochs 100 --eval_every 10 --agg_layers 5 --compute_layers 2 --seed 0 --device cuda:1
ep10 val_rocauc=0.69034 halt=0.00 train_steps=3.00
ep20 val_rocauc=0.74088 halt=0.00 train_steps=3.00
ep30 val_rocauc=0.76331 halt=0.00 train_steps=3.00
ep40 val_rocauc=0.76182 halt=0.00 train_steps=3.00
ep50 val_rocauc=0.73312 halt=0.00 train_steps=3.00
ep60 val_rocauc=0.75706 halt=0.00 train_steps=3.00
ep70 val_rocauc=0.72671 halt=0.00 train_steps=3.00
ep80 val_rocauc=0.70616 halt=0.00 train_steps=3.00
ep90 val_rocauc=0.71340 halt=0.00 train_steps=3.00
ep100 val_rocauc=0.70973 halt=0.00 train_steps=3.00
[ogbg-molhiv_film_fixed-rrog_T3_ns3_h128_e100_s0] best_ep=30 val={'rocauc': 0.7633101851851851} test={'rocauc': 0.7727785395623709} adaptive=None steps=None
  wrote /orion/u/oscarwan/rrog-gnn-runner/runs/ogbg-molhiv_film_fixed-rrog_T3_ns3_h128_e100_s0.json
[run] ogbg-molhiv view=resgated compute=classic T=0 ns=1 device=cuda:1
python3 rrog/train_ogb_graphprop.py --dataset ogbg-molhiv --view resgated --compute classic --T 0 --n_sup 1 --hidden 128 --bs 128 --epochs 100 --eval_every 10 --agg_layers 5 --compute_layers 2 --seed 0 --device cuda:1
ep10 val_rocauc=0.68158 halt=0.00 train_steps=1.00
ep20 val_rocauc=0.79255 halt=0.00 train_steps=1.00
ep30 val_rocauc=0.79856 halt=0.00 train_steps=1.00
ep40 val_rocauc=0.78576 halt=0.00 train_steps=1.00
ep50 val_rocauc=0.80478 halt=0.00 train_steps=1.00
ep60 val_rocauc=0.79662 halt=0.00 train_steps=1.00
ep70 val_rocauc=0.79878 halt=0.00 train_steps=1.00
ep80 val_rocauc=0.81437 halt=0.00 train_steps=1.00
ep90 val_rocauc=0.80889 halt=0.00 train_steps=1.00
ep100 val_rocauc=0.80836 halt=0.00 train_steps=1.00
[ogbg-molhiv_resgated_classic_T0_ns1_h128_e100_s0] best_ep=80 val={'rocauc': 0.8143738977072311} test={'rocauc': 0.7241738928909403} adaptive=None steps=None
  wrote /orion/u/oscarwan/rrog-gnn-runner/runs/ogbg-molhiv_resgated_classic_T0_ns1_h128_e100_s0.json
[run] ogbg-molhiv view=resgated compute=fixed-rrog T=3 ns=3 device=cuda:1
python3 rrog/train_ogb_graphprop.py --dataset ogbg-molhiv --view resgated --compute fixed-rrog --T 3 --n_sup 3 --hidden 128 --bs 128 --epochs 100 --eval_every 10 --agg_layers 5 --compute_layers 2 --seed 0 --device cuda:1
ep10 val_rocauc=0.68104 halt=0.00 train_steps=3.00
ep20 val_rocauc=0.77799 halt=0.00 train_steps=3.00
ep30 val_rocauc=0.78871 halt=0.00 train_steps=3.00
ep40 val_rocauc=0.79660 halt=0.00 train_steps=3.00
ep50 val_rocauc=0.78792 halt=0.00 train_steps=3.00
ep60 val_rocauc=0.80129 halt=0.00 train_steps=3.00
ep70 val_rocauc=0.80478 halt=0.00 train_steps=3.00
ep80 val_rocauc=0.81735 halt=0.00 train_steps=3.00
ep90 val_rocauc=0.80159 halt=0.00 train_steps=3.00
