[run] ogbg-molhiv view=gin compute=rrog-act mode=stream T=1 ns=3 seed=0 device=cuda:0
python3 rrog/train_ogb_graphprop.py --dataset ogbg-molhiv --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=0.50465 val_adapt_rocauc=0.51605 adapt_steps=2.17 halt=0.44 train_steps=1.86
ep20 val_rocauc=0.68965 val_adapt_rocauc=0.70338 adapt_steps=2.05 halt=0.43 train_steps=1.93
ep30 val_rocauc=0.74705 val_adapt_rocauc=0.71674 adapt_steps=2.16 halt=0.44 train_steps=1.88
ep40 val_rocauc=0.66882 val_adapt_rocauc=0.69730 adapt_steps=2.23 halt=0.44 train_steps=1.90
ep50 val_rocauc=0.69645 val_adapt_rocauc=0.75311 adapt_steps=2.17 halt=0.44 train_steps=1.88
ep60 val_rocauc=0.72560 val_adapt_rocauc=0.75246 adapt_steps=2.12 halt=0.44 train_steps=1.87
ep70 val_rocauc=0.73678 val_adapt_rocauc=0.76610 adapt_steps=2.13 halt=0.44 train_steps=1.86
ep80 val_rocauc=0.73638 val_adapt_rocauc=0.77730 adapt_steps=2.07 halt=0.44 train_steps=1.85
ep90 val_rocauc=0.76228 val_adapt_rocauc=0.78163 adapt_steps=2.11 halt=0.44 train_steps=1.85
ep100 val_rocauc=0.75586 val_adapt_rocauc=0.78034 adapt_steps=2.10 halt=0.44 train_steps=1.85
[ogbg-molhiv_gin_rrog-act_T1_ns3_stream_hm8_hmin2_loss0.2_lq0.1_hex0.1_qw0_h128_e100_s0] best_ep=90 val={'rocauc': 0.7622844405251813} test={'rocauc': 0.728463276617934} adaptive={'rocauc': 0.7414685490256668} steps=2.1835643082907854
  wrote /orion/u/oscarwan/rrog-gnn-runner/runs/ogbg-molhiv_gin_rrog-act_T1_ns3_stream_hm8_hmin2_loss0.2_lq0.1_hex0.1_qw0_h128_e100_s0.json
[run] ogbg-molhiv view=gine compute=rrog-act mode=stream T=1 ns=3 seed=0 device=cuda:0
python3 rrog/train_ogb_graphprop.py --dataset ogbg-molhiv --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=0.69659 val_adapt_rocauc=0.67019 adapt_steps=2.27 halt=0.43 train_steps=1.91
ep20 val_rocauc=0.71128 val_adapt_rocauc=0.74182 adapt_steps=2.25 halt=0.44 train_steps=1.90
ep30 val_rocauc=0.71784 val_adapt_rocauc=0.71432 adapt_steps=2.09 halt=0.43 train_steps=1.92
ep40 val_rocauc=0.57809 val_adapt_rocauc=0.61863 adapt_steps=2.07 halt=0.43 train_steps=1.95
ep50 val_rocauc=0.71898 val_adapt_rocauc=0.72970 adapt_steps=2.15 halt=0.44 train_steps=1.90
ep60 val_rocauc=0.74272 val_adapt_rocauc=0.75436 adapt_steps=2.17 halt=0.44 train_steps=1.91
ep70 val_rocauc=0.64453 val_adapt_rocauc=0.67305 adapt_steps=2.09 halt=0.43 train_steps=1.92
ep80 val_rocauc=0.66148 val_adapt_rocauc=0.71639 adapt_steps=2.11 halt=0.44 train_steps=1.89
ep90 val_rocauc=0.68795 val_adapt_rocauc=0.73859 adapt_steps=2.10 halt=0.44 train_steps=1.89
ep100 val_rocauc=0.69479 val_adapt_rocauc=0.73030 adapt_steps=2.10 halt=0.44 train_steps=1.88
[ogbg-molhiv_gine_rrog-act_T1_ns3_stream_hm8_hmin2_loss0.2_lq0.1_hex0.1_qw0_h128_e100_s0] best_ep=60 val={'rocauc': 0.7427218058005095} test={'rocauc': 0.7216728789663763} adaptive={'rocauc': 0.7219567778442999} steps=2.1913445173839046
  wrote /orion/u/oscarwan/rrog-gnn-runner/runs/ogbg-molhiv_gine_rrog-act_T1_ns3_stream_hm8_hmin2_loss0.2_lq0.1_hex0.1_qw0_h128_e100_s0.json
[run] ogbg-molhiv view=gcn compute=rrog-act mode=stream T=1 ns=3 seed=0 device=cuda:0
python3 rrog/train_ogb_graphprop.py --dataset ogbg-molhiv --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=0.65285 val_adapt_rocauc=0.67983 adapt_steps=2.17 halt=0.43 train_steps=1.95
