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[run] ogbg-molsider view=gin compute=rrog-act mode=stream T=1 ns=3 seed=0 device=cuda:3
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 30 --device cuda:3 --num_workers 0
ep10 val_rocauc=0.51835 val_adapt_rocauc=0.51835 adapt_steps=8.00 halt=0.12 train_steps=4.50
ep20 val_rocauc=0.55881 val_adapt_rocauc=0.55881 adapt_steps=8.00 halt=0.12 train_steps=4.50
ep30 val_rocauc=0.52823 val_adapt_rocauc=0.52823 adapt_steps=8.00 halt=0.12 train_steps=4.50
ep40 val_rocauc=0.52164 val_adapt_rocauc=0.52164 adapt_steps=8.00 halt=0.12 train_steps=4.50
ep50 val_rocauc=0.55790 val_adapt_rocauc=0.55790 adapt_steps=8.00 halt=0.12 train_steps=4.50
ep60 val_rocauc=0.57934 val_adapt_rocauc=0.57937 adapt_steps=7.83 halt=0.12 train_steps=4.46
ep70 val_rocauc=0.57559 val_adapt_rocauc=0.57449 adapt_steps=7.95 halt=0.12 train_steps=4.50
ep80 val_rocauc=0.60248 val_adapt_rocauc=0.60248 adapt_steps=8.00 halt=0.12 train_steps=4.50
ep90 val_rocauc=0.58266 val_adapt_rocauc=0.58280 adapt_steps=7.99 halt=0.12 train_steps=4.50
ep100 val_rocauc=0.59116 val_adapt_rocauc=0.59116 adapt_steps=8.00 halt=0.12 train_steps=4.50
[ogbg-molsider_gin_rrog-act_T1_ns3_stream_hm8_hmin2_loss0.2_lq0.1_hex0.1_qw30_h128_e100_s0] best_ep=80 val={'rocauc': 0.6024836436339023} test={'rocauc': 0.5932734621249586} adaptive={'rocauc': 0.5932734621249586} steps=8.0
wrote /home/yurenh2/rrog-gnn-runner/runs/ogbg-molsider_gin_rrog-act_T1_ns3_stream_hm8_hmin2_loss0.2_lq0.1_hex0.1_qw30_h128_e100_s0.json
[run] ogbg-molsider view=gcn compute=rrog-act mode=stream T=1 ns=3 seed=0 device=cuda:3
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 30 --device cuda:3 --num_workers 0
ep10 val_rocauc=0.56195 val_adapt_rocauc=0.56195 adapt_steps=8.00 halt=0.12 train_steps=4.50
ep20 val_rocauc=0.59090 val_adapt_rocauc=0.59090 adapt_steps=8.00 halt=0.12 train_steps=4.50
ep30 val_rocauc=0.56785 val_adapt_rocauc=0.56785 adapt_steps=8.00 halt=0.12 train_steps=4.50
ep40 val_rocauc=0.55460 val_adapt_rocauc=0.55460 adapt_steps=8.00 halt=0.12 train_steps=4.50
ep50 val_rocauc=0.59103 val_adapt_rocauc=0.59103 adapt_steps=8.00 halt=0.12 train_steps=4.50
ep60 val_rocauc=0.56623 val_adapt_rocauc=0.56623 adapt_steps=8.00 halt=0.12 train_steps=4.50
ep70 val_rocauc=0.61162 val_adapt_rocauc=0.61169 adapt_steps=7.98 halt=0.12 train_steps=4.43
ep80 val_rocauc=0.62851 val_adapt_rocauc=0.62851 adapt_steps=8.00 halt=0.12 train_steps=4.50
ep90 val_rocauc=0.63179 val_adapt_rocauc=0.63179 adapt_steps=7.99 halt=0.12 train_steps=4.47
ep100 val_rocauc=0.62571 val_adapt_rocauc=0.62571 adapt_steps=8.00 halt=0.13 train_steps=4.46
[ogbg-molsider_gcn_rrog-act_T1_ns3_stream_hm8_hmin2_loss0.2_lq0.1_hex0.1_qw30_h128_e100_s0] best_ep=90 val={'rocauc': 0.6317896497739357} test={'rocauc': 0.640011295277304} adaptive={'rocauc': 0.6402407710265505} steps=7.937062937062937
wrote /home/yurenh2/rrog-gnn-runner/runs/ogbg-molsider_gcn_rrog-act_T1_ns3_stream_hm8_hmin2_loss0.2_lq0.1_hex0.1_qw30_h128_e100_s0.json
