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[run] ogbg-moltox21 view=gin compute=rrog-act mode=stream T=1 ns=3 seed=0 device=cuda:3
python3 rrog/train_ogb_graphprop.py --dataset ogbg-moltox21 --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
Downloading http://snap.stanford.edu/ogb/data/graphproppred/csv_mol_download/tox21.zip
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Processing...
Extracting /home/yurenh2/rrog-gnn-runner/data/ogb/tox21.zip
Loading necessary files...
This might take a while.
Processing graphs...
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Done!
Saving...
ep10 val_rocauc=0.71371 val_adapt_rocauc=0.69966 adapt_steps=3.11 halt=0.29 train_steps=2.95
ep20 val_rocauc=0.63487 val_adapt_rocauc=0.60317 adapt_steps=2.49 halt=0.29 train_steps=2.93
ep30 val_rocauc=0.71755 val_adapt_rocauc=0.70846 adapt_steps=3.98 halt=0.28 train_steps=2.98
ep40 val_rocauc=0.71255 val_adapt_rocauc=0.68936 adapt_steps=3.00 halt=0.29 train_steps=2.94
ep50 val_rocauc=0.72362 val_adapt_rocauc=0.72481 adapt_steps=3.24 halt=0.28 train_steps=3.16
ep60 val_rocauc=0.71946 val_adapt_rocauc=0.72876 adapt_steps=3.93 halt=0.28 train_steps=3.08
ep70 val_rocauc=0.72799 val_adapt_rocauc=0.73083 adapt_steps=3.71 halt=0.28 train_steps=3.13
ep80 val_rocauc=0.75666 val_adapt_rocauc=0.76164 adapt_steps=4.28 halt=0.29 train_steps=3.06
ep90 val_rocauc=0.75393 val_adapt_rocauc=0.76254 adapt_steps=3.78 halt=0.29 train_steps=3.06
ep100 val_rocauc=0.76113 val_adapt_rocauc=0.76936 adapt_steps=3.90 halt=0.29 train_steps=3.04
[ogbg-moltox21_gin_rrog-act_T1_ns3_stream_hm8_hmin2_exact_lq0.1_hex0.1_qw0_h128_e100_s0] best_ep=100 val={'rocauc': 0.7611296131897838} test={'rocauc': 0.7089667909334546} adaptive={'rocauc': 0.7126552901719999} steps=3.826530612244898
wrote /home/yurenh2/rrog-gnn-runner/runs/ogbg-moltox21_gin_rrog-act_T1_ns3_stream_hm8_hmin2_exact_lq0.1_hex0.1_qw0_h128_e100_s0.json
[run] ogbg-moltox21 view=gcn compute=rrog-act mode=stream T=1 ns=3 seed=0 device=cuda:3
python3 rrog/train_ogb_graphprop.py --dataset ogbg-moltox21 --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=0.59058 val_adapt_rocauc=0.61606 adapt_steps=2.60 halt=0.29 train_steps=2.95
ep20 val_rocauc=0.73407 val_adapt_rocauc=0.73772 adapt_steps=4.45 halt=0.28 train_steps=3.03
ep30 val_rocauc=0.66805 val_adapt_rocauc=0.67628 adapt_steps=2.39 halt=0.31 train_steps=2.83
ep40 val_rocauc=0.75097 val_adapt_rocauc=0.75197 adapt_steps=3.77 halt=0.30 train_steps=2.93
ep50 val_rocauc=0.75364 val_adapt_rocauc=0.75297 adapt_steps=2.95 halt=0.32 train_steps=2.68
ep60 val_rocauc=0.76552 val_adapt_rocauc=0.77043 adapt_steps=3.76 halt=0.31 train_steps=2.86
ep70 val_rocauc=0.75776 val_adapt_rocauc=0.75709 adapt_steps=3.80 halt=0.34 train_steps=2.62
ep80 val_rocauc=0.75257 val_adapt_rocauc=0.76572 adapt_steps=2.98 halt=0.35 train_steps=2.57
ep90 val_rocauc=0.75894 val_adapt_rocauc=0.76629 adapt_steps=2.81 halt=0.37 train_steps=2.40
ep100 val_rocauc=0.75414 val_adapt_rocauc=0.76330 adapt_steps=2.75 halt=0.37 train_steps=2.36
[ogbg-moltox21_gcn_rrog-act_T1_ns3_stream_hm8_hmin2_exact_lq0.1_hex0.1_qw0_h128_e100_s0] best_ep=60 val={'rocauc': 0.7655200134252199} test={'rocauc': 0.7134396419831454} adaptive={'rocauc': 0.7234036772214454} steps=3.764030612244898
