[run] ogbg-molsider view=gin compute=rrog-act mode=stream T=1 ns=3 seed=0 device=cuda:0
python3 rrog/train_ogb_graphprop.py --dataset ogbg-molsider --view gin --compute rrog-act --T 1 --n_sup 3 --hidden 128 --bs 128 --epochs 100 --eval_every 10 --agg_layers 5 --compute_layers 2 --seed 0 --lam_q 0.1 --halt_max_steps 8 --halt_min_steps 2 --halt_target loss --halt_loss_threshold 0.2 --halt_exploration_prob 0.1 --act_train_mode stream --q_warmup_epochs 0 --device cuda:0 --num_workers 0
ep10 val_rocauc=0.52242 val_adapt_rocauc=0.52242 adapt_steps=8.00 halt=0.12 train_steps=4.50
ep20 val_rocauc=0.57120 val_adapt_rocauc=0.57031 adapt_steps=7.99 halt=0.12 train_steps=4.50
ep30 val_rocauc=0.52140 val_adapt_rocauc=0.52140 adapt_steps=8.00 halt=0.12 train_steps=4.45
ep40 val_rocauc=0.54349 val_adapt_rocauc=0.54351 adapt_steps=7.93 halt=0.12 train_steps=4.50
ep50 val_rocauc=0.52355 val_adapt_rocauc=0.52355 adapt_steps=8.00 halt=0.12 train_steps=4.50
ep60 val_rocauc=0.57651 val_adapt_rocauc=0.57763 adapt_steps=7.97 halt=0.12 train_steps=4.47
ep70 val_rocauc=0.59417 val_adapt_rocauc=0.59417 adapt_steps=8.00 halt=0.12 train_steps=4.50
ep80 val_rocauc=0.57162 val_adapt_rocauc=0.57204 adapt_steps=7.96 halt=0.12 train_steps=4.50
ep90 val_rocauc=0.58977 val_adapt_rocauc=0.58977 adapt_steps=8.00 halt=0.12 train_steps=4.50
ep100 val_rocauc=0.59364 val_adapt_rocauc=0.59378 adapt_steps=7.99 halt=0.12 train_steps=4.50
[ogbg-molsider_gin_rrog-act_T1_ns3_stream_hm8_hmin2_loss0.2_lq0.1_hex0.1_qw0_h128_e100_s0] best_ep=70 val={'rocauc': 0.5941723817458207} test={'rocauc': 0.5949106211087469} adaptive={'rocauc': 0.5949106211087469} steps=8.0
  wrote /orion/u/oscarwan/rrog-gnn-runner/runs/ogbg-molsider_gin_rrog-act_T1_ns3_stream_hm8_hmin2_loss0.2_lq0.1_hex0.1_qw0_h128_e100_s0.json
[run] ogbg-molsider view=gine compute=rrog-act mode=stream T=1 ns=3 seed=0 device=cuda:0
python3 rrog/train_ogb_graphprop.py --dataset ogbg-molsider --view gine --compute rrog-act --T 1 --n_sup 3 --hidden 128 --bs 128 --epochs 100 --eval_every 10 --agg_layers 5 --compute_layers 2 --seed 0 --lam_q 0.1 --halt_max_steps 8 --halt_min_steps 2 --halt_target loss --halt_loss_threshold 0.2 --halt_exploration_prob 0.1 --act_train_mode stream --q_warmup_epochs 0 --device cuda:0 --num_workers 0
ep10 val_rocauc=0.52714 val_adapt_rocauc=0.52714 adapt_steps=8.00 halt=0.12 train_steps=4.50
ep20 val_rocauc=0.53410 val_adapt_rocauc=0.53410 adapt_steps=8.00 halt=0.12 train_steps=4.50
ep30 val_rocauc=0.55999 val_adapt_rocauc=0.55999 adapt_steps=8.00 halt=0.12 train_steps=4.50
ep40 val_rocauc=0.58039 val_adapt_rocauc=0.58039 adapt_steps=8.00 halt=0.12 train_steps=4.50
ep50 val_rocauc=0.59855 val_adapt_rocauc=0.59871 adapt_steps=7.99 halt=0.12 train_steps=4.50
ep60 val_rocauc=0.58511 val_adapt_rocauc=0.58521 adapt_steps=7.98 halt=0.12 train_steps=4.50
ep70 val_rocauc=0.58872 val_adapt_rocauc=0.58872 adapt_steps=8.00 halt=0.12 train_steps=4.50
ep80 val_rocauc=0.59935 val_adapt_rocauc=0.59935 adapt_steps=8.00 halt=0.12 train_steps=4.50
ep90 val_rocauc=0.60537 val_adapt_rocauc=0.60495 adapt_steps=7.96 halt=0.12 train_steps=4.47
ep100 val_rocauc=0.61174 val_adapt_rocauc=0.61174 adapt_steps=8.00 halt=0.12 train_steps=4.50
[ogbg-molsider_gine_rrog-act_T1_ns3_stream_hm8_hmin2_loss0.2_lq0.1_hex0.1_qw0_h128_e100_s0] best_ep=100 val={'rocauc': 0.6117392254931898} test={'rocauc': 0.6143023355631505} adaptive={'rocauc': 0.6147815453760646} steps=7.958041958041958
  wrote /orion/u/oscarwan/rrog-gnn-runner/runs/ogbg-molsider_gine_rrog-act_T1_ns3_stream_hm8_hmin2_loss0.2_lq0.1_hex0.1_qw0_h128_e100_s0.json
[run] ogbg-molsider view=gcn compute=rrog-act mode=stream T=1 ns=3 seed=0 device=cuda:0
python3 rrog/train_ogb_graphprop.py --dataset ogbg-molsider --view gcn --compute rrog-act --T 1 --n_sup 3 --hidden 128 --bs 128 --epochs 100 --eval_every 10 --agg_layers 5 --compute_layers 2 --seed 0 --lam_q 0.1 --halt_max_steps 8 --halt_min_steps 2 --halt_target loss --halt_loss_threshold 0.2 --halt_exploration_prob 0.1 --act_train_mode stream --q_warmup_epochs 0 --device cuda:0 --num_workers 0