[run] ogbg-molsider view=sgc compute=rrog-act mode=stream T=1 ns=3 seed=0 device=cuda:3
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 30 --device cuda:3 --num_workers 0
ep10 val_rocauc=0.52921 val_adapt_rocauc=0.52921 adapt_steps=8.00 halt=0.12 train_steps=4.50
ep20 val_rocauc=0.51742 val_adapt_rocauc=0.51742 adapt_steps=8.00 halt=0.12 train_steps=4.50
ep30 val_rocauc=0.57194 val_adapt_rocauc=0.57194 adapt_steps=8.00 halt=0.12 train_steps=4.50
ep40 val_rocauc=0.57726 val_adapt_rocauc=0.57637 adapt_steps=7.68 halt=0.12 train_steps=4.40
ep50 val_rocauc=0.56214 val_adapt_rocauc=0.56238 adapt_steps=7.98 halt=0.12 train_steps=4.50
ep60 val_rocauc=0.59428 val_adapt_rocauc=0.59428 adapt_steps=8.00 halt=0.12 train_steps=4.50
ep70 val_rocauc=0.62022 val_adapt_rocauc=0.61898 adapt_steps=7.87 halt=0.12 train_steps=4.42
ep80 val_rocauc=0.61458 val_adapt_rocauc=0.61451 adapt_steps=7.99 halt=0.12 train_steps=4.50
ep90 val_rocauc=0.62914 val_adapt_rocauc=0.62914 adapt_steps=8.00 halt=0.12 train_steps=4.49
ep100 val_rocauc=0.61681 val_adapt_rocauc=0.61660 adapt_steps=7.94 halt=0.13 train_steps=4.49
[ogbg-molsider_sgc_rrog-act_T1_ns3_stream_hm8_hmin2_loss0.2_lq0.1_hex0.1_qw30_h128_e100_s0] best_ep=90 val={'rocauc': 0.6291378293840166} test={'rocauc': 0.622805287595805} adaptive={'rocauc': 0.6223183091184133} steps=7.916083916083916
wrote /home/yurenh2/rrog-gnn-runner/runs/ogbg-molsider_sgc_rrog-act_T1_ns3_stream_hm8_hmin2_loss0.2_lq0.1_hex0.1_qw30_h128_e100_s0.json
[run] ogbg-molsider view=tag compute=rrog-act mode=stream T=1 ns=3 seed=0 device=cuda:3
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 30 --device cuda:3 --num_workers 0
ep10 val_rocauc=0.53256 val_adapt_rocauc=0.53256 adapt_steps=8.00 halt=0.12 train_steps=4.50
ep20 val_rocauc=0.54926 val_adapt_rocauc=0.54926 adapt_steps=8.00 halt=0.12 train_steps=4.50
ep30 val_rocauc=0.54957 val_adapt_rocauc=0.54957 adapt_steps=8.00 halt=0.12 train_steps=4.50
ep40 val_rocauc=0.51981 val_adapt_rocauc=0.52396 adapt_steps=7.78 halt=0.12 train_steps=4.44
ep50 val_rocauc=0.60657 val_adapt_rocauc=0.60657 adapt_steps=8.00 halt=0.12 train_steps=4.50
ep60 val_rocauc=0.58438 val_adapt_rocauc=0.58438 adapt_steps=8.00 halt=0.12 train_steps=4.47
ep70 val_rocauc=0.57212 val_adapt_rocauc=0.57212 adapt_steps=8.00 halt=0.12 train_steps=4.50
ep80 val_rocauc=0.58304 val_adapt_rocauc=0.58304 adapt_steps=8.00 halt=0.12 train_steps=4.49
ep90 val_rocauc=0.61059 val_adapt_rocauc=0.61099 adapt_steps=7.98 halt=0.12 train_steps=4.49
ep100 val_rocauc=0.60016 val_adapt_rocauc=0.60046 adapt_steps=7.99 halt=0.12 train_steps=4.48
[ogbg-molsider_tag_rrog-act_T1_ns3_stream_hm8_hmin2_loss0.2_lq0.1_hex0.1_qw30_h128_e100_s0] best_ep=90 val={'rocauc': 0.6105933624956364} test={'rocauc': 0.624320975867962} adaptive={'rocauc': 0.6243824341662988} steps=7.958041958041958