wrote /home/yurenh2/rrog-gnn-runner/runs/ogbg-moltox21_gcn_rrog-act_T1_ns3_stream_hm8_hmin2_exact_lq0.1_hex0.1_qw0_h128_e100_s0.json
[run] ogbg-moltox21 view=sgc compute=rrog-act mode=stream T=1 ns=3 seed=0 device=cuda:3
python3 rrog/train_ogb_graphprop.py --dataset ogbg-moltox21 --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=0.66429 val_adapt_rocauc=0.65320 adapt_steps=3.43 halt=0.30 train_steps=2.88
ep20 val_rocauc=0.68343 val_adapt_rocauc=0.70058 adapt_steps=2.30 halt=0.28 train_steps=3.01
ep30 val_rocauc=0.74431 val_adapt_rocauc=0.74038 adapt_steps=3.91 halt=0.28 train_steps=3.06
ep40 val_rocauc=0.77575 val_adapt_rocauc=0.78093 adapt_steps=3.57 halt=0.29 train_steps=3.01
ep50 val_rocauc=0.76411 val_adapt_rocauc=0.76469 adapt_steps=3.98 halt=0.28 train_steps=3.10
ep60 val_rocauc=0.74714 val_adapt_rocauc=0.75263 adapt_steps=4.61 halt=0.29 train_steps=2.98
ep70 val_rocauc=0.76848 val_adapt_rocauc=0.77476 adapt_steps=3.52 halt=0.31 train_steps=2.93
ep80 val_rocauc=0.77621 val_adapt_rocauc=0.77758 adapt_steps=3.50 halt=0.30 train_steps=2.90
ep90 val_rocauc=0.77792 val_adapt_rocauc=0.77851 adapt_steps=3.40 halt=0.32 train_steps=2.79
ep100 val_rocauc=0.77758 val_adapt_rocauc=0.77971 adapt_steps=3.31 halt=0.32 train_steps=2.76
[ogbg-moltox21_sgc_rrog-act_T1_ns3_stream_hm8_hmin2_exact_lq0.1_hex0.1_qw0_h128_e100_s0] best_ep=90 val={'rocauc': 0.7779156296831152} test={'rocauc': 0.7359551442188313} adaptive={'rocauc': 0.7477856114096909} steps=3.485969387755102
wrote /home/yurenh2/rrog-gnn-runner/runs/ogbg-moltox21_sgc_rrog-act_T1_ns3_stream_hm8_hmin2_exact_lq0.1_hex0.1_qw0_h128_e100_s0.json
[run] ogbg-moltox21 view=tag compute=rrog-act mode=stream T=1 ns=3 seed=0 device=cuda:3
python3 rrog/train_ogb_graphprop.py --dataset ogbg-moltox21 --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=0.65297 val_adapt_rocauc=0.67456 adapt_steps=3.02 halt=0.29 train_steps=2.89
ep20 val_rocauc=0.58464 val_adapt_rocauc=0.57802 adapt_steps=2.47 halt=0.28 train_steps=3.08
ep30 val_rocauc=0.67479 val_adapt_rocauc=0.66288 adapt_steps=2.71 halt=0.28 train_steps=3.03
ep40 val_rocauc=0.73567 val_adapt_rocauc=0.72464 adapt_steps=4.40 halt=0.29 train_steps=2.94
ep50 val_rocauc=0.74825 val_adapt_rocauc=0.74753 adapt_steps=3.90 halt=0.30 train_steps=2.89
ep60 val_rocauc=0.74074 val_adapt_rocauc=0.74859 adapt_steps=3.41 halt=0.29 train_steps=3.03
ep70 val_rocauc=0.76016 val_adapt_rocauc=0.76365 adapt_steps=3.69 halt=0.32 train_steps=2.78
ep80 val_rocauc=0.76849 val_adapt_rocauc=0.77008 adapt_steps=3.87 halt=0.32 train_steps=2.79
ep90 val_rocauc=0.76637 val_adapt_rocauc=0.76973 adapt_steps=3.59 halt=0.31 train_steps=2.86
ep100 val_rocauc=0.77008 val_adapt_rocauc=0.77327 adapt_steps=3.50 halt=0.32 train_steps=2.79
[ogbg-moltox21_tag_rrog-act_T1_ns3_stream_hm8_hmin2_exact_lq0.1_hex0.1_qw0_h128_e100_s0] best_ep=100 val={'rocauc': 0.7700778158549199} test={'rocauc': 0.7162575064186166} adaptive={'rocauc': 0.7254468026027986} steps=3.506377551020408
wrote /home/yurenh2/rrog-gnn-runner/runs/ogbg-moltox21_tag_rrog-act_T1_ns3_stream_hm8_hmin2_exact_lq0.1_hex0.1_qw0_h128_e100_s0.json