ep10 val_rocauc=0.53639 val_adapt_rocauc=0.53639 adapt_steps=8.00 halt=0.12 train_steps=4.50
ep20 val_rocauc=0.54446 val_adapt_rocauc=0.54446 adapt_steps=8.00 halt=0.12 train_steps=4.50
ep30 val_rocauc=0.56844 val_adapt_rocauc=0.56844 adapt_steps=8.00 halt=0.12 train_steps=4.50
ep40 val_rocauc=0.59523 val_adapt_rocauc=0.59523 adapt_steps=8.00 halt=0.12 train_steps=4.50
ep50 val_rocauc=0.58925 val_adapt_rocauc=0.58925 adapt_steps=8.00 halt=0.12 train_steps=4.50
ep60 val_rocauc=0.61196 val_adapt_rocauc=0.60952 adapt_steps=7.48 halt=0.13 train_steps=4.43
ep70 val_rocauc=0.60679 val_adapt_rocauc=0.60935 adapt_steps=7.81 halt=0.12 train_steps=4.49
ep80 val_rocauc=0.62425 val_adapt_rocauc=0.62328 adapt_steps=7.92 halt=0.12 train_steps=4.49
ep90 val_rocauc=0.63455 val_adapt_rocauc=0.63464 adapt_steps=7.98 halt=0.12 train_steps=4.50
ep100 val_rocauc=0.63261 val_adapt_rocauc=0.63316 adapt_steps=7.90 halt=0.13 train_steps=4.48
[ogbg-molsider_gcn_rrog-act_T1_ns3_stream_hm8_hmin2_loss0.2_lq0.1_hex0.1_qw0_h128_e100_s0] best_ep=90 val={'rocauc': 0.634553246023819} test={'rocauc': 0.6445134528927392} adaptive={'rocauc': 0.644509359822436} steps=7.93006993006993
  wrote /orion/u/oscarwan/rrog-gnn-runner/runs/ogbg-molsider_gcn_rrog-act_T1_ns3_stream_hm8_hmin2_loss0.2_lq0.1_hex0.1_qw0_h128_e100_s0.json
[run] ogbg-molsider view=graphsage compute=rrog-act mode=stream T=1 ns=3 seed=0 device=cuda:0
python3 rrog/train_ogb_graphprop.py --dataset ogbg-molsider --view graphsage --compute rrog-act --T 1 --n_sup 3 --hidden 128 --bs 128 --epochs 100 --eval_every 10 --agg_layers 5 --compute_layers 2 --seed 0 --lam_q 0.1 --halt_max_steps 8 --halt_min_steps 2 --halt_target loss --halt_loss_threshold 0.2 --halt_exploration_prob 0.1 --act_train_mode stream --q_warmup_epochs 0 --device cuda:0 --num_workers 0
ep10 val_rocauc=0.53993 val_adapt_rocauc=0.53993 adapt_steps=8.00 halt=0.12 train_steps=4.50
ep20 val_rocauc=0.51721 val_adapt_rocauc=0.51820 adapt_steps=7.81 halt=0.12 train_steps=4.50
ep30 val_rocauc=0.57455 val_adapt_rocauc=0.57455 adapt_steps=8.00 halt=0.12 train_steps=4.50
ep40 val_rocauc=0.54410 val_adapt_rocauc=0.54400 adapt_steps=7.98 halt=0.12 train_steps=4.50
ep50 val_rocauc=0.57426 val_adapt_rocauc=0.57426 adapt_steps=8.00 halt=0.12 train_steps=4.50
ep60 val_rocauc=0.57583 val_adapt_rocauc=0.57668 adapt_steps=7.83 halt=0.12 train_steps=4.49
ep70 val_rocauc=0.57077 val_adapt_rocauc=0.57333 adapt_steps=7.81 halt=0.13 train_steps=4.46
ep80 val_rocauc=0.60329 val_adapt_rocauc=0.60324 adapt_steps=7.98 halt=0.12 train_steps=4.50
ep90 val_rocauc=0.60080 val_adapt_rocauc=0.60075 adapt_steps=7.99 halt=0.12 train_steps=4.48
ep100 val_rocauc=0.61496 val_adapt_rocauc=0.61461 adapt_steps=7.97 halt=0.13 train_steps=4.48
[ogbg-molsider_graphsage_rrog-act_T1_ns3_stream_hm8_hmin2_loss0.2_lq0.1_hex0.1_qw0_h128_e100_s0] best_ep=100 val={'rocauc': 0.6149561902831908} test={'rocauc': 0.6082036653138152} adaptive={'rocauc': 0.6072637395395359} steps=7.923076923076923
  wrote /orion/u/oscarwan/rrog-gnn-runner/runs/ogbg-molsider_graphsage_rrog-act_T1_ns3_stream_hm8_hmin2_loss0.2_lq0.1_hex0.1_qw0_h128_e100_s0.json
[run] ogbg-molsider view=gatv2 compute=rrog-act mode=stream T=1 ns=3 seed=0 device=cuda:0
python3 rrog/train_ogb_graphprop.py --dataset ogbg-molsider --view gatv2 --compute rrog-act --T 1 --n_sup 3 --hidden 128 --bs 128 --epochs 100 --eval_every 10 --agg_layers 5 --compute_layers 2 --seed 0 --lam_q 0.1 --halt_max_steps 8 --halt_min_steps 2 --halt_target loss --halt_loss_threshold 0.2 --halt_exploration_prob 0.1 --act_train_mode stream --q_warmup_epochs 0 --device cuda:0 --num_workers 0
ep10 val_rocauc=0.51326 val_adapt_rocauc=0.51326 adapt_steps=8.00 halt=0.12 train_steps=4.50