wrote /home/yurenh2/rrog-gnn-runner/runs/ogbg-molsider_tag_rrog-act_T1_ns3_stream_hm8_hmin2_loss0.2_lq0.1_hex0.1_qw30_h128_e100_s0.json
[run] ogbg-molsider view=graphconv compute=rrog-act mode=stream T=1 ns=3 seed=0 device=cuda:3
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 30 --device cuda:3 --num_workers 0
ep10 val_rocauc=0.53539 val_adapt_rocauc=0.53539 adapt_steps=8.00 halt=0.12 train_steps=4.50
ep20 val_rocauc=0.54274 val_adapt_rocauc=0.54274 adapt_steps=8.00 halt=0.12 train_steps=4.50
ep30 val_rocauc=0.56668 val_adapt_rocauc=0.56668 adapt_steps=8.00 halt=0.12 train_steps=4.50
ep40 val_rocauc=0.58346 val_adapt_rocauc=0.58251 adapt_steps=7.85 halt=0.12 train_steps=4.49
ep50 val_rocauc=0.57517 val_adapt_rocauc=0.57482 adapt_steps=7.76 halt=0.12 train_steps=4.49
ep60 val_rocauc=0.57520 val_adapt_rocauc=0.57520 adapt_steps=8.00 halt=0.13 train_steps=4.50
ep70 val_rocauc=0.58768 val_adapt_rocauc=0.58768 adapt_steps=8.00 halt=0.12 train_steps=4.50
ep80 val_rocauc=0.60462 val_adapt_rocauc=0.60442 adapt_steps=7.89 halt=0.13 train_steps=4.46
ep90 val_rocauc=0.60757 val_adapt_rocauc=0.60786 adapt_steps=7.91 halt=0.12 train_steps=4.49
ep100 val_rocauc=0.61648 val_adapt_rocauc=0.61625 adapt_steps=7.95 halt=0.12 train_steps=4.49
[ogbg-molsider_graphconv_rrog-act_T1_ns3_stream_hm8_hmin2_loss0.2_lq0.1_hex0.1_qw30_h128_e100_s0] best_ep=100 val={'rocauc': 0.6164847255453625} test={'rocauc': 0.6344146740580251} adaptive={'rocauc': 0.6358083894071243} steps=7.916083916083916
wrote /home/yurenh2/rrog-gnn-runner/runs/ogbg-molsider_graphconv_rrog-act_T1_ns3_stream_hm8_hmin2_loss0.2_lq0.1_hex0.1_qw30_h128_e100_s0.json
[run] ogbg-molsider view=appnp compute=rrog-act mode=stream T=1 ns=3 seed=0 device=cuda:3
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 30 --device cuda:3 --num_workers 0
ep10 val_rocauc=0.50415 val_adapt_rocauc=0.50415 adapt_steps=8.00 halt=0.12 train_steps=4.50
ep20 val_rocauc=0.55422 val_adapt_rocauc=0.55422 adapt_steps=8.00 halt=0.12 train_steps=4.50
ep30 val_rocauc=0.54565 val_adapt_rocauc=0.54565 adapt_steps=8.00 halt=0.12 train_steps=4.50
ep40 val_rocauc=0.56511 val_adapt_rocauc=0.56078 adapt_steps=7.85 halt=0.13 train_steps=4.44
ep50 val_rocauc=0.55631 val_adapt_rocauc=0.55109 adapt_steps=7.76 halt=0.13 train_steps=4.48
ep60 val_rocauc=0.57521 val_adapt_rocauc=0.56829 adapt_steps=7.38 halt=0.13 train_steps=4.46
ep70 val_rocauc=0.56234 val_adapt_rocauc=0.54971 adapt_steps=7.09 halt=0.13 train_steps=4.41
ep80 val_rocauc=0.58328 val_adapt_rocauc=0.57728 adapt_steps=7.38 halt=0.13 train_steps=4.44
ep90 val_rocauc=0.58378 val_adapt_rocauc=0.56802 adapt_steps=7.01 halt=0.13 train_steps=4.39
ep100 val_rocauc=0.58739 val_adapt_rocauc=0.57353 adapt_steps=7.03 halt=0.13 train_steps=4.38
[ogbg-molsider_appnp_rrog-act_T1_ns3_stream_hm8_hmin2_loss0.2_lq0.1_hex0.1_qw30_h128_e100_s0] best_ep=100 val={'rocauc': 0.5873882396417854} test={'rocauc': 0.5819785573765826} adaptive={'rocauc': 0.5894161989454557} steps=7.13986013986014
wrote /home/yurenh2/rrog-gnