[run] ogbg-molhiv view=gin compute=rrog-act mode=stream T=1 ns=3 seed=0 device=cuda:3
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 exact --halt_loss_threshold 0.2 --halt_exploration_prob 0.1 --act_train_mode stream --q_warmup_epochs 0 --device cuda:3 --num_workers 0
ep10 val_rocauc=0.57684 val_adapt_rocauc=0.58405 adapt_steps=2.00 halt=0.47 train_steps=1.59
ep20 val_rocauc=0.60452 val_adapt_rocauc=0.59144 adapt_steps=2.01 halt=0.47 train_steps=1.59
ep30 val_rocauc=0.71542 val_adapt_rocauc=0.72246 adapt_steps=2.00 halt=0.48 train_steps=1.58
ep40 val_rocauc=0.70211 val_adapt_rocauc=0.68285 adapt_steps=2.00 halt=0.48 train_steps=1.57
ep50 val_rocauc=0.59821 val_adapt_rocauc=0.72487 adapt_steps=2.00 halt=0.48 train_steps=1.57
ep60 val_rocauc=0.64796 val_adapt_rocauc=0.71730 adapt_steps=2.00 halt=0.48 train_steps=1.57
ep70 val_rocauc=0.70277 val_adapt_rocauc=0.73025 adapt_steps=2.00 halt=0.48 train_steps=1.57
ep80 val_rocauc=0.68902 val_adapt_rocauc=0.72341 adapt_steps=2.00 halt=0.48 train_steps=1.57
ep90 val_rocauc=0.72762 val_adapt_rocauc=0.74793 adapt_steps=2.00 halt=0.48 train_steps=1.57
ep100 val_rocauc=0.74125 val_adapt_rocauc=0.75760 adapt_steps=2.00 halt=0.48 train_steps=1.57
[ogbg-molhiv_gin_rrog-act_T1_ns3_stream_hm8_hmin2_exact_lq0.1_hex0.1_qw0_h128_e100_s0] best_ep=100 val={'rocauc': 0.7412490201842054} test={'rocauc': 0.7254234342107805} adaptive={'rocauc': 0.7567005929044593} steps=2.00024313153416
  wrote /home/yurenh2/rrog-gnn-runner/runs/ogbg-molhiv_gin_rrog-act_T1_ns3_stream_hm8_hmin2_exact_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:3
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 exact --halt_loss_threshold 0.2 --halt_exploration_prob 0.1 --act_train_mode stream --q_warmup_epochs 0 --device cuda:3 --num_workers 0
ep10 val_rocauc=0.67913 val_adapt_rocauc=0.65745 adapt_steps=2.00 halt=0.47 train_steps=1.60
ep20 val_rocauc=0.53086 val_adapt_rocauc=0.70728 adapt_steps=2.02 halt=0.47 train_steps=1.59
ep30 val_rocauc=0.73614 val_adapt_rocauc=0.73784 adapt_steps=2.04 halt=0.48 train_steps=1.57
ep40 val_rocauc=0.74062 val_adapt_rocauc=0.74468 adapt_steps=2.00 halt=0.48 train_steps=1.58
ep50 val_rocauc=0.64917 val_adapt_rocauc=0.74024 adapt_steps=2.00 halt=0.48 train_steps=1.57
ep60 val_rocauc=0.74111 val_adapt_rocauc=0.74943 adapt_steps=2.00 halt=0.48 train_steps=1.58
ep70 val_rocauc=0.69073 val_adapt_rocauc=0.75506 adapt_steps=2.00 halt=0.48 train_steps=1.57
ep80 val_rocauc=0.75837 val_adapt_rocauc=0.74771 adapt_steps=2.00 halt=0.48 train_steps=1.57
ep90 val_rocauc=0.77126 val_adapt_rocauc=0.75543 adapt_steps=2.00 halt=0.48 train_steps=1.57
ep100 val_rocauc=0.76237 val_adapt_rocauc=0.74920 adapt_steps=2.00 halt=0.48 train_steps=1.57
[ogbg-molhiv_gcn_rrog-act_T1_ns3_stream_hm8_hmin2_exact_lq0.1_hex0.1_qw0_h128_e100_s0] best_ep=90 val={'rocauc': 0.7712620027434841} test={'rocauc': 0.7476081036713725} adaptive={'rocauc': 0.7387782691824871} steps=2.012156576707999
  wrote /home/yurenh2/rrog-gnn-runner/runs/ogbg-molhiv_gcn_rrog-act_T1_ns3_stream_hm8_hmin2_exact_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:3
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 exact --halt_loss_threshold 0.2 --halt_exploration_prob 0.1 --act_train_mode stream --q_warmup_epochs 0 --device cuda:3 --num_workers 0
ep10 val_rocauc=0.56968 val_adapt_rocauc=0.68573 adapt_steps=2.01 halt=0.47 train_steps=1.60