ep100 val_rocauc=0.80293 halt=0.00 train_steps=3.00
[ogbg-molhiv_resgated_fixed-rrog_T3_ns3_h128_e100_s0] best_ep=80 val={'rocauc': 0.8173500881834216} test={'rocauc': 0.7054809865003185} adaptive=None steps=None
  wrote /orion/u/oscarwan/rrog-gnn-runner/runs/ogbg-molhiv_resgated_fixed-rrog_T3_ns3_h128_e100_s0.json
[run] ogbg-molhiv view=tag compute=classic T=0 ns=1 device=cuda:1
python3 rrog/train_ogb_graphprop.py --dataset ogbg-molhiv --view tag --compute classic --T 0 --n_sup 1 --hidden 128 --bs 128 --epochs 100 --eval_every 10 --agg_layers 5 --compute_layers 2 --seed 0 --device cuda:1
ep10 val_rocauc=0.57852 halt=0.00 train_steps=1.00
ep20 val_rocauc=0.71945 halt=0.00 train_steps=1.00
ep30 val_rocauc=0.68145 halt=0.00 train_steps=1.00
ep40 val_rocauc=0.74554 halt=0.00 train_steps=1.00
ep50 val_rocauc=0.73004 halt=0.00 train_steps=1.00
ep60 val_rocauc=0.75214 halt=0.00 train_steps=1.00
ep70 val_rocauc=0.75283 halt=0.00 train_steps=1.00
ep80 val_rocauc=0.74456 halt=0.00 train_steps=1.00
ep90 val_rocauc=0.74209 halt=0.00 train_steps=1.00
ep100 val_rocauc=0.74289 halt=0.00 train_steps=1.00
[ogbg-molhiv_tag_classic_T0_ns1_h128_e100_s0] best_ep=70 val={'rocauc': 0.7528292181069958} test={'rocauc': 0.7255354487340427} adaptive=None steps=None
  wrote /orion/u/oscarwan/rrog-gnn-runner/runs/ogbg-molhiv_tag_classic_T0_ns1_h128_e100_s0.json
[run] ogbg-molhiv view=tag compute=fixed-rrog T=3 ns=3 device=cuda:1
python3 rrog/train_ogb_graphprop.py --dataset ogbg-molhiv --view tag --compute fixed-rrog --T 3 --n_sup 3 --hidden 128 --bs 128 --epochs 100 --eval_every 10 --agg_layers 5 --compute_layers 2 --seed 0 --device cuda:1
ep10 val_rocauc=0.60822 halt=0.00 train_steps=3.00
ep20 val_rocauc=0.72299 halt=0.00 train_steps=3.00
ep30 val_rocauc=0.72619 halt=0.00 train_steps=3.00
ep40 val_rocauc=0.76570 halt=0.00 train_steps=3.00
ep50 val_rocauc=0.77329 halt=0.00 train_steps=3.00
ep60 val_rocauc=0.76888 halt=0.00 train_steps=3.00
ep70 val_rocauc=0.78038 halt=0.00 train_steps=3.00
ep80 val_rocauc=0.77078 halt=0.00 train_steps=3.00
ep90 val_rocauc=0.77102 halt=0.00 train_steps=3.00
ep100 val_rocauc=0.77014 halt=0.00 train_steps=3.00
[ogbg-molhiv_tag_fixed-rrog_T3_ns3_h128_e100_s0] best_ep=70 val={'rocauc': 0.7803834754066236} test={'rocauc': 0.7351764228741382} adaptive=None steps=None
  wrote /orion/u/oscarwan/rrog-gnn-runner/runs/ogbg-molhiv_tag_fixed-rrog_T3_ns3_h128_e100_s0.json
[run] ogbg-molhiv view=sgc compute=classic T=0 ns=1 device=cuda:1
python3 rrog/train_ogb_graphprop.py --dataset ogbg-molhiv --view sgc --compute classic --T 0 --n_sup 1 --hidden 128 --bs 128 --epochs 100 --eval_every 10 --agg_layers 5 --compute_layers 2 --seed 0 --device cuda:1