ep20 val_rocauc=0.53070 val_adapt_rocauc=0.53070 adapt_steps=8.00 halt=0.12 train_steps=4.50
ep30 val_rocauc=0.52081 val_adapt_rocauc=0.52081 adapt_steps=8.00 halt=0.12 train_steps=4.50
ep40 val_rocauc=0.53950 val_adapt_rocauc=0.53893 adapt_steps=7.83 halt=0.12 train_steps=4.47
ep50 val_rocauc=0.55260 val_adapt_rocauc=0.55337 adapt_steps=7.90 halt=0.12 train_steps=4.48
ep60 val_rocauc=0.55241 val_adapt_rocauc=0.55241 adapt_steps=8.00 halt=0.12 train_steps=4.50
ep70 val_rocauc=0.56511 val_adapt_rocauc=0.56511 adapt_steps=8.00 halt=0.12 train_steps=4.50
ep80 val_rocauc=0.56634 val_adapt_rocauc=0.56626 adapt_steps=7.99 halt=0.12 train_steps=4.50
ep90 val_rocauc=0.57675 val_adapt_rocauc=0.57675 adapt_steps=8.00 halt=0.12 train_steps=4.49
ep100 val_rocauc=0.57777 val_adapt_rocauc=0.57777 adapt_steps=8.00 halt=0.12 train_steps=4.49
[ogbg-molsider_gatv2_rrog-act_T1_ns3_stream_hm8_hmin2_loss0.2_lq0.1_hex0.1_qw0_h128_e100_s0] best_ep=100 val={'rocauc': 0.5777678714357024} test={'rocauc': 0.6337321824820535} adaptive={'rocauc': 0.6339582691134998} steps=7.958041958041958
  wrote /orion/u/oscarwan/rrog-gnn-runner/runs/ogbg-molsider_gatv2_rrog-act_T1_ns3_stream_hm8_hmin2_loss0.2_lq0.1_hex0.1_qw0_h128_e100_s0.json
[run] ogbg-molsider view=graphconv compute=rrog-act mode=stream T=1 ns=3 seed=0 device=cuda:0
python3 rrog/train_ogb_graphprop.py --dataset ogbg-molsider --view graphconv --compute rrog-act --T 1 --n_sup 3 --hidden 128 --bs 128 --epochs 100 --eval_every 10 --agg_layers 5 --compute_layers 2 --seed 0 --lam_q 0.1 --halt_max_steps 8 --halt_min_steps 2 --halt_target loss --halt_loss_threshold 0.2 --halt_exploration_prob 0.1 --act_train_mode stream --q_warmup_epochs 0 --device cuda:0 --num_workers 0
ep10 val_rocauc=0.53249 val_adapt_rocauc=0.52907 adapt_steps=7.41 halt=0.12 train_steps=4.47
ep20 val_rocauc=0.53857 val_adapt_rocauc=0.53838 adapt_steps=7.96 halt=0.12 train_steps=4.50
ep30 val_rocauc=0.55430 val_adapt_rocauc=0.55430 adapt_steps=8.00 halt=0.12 train_steps=4.50
ep40 val_rocauc=0.55775 val_adapt_rocauc=0.55775 adapt_steps=8.00 halt=0.12 train_steps=4.50
ep50 val_rocauc=0.53048 val_adapt_rocauc=0.53048 adapt_steps=8.00 halt=0.12 train_steps=4.50
ep60 val_rocauc=0.58757 val_adapt_rocauc=0.58768 adapt_steps=7.99 halt=0.12 train_steps=4.50
ep70 val_rocauc=0.58127 val_adapt_rocauc=0.58127 adapt_steps=8.00 halt=0.12 train_steps=4.50
ep80 val_rocauc=0.58969 val_adapt_rocauc=0.58958 adapt_steps=7.97 halt=0.12 train_steps=4.50
ep90 val_rocauc=0.59525 val_adapt_rocauc=0.59512 adapt_steps=7.94 halt=0.13 train_steps=4.44
ep100 val_rocauc=0.61397 val_adapt_rocauc=0.61302 adapt_steps=7.97 halt=0.12 train_steps=4.45
[ogbg-molsider_graphconv_rrog-act_T1_ns3_stream_hm8_hmin2_loss0.2_lq0.1_hex0.1_qw0_h128_e100_s0] best_ep=100 val={'rocauc': 0.6139745501285409} test={'rocauc': 0.6068834635828694} adaptive={'rocauc': 0.6064255172013142} steps=7.916083916083916
  wrote /orion/u/oscarwan/rrog-gnn-runner/runs/ogbg-molsider_graphconv_rrog-act_T1_ns3_stream_hm8_hmin2_loss0.2_lq0.1_hex0.1_qw0_h128_e100_s0.json
[run] ogbg-molsider view=transformer compute=rrog-act mode=stream T=1 ns=3 seed=0 device=cuda:0
python3 rrog/train_ogb_graphprop.py --dataset ogbg-molsider --view transformer --compute rrog-act --T 1 --n_sup 3 --hidden 128 --bs 128 --epochs 100 --eval_every 10 --agg_layers 5 --compute_layers 2 --seed 0 --lam_q 0.1 --halt_max_steps 8 --halt_min_steps 2 --halt_target loss --halt_loss_threshold 0.2 --halt_exploration_prob 0.1 --act_train_mode stream --q_warmup_epochs 0 --device cuda:0 --num_workers 0
ep10 val_rocauc=0.53954 val_adapt_rocauc=0.53954 adapt_steps=8.00 halt=0.12 train_steps=4.50
ep20 val_rocauc=0.54449 val_adapt_rocauc=0.54449 adapt_steps=8.00 halt=0.12 train_steps=4.50
ep30 val_rocauc=0.58517 val_adapt_rocauc=0.58517 adapt_steps=8.00 halt=0.12 train_steps=4.50