ep20 val_rocauc=0.65960 val_adapt_rocauc=0.64533 adapt_steps=2.00 halt=0.47 train_steps=1.59
ep30 val_rocauc=0.73782 val_adapt_rocauc=0.74603 adapt_steps=2.03 halt=0.48 train_steps=1.57
ep40 val_rocauc=0.70390 val_adapt_rocauc=0.72870 adapt_steps=2.00 halt=0.48 train_steps=1.57
ep50 val_rocauc=0.68717 val_adapt_rocauc=0.73036 adapt_steps=2.00 halt=0.48 train_steps=1.57
ep60 val_rocauc=0.68431 val_adapt_rocauc=0.72508 adapt_steps=2.00 halt=0.48 train_steps=1.57
ep70 val_rocauc=0.69653 val_adapt_rocauc=0.73800 adapt_steps=2.00 halt=0.48 train_steps=1.57
ep80 val_rocauc=0.70493 val_adapt_rocauc=0.74137 adapt_steps=2.00 halt=0.48 train_steps=1.57
ep90 val_rocauc=0.69423 val_adapt_rocauc=0.73412 adapt_steps=2.00 halt=0.48 train_steps=1.57
ep100 val_rocauc=0.69464 val_adapt_rocauc=0.74486 adapt_steps=2.00 halt=0.48 train_steps=1.57
[ogbg-molhiv_sgc_rrog-act_T1_ns3_stream_hm8_hmin2_exact_lq0.1_hex0.1_qw0_h128_e100_s0] best_ep=30 val={'rocauc': 0.7378227268273565} test={'rocauc': 0.7035709457502076} adaptive={'rocauc': 0.724386334228162} steps=2.0269876002917577
  wrote /home/yurenh2/rrog-gnn-runner/runs/ogbg-molhiv_sgc_rrog-act_T1_ns3_stream_hm8_hmin2_exact_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:3
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 exact --halt_loss_threshold 0.2 --halt_exploration_prob 0.1 --act_train_mode stream --q_warmup_epochs 0 --device cuda:3 --num_workers 0
ep10 val_rocauc=0.64438 val_adapt_rocauc=0.62773 adapt_steps=2.01 halt=0.47 train_steps=1.60
ep20 val_rocauc=0.64326 val_adapt_rocauc=0.64524 adapt_steps=2.01 halt=0.47 train_steps=1.61
ep30 val_rocauc=0.68766 val_adapt_rocauc=0.66943 adapt_steps=2.00 halt=0.48 train_steps=1.58
ep40 val_rocauc=0.63871 val_adapt_rocauc=0.72422 adapt_steps=2.00 halt=0.48 train_steps=1.57
ep50 val_rocauc=0.63257 val_adapt_rocauc=0.73165 adapt_steps=2.00 halt=0.48 train_steps=1.57
ep60 val_rocauc=0.68678 val_adapt_rocauc=0.75939 adapt_steps=2.00 halt=0.48 train_steps=1.57
ep70 val_rocauc=0.73265 val_adapt_rocauc=0.76437 adapt_steps=2.00 halt=0.48 train_steps=1.57
ep80 val_rocauc=0.71019 val_adapt_rocauc=0.75081 adapt_steps=2.00 halt=0.48 train_steps=1.57
ep90 val_rocauc=0.71181 val_adapt_rocauc=0.75327 adapt_steps=2.00 halt=0.48 train_steps=1.57
ep100 val_rocauc=0.72871 val_adapt_rocauc=0.76052 adapt_steps=2.00 halt=0.48 train_steps=1.57
[ogbg-molhiv_tag_rrog-act_T1_ns3_stream_hm8_hmin2_exact_lq0.1_hex0.1_qw0_h128_e100_s0] best_ep=70 val={'rocauc': 0.7326450127376054} test={'rocauc': 0.759611039224396} adaptive={'rocauc': 0.7786419204696885} steps=2.0
  wrote /home/yurenh2/rrog-gnn-runner/runs/ogbg-molhiv_tag_rrog-act_T1_ns3_stream_hm8_hmin2_exact_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:3
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 exact --halt_loss_threshold 0.2 --halt_exploration_prob 0.1 --act_train_mode stream --q_warmup_epochs 0 --device cuda:3 --num_workers 0
ep10 val_rocauc=0.62212 val_adapt_rocauc=0.62817 adapt_steps=2.01 halt=0.47 train_steps=1.60
ep20 val_rocauc=0.61445 val_adapt_rocauc=0.69759 adapt_steps=2.00 halt=0.47 train_steps=1.59
ep30 val_rocauc=0.68557 val_adapt_rocauc=0.71732 adapt_steps=2.00 halt=0.48 train_steps=1.58
ep40 val_rocauc=0.47925 val_adapt_rocauc=0.71316 adapt_steps=2.00 halt=0.48 train_steps=1.57
ep50 val_rocauc=0.61766 val_adapt_rocauc=0.69094 adapt_steps=2.00 halt=0.48 train_steps=1.57