ep20 val_rocauc=0.63964 val_adapt_rocauc=0.67770 adapt_steps=2.12 halt=0.43 train_steps=1.91
ep30 val_rocauc=0.70439 val_adapt_rocauc=0.72607 adapt_steps=2.28 halt=0.43 train_steps=1.93
ep40 val_rocauc=0.67857 val_adapt_rocauc=0.67646 adapt_steps=2.09 halt=0.44 train_steps=1.90
ep50 val_rocauc=0.66299 val_adapt_rocauc=0.70493 adapt_steps=2.10 halt=0.44 train_steps=1.89
ep60 val_rocauc=0.72309 val_adapt_rocauc=0.75250 adapt_steps=2.10 halt=0.43 train_steps=1.91
ep70 val_rocauc=0.69147 val_adapt_rocauc=0.71552 adapt_steps=2.06 halt=0.44 train_steps=1.88
ep80 val_rocauc=0.75410 val_adapt_rocauc=0.76395 adapt_steps=2.12 halt=0.44 train_steps=1.88
ep90 val_rocauc=0.76238 val_adapt_rocauc=0.75313 adapt_steps=2.09 halt=0.44 train_steps=1.85
ep100 val_rocauc=0.76521 val_adapt_rocauc=0.75985 adapt_steps=2.09 halt=0.44 train_steps=1.87
[ogbg-molhiv_gcn_rrog-act_T1_ns3_stream_hm8_hmin2_loss0.2_lq0.1_hex0.1_qw0_h128_e100_s0] best_ep=100 val={'rocauc': 0.76521164021164} test={'rocauc': 0.7279630738330211} adaptive={'rocauc': 0.7195040460418316} steps=2.1412594213469487
  wrote /orion/u/oscarwan/rrog-gnn-runner/runs/ogbg-molhiv_gcn_rrog-act_T1_ns3_stream_hm8_hmin2_loss0.2_lq0.1_hex0.1_qw0_h128_e100_s0.json
[run] ogbg-molhiv view=graphsage compute=rrog-act mode=stream T=1 ns=3 seed=0 device=cuda:0
python3 rrog/train_ogb_graphprop.py --dataset ogbg-molhiv --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=0.60161 val_adapt_rocauc=0.64669 adapt_steps=2.08 halt=0.43 train_steps=1.92
ep20 val_rocauc=0.71865 val_adapt_rocauc=0.72325 adapt_steps=2.51 halt=0.43 train_steps=1.95
ep30 val_rocauc=0.72758 val_adapt_rocauc=0.72496 adapt_steps=2.13 halt=0.43 train_steps=1.95
ep40 val_rocauc=0.75529 val_adapt_rocauc=0.73154 adapt_steps=2.08 halt=0.43 train_steps=1.93
ep50 val_rocauc=0.74199 val_adapt_rocauc=0.74141 adapt_steps=2.12 halt=0.43 train_steps=1.96
ep60 val_rocauc=0.70099 val_adapt_rocauc=0.73010 adapt_steps=2.10 halt=0.43 train_steps=1.95
ep70 val_rocauc=0.76185 val_adapt_rocauc=0.77202 adapt_steps=2.10 halt=0.44 train_steps=1.91
ep80 val_rocauc=0.77242 val_adapt_rocauc=0.76566 adapt_steps=2.14 halt=0.44 train_steps=1.91
ep90 val_rocauc=0.75081 val_adapt_rocauc=0.75828 adapt_steps=2.14 halt=0.44 train_steps=1.90
ep100 val_rocauc=0.74508 val_adapt_rocauc=0.74839 adapt_steps=2.13 halt=0.44 train_steps=1.90
[ogbg-molhiv_graphsage_rrog-act_T1_ns3_stream_hm8_hmin2_loss0.2_lq0.1_hex0.1_qw0_h128_e100_s0] best_ep=80 val={'rocauc': 0.7724224720752498} test={'rocauc': 0.7540721141775624} adaptive={'rocauc': 0.7493028061569362} steps=2.225382932166302
  wrote /orion/u/oscarwan/rrog-gnn-runner/runs/ogbg-molhiv_graphsage_rrog-act_T1_ns3_stream_hm8_hmin2_loss0.2_lq0.1_hex0.1_qw0_h128_e100_s0.json
[run] ogbg-molhiv view=gatv2 compute=rrog-act mode=stream T=1 ns=3 seed=0 device=cuda:0
python3 rrog/train_ogb_graphprop.py --dataset ogbg-molhiv --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=0.63791 val_adapt_rocauc=0.65941 adapt_steps=2.16 halt=0.44 train_steps=1.86
ep20 val_rocauc=0.65958 val_adapt_rocauc=0.68114 adapt_steps=2.14 halt=0.43 train_steps=1.98
ep30 val_rocauc=0.70813 val_adapt_rocauc=0.71648 adapt_steps=2.10 halt=0.44 train_steps=1.91