ep10 val_rocauc=0.71821 halt=0.00 train_steps=1.00
ep20 val_rocauc=0.72852 halt=0.00 train_steps=1.00
ep30 val_rocauc=0.71183 halt=0.00 train_steps=1.00
ep40 val_rocauc=0.73111 halt=0.00 train_steps=1.00
ep50 val_rocauc=0.72198 halt=0.00 train_steps=1.00
ep60 val_rocauc=0.74022 halt=0.00 train_steps=1.00
ep70 val_rocauc=0.73184 halt=0.00 train_steps=1.00
ep80 val_rocauc=0.74786 halt=0.00 train_steps=1.00
ep90 val_rocauc=0.74065 halt=0.00 train_steps=1.00
ep100 val_rocauc=0.73686 halt=0.00 train_steps=1.00
[ogbg-molhiv_sgc_classic_T0_ns1_h128_e100_s0] best_ep=80 val={'rocauc': 0.7478597148736037} test={'rocauc': 0.7019100407501111} adaptive=None steps=None
  wrote /orion/u/oscarwan/rrog-gnn-runner/runs/ogbg-molhiv_sgc_classic_T0_ns1_h128_e100_s0.json
[run] ogbg-molhiv view=sgc compute=fixed-rrog T=3 ns=3 device=cuda:1
python3 rrog/train_ogb_graphprop.py --dataset ogbg-molhiv --view sgc --compute fixed-rrog --T 3 --n_sup 3 --hidden 128 --bs 128 --epochs 100 --eval_every 10 --agg_layers 5 --compute_layers 2 --seed 0 --device cuda:1
ep10 val_rocauc=0.69664 halt=0.00 train_steps=3.00
ep20 val_rocauc=0.68565 halt=0.00 train_steps=3.00
ep30 val_rocauc=0.69235 halt=0.00 train_steps=3.00
ep40 val_rocauc=0.71844 halt=0.00 train_steps=3.00
ep50 val_rocauc=0.74817 halt=0.00 train_steps=3.00
ep60 val_rocauc=0.72886 halt=0.00 train_steps=3.00
ep70 val_rocauc=0.72618 halt=0.00 train_steps=3.00
ep80 val_rocauc=0.73125 halt=0.00 train_steps=3.00
ep90 val_rocauc=0.73147 halt=0.00 train_steps=3.00
ep100 val_rocauc=0.72471 halt=0.00 train_steps=3.00
[ogbg-molhiv_sgc_fixed-rrog_T3_ns3_h128_e100_s0] best_ep=50 val={'rocauc': 0.7481720311581422} test={'rocauc': 0.7177311265184728} adaptive=None steps=None
  wrote /orion/u/oscarwan/rrog-gnn-runner/runs/ogbg-molhiv_sgc_fixed-rrog_T3_ns3_h128_e100_s0.json
[run] ogbg-molhiv view=cheb compute=classic T=0 ns=1 device=cuda:1
python3 rrog/train_ogb_graphprop.py --dataset ogbg-molhiv --view cheb --compute classic --T 0 --n_sup 1 --hidden 128 --bs 128 --epochs 100 --eval_every 10 --agg_layers 5 --compute_layers 2 --seed 0 --device cuda:1
ep10 val_rocauc=0.75239 halt=0.00 train_steps=1.00
ep20 val_rocauc=0.75098 halt=0.00 train_steps=1.00
ep30 val_rocauc=0.78237 halt=0.00 train_steps=1.00
ep40 val_rocauc=0.74148 halt=0.00 train_steps=1.00
ep50 val_rocauc=0.78134 halt=0.00 train_steps=1.00
ep60 val_rocauc=0.78312 halt=0.00 train_steps=1.00
ep70 val_rocauc=0.77063 halt=0.00 train_steps=1.00
ep80 val_rocauc=0.75010 halt=0.00 train_steps=1.00
ep90 val_rocauc=0.76070 halt=0.00 train_steps=1.00
ep100 val_rocauc=0.76190 halt=0.00 train_steps=1.00