ep60 val_rocauc=0.73183 val_adapt_rocauc=0.73302 adapt_steps=2.00 halt=0.48 train_steps=1.57
ep70 val_rocauc=0.72858 val_adapt_rocauc=0.73825 adapt_steps=2.00 halt=0.48 train_steps=1.57
ep80 val_rocauc=0.74850 val_adapt_rocauc=0.74077 adapt_steps=2.00 halt=0.48 train_steps=1.57
ep90 val_rocauc=0.73303 val_adapt_rocauc=0.74217 adapt_steps=2.00 halt=0.48 train_steps=1.57
ep100 val_rocauc=0.73150 val_adapt_rocauc=0.73659 adapt_steps=2.00 halt=0.48 train_steps=1.57
[ogbg-molhiv_graphconv_rrog-act_T1_ns3_stream_hm8_hmin2_exact_lq0.1_hex0.1_qw0_h128_e100_s0] best_ep=80 val={'rocauc': 0.7484965951401137} test={'rocauc': 0.722140249908264} adaptive={'rocauc': 0.7285405280132873} steps=2.00024313153416
  wrote /home/yurenh2/rrog-gnn-runner/runs/ogbg-molhiv_graphconv_rrog-act_T1_ns3_stream_hm8_hmin2_exact_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:3
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 exact --halt_loss_threshold 0.2 --halt_exploration_prob 0.1 --act_train_mode stream --q_warmup_epochs 0 --device cuda:3 --num_workers 0
ep10 val_rocauc=0.70638 val_adapt_rocauc=0.68842 adapt_steps=2.01 halt=0.47 train_steps=1.60
ep20 val_rocauc=0.64695 val_adapt_rocauc=0.67193 adapt_steps=2.00 halt=0.47 train_steps=1.59
ep30 val_rocauc=0.71509 val_adapt_rocauc=0.75189 adapt_steps=2.01 halt=0.48 train_steps=1.58
ep40 val_rocauc=0.65625 val_adapt_rocauc=0.68931 adapt_steps=2.00 halt=0.48 train_steps=1.58
ep50 val_rocauc=0.73236 val_adapt_rocauc=0.74043 adapt_steps=2.00 halt=0.48 train_steps=1.57
ep60 val_rocauc=0.68610 val_adapt_rocauc=0.70942 adapt_steps=2.01 halt=0.48 train_steps=1.58
ep70 val_rocauc=0.71282 val_adapt_rocauc=0.71800 adapt_steps=2.00 halt=0.48 train_steps=1.57
ep80 val_rocauc=0.70420 val_adapt_rocauc=0.71488 adapt_steps=2.00 halt=0.48 train_steps=1.57
ep90 val_rocauc=0.71668 val_adapt_rocauc=0.72406 adapt_steps=2.00 halt=0.48 train_steps=1.57
ep100 val_rocauc=0.71284 val_adapt_rocauc=0.72607 adapt_steps=2.00 halt=0.48 train_steps=1.57
[ogbg-molhiv_appnp_rrog-act_T1_ns3_stream_hm8_hmin2_exact_lq0.1_hex0.1_qw0_h128_e100_s0] best_ep=50 val={'rocauc': 0.7323571918479326} test={'rocauc': 0.7487070047702736} adaptive={'rocauc': 0.7287433129260897} steps=2.0063214198881596
  wrote /home/yurenh2/rrog-gnn-runner/runs/ogbg-molhiv_appnp_rrog-act_T1_ns3_stream_hm8_hmin2_exact_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:3
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 exact --halt_loss_threshold 0.2 --halt_exploration_prob 0.1 --act_train_mode stream --q_warmup_epochs 0 --device cuda:3 --num_workers 0
/home/yurenh2/miniconda3/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(
/home/yurenh2/miniconda3/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=0.52661 val_adapt_rocauc=0.57540 adapt_steps=2.02 halt=0.47 train_steps=1.60
/home/yurenh2/miniconda3/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(
/home/yurenh2/miniconda3/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=0.53124 val_adapt_rocauc=0.54271 adapt_steps=2.00 halt=0.48 train_steps=1.58
/home/yurenh2/miniconda3/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(
/home/yurenh2/miniconda3/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=0.64086 val_adapt_rocauc=0.71386 adapt_steps=2.00 halt=0.48 train_steps=1.57
/home/yurenh2/miniconda3/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(