ep40 val_rocauc=0.71160 val_adapt_rocauc=0.71489 adapt_steps=2.12 halt=0.43 train_steps=1.92
ep50 val_rocauc=0.74629 val_adapt_rocauc=0.72922 adapt_steps=2.12 halt=0.43 train_steps=1.93
ep60 val_rocauc=0.77221 val_adapt_rocauc=0.77362 adapt_steps=2.10 halt=0.44 train_steps=1.91
ep70 val_rocauc=0.73550 val_adapt_rocauc=0.75380 adapt_steps=2.22 halt=0.43 train_steps=1.96
ep80 val_rocauc=0.75176 val_adapt_rocauc=0.76963 adapt_steps=2.17 halt=0.43 train_steps=1.95
ep90 val_rocauc=0.77064 val_adapt_rocauc=0.77482 adapt_steps=2.17 halt=0.44 train_steps=1.89
ep100 val_rocauc=0.77092 val_adapt_rocauc=0.77462 adapt_steps=2.15 halt=0.44 train_steps=1.89
[ogbg-molhiv_gatv2_rrog-act_T1_ns3_stream_hm8_hmin2_loss0.2_lq0.1_hex0.1_qw0_h128_e100_s0] best_ep=60 val={'rocauc': 0.7722111992945326} test={'rocauc': 0.7005330346279379} adaptive={'rocauc': 0.7209988605419185} steps=2.161196207148067
  wrote /orion/u/oscarwan/rrog-gnn-runner/runs/ogbg-molhiv_gatv2_rrog-act_T1_ns3_stream_hm8_hmin2_loss0.2_lq0.1_hex0.1_qw0_h128_e100_s0.json
[run] ogbg-molhiv view=graphconv compute=rrog-act mode=stream T=1 ns=3 seed=0 device=cuda:0
python3 rrog/train_ogb_graphprop.py --dataset ogbg-molhiv --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=0.62286 val_adapt_rocauc=0.63631 adapt_steps=2.12 halt=0.43 train_steps=1.92
ep20 val_rocauc=0.50681 val_adapt_rocauc=0.62452 adapt_steps=2.03 halt=0.44 train_steps=1.88
ep30 val_rocauc=0.71981 val_adapt_rocauc=0.71587 adapt_steps=2.12 halt=0.44 train_steps=1.92
ep40 val_rocauc=0.63676 val_adapt_rocauc=0.69827 adapt_steps=2.10 halt=0.44 train_steps=1.89
ep50 val_rocauc=0.71987 val_adapt_rocauc=0.71579 adapt_steps=2.10 halt=0.43 train_steps=1.95
ep60 val_rocauc=0.77652 val_adapt_rocauc=0.77421 adapt_steps=2.13 halt=0.43 train_steps=1.94
ep70 val_rocauc=0.73578 val_adapt_rocauc=0.73905 adapt_steps=2.14 halt=0.44 train_steps=1.90
ep80 val_rocauc=0.77909 val_adapt_rocauc=0.77380 adapt_steps=2.21 halt=0.43 train_steps=1.93
ep90 val_rocauc=0.77284 val_adapt_rocauc=0.76992 adapt_steps=2.20 halt=0.43 train_steps=1.95
ep100 val_rocauc=0.75856 val_adapt_rocauc=0.75794 adapt_steps=2.19 halt=0.43 train_steps=1.95
[ogbg-molhiv_graphconv_rrog-act_T1_ns3_stream_hm8_hmin2_loss0.2_lq0.1_hex0.1_qw0_h128_e100_s0] best_ep=80 val={'rocauc': 0.7790944052518126} test={'rocauc': 0.7510651036134339} adaptive={'rocauc': 0.7493443287819387} steps=2.2287867736445417
  wrote /orion/u/oscarwan/rrog-gnn-runner/runs/ogbg-molhiv_graphconv_rrog-act_T1_ns3_stream_hm8_hmin2_loss0.2_lq0.1_hex0.1_qw0_h128_e100_s0.json
[run] ogbg-molhiv view=transformer compute=rrog-act mode=stream T=1 ns=3 seed=0 device=cuda:0
python3 rrog/train_ogb_graphprop.py --dataset ogbg-molhiv --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=0.69482 val_adapt_rocauc=0.69337 adapt_steps=2.09 halt=0.43 train_steps=1.95
ep20 val_rocauc=0.69048 val_adapt_rocauc=0.68828 adapt_steps=2.11 halt=0.43 train_steps=1.92
ep30 val_rocauc=0.70734 val_adapt_rocauc=0.68263 adapt_steps=2.08 halt=0.43 train_steps=1.96
ep40 val_rocauc=0.75896 val_adapt_rocauc=0.75541 adapt_steps=2.17 halt=0.43 train_steps=1.96
ep50 val_rocauc=0.63094 val_adapt_rocauc=0.71781 adapt_steps=2.15 halt=0.43 train_steps=2.00
ep60 val_rocauc=0.78134 val_adapt_rocauc=0.78253 adapt_steps=2.15 halt=0.43 train_steps=1.95
ep70 val_rocauc=0.74767 val_adapt_rocauc=0.77658 adapt_steps=2.14 halt=0.43 train_steps=1.93
ep80 val_rocauc=0.75221 val_adapt_rocauc=0.76723 adapt_steps=2.19 halt=0.43 train_steps=1.98
ep90 val_rocauc=0.73516 val_adapt_rocauc=0.74942 adapt_steps=2.14 halt=0.43 train_steps=1.92
ep100 val_rocauc=0.74673 val_adapt_rocauc=0.75054 adapt_steps=2.13 halt=0.44 train_steps=1.91
[ogbg-molhiv_transformer_rrog-act_T1_ns3_stream_hm8_hmin2_loss0.2_lq0.1_hex0.1_qw0_h128_e100_s0] best_ep=60 val={'rocauc': 0.7813418577307466} test={'rocauc': 0.7294279534174086} adaptive={'rocauc': 0.7396338283860252} steps=2.215171407731583
  wrote /orion/u/oscarwan/rrog-gnn-runner/runs/ogbg-molhiv_transformer_rrog-act_T1_ns3_stream_hm8_hmin2_loss0.2_lq0.1_hex0.1_qw0_h128_e100_s0.json
[run] ogbg-molhiv view=pna compute=rrog-act mode=stream T=1 ns=3 seed=0 device=cuda:0
python3 rrog/train_ogb_graphprop.py --dataset ogbg-molhiv --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=0.65392 val_adapt_rocauc=0.65972 adapt_steps=2.26 halt=0.45 train_steps=1.76
/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=0.70167 val_adapt_rocauc=0.73359 adapt_steps=2.14 halt=0.45 train_steps=1.83
/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=0.57289 val_adapt_rocauc=0.60013 adapt_steps=2.02 halt=0.45 train_steps=1.83
/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=0.74876 val_adapt_rocauc=0.72038 adapt_steps=2.03 halt=0.44 train_steps=1.89
/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=0.69108 val_adapt_rocauc=0.72229 adapt_steps=2.11 halt=0.43 train_steps=1.92
/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=0.69969 val_adapt_rocauc=0.70883 adapt_steps=2.12 halt=0.44 train_steps=1.89
/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=0.72681 val_adapt_rocauc=0.77015 adapt_steps=2.16 halt=0.43 train_steps=1.93
/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=0.74562 val_adapt_rocauc=0.77410 adapt_steps=2.13 halt=0.43 train_steps=1.92
/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=0.71370 val_adapt_rocauc=0.78801 adapt_steps=2.16 halt=0.44 train_steps=1.90
/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=0.71775 val_adapt_rocauc=0.78617 adapt_steps=2.14 halt=0.44 train_steps=1.89
[ogbg-molhiv_pna_rrog-act_T1_ns3_stream_hm8_hmin2_loss0.2_lq0.1_hex0.1_qw0_h128_e100_s0] best_ep=40 val={'rocauc': 0.7487599206349206} test={'rocauc': 0.6730373317368046} adaptive={'rocauc': 0.6877382722725429} steps=2.0588378312667155
  wrote /orion/u/oscarwan/rrog-gnn-runner/runs/ogbg-molhiv_pna_rrog-act_T1_ns3_stream_hm8_hmin2_loss0.2_lq0.1_hex0.1_qw0_h128_e100_s0.json
[run] ogbg-molhiv view=gen compute=rrog-act mode=stream T=1 ns=3 seed=0 device=cuda:0
python3 rrog/train_ogb_graphprop.py --dataset ogbg-molhiv --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=0.58244 val_adapt_rocauc=0.62459 adapt_steps=2.17 halt=0.43 train_steps=1.95
ep20 val_rocauc=0.65886 val_adapt_rocauc=0.66235 adapt_steps=2.06 halt=0.44 train_steps=1.89
ep30 val_rocauc=0.69707 val_adapt_rocauc=0.69637 adapt_steps=2.06 halt=0.44 train_steps=1.92
ep40 val_rocauc=0.73515 val_adapt_rocauc=0.77200 adapt_steps=2.17 halt=0.44 train_steps=1.91