[run] ogbg-moltox21 view=graphconv compute=rrog-act mode=stream T=1 ns=3 seed=0 device=cuda:3
python3 rrog/train_ogb_graphprop.py --dataset ogbg-moltox21 --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=0.67403 val_adapt_rocauc=0.66074 adapt_steps=5.50 halt=0.30 train_steps=2.85
ep20 val_rocauc=0.72382 val_adapt_rocauc=0.72638 adapt_steps=4.50 halt=0.29 train_steps=2.98
ep30 val_rocauc=0.74375 val_adapt_rocauc=0.74426 adapt_steps=4.91 halt=0.30 train_steps=2.93
ep40 val_rocauc=0.76136 val_adapt_rocauc=0.75836 adapt_steps=4.87 halt=0.30 train_steps=2.95
ep50 val_rocauc=0.77110 val_adapt_rocauc=0.76887 adapt_steps=3.39 halt=0.31 train_steps=2.88
ep60 val_rocauc=0.76514 val_adapt_rocauc=0.76428 adapt_steps=3.77 halt=0.30 train_steps=2.93
ep70 val_rocauc=0.77659 val_adapt_rocauc=0.78194 adapt_steps=3.61 halt=0.33 train_steps=2.73
ep80 val_rocauc=0.78163 val_adapt_rocauc=0.78200 adapt_steps=3.14 halt=0.32 train_steps=2.79
ep90 val_rocauc=0.77395 val_adapt_rocauc=0.77600 adapt_steps=3.46 halt=0.34 train_steps=2.63
ep100 val_rocauc=0.77629 val_adapt_rocauc=0.78067 adapt_steps=3.51 halt=0.34 train_steps=2.67
[ogbg-moltox21_graphconv_rrog-act_T1_ns3_stream_hm8_hmin2_exact_lq0.1_hex0.1_qw0_h128_e100_s0] best_ep=80 val={'rocauc': 0.7816268997660386} test={'rocauc': 0.7115162457844392} adaptive={'rocauc': 0.7190898385325345} steps=3.205357142857143
wrote /home/yurenh2/rrog-gnn-runner/runs/ogbg-moltox21_graphconv_rrog-act_T1_ns3_stream_hm8_hmin2_exact_lq0.1_hex0.1_qw0_h128_e100_s0.json
[run] ogbg-moltox21 view=appnp compute=rrog-act mode=stream T=1 ns=3 seed=0 device=cuda:3
python3 rrog/train_ogb_graphprop.py --dataset ogbg-moltox21 --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=0.67337 val_adapt_rocauc=0.68763 adapt_steps=3.64 halt=0.31 train_steps=2.73
ep20 val_rocauc=0.70783 val_adapt_rocauc=0.71959 adapt_steps=3.12 halt=0.30 train_steps=2.88
ep30 val_rocauc=0.70171 val_adapt_rocauc=0.71441 adapt_steps=3.00 halt=0.30 train_steps=2.88
ep40 val_rocauc=0.71979 val_adapt_rocauc=0.72206 adapt_steps=3.04 halt=0.31 train_steps=2.84
ep50 val_rocauc=0.71376 val_adapt_rocauc=0.72112 adapt_steps=2.58 halt=0.32 train_steps=2.70
ep60 val_rocauc=0.71062 val_adapt_rocauc=0.72892 adapt_steps=2.53 halt=0.37 train_steps=2.29
ep70 val_rocauc=0.72376 val_adapt_rocauc=0.73807 adapt_steps=2.52 halt=0.38 train_steps=2.29
ep80 val_rocauc=0.71834 val_adapt_rocauc=0.74502 adapt_steps=2.27 halt=0.38 train_steps=2.25
ep90 val_rocauc=0.71743 val_adapt_rocauc=0.73867 adapt_steps=2.35 halt=0.40 train_steps=2.13
ep100 val_rocauc=0.72314 val_adapt_rocauc=0.74270 adapt_steps=2.30 halt=0.39 train_steps=2.20
[ogbg-moltox21_appnp_rrog-act_T1_ns3_stream_hm8_hmin2_exact_lq0.1_hex0.1_qw0_h128_e100_s0] best_ep=70 val={'rocauc': 0.7237576716525247} test={'rocauc': 0.6822930471359431} adaptive={'rocauc': 0.7137838364459371} steps=2.63265306122449
wrote /home/yurenh2/rrog-gnn-runner/runs/ogbg-moltox21_appnp_rrog-act_T1_ns3_stream_hm8_hmin2_exact_lq0.1_hex0.1_qw0_h128_e100_s0.json
[run] ogbg-moltox21 view=pna compute=rrog-act mode=stream T=1 ns=3 seed=0 device=cuda:3
python3 rrog/train_ogb_graphprop.py --dataset ogbg-moltox21 --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=0.67622 val_adapt_rocauc=0.68638 adapt_steps=2.73 halt=0.29 train_steps=2.84