ep40 val_rocauc=0.57396 val_adapt_rocauc=0.57671 adapt_steps=7.88 halt=0.12 train_steps=4.45
ep50 val_rocauc=0.58068 val_adapt_rocauc=0.58068 adapt_steps=8.00 halt=0.12 train_steps=4.50
ep60 val_rocauc=0.60139 val_adapt_rocauc=0.60139 adapt_steps=8.00 halt=0.12 train_steps=4.49
ep70 val_rocauc=0.61355 val_adapt_rocauc=0.61355 adapt_steps=8.00 halt=0.12 train_steps=4.45
ep80 val_rocauc=0.57128 val_adapt_rocauc=0.57180 adapt_steps=7.95 halt=0.12 train_steps=4.48
ep90 val_rocauc=0.62410 val_adapt_rocauc=0.62410 adapt_steps=8.00 halt=0.12 train_steps=4.50
ep100 val_rocauc=0.62875 val_adapt_rocauc=0.62875 adapt_steps=8.00 halt=0.12 train_steps=4.50
[ogbg-molsider_transformer_rrog-act_T1_ns3_stream_hm8_hmin2_loss0.2_lq0.1_hex0.1_qw0_h128_e100_s0] best_ep=100 val={'rocauc': 0.6287543036419472} test={'rocauc': 0.621503855814713} adaptive={'rocauc': 0.6212391169096168} steps=7.951048951048951
  wrote /orion/u/oscarwan/rrog-gnn-runner/runs/ogbg-molsider_transformer_rrog-act_T1_ns3_stream_hm8_hmin2_loss0.2_lq0.1_hex0.1_qw0_h128_e100_s0.json
[run] ogbg-molsider view=pna compute=rrog-act mode=stream T=1 ns=3 seed=0 device=cuda:0
python3 rrog/train_ogb_graphprop.py --dataset ogbg-molsider --view pna --compute rrog-act --T 1 --n_sup 3 --hidden 128 --bs 128 --epochs 100 --eval_every 10 --agg_layers 5 --compute_layers 2 --seed 0 --lam_q 0.1 --halt_max_steps 8 --halt_min_steps 2 --halt_target loss --halt_loss_threshold 0.2 --halt_exploration_prob 0.1 --act_train_mode stream --q_warmup_epochs 0 --device cuda:0 --num_workers 0
/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=0.53705 val_adapt_rocauc=0.53705 adapt_steps=8.00 halt=0.12 train_steps=4.50
/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=0.49938 val_adapt_rocauc=0.49938 adapt_steps=8.00 halt=0.12 train_steps=4.43
/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=0.50914 val_adapt_rocauc=0.50914 adapt_steps=8.00 halt=0.12 train_steps=4.50
/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=0.52533 val_adapt_rocauc=0.52533 adapt_steps=8.00 halt=0.12 train_steps=4.50
/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=0.56368 val_adapt_rocauc=0.56368 adapt_steps=8.00 halt=0.12 train_steps=4.50
/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=0.58102 val_adapt_rocauc=0.58102 adapt_steps=8.00 halt=0.12 train_steps=4.50
/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=0.55427 val_adapt_rocauc=0.55427 adapt_steps=8.00 halt=0.12 train_steps=4.50
/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=0.56861 val_adapt_rocauc=0.56861 adapt_steps=8.00 halt=0.12 train_steps=4.50
/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=0.54785 val_adapt_rocauc=0.54785 adapt_steps=8.00 halt=0.12 train_steps=4.50
/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=0.56012 val_adapt_rocauc=0.56012 adapt_steps=8.00 halt=0.12 train_steps=4.50
[ogbg-molsider_pna_rrog-act_T1_ns3_stream_hm8_hmin2_loss0.2_lq0.1_hex0.1_qw0_h128_e100_s0] best_ep=60 val={'rocauc': 0.5810212759423172} test={'rocauc': 0.5397701280540309} adaptive={'rocauc': 0.5397701280540309} steps=8.0
  wrote /orion/u/oscarwan/rrog-gnn-runner/runs/ogbg-molsider_pna_rrog-act_T1_ns3_stream_hm8_hmin2_loss0.2_lq0.1_hex0.1_qw0_h128_e100_s0.json
[run] ogbg-molsider view=gen compute=rrog-act mode=stream T=1 ns=3 seed=0 device=cuda:0
python3 rrog/train_ogb_graphprop.py --dataset ogbg-molsider --view gen --compute rrog-act --T 1 --n_sup 3 --hidden 128 --bs 128 --epochs 100 --eval_every 10 --agg_layers 5 --compute_layers 2 --seed 0 --lam_q 0.1 --halt_max_steps 8 --halt_min_steps 2 --halt_target loss --halt_loss_threshold 0.2 --halt_exploration_prob 0.1 --act_train_mode stream --q_warmup_epochs 0 --device cuda:0 --num_workers 0
ep10 val_rocauc=0.52015 val_adapt_rocauc=0.51409 adapt_steps=7.45 halt=0.12 train_steps=4.50
ep20 val_rocauc=0.56979 val_adapt_rocauc=0.56338 adapt_steps=7.21 halt=0.13 train_steps=4.43
ep30 val_rocauc=0.57163 val_adapt_rocauc=0.57163 adapt_steps=8.00 halt=0.12 train_steps=4.50
ep40 val_rocauc=0.50300 val_adapt_rocauc=0.50300 adapt_steps=8.00 halt=0.12 train_steps=4.50
ep50 val_rocauc=0.55048 val_adapt_rocauc=0.55135 adapt_steps=7.97 halt=0.12 train_steps=4.50
ep60 val_rocauc=0.55929 val_adapt_rocauc=0.55929 adapt_steps=8.00 halt=0.13 train_steps=4.50
ep70 val_rocauc=0.61128 val_adapt_rocauc=0.61133 adapt_steps=7.96 halt=0.12 train_steps=4.50
ep80 val_rocauc=0.61143 val_adapt_rocauc=0.61143 adapt_steps=8.00 halt=0.12 train_steps=4.50
ep90 val_rocauc=0.59636 val_adapt_rocauc=0.59635 adapt_steps=7.96 halt=0.12 train_steps=4.50
ep100 val_rocauc=0.59800 val_adapt_rocauc=0.59800 adapt_steps=8.00 halt=0.12 train_steps=4.49
[ogbg-molsider_gen_rrog-act_T1_ns3_stream_hm8_hmin2_loss0.2_lq0.1_hex0.1_qw0_h128_e100_s0] best_ep=80 val={'rocauc': 0.6114344272964439} test={'rocauc': 0.6071293608886619} adaptive={'rocauc': 0.6071293608886619} steps=8.0
  wrote /orion/u/oscarwan/rrog-gnn-runner/runs/ogbg-molsider_gen_rrog-act_T1_ns3_stream_hm8_hmin2_loss0.2_lq0.1_hex0.1_qw0_h128_e100_s0.json
[run] ogbg-molsider view=film compute=rrog-act mode=stream T=1 ns=3 seed=0 device=cuda:0
python3 rrog/train_ogb_graphprop.py --dataset ogbg-molsider --view film --compute rrog-act --T 1 --n_sup 3 --hidden 128 --bs 128 --epochs 100 --eval_every 10 --agg_layers 5 --compute_layers 2 --seed 0 --lam_q 0.1 --halt_max_steps 8 --halt_min_steps 2 --halt_target loss --halt_loss_threshold 0.2 --halt_exploration_prob 0.1 --act_train_mode stream --q_warmup_epochs 0 --device cuda:0 --num_workers 0
ep10 val_rocauc=0.50663 val_adapt_rocauc=0.50663 adapt_steps=8.00 halt=0.12 train_steps=4.50
ep20 val_rocauc=0.53836 val_adapt_rocauc=0.53836 adapt_steps=8.00 halt=0.12 train_steps=4.50
ep30 val_rocauc=0.54072 val_adapt_rocauc=0.54072 adapt_steps=8.00 halt=0.12 train_steps=4.49
ep40 val_rocauc=0.52242 val_adapt_rocauc=0.52242 adapt_steps=8.00 halt=0.12 train_steps=4.50
ep50 val_rocauc=0.52826 val_adapt_rocauc=0.52826 adapt_steps=8.00 halt=0.12 train_steps=4.50
ep60 val_rocauc=0.52041 val_adapt_rocauc=0.52041 adapt_steps=8.00 halt=0.12 train_steps=4.50
ep70 val_rocauc=0.53993 val_adapt_rocauc=0.53993 adapt_steps=8.00 halt=0.12 train_steps=4.50
ep80 val_rocauc=0.52758 val_adapt_rocauc=0.52758 adapt_steps=8.00 halt=0.12 train_steps=4.50
ep90 val_rocauc=0.53864 val_adapt_rocauc=0.53864 adapt_steps=8.00 halt=0.12 train_steps=4.50
ep100 val_rocauc=0.54079 val_adapt_rocauc=0.54079 adapt_steps=8.00 halt=0.12 train_steps=4.50
[ogbg-molsider_film_rrog-act_T1_ns3_stream_hm8_hmin2_loss0.2_lq0.1_hex0.1_qw0_h128_e100_s0] best_ep=100 val={'rocauc': 0.5407876481859505} test={'rocauc': 0.5825278400429263} adaptive={'rocauc': 0.5825278400429263} steps=8.0
  wrote /orion/u/oscarwan/rrog-gnn-runner/runs/ogbg-molsider_film_rrog-act_T1_ns3_stream_hm8_hmin2_loss0.2_lq0.1_hex0.1_qw0_h128_e100_s0.json
[run] ogbg-molsider view=resgated compute=rrog-act mode=stream T=1 ns=3 seed=0 device=cuda:0
python3 rrog/train_ogb_graphprop.py --dataset ogbg-molsider --view resgated --compute rrog-act --T 1 --n_sup 3 --hidden 128 --bs 128 --epochs 100 --eval_every 10 --agg_layers 5 --compute_layers 2 --seed 0 --lam_q 0.1 --halt_max_steps 8 --halt_min_steps 2 --halt_target loss --halt_loss_threshold 0.2 --halt_exploration_prob 0.1 --act_train_mode stream --q_warmup_epochs 0 --device cuda:0 --num_workers 0
ep10 val_rocauc=0.51625 val_adapt_rocauc=0.51625 adapt_steps=8.00 halt=0.12 train_steps=4.50
ep20 val_rocauc=0.55876 val_adapt_rocauc=0.55876 adapt_steps=8.00 halt=0.12 train_steps=4.50
ep30 val_rocauc=0.58353 val_adapt_rocauc=0.58374 adapt_steps=7.99 halt=0.12 train_steps=4.50
ep40 val_rocauc=0.59270 val_adapt_rocauc=0.59269 adapt_steps=8.00 halt=0.12 train_steps=4.50
ep50 val_rocauc=0.59117 val_adapt_rocauc=0.59117 adapt_steps=8.00 halt=0.12 train_steps=4.50
ep60 val_rocauc=0.59394 val_adapt_rocauc=0.59559 adapt_steps=7.96 halt=0.12 train_steps=4.48
ep70 val_rocauc=0.57628 val_adapt_rocauc=0.57628 adapt_steps=8.00 halt=0.12 train_steps=4.50
ep80 val_rocauc=0.59810 val_adapt_rocauc=0.59810 adapt_steps=8.00 halt=0.12 train_steps=4.50
ep90 val_rocauc=0.59935 val_adapt_rocauc=0.59919 adapt_steps=7.96 halt=0.12 train_steps=4.48
ep100 val_rocauc=0.60973 val_adapt_rocauc=0.60972 adapt_steps=7.97 halt=0.12 train_steps=4.48
[ogbg-molsider_resgated_rrog-act_T1_ns3_stream_hm8_hmin2_loss0.2_lq0.1_hex0.1_qw0_h128_e100_s0] best_ep=100 val={'rocauc': 0.609727680398977} test={'rocauc': 0.6245839801623748} adaptive={'rocauc': 0.624652550767547} steps=7.951048951048951
  wrote /orion/u/oscarwan/rrog-gnn-runner/runs/ogbg-molsider_resgated_rrog-act_T1_ns3_stream_hm8_hmin2_loss0.2_lq0.1_hex0.1_qw0_h128_e100_s0.json
[run] ogbg-molsider view=tag compute=rrog-act mode=stream T=1 ns=3 seed=0 device=cuda:0
python3 rrog/train_ogb_graphprop.py --dataset ogbg-molsider --view tag --compute rrog-act --T 1 --n_sup 3 --hidden 128 --bs 128 --epochs 100 --eval_every 10 --agg_layers 5 --compute_layers 2 --seed 0 --lam_q 0.1 --halt_max_steps 8 --halt_min_steps 2 --halt_target loss --halt_loss_threshold 0.2 --halt_exploration_prob 0.1 --act_train_mode stream --q_warmup_epochs 0 --device cuda:0 --num_workers 0
ep10 val_rocauc=0.53875 val_adapt_rocauc=0.53875 adapt_steps=8.00 halt=0.12 train_steps=4.50
ep20 val_rocauc=0.55745 val_adapt_rocauc=0.55745 adapt_steps=8.00 halt=0.12 train_steps=4.49
ep30 val_rocauc=0.52523 val_adapt_rocauc=0.52508 adapt_steps=7.99 halt=0.12 train_steps=4.50
ep40 val_rocauc=0.57020 val_adapt_rocauc=0.57005 adapt_steps=7.99 halt=0.12 train_steps=4.43
ep50 val_rocauc=0.57818 val_adapt_rocauc=0.57818 adapt_steps=8.00 halt=0.12 train_steps=4.50
ep60 val_rocauc=0.58430 val_adapt_rocauc=0.58478 adapt_steps=7.88 halt=0.13 train_steps=4.46
ep70 val_rocauc=0.62524 val_adapt_rocauc=0.62524 adapt_steps=8.00 halt=0.12 train_steps=4.49
ep80 val_rocauc=0.58782 val_adapt_rocauc=0.58782 adapt_steps=8.00 halt=0.12 train_steps=4.50
ep90 val_rocauc=0.63291 val_adapt_rocauc=0.63291 adapt_steps=8.00 halt=0.13 train_steps=4.50
ep100 val_rocauc=0.63016 val_adapt_rocauc=0.63032 adapt_steps=7.97 halt=0.13 train_steps=4.47
[ogbg-molsider_tag_rrog-act_T1_ns3_stream_hm8_hmin2_loss0.2_lq0.1_hex0.1_qw0_h128_e100_s0] best_ep=90 val={'rocauc': 0.6329113613630306} test={'rocauc': 0.6060675375955825} adaptive={'rocauc': 0.6066839663477379} steps=7.909090909090909
  wrote /orion/u/oscarwan/rrog-gnn-runner/runs/ogbg-molsider_tag_rrog-act_T1_ns3_stream_hm8_hmin2_loss0.2_lq0.1_hex0.1_qw0_h128_e100_s0.json
[run] ogbg-molsider view=sgc compute=rrog-act mode=stream T=1 ns=3 seed=0 device=cuda:0
python3 rrog/train_ogb_graphprop.py --dataset ogbg-molsider --view sgc --compute rrog-act --T 1 --n_sup 3 --hidden 128 --bs 128 --epochs 100 --eval_every 10 --agg_layers 5 --compute_layers 2 --seed 0 --lam_q 0.1 --halt_max_steps 8 --halt_min_steps 2 --halt_target loss --halt_loss_threshold 0.2 --halt_exploration_prob 0.1 --act_train_mode stream --q_warmup_epochs 0 --device cuda:0 --num_workers 0
ep10 val_rocauc=0.51769 val_adapt_rocauc=0.51769 adapt_steps=8.00 halt=0.12 train_steps=4.50
ep20 val_rocauc=0.54554 val_adapt_rocauc=0.54554 adapt_steps=8.00 halt=0.12 train_steps=4.50
ep30 val_rocauc=0.52433 val_adapt_rocauc=0.52433 adapt_steps=8.00 halt=0.12 train_steps=4.50
ep40 val_rocauc=0.54839 val_adapt_rocauc=0.54757 adapt_steps=7.82 halt=0.12 train_steps=4.40
ep50 val_rocauc=0.56667 val_adapt_rocauc=0.56667 adapt_steps=8.00 halt=0.12 train_steps=4.50
ep60 val_rocauc=0.56001 val_adapt_rocauc=0.56001 adapt_steps=8.00 halt=0.12 train_steps=4.50
ep70 val_rocauc=0.56325 val_adapt_rocauc=0.56263 adapt_steps=7.93 halt=0.12 train_steps=4.38
ep80 val_rocauc=0.57410 val_adapt_rocauc=0.57405 adapt_steps=7.99 halt=0.12 train_steps=4.49