ep50 val_rocauc=0.72530 val_adapt_rocauc=0.75137 adapt_steps=2.12 halt=0.43 train_steps=1.97
ep60 val_rocauc=0.69888 val_adapt_rocauc=0.73379 adapt_steps=2.09 halt=0.43 train_steps=1.98
ep70 val_rocauc=0.71051 val_adapt_rocauc=0.74897 adapt_steps=2.12 halt=0.43 train_steps=1.94
ep80 val_rocauc=0.76901 val_adapt_rocauc=0.77657 adapt_steps=2.10 halt=0.43 train_steps=1.93
ep90 val_rocauc=0.73056 val_adapt_rocauc=0.76530 adapt_steps=2.11 halt=0.44 train_steps=1.92
ep100 val_rocauc=0.73376 val_adapt_rocauc=0.75580 adapt_steps=2.13 halt=0.43 train_steps=1.93
[ogbg-molhiv_gen_rrog-act_T1_ns3_stream_hm8_hmin2_loss0.2_lq0.1_hex0.1_qw0_h128_e100_s0] best_ep=80 val={'rocauc': 0.769011488340192} test={'rocauc': 0.7434075590490352} adaptive={'rocauc': 0.7426736707931786} steps=2.178215414539266
  wrote /orion/u/oscarwan/rrog-gnn-runner/runs/ogbg-molhiv_gen_rrog-act_T1_ns3_stream_hm8_hmin2_loss0.2_lq0.1_hex0.1_qw0_h128_e100_s0.json
[run] ogbg-molhiv view=film compute=rrog-act mode=stream T=1 ns=3 seed=0 device=cuda:0
python3 rrog/train_ogb_graphprop.py --dataset ogbg-molhiv --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=0.60803 val_adapt_rocauc=0.69045 adapt_steps=2.06 halt=0.47 train_steps=1.67
ep20 val_rocauc=0.64308 val_adapt_rocauc=0.78621 adapt_steps=2.01 halt=0.45 train_steps=1.79
ep30 val_rocauc=0.74069 val_adapt_rocauc=0.75513 adapt_steps=2.21 halt=0.45 train_steps=1.83
ep40 val_rocauc=0.75046 val_adapt_rocauc=0.78035 adapt_steps=2.23 halt=0.43 train_steps=1.93
ep50 val_rocauc=0.71486 val_adapt_rocauc=0.75589 adapt_steps=2.34 halt=0.43 train_steps=2.01
ep60 val_rocauc=0.77074 val_adapt_rocauc=0.80191 adapt_steps=2.10 halt=0.43 train_steps=1.99
ep70 val_rocauc=0.78228 val_adapt_rocauc=0.80466 adapt_steps=2.21 halt=0.43 train_steps=1.97
ep80 val_rocauc=0.81468 val_adapt_rocauc=0.81506 adapt_steps=2.13 halt=0.43 train_steps=1.96
ep90 val_rocauc=0.81502 val_adapt_rocauc=0.81015 adapt_steps=2.14 halt=0.43 train_steps=1.95
ep100 val_rocauc=0.81203 val_adapt_rocauc=0.80753 adapt_steps=2.11 halt=0.43 train_steps=1.95
[ogbg-molhiv_film_rrog-act_T1_ns3_stream_hm8_hmin2_loss0.2_lq0.1_hex0.1_qw0_h128_e100_s0] best_ep=90 val={'rocauc': 0.8150199637468156} test={'rocauc': 0.7415303501419496} adaptive={'rocauc': 0.7291546766063463} steps=2.2144420131291027
  wrote /orion/u/oscarwan/rrog-gnn-runner/runs/ogbg-molhiv_film_rrog-act_T1_ns3_stream_hm8_hmin2_loss0.2_lq0.1_hex0.1_qw0_h128_e100_s0.json
[run] ogbg-molhiv view=resgated compute=rrog-act mode=stream T=1 ns=3 seed=0 device=cuda:0
python3 rrog/train_ogb_graphprop.py --dataset ogbg-molhiv --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=0.57563 val_adapt_rocauc=0.57248 adapt_steps=2.08 halt=0.43 train_steps=1.96
ep20 val_rocauc=0.68901 val_adapt_rocauc=0.69659 adapt_steps=2.07 halt=0.43 train_steps=1.97
ep30 val_rocauc=0.72785 val_adapt_rocauc=0.73945 adapt_steps=2.17 halt=0.43 train_steps=1.95
ep40 val_rocauc=0.80307 val_adapt_rocauc=0.79528 adapt_steps=2.10 halt=0.43 train_steps=1.93
ep50 val_rocauc=0.74046 val_adapt_rocauc=0.74882 adapt_steps=2.15 halt=0.43 train_steps=1.96
ep60 val_rocauc=0.76188 val_adapt_rocauc=0.76877 adapt_steps=2.13 halt=0.43 train_steps=1.95