/home/yurenh2/miniconda3/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=0.66964 val_adapt_rocauc=0.69129 adapt_steps=2.00 halt=0.48 train_steps=1.58
/home/yurenh2/miniconda3/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(
/home/yurenh2/miniconda3/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=0.63916 val_adapt_rocauc=0.69673 adapt_steps=2.00 halt=0.48 train_steps=1.57
/home/yurenh2/miniconda3/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(
/home/yurenh2/miniconda3/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=0.73238 val_adapt_rocauc=0.74287 adapt_steps=2.00 halt=0.48 train_steps=1.57
/home/yurenh2/miniconda3/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(
/home/yurenh2/miniconda3/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=0.73849 val_adapt_rocauc=0.76782 adapt_steps=2.00 halt=0.48 train_steps=1.57
/home/yurenh2/miniconda3/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(
/home/yurenh2/miniconda3/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=0.72881 val_adapt_rocauc=0.78390 adapt_steps=2.00 halt=0.48 train_steps=1.57
/home/yurenh2/miniconda3/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(
/home/yurenh2/miniconda3/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=0.73387 val_adapt_rocauc=0.77480 adapt_steps=2.00 halt=0.48 train_steps=1.57
/home/yurenh2/miniconda3/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(
/home/yurenh2/miniconda3/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=0.73341 val_adapt_rocauc=0.77625 adapt_steps=2.00 halt=0.48 train_steps=1.57
[ogbg-molhiv_pna_rrog-act_T1_ns3_stream_hm8_hmin2_exact_lq0.1_hex0.1_qw0_h128_e100_s0] best_ep=70 val={'rocauc': 0.7384932882618068} test={'rocauc': 0.7185075030417737} adaptive={'rocauc': 0.7421078043222156} steps=2.0
  wrote /home/yurenh2/rrog-gnn-runner/runs/ogbg-molhiv_pna_rrog-act_T1_ns3_stream_hm8_hmin2_exact_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:3
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 exact --halt_loss_threshold 0.2 --halt_exploration_prob 0.1 --act_train_mode stream --q_warmup_epochs 0 --device cuda:3 --num_workers 0
ep10 val_rocauc=0.50096 val_adapt_rocauc=0.63103 adapt_steps=2.00 halt=0.47 train_steps=1.60
ep20 val_rocauc=0.65405 val_adapt_rocauc=0.70279 adapt_steps=2.00 halt=0.47 train_steps=1.58
ep30 val_rocauc=0.68276 val_adapt_rocauc=0.65325 adapt_steps=2.00 halt=0.48 train_steps=1.58
ep40 val_rocauc=0.80097 val_adapt_rocauc=0.78965 adapt_steps=2.00 halt=0.48 train_steps=1.58
ep50 val_rocauc=0.77943 val_adapt_rocauc=0.71584 adapt_steps=2.00 halt=0.48 train_steps=1.57
ep60 val_rocauc=0.77564 val_adapt_rocauc=0.75634 adapt_steps=2.00 halt=0.48 train_steps=1.57
ep70 val_rocauc=0.77493 val_adapt_rocauc=0.78545 adapt_steps=2.00 halt=0.48 train_steps=1.58
ep80 val_rocauc=0.77393 val_adapt_rocauc=0.77547 adapt_steps=2.00 halt=0.48 train_steps=1.57
ep90 val_rocauc=0.76533 val_adapt_rocauc=0.76729 adapt_steps=2.00 halt=0.48 train_steps=1.57
ep100 val_rocauc=0.76010 val_adapt_rocauc=0.76356 adapt_steps=2.00 halt=0.48 train_steps=1.57
[ogbg-molhiv_resgated_rrog-act_T1_ns3_stream_hm8_hmin2_exact_lq0.1_hex0.1_qw0_h128_e100_s0] best_ep=40 val={'rocauc': 0.8009657309425828} test={'rocauc': 0.7787017903010873} adaptive={'rocauc': 0.7875702504876494} steps=2.0
  wrote /home/yurenh2/rrog-gnn-runner/runs/ogbg-molhiv_resgated_rrog-act_T1_ns3_stream_hm8_hmin2_exact_lq0.1_hex0.1_qw0_h128_e100_s0.json