/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=0.70913 val_adapt_rocauc=0.71306 adapt_steps=3.51 halt=0.30 train_steps=2.87
/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=0.72224 val_adapt_rocauc=0.71267 adapt_steps=3.47 halt=0.29 train_steps=2.94
/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=0.72374 val_adapt_rocauc=0.72731 adapt_steps=2.78 halt=0.30 train_steps=2.99
/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=0.68200 val_adapt_rocauc=0.67391 adapt_steps=2.33 halt=0.29 train_steps=2.98
/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=0.67336 val_adapt_rocauc=0.67501 adapt_steps=6.05 halt=0.31 train_steps=2.84
/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=0.73655 val_adapt_rocauc=0.74091 adapt_steps=3.35 halt=0.30 train_steps=2.92
/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=0.74975 val_adapt_rocauc=0.75746 adapt_steps=4.96 halt=0.29 train_steps=3.02
/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=0.75471 val_adapt_rocauc=0.76064 adapt_steps=4.10 halt=0.29 train_steps=3.02
/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=0.75208 val_adapt_rocauc=0.76070 adapt_steps=3.94 halt=0.30 train_steps=2.96
[ogbg-moltox21_pna_rrog-act_T1_ns3_stream_hm8_hmin2_exact_lq0.1_hex0.1_qw0_h128_e100_s0] best_ep=90 val={'rocauc': 0.7547109180572619} test={'rocauc': 0.6948890646179361} adaptive={'rocauc': 0.7075025478713003} steps=4.220663265306122
wrote /home/yurenh2/rrog-gnn-runner/runs/ogbg-moltox21_pna_rrog-act_T1_ns3_stream_hm8_hmin2_exact_lq0.1_hex0.1_qw0_h128_e100_s0.json
[run] ogbg-moltox21 view=resgated compute=rrog-act mode=stream T=1 ns=3 seed=0 device=cuda:3
python3 rrog/train_ogb_graphprop.py --dataset ogbg-moltox21 --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=0.70487 val_adapt_rocauc=0.70394 adapt_steps=5.69 halt=0.30 train_steps=2.83
ep20 val_rocauc=0.69675 val_adapt_rocauc=0.69557 adapt_steps=4.74 halt=0.29 train_steps=2.99
ep30 val_rocauc=0.66699 val_adapt_rocauc=0.63127 adapt_steps=5.03 halt=0.28 train_steps=3.06
ep40 val_rocauc=0.74485 val_adapt_rocauc=0.75016 adapt_steps=3.17 halt=0.29 train_steps=2.99
ep50 val_rocauc=0.76238 val_adapt_rocauc=0.76296 adapt_steps=3.47 halt=0.30 train_steps=2.94
ep60 val_rocauc=0.75613 val_adapt_rocauc=0.75760 adapt_steps=3.12 halt=0.30 train_steps=2.92
ep70 val_rocauc=0.75660 val_adapt_rocauc=0.75749 adapt_steps=3.60 halt=0.33 train_steps=2.70
ep80 val_rocauc=0.77965 val_adapt_rocauc=0.78270 adapt_steps=3.04 halt=0.33 train_steps=2.71
ep90 val_rocauc=0.77346 val_adapt_rocauc=0.77623 adapt_steps=3.25 halt=0.35 train_steps=2.54
ep100 val_rocauc=0.77053 val_adapt_rocauc=0.77614 adapt_steps=3.10 halt=0.36 train_steps=2.49
[ogbg-moltox21_resgated_rrog-act_T1_ns3_stream_hm8_hmin2_exact_lq0.1_hex0.1_qw0_h128_e100_s0] best_ep=80 val={'rocauc': 0.7796493691111651} test={'rocauc': 0.7201514791915015} adaptive={'rocauc': 0.7223894069034307} steps=3.0778061224489797
wrote /home/yurenh2/rrog-gnn-runner/runs/ogbg-moltox21_resgated_rrog-act_T1_ns3_stream_hm8_hmin2_exact_lq0.1_hex0.1_qw0_h128_e100_s0.json
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