ep70 val_rocauc=0.74584 val_adapt_rocauc=0.74880 adapt_steps=2.14 halt=0.43 train_steps=1.99
ep80 val_rocauc=0.72576 val_adapt_rocauc=0.72937 adapt_steps=2.09 halt=0.43 train_steps=1.95
ep90 val_rocauc=0.72962 val_adapt_rocauc=0.72854 adapt_steps=2.17 halt=0.43 train_steps=1.98
ep100 val_rocauc=0.73262 val_adapt_rocauc=0.73243 adapt_steps=2.13 halt=0.43 train_steps=1.97
[ogbg-molhiv_resgated_rrog-act_T1_ns3_stream_hm8_hmin2_loss0.2_lq0.1_hex0.1_qw0_h128_e100_s0] best_ep=40 val={'rocauc': 0.8030723349010386} test={'rocauc': 0.7502983835145524} adaptive={'rocauc': 0.7566658297765504} steps=2.2205203014831025
  wrote /orion/u/oscarwan/rrog-gnn-runner/runs/ogbg-molhiv_resgated_rrog-act_T1_ns3_stream_hm8_hmin2_loss0.2_lq0.1_hex0.1_qw0_h128_e100_s0.json
[run] ogbg-molhiv view=tag compute=rrog-act mode=stream T=1 ns=3 seed=0 device=cuda:0
python3 rrog/train_ogb_graphprop.py --dataset ogbg-molhiv --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=0.55912 val_adapt_rocauc=0.62342 adapt_steps=2.04 halt=0.43 train_steps=1.90
ep20 val_rocauc=0.68402 val_adapt_rocauc=0.67609 adapt_steps=2.15 halt=0.44 train_steps=1.84
ep30 val_rocauc=0.70234 val_adapt_rocauc=0.71080 adapt_steps=2.18 halt=0.44 train_steps=1.90
ep40 val_rocauc=0.71746 val_adapt_rocauc=0.70275 adapt_steps=2.42 halt=0.44 train_steps=1.86
ep50 val_rocauc=0.74377 val_adapt_rocauc=0.74631 adapt_steps=2.14 halt=0.44 train_steps=1.90
ep60 val_rocauc=0.68302 val_adapt_rocauc=0.74882 adapt_steps=2.18 halt=0.44 train_steps=1.91
ep70 val_rocauc=0.70337 val_adapt_rocauc=0.73207 adapt_steps=2.09 halt=0.44 train_steps=1.90
ep80 val_rocauc=0.75679 val_adapt_rocauc=0.76447 adapt_steps=2.14 halt=0.44 train_steps=1.90
ep90 val_rocauc=0.74719 val_adapt_rocauc=0.76515 adapt_steps=2.12 halt=0.44 train_steps=1.88
ep100 val_rocauc=0.74130 val_adapt_rocauc=0.75597 adapt_steps=2.12 halt=0.44 train_steps=1.89
[ogbg-molhiv_tag_rrog-act_T1_ns3_stream_hm8_hmin2_loss0.2_lq0.1_hex0.1_qw0_h128_e100_s0] best_ep=80 val={'rocauc': 0.7567852243778168} test={'rocauc': 0.7480619556190734} adaptive={'rocauc': 0.7477104617702157} steps=2.225869195234622
  wrote /orion/u/oscarwan/rrog-gnn-runner/runs/ogbg-molhiv_tag_rrog-act_T1_ns3_stream_hm8_hmin2_loss0.2_lq0.1_hex0.1_qw0_h128_e100_s0.json
[run] ogbg-molhiv view=sgc compute=rrog-act mode=stream T=1 ns=3 seed=0 device=cuda:0
python3 rrog/train_ogb_graphprop.py --dataset ogbg-molhiv --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=0.62000 val_adapt_rocauc=0.63880 adapt_steps=2.22 halt=0.44 train_steps=1.89
ep20 val_rocauc=0.59820 val_adapt_rocauc=0.61098 adapt_steps=2.16 halt=0.44 train_steps=1.89
ep30 val_rocauc=0.67764 val_adapt_rocauc=0.68890 adapt_steps=2.08 halt=0.44 train_steps=1.86
ep40 val_rocauc=0.66809 val_adapt_rocauc=0.66128 adapt_steps=2.09 halt=0.44 train_steps=1.89
ep50 val_rocauc=0.73528 val_adapt_rocauc=0.71478 adapt_steps=2.09 halt=0.44 train_steps=1.91
ep60 val_rocauc=0.69053 val_adapt_rocauc=0.68013 adapt_steps=2.16 halt=0.44 train_steps=1.89
ep70 val_rocauc=0.68528 val_adapt_rocauc=0.72841 adapt_steps=2.10 halt=0.44 train_steps=1.89
ep80 val_rocauc=0.68138 val_adapt_rocauc=0.69448 adapt_steps=2.11 halt=0.44 train_steps=1.88
ep90 val_rocauc=0.68754 val_adapt_rocauc=0.69915 adapt_steps=2.12 halt=0.43 train_steps=1.93