[ogbg-molhiv_cheb_classic_T0_ns1_h128_e100_s0] best_ep=60 val={'rocauc': 0.783120835782873} test={'rocauc': 0.7281735838853589} adaptive=None steps=None
  wrote /orion/u/oscarwan/rrog-gnn-runner/runs/ogbg-molhiv_cheb_classic_T0_ns1_h128_e100_s0.json
[run] ogbg-molhiv view=cheb compute=fixed-rrog T=3 ns=3 device=cuda:1
python3 rrog/train_ogb_graphprop.py --dataset ogbg-molhiv --view cheb --compute fixed-rrog --T 3 --n_sup 3 --hidden 128 --bs 128 --epochs 100 --eval_every 10 --agg_layers 5 --compute_layers 2 --seed 0 --device cuda:1
ep10 val_rocauc=0.74773 halt=0.00 train_steps=3.00
ep20 val_rocauc=0.72303 halt=0.00 train_steps=3.00
ep30 val_rocauc=0.75490 halt=0.00 train_steps=3.00
ep40 val_rocauc=0.77348 halt=0.00 train_steps=3.00
ep50 val_rocauc=0.76761 halt=0.00 train_steps=3.00
ep60 val_rocauc=0.76278 halt=0.00 train_steps=3.00
ep70 val_rocauc=0.75326 halt=0.00 train_steps=3.00
ep80 val_rocauc=0.73576 halt=0.00 train_steps=3.00
ep90 val_rocauc=0.74163 halt=0.00 train_steps=3.00
ep100 val_rocauc=0.73885 halt=0.00 train_steps=3.00
[ogbg-molhiv_cheb_fixed-rrog_T3_ns3_h128_e100_s0] best_ep=40 val={'rocauc': 0.7734757740544776} test={'rocauc': 0.7425713126943356} adaptive=None steps=None
  wrote /orion/u/oscarwan/rrog-gnn-runner/runs/ogbg-molhiv_cheb_fixed-rrog_T3_ns3_h128_e100_s0.json
[run] ogbg-molhiv view=arma compute=classic T=0 ns=1 device=cuda:1
python3 rrog/train_ogb_graphprop.py --dataset ogbg-molhiv --view arma --compute classic --T 0 --n_sup 1 --hidden 128 --bs 128 --epochs 100 --eval_every 10 --agg_layers 5 --compute_layers 2 --seed 0 --device cuda:1
ep10 val_rocauc=0.72501 halt=0.00 train_steps=1.00
ep20 val_rocauc=0.70732 halt=0.00 train_steps=1.00
ep30 val_rocauc=0.71658 halt=0.00 train_steps=1.00
ep40 val_rocauc=0.75121 halt=0.00 train_steps=1.00
ep50 val_rocauc=0.78409 halt=0.00 train_steps=1.00
ep60 val_rocauc=0.77785 halt=0.00 train_steps=1.00
ep70 val_rocauc=0.78721 halt=0.00 train_steps=1.00
ep80 val_rocauc=0.77699 halt=0.00 train_steps=1.00
ep90 val_rocauc=0.78586 halt=0.00 train_steps=1.00
ep100 val_rocauc=0.78516 halt=0.00 train_steps=1.00
[ogbg-molhiv_arma_classic_T0_ns1_h128_e100_s0] best_ep=70 val={'rocauc': 0.7872115667254557} test={'rocauc': 0.7296336352575368} adaptive=None steps=None
  wrote /orion/u/oscarwan/rrog-gnn-runner/runs/ogbg-molhiv_arma_classic_T0_ns1_h128_e100_s0.json
[run] ogbg-molhiv view=arma compute=fixed-rrog T=3 ns=3 device=cuda:1
python3 rrog/train_ogb_graphprop.py --dataset ogbg-molhiv --view arma --compute fixed-rrog --T 3 --n_sup 3 --hidden 128 --bs 128 --epochs 100 --eval_every 10 --agg_layers 5 --compute_layers 2 --seed 0 --device cuda:1
ep10 val_rocauc=0.65686 halt=0.00 train_steps=3.00