ep90 val_rocauc=0.58333 val_adapt_rocauc=0.58333 adapt_steps=7.92 halt=0.13 train_steps=4.47
ep100 val_rocauc=0.58659 val_adapt_rocauc=0.58673 adapt_steps=7.91 halt=0.13 train_steps=4.47
[ogbg-molsider_sgc_rrog-act_T1_ns3_stream_hm8_hmin2_loss0.2_lq0.1_hex0.1_qw0_h128_e100_s0] best_ep=100 val={'rocauc': 0.5865936638036943} test={'rocauc': 0.624583829207758} adaptive={'rocauc': 0.6241791163198843} steps=7.881118881118881
  wrote /orion/u/oscarwan/rrog-gnn-runner/runs/ogbg-molsider_sgc_rrog-act_T1_ns3_stream_hm8_hmin2_loss0.2_lq0.1_hex0.1_qw0_h128_e100_s0.json
[run] ogbg-molsider view=cheb compute=rrog-act mode=stream T=1 ns=3 seed=0 device=cuda:0
python3 rrog/train_ogb_graphprop.py --dataset ogbg-molsider --view cheb --compute rrog-act --T 1 --n_sup 3 --hidden 128 --bs 128 --epochs 100 --eval_every 10 --agg_layers 5 --compute_layers 2 --seed 0 --lam_q 0.1 --halt_max_steps 8 --halt_min_steps 2 --halt_target loss --halt_loss_threshold 0.2 --halt_exploration_prob 0.1 --act_train_mode stream --q_warmup_epochs 0 --device cuda:0 --num_workers 0
ep10 val_rocauc=0.52959 val_adapt_rocauc=0.53014 adapt_steps=7.83 halt=0.12 train_steps=4.50
ep20 val_rocauc=0.59000 val_adapt_rocauc=0.59000 adapt_steps=8.00 halt=0.12 train_steps=4.50
ep30 val_rocauc=0.57720 val_adapt_rocauc=0.57720 adapt_steps=8.00 halt=0.12 train_steps=4.49
ep40 val_rocauc=0.58010 val_adapt_rocauc=0.58016 adapt_steps=7.99 halt=0.12 train_steps=4.50
ep50 val_rocauc=0.56105 val_adapt_rocauc=0.56396 adapt_steps=7.42 halt=0.12 train_steps=4.47
ep60 val_rocauc=0.60272 val_adapt_rocauc=0.60144 adapt_steps=7.73 halt=0.13 train_steps=4.46
ep70 val_rocauc=0.63704 val_adapt_rocauc=0.63704 adapt_steps=8.00 halt=0.12 train_steps=4.49
ep80 val_rocauc=0.63367 val_adapt_rocauc=0.63378 adapt_steps=7.95 halt=0.12 train_steps=4.46
ep90 val_rocauc=0.64091 val_adapt_rocauc=0.64157 adapt_steps=7.88 halt=0.13 train_steps=4.47
ep100 val_rocauc=0.63703 val_adapt_rocauc=0.63925 adapt_steps=7.80 halt=0.13 train_steps=4.46
[ogbg-molsider_cheb_rrog-act_T1_ns3_stream_hm8_hmin2_loss0.2_lq0.1_hex0.1_qw0_h128_e100_s0] best_ep=90 val={'rocauc': 0.6409076943311921} test={'rocauc': 0.6149744329414113} adaptive={'rocauc': 0.6144652607315165} steps=7.8671328671328675
  wrote /orion/u/oscarwan/rrog-gnn-runner/runs/ogbg-molsider_cheb_rrog-act_T1_ns3_stream_hm8_hmin2_loss0.2_lq0.1_hex0.1_qw0_h128_e100_s0.json
[run] ogbg-molsider view=arma compute=rrog-act mode=stream T=1 ns=3 seed=0 device=cuda:0
python3 rrog/train_ogb_graphprop.py --dataset ogbg-molsider --view arma --compute rrog-act --T 1 --n_sup 3 --hidden 128 --bs 128 --epochs 100 --eval_every 10 --agg_layers 5 --compute_layers 2 --seed 0 --lam_q 0.1 --halt_max_steps 8 --halt_min_steps 2 --halt_target loss --halt_loss_threshold 0.2 --halt_exploration_prob 0.1 --act_train_mode stream --q_warmup_epochs 0 --device cuda:0 --num_workers 0
ep10 val_rocauc=0.51335 val_adapt_rocauc=0.51335 adapt_steps=8.00 halt=0.12 train_steps=4.50
ep20 val_rocauc=0.51105 val_adapt_rocauc=0.50798 adapt_steps=7.59 halt=0.13 train_steps=4.47
ep30 val_rocauc=0.54401 val_adapt_rocauc=0.54401 adapt_steps=8.00 halt=0.12 train_steps=4.50
ep40 val_rocauc=0.57421 val_adapt_rocauc=0.57421 adapt_steps=8.00 halt=0.12 train_steps=4.50
ep50 val_rocauc=0.58087 val_adapt_rocauc=0.58094 adapt_steps=7.98 halt=0.12 train_steps=4.50
ep60 val_rocauc=0.58740 val_adapt_rocauc=0.58740 adapt_steps=8.00 halt=0.12 train_steps=4.50
ep70 val_rocauc=0.62738 val_adapt_rocauc=0.62725 adapt_steps=7.99 halt=0.12 train_steps=4.50
ep80 val_rocauc=0.66049 val_adapt_rocauc=0.66028 adapt_steps=7.93 halt=0.13 train_steps=4.46
ep90 val_rocauc=0.63467 val_adapt_rocauc=0.63512 adapt_steps=7.97 halt=0.13 train_steps=4.45
ep100 val_rocauc=0.64614 val_adapt_rocauc=0.64687 adapt_steps=7.94 halt=0.12 train_steps=4.50