ep100 val_rocauc=0.69254 val_adapt_rocauc=0.70377 adapt_steps=2.12 halt=0.44 train_steps=1.88
[ogbg-molhiv_sgc_rrog-act_T1_ns3_stream_hm8_hmin2_loss0.2_lq0.1_hex0.1_qw0_h128_e100_s0] best_ep=50 val={'rocauc': 0.7352782676856752} test={'rocauc': 0.6961702620753587} adaptive={'rocauc': 0.7016995306977732} steps=2.0795040116703136
  wrote /orion/u/oscarwan/rrog-gnn-runner/runs/ogbg-molhiv_sgc_rrog-act_T1_ns3_stream_hm8_hmin2_loss0.2_lq0.1_hex0.1_qw0_h128_e100_s0.json
[run] ogbg-molhiv view=cheb compute=rrog-act mode=stream T=1 ns=3 seed=0 device=cuda:0
python3 rrog/train_ogb_graphprop.py --dataset ogbg-molhiv --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=0.63285 val_adapt_rocauc=0.63797 adapt_steps=2.09 halt=0.43 train_steps=1.94
ep20 val_rocauc=0.59546 val_adapt_rocauc=0.62621 adapt_steps=2.09 halt=0.43 train_steps=1.94
ep30 val_rocauc=0.64157 val_adapt_rocauc=0.64537 adapt_steps=2.07 halt=0.43 train_steps=1.95
ep40 val_rocauc=0.78253 val_adapt_rocauc=0.79697 adapt_steps=2.21 halt=0.43 train_steps=1.94
ep50 val_rocauc=0.78099 val_adapt_rocauc=0.78545 adapt_steps=2.23 halt=0.42 train_steps=2.02
ep60 val_rocauc=0.75496 val_adapt_rocauc=0.75580 adapt_steps=2.16 halt=0.43 train_steps=1.92
ep70 val_rocauc=0.76923 val_adapt_rocauc=0.76459 adapt_steps=2.17 halt=0.43 train_steps=1.94
ep80 val_rocauc=0.75888 val_adapt_rocauc=0.75674 adapt_steps=2.10 halt=0.45 train_steps=1.83
ep90 val_rocauc=0.73937 val_adapt_rocauc=0.73410 adapt_steps=2.10 halt=0.45 train_steps=1.77
ep100 val_rocauc=0.71941 val_adapt_rocauc=0.71715 adapt_steps=2.07 halt=0.46 train_steps=1.74
[ogbg-molhiv_cheb_rrog-act_T1_ns3_stream_hm8_hmin2_loss0.2_lq0.1_hex0.1_qw0_h128_e100_s0] best_ep=40 val={'rocauc': 0.7825329463060945} test={'rocauc': 0.7205932907163135} adaptive={'rocauc': 0.7218775951640628} steps=2.2939460247994163
  wrote /orion/u/oscarwan/rrog-gnn-runner/runs/ogbg-molhiv_cheb_rrog-act_T1_ns3_stream_hm8_hmin2_loss0.2_lq0.1_hex0.1_qw0_h128_e100_s0.json
[run] ogbg-molhiv view=arma compute=rrog-act mode=stream T=1 ns=3 seed=0 device=cuda:0
python3 rrog/train_ogb_graphprop.py --dataset ogbg-molhiv --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=0.58405 val_adapt_rocauc=0.62652 adapt_steps=2.13 halt=0.44 train_steps=1.87
ep20 val_rocauc=0.63653 val_adapt_rocauc=0.65469 adapt_steps=2.04 halt=0.44 train_steps=1.83
ep30 val_rocauc=0.68206 val_adapt_rocauc=0.66472 adapt_steps=2.09 halt=0.44 train_steps=1.85
ep40 val_rocauc=0.73261 val_adapt_rocauc=0.71919 adapt_steps=2.08 halt=0.44 train_steps=1.82
ep50 val_rocauc=0.76797 val_adapt_rocauc=0.79653 adapt_steps=2.13 halt=0.44 train_steps=1.86
ep60 val_rocauc=0.74831 val_adapt_rocauc=0.72643 adapt_steps=2.04 halt=0.45 train_steps=1.77
ep70 val_rocauc=0.73358 val_adapt_rocauc=0.77502 adapt_steps=2.03 halt=0.46 train_steps=1.73
ep80 val_rocauc=0.77783 val_adapt_rocauc=0.77223 adapt_steps=2.06 halt=0.45 train_steps=1.75
ep90 val_rocauc=0.78442 val_adapt_rocauc=0.76721 adapt_steps=2.05 halt=0.46 train_steps=1.71
ep100 val_rocauc=0.78612 val_adapt_rocauc=0.76507 adapt_steps=2.06 halt=0.46 train_steps=1.73
[ogbg-molhiv_arma_rrog-act_T1_ns3_stream_hm8_hmin2_loss0.2_lq0.1_hex0.1_qw0_h128_e100_s0] best_ep=100 val={'rocauc': 0.7861153978052127} test={'rocauc': 0.7306939106587612} adaptive={'rocauc': 0.7378782904266208} steps=2.085825431558473