ep20 val_rocauc=0.76562 halt=0.00 train_steps=3.00
ep30 val_rocauc=0.70968 halt=0.00 train_steps=3.00
ep40 val_rocauc=0.75652 halt=0.00 train_steps=3.00
ep50 val_rocauc=0.76905 halt=0.00 train_steps=3.00
ep60 val_rocauc=0.75629 halt=0.00 train_steps=3.00
ep70 val_rocauc=0.77282 halt=0.00 train_steps=3.00
ep80 val_rocauc=0.78131 halt=0.00 train_steps=3.00
ep90 val_rocauc=0.79265 halt=0.00 train_steps=3.00
ep100 val_rocauc=0.79150 halt=0.00 train_steps=3.00
[ogbg-molhiv_arma_fixed-rrog_T3_ns3_h128_e100_s0] best_ep=90 val={'rocauc': 0.7926495443856555} test={'rocauc': 0.7317541860599857} adaptive=None steps=None
  wrote /orion/u/oscarwan/rrog-gnn-runner/runs/ogbg-molhiv_arma_fixed-rrog_T3_ns3_h128_e100_s0.json
[run] ogbg-molhiv view=mf compute=classic T=0 ns=1 device=cuda:1
python3 rrog/train_ogb_graphprop.py --dataset ogbg-molhiv --view mf --compute classic --T 0 --n_sup 1 --hidden 128 --bs 128 --epochs 100 --eval_every 10 --agg_layers 5 --compute_layers 2 --seed 0 --device cuda:1
ep10 val_rocauc=0.71708 halt=0.00 train_steps=1.00
ep20 val_rocauc=0.77319 halt=0.00 train_steps=1.00
ep30 val_rocauc=0.76736 halt=0.00 train_steps=1.00
ep40 val_rocauc=0.76895 halt=0.00 train_steps=1.00
ep50 val_rocauc=0.76902 halt=0.00 train_steps=1.00
ep60 val_rocauc=0.78454 halt=0.00 train_steps=1.00
ep70 val_rocauc=0.77621 halt=0.00 train_steps=1.00
ep80 val_rocauc=0.77615 halt=0.00 train_steps=1.00
ep90 val_rocauc=0.76938 halt=0.00 train_steps=1.00
ep100 val_rocauc=0.76895 halt=0.00 train_steps=1.00
[ogbg-molhiv_mf_classic_T0_ns1_h128_e100_s0] best_ep=60 val={'rocauc': 0.784538506760729} test={'rocauc': 0.7073504702678692} adaptive=None steps=None
  wrote /orion/u/oscarwan/rrog-gnn-runner/runs/ogbg-molhiv_mf_classic_T0_ns1_h128_e100_s0.json
[run] ogbg-molhiv view=mf compute=fixed-rrog T=3 ns=3 device=cuda:1
python3 rrog/train_ogb_graphprop.py --dataset ogbg-molhiv --view mf --compute fixed-rrog --T 3 --n_sup 3 --hidden 128 --bs 128 --epochs 100 --eval_every 10 --agg_layers 5 --compute_layers 2 --seed 0 --device cuda:1
ep10 val_rocauc=0.70505 halt=0.00 train_steps=3.00
ep20 val_rocauc=0.76439 halt=0.00 train_steps=3.00
ep30 val_rocauc=0.77684 halt=0.00 train_steps=3.00
ep40 val_rocauc=0.77297 halt=0.00 train_steps=3.00
ep50 val_rocauc=0.79308 halt=0.00 train_steps=3.00
ep60 val_rocauc=0.75479 halt=0.00 train_steps=3.00
ep70 val_rocauc=0.75477 halt=0.00 train_steps=3.00
ep80 val_rocauc=0.75694 halt=0.00 train_steps=3.00
ep90 val_rocauc=0.74535 halt=0.00 train_steps=3.00
ep100 val_rocauc=0.74554 halt=0.00 train_steps=3.00
[ogbg-molhiv_mf_fixed-rrog_T3_ns3_h128_e100_s0] best_ep=50 val={'rocauc': 0.7930782137958065} test={'rocauc': 0.7216728789663763} adaptive=None steps=None