[ogbg-molsider_arma_rrog-act_T1_ns3_stream_hm8_hmin2_loss0.2_lq0.1_hex0.1_qw0_h128_e100_s0] best_ep=80 val={'rocauc': 0.6604919913889579} test={'rocauc': 0.5589855993934405} adaptive={'rocauc': 0.5580832949005652} steps=7.895104895104895
  wrote /orion/u/oscarwan/rrog-gnn-runner/runs/ogbg-molsider_arma_rrog-act_T1_ns3_stream_hm8_hmin2_loss0.2_lq0.1_hex0.1_qw0_h128_e100_s0.json
[run] ogbg-molsider view=mf compute=rrog-act mode=stream T=1 ns=3 seed=0 device=cuda:0
python3 rrog/train_ogb_graphprop.py --dataset ogbg-molsider --view mf --compute rrog-act --T 1 --n_sup 3 --hidden 128 --bs 128 --epochs 100 --eval_every 10 --agg_layers 5 --compute_layers 2 --seed 0 --lam_q 0.1 --halt_max_steps 8 --halt_min_steps 2 --halt_target loss --halt_loss_threshold 0.2 --halt_exploration_prob 0.1 --act_train_mode stream --q_warmup_epochs 0 --device cuda:0 --num_workers 0
ep10 val_rocauc=0.52188 val_adapt_rocauc=0.52188 adapt_steps=8.00 halt=0.12 train_steps=4.49
ep20 val_rocauc=0.55853 val_adapt_rocauc=0.55853 adapt_steps=8.00 halt=0.12 train_steps=4.50
ep30 val_rocauc=0.55126 val_adapt_rocauc=0.55126 adapt_steps=8.00 halt=0.12 train_steps=4.50
ep40 val_rocauc=0.55645 val_adapt_rocauc=0.55527 adapt_steps=7.83 halt=0.12 train_steps=4.50
ep50 val_rocauc=0.55799 val_adapt_rocauc=0.55799 adapt_steps=8.00 halt=0.12 train_steps=4.50
ep60 val_rocauc=0.59604 val_adapt_rocauc=0.59534 adapt_steps=7.92 halt=0.12 train_steps=4.49
ep70 val_rocauc=0.57920 val_adapt_rocauc=0.57921 adapt_steps=7.96 halt=0.12 train_steps=4.50
ep80 val_rocauc=0.63001 val_adapt_rocauc=0.62920 adapt_steps=7.96 halt=0.12 train_steps=4.49
ep90 val_rocauc=0.61962 val_adapt_rocauc=0.61885 adapt_steps=7.95 halt=0.12 train_steps=4.50
ep100 val_rocauc=0.61522 val_adapt_rocauc=0.61430 adapt_steps=7.93 halt=0.12 train_steps=4.47
[ogbg-molsider_mf_rrog-act_T1_ns3_stream_hm8_hmin2_loss0.2_lq0.1_hex0.1_qw0_h128_e100_s0] best_ep=80 val={'rocauc': 0.630007512812611} test={'rocauc': 0.5843445908586726} adaptive={'rocauc': 0.5856297203566897} steps=7.8671328671328675
  wrote /orion/u/oscarwan/rrog-gnn-runner/runs/ogbg-molsider_mf_rrog-act_T1_ns3_stream_hm8_hmin2_loss0.2_lq0.1_hex0.1_qw0_h128_e100_s0.json
[run] ogbg-molsider view=appnp compute=rrog-act mode=stream T=1 ns=3 seed=0 device=cuda:0
python3 rrog/train_ogb_graphprop.py --dataset ogbg-molsider --view appnp --compute rrog-act --T 1 --n_sup 3 --hidden 128 --bs 128 --epochs 100 --eval_every 10 --agg_layers 5 --compute_layers 2 --seed 0 --lam_q 0.1 --halt_max_steps 8 --halt_min_steps 2 --halt_target loss --halt_loss_threshold 0.2 --halt_exploration_prob 0.1 --act_train_mode stream --q_warmup_epochs 0 --device cuda:0 --num_workers 0
ep10 val_rocauc=0.52120 val_adapt_rocauc=0.52120 adapt_steps=8.00 halt=0.12 train_steps=4.50
ep20 val_rocauc=0.54017 val_adapt_rocauc=0.53843 adapt_steps=7.87 halt=0.12 train_steps=4.49
ep30 val_rocauc=0.53233 val_adapt_rocauc=0.53233 adapt_steps=8.00 halt=0.12 train_steps=4.48
ep40 val_rocauc=0.53931 val_adapt_rocauc=0.53735 adapt_steps=7.72 halt=0.13 train_steps=4.46
ep50 val_rocauc=0.56152 val_adapt_rocauc=0.56188 adapt_steps=7.75 halt=0.13 train_steps=4.47
ep60 val_rocauc=0.57531 val_adapt_rocauc=0.57388 adapt_steps=7.59 halt=0.13 train_steps=4.41
ep70 val_rocauc=0.55453 val_adapt_rocauc=0.56066 adapt_steps=7.09 halt=0.13 train_steps=4.44
ep80 val_rocauc=0.58010 val_adapt_rocauc=0.57429 adapt_steps=7.18 halt=0.13 train_steps=4.45
ep90 val_rocauc=0.56976 val_adapt_rocauc=0.56750 adapt_steps=7.34 halt=0.13 train_steps=4.44
ep100 val_rocauc=0.57081 val_adapt_rocauc=0.56938 adapt_steps=7.11 halt=0.13 train_steps=4.39
[ogbg-molsider_appnp_rrog-act_T1_ns3_stream_hm8_hmin2_loss0.2_lq0.1_hex0.1_qw0_h128_e100_s0] best_ep=80 val={'rocauc': 0.58009596629467} test={'rocauc': 0.6009364115000575} adaptive={'rocauc': 0.5959003553252203} steps=7.27972027972028
  wrote /orion/u/oscarwan/rrog-gnn-runner/runs/ogbg-molsider_appnp_rrog-act_T1_ns3_stream_hm8_hmin2_loss0.2_lq0.1_hex0.1_qw0_h128_e100_s0.json