  wrote /orion/u/oscarwan/rrog-gnn-runner/runs/ogbg-molhiv_arma_rrog-act_T1_ns3_stream_hm8_hmin2_loss0.2_lq0.1_hex0.1_qw0_h128_e100_s0.json
[run] ogbg-molhiv view=mf compute=rrog-act mode=stream T=1 ns=3 seed=0 device=cuda:0
python3 rrog/train_ogb_graphprop.py --dataset ogbg-molhiv --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=0.65109 val_adapt_rocauc=0.65071 adapt_steps=2.09 halt=0.43 train_steps=1.91
ep20 val_rocauc=0.66377 val_adapt_rocauc=0.70133 adapt_steps=2.15 halt=0.43 train_steps=1.92
ep30 val_rocauc=0.69912 val_adapt_rocauc=0.72873 adapt_steps=2.15 halt=0.43 train_steps=1.91
ep40 val_rocauc=0.75265 val_adapt_rocauc=0.77675 adapt_steps=2.15 halt=0.43 train_steps=1.95
ep50 val_rocauc=0.71979 val_adapt_rocauc=0.76668 adapt_steps=2.10 halt=0.45 train_steps=1.82
ep60 val_rocauc=0.78688 val_adapt_rocauc=0.78537 adapt_steps=2.27 halt=0.44 train_steps=1.89
ep70 val_rocauc=0.80465 val_adapt_rocauc=0.78273 adapt_steps=2.15 halt=0.43 train_steps=1.93
ep80 val_rocauc=0.77804 val_adapt_rocauc=0.78100 adapt_steps=2.15 halt=0.43 train_steps=1.94
ep90 val_rocauc=0.76305 val_adapt_rocauc=0.77873 adapt_steps=2.18 halt=0.44 train_steps=1.91
ep100 val_rocauc=0.76926 val_adapt_rocauc=0.78354 adapt_steps=2.14 halt=0.44 train_steps=1.90
[ogbg-molhiv_mf_rrog-act_T1_ns3_stream_hm8_hmin2_loss0.2_lq0.1_hex0.1_qw0_h128_e100_s0] best_ep=70 val={'rocauc': 0.8046522878698804} test={'rocauc': 0.7416423646652118} adaptive={'rocauc': 0.7333127329612392} steps=2.1964502796012644
  wrote /orion/u/oscarwan/rrog-gnn-runner/runs/ogbg-molhiv_mf_rrog-act_T1_ns3_stream_hm8_hmin2_loss0.2_lq0.1_hex0.1_qw0_h128_e100_s0.json
[run] ogbg-molhiv view=appnp compute=rrog-act mode=stream T=1 ns=3 seed=0 device=cuda:0
python3 rrog/train_ogb_graphprop.py --dataset ogbg-molhiv --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=0.62676 val_adapt_rocauc=0.67960 adapt_steps=2.09 halt=0.44 train_steps=1.87
ep20 val_rocauc=0.65528 val_adapt_rocauc=0.72533 adapt_steps=2.03 halt=0.44 train_steps=1.88
ep30 val_rocauc=0.72398 val_adapt_rocauc=0.73609 adapt_steps=2.22 halt=0.43 train_steps=1.92
ep40 val_rocauc=0.67653 val_adapt_rocauc=0.71372 adapt_steps=2.12 halt=0.44 train_steps=1.88
ep50 val_rocauc=0.72748 val_adapt_rocauc=0.72608 adapt_steps=2.14 halt=0.44 train_steps=1.90
ep60 val_rocauc=0.69598 val_adapt_rocauc=0.70466 adapt_steps=2.12 halt=0.44 train_steps=1.87
ep70 val_rocauc=0.73788 val_adapt_rocauc=0.71230 adapt_steps=2.10 halt=0.44 train_steps=1.88
ep80 val_rocauc=0.72564 val_adapt_rocauc=0.74313 adapt_steps=2.06 halt=0.45 train_steps=1.82
ep90 val_rocauc=0.73463 val_adapt_rocauc=0.75132 adapt_steps=2.07 halt=0.45 train_steps=1.78
ep100 val_rocauc=0.72183 val_adapt_rocauc=0.73975 adapt_steps=2.05 halt=0.45 train_steps=1.76
[ogbg-molhiv_appnp_rrog-act_T1_ns3_stream_hm8_hmin2_loss0.2_lq0.1_hex0.1_qw0_h128_e100_s0] best_ep=70 val={'rocauc': 0.7378778414658045} test={'rocauc': 0.6883099325981575} adaptive={'rocauc': 0.7290619749319223} steps=2.102358375881352
  wrote /orion/u/oscarwan/rrog-gnn-runner/runs/ogbg-molhiv_appnp_rrog-act_T1_ns3_stream_hm8_hmin2_loss0.2_lq0.1_hex0.1_qw0_h128_e100_s0.json