  wrote /orion/u/oscarwan/rrog-gnn-runner/runs/ogbg-molhiv_mf_fixed-rrog_T3_ns3_h128_e100_s0.json
[run] ogbg-molhiv view=appnp compute=classic T=0 ns=1 device=cuda:1
python3 rrog/train_ogb_graphprop.py --dataset ogbg-molhiv --view appnp --compute classic --T 0 --n_sup 1 --hidden 128 --bs 128 --epochs 100 --eval_every 10 --agg_layers 5 --compute_layers 2 --seed 0 --device cuda:1
ep10 val_rocauc=0.69287 halt=0.00 train_steps=1.00
ep20 val_rocauc=0.71781 halt=0.00 train_steps=1.00
ep30 val_rocauc=0.76749 halt=0.00 train_steps=1.00
ep40 val_rocauc=0.69366 halt=0.00 train_steps=1.00
ep50 val_rocauc=0.69388 halt=0.00 train_steps=1.00
ep60 val_rocauc=0.70828 halt=0.00 train_steps=1.00
ep70 val_rocauc=0.69664 halt=0.00 train_steps=1.00
ep80 val_rocauc=0.71279 halt=0.00 train_steps=1.00
ep90 val_rocauc=0.72196 halt=0.00 train_steps=1.00
ep100 val_rocauc=0.72063 halt=0.00 train_steps=1.00
[ogbg-molhiv_appnp_classic_T0_ns1_h128_e100_s0] best_ep=30 val={'rocauc': 0.7674851190476192} test={'rocauc': 0.7000000000000001} adaptive=None steps=None
  wrote /orion/u/oscarwan/rrog-gnn-runner/runs/ogbg-molhiv_appnp_classic_T0_ns1_h128_e100_s0.json
[run] ogbg-molhiv view=appnp compute=fixed-rrog T=3 ns=3 device=cuda:1
python3 rrog/train_ogb_graphprop.py --dataset ogbg-molhiv --view appnp --compute fixed-rrog --T 3 --n_sup 3 --hidden 128 --bs 128 --epochs 100 --eval_every 10 --agg_layers 5 --compute_layers 2 --seed 0 --device cuda:1
ep10 val_rocauc=0.67495 halt=0.00 train_steps=3.00
ep20 val_rocauc=0.71270 halt=0.00 train_steps=3.00
ep30 val_rocauc=0.72034 halt=0.00 train_steps=3.00
ep40 val_rocauc=0.69504 halt=0.00 train_steps=3.00
ep50 val_rocauc=0.64499 halt=0.00 train_steps=3.00
ep60 val_rocauc=0.66316 halt=0.00 train_steps=3.00
ep70 val_rocauc=0.66686 halt=0.00 train_steps=3.00
ep80 val_rocauc=0.64643 halt=0.00 train_steps=3.00
ep90 val_rocauc=0.66936 halt=0.00 train_steps=3.00
ep100 val_rocauc=0.67262 halt=0.00 train_steps=3.00
[ogbg-molhiv_appnp_fixed-rrog_T3_ns3_h128_e100_s0] best_ep=30 val={'rocauc': 0.7203422006662747} test={'rocauc': 0.682502558952471} adaptive=None steps=None
  wrote /orion/u/oscarwan/rrog-gnn-runner/runs/ogbg-molhiv_appnp_fixed-rrog_T3_ns3_h128_e100_s0.json

Classic baseline: task x backbone
| task | backbone | metric | n | val | test |
| --- | --- | --- | --- | --- | --- |
| ogbg-molhiv | appnp | rocauc | 1 | 0.7675 +/- 0.0000 | 0.7000 +/- 0.0000 |
| ogbg-molhiv | arma | rocauc | 1 | 0.7872 +/- 0.0000 | 0.7296 +/- 0.0000 |
| ogbg-molhiv | cheb | rocauc | 1 | 0.7831 +/- 0.0000 | 0.7282 +/- 0.0000 |
| ogbg-molhiv | film | rocauc | 1 | 0.7922 +/- 0.0000 | 0.7842 +/- 0.0000 |
| ogbg-molhiv | gatv2 | rocauc | 1 | 0.7835 +/- 0.0000 | 0.7563 +/- 0.0000 |
| ogbg-molhiv | gcn | rocauc | 1 | 0.7754 +/- 0.0000 | 0.7198 +/- 0.0000 |
| ogbg-molhiv | gen | rocauc | 1 | 0.7530 +/- 0.0000 | 0.7314 +/- 0.0000 |
| ogbg-molhiv | gin | rocauc | 1 | 0.8167 +/- 0.0000 | 0.7724 +/- 0.0000 |
| ogbg-molhiv | gine | rocauc | 1 | 0.7942 +/- 0.0000 | 0.7445 +/- 0.0000 |
| ogbg-molhiv | graphconv | rocauc | 1 | 0.7707 +/- 0.0000 | 0.7108 +/- 0.0000 |
| ogbg-molhiv | graphsage | rocauc | 1 | 0.7969 +/- 0.0000 | 0.7625 +/- 0.0000 |
| ogbg-molhiv | mf | rocauc | 1 | 0.7845 +/- 0.0000 | 0.7074 +/- 0.0000 |
| ogbg-molhiv | pna | rocauc | 1 | 0.7780 +/- 0.0000 | 0.7648 +/- 0.0000 |
| ogbg-molhiv | resgated | rocauc | 1 | 0.8144 +/- 0.0000 | 0.7242 +/- 0.0000 |
| ogbg-molhiv | sgc | rocauc | 1 | 0.7479 +/- 0.0000 | 0.7019 +/- 0.0000 |
| ogbg-molhiv | tag | rocauc | 1 | 0.7528 +/- 0.0000 | 0.7255 +/- 0.0000 |
| ogbg-molhiv | transformer | rocauc | 1 | 0.7617 +/- 0.0000 | 0.7547 +/- 0.0000 |

Delta vs matching classic
| task | backbone | compute | metric | n | val score (delta) | test score (delta) | steps |
| --- | --- | --- | --- | --- | --- | --- | --- |
| ogbg-molhiv | appnp | fixed-rrog | rocauc | 1 | 0.7203 (-0.0471) | 0.6825 (-0.0175) |  |
| ogbg-molhiv | arma | fixed-rrog | rocauc | 1 | 0.7926 (0.0054) | 0.7318 (0.0021) |  |
| ogbg-molhiv | cheb | fixed-rrog | rocauc | 1 | 0.7735 (-0.0096) | 0.7426 (0.0144) |  |
| ogbg-molhiv | film | fixed-rrog | rocauc | 1 | 0.7633 (-0.0288) | 0.7728 (-0.0114) |  |
| ogbg-molhiv | gatv2 | fixed-rrog | rocauc | 1 | 0.7835 (0.0001) | 0.7466 (-0.0096) |  |
| ogbg-molhiv | gcn | fixed-rrog | rocauc | 1 | 0.7455 (-0.0300) | 0.7483 (0.0285) |  |
| ogbg-molhiv | gen | fixed-rrog | rocauc | 1 | 0.8128 (0.0598) | 0.7631 (0.0318) |  |
| ogbg-molhiv | gin | fixed-rrog | rocauc | 1 | 0.7524 (-0.0644) | 0.7324 (-0.0400) |  |
| ogbg-molhiv | gine | fixed-rrog | rocauc | 1 | 0.7575 (-0.0368) | 0.7401 (-0.0044) |  |
| ogbg-molhiv | graphconv | fixed-rrog | rocauc | 1 | 0.7713 (0.0006) | 0.6979 (-0.0129) |  |
| ogbg-molhiv | graphsage | fixed-rrog | rocauc | 1 | 0.7587 (-0.0382) | 0.7641 (0.0016) |  |
| ogbg-molhiv | mf | fixed-rrog | rocauc | 1 | 0.7931 (0.0085) | 0.7217 (0.0143) |  |
| ogbg-molhiv | pna | fixed-rrog | rocauc | 1 | 0.7890 (0.0111) | 0.7630 (-0.0018) |  |
| ogbg-molhiv | resgated | fixed-rrog | rocauc | 1 | 0.8174 (0.0030) | 0.7055 (-0.0187) |  |
| ogbg-molhiv | sgc | fixed-rrog | rocauc | 1 | 0.7482 (0.0003) | 0.7177 (0.0158) |  |
| ogbg-molhiv | tag | fixed-rrog | rocauc | 1 | 0.7804 (0.0276) | 0.7352 (0.0096) |  |
| ogbg-molhiv | transformer | fixed-rrog | rocauc | 1 | 0.8088 (0.0471) | 0.7413 (-0.0134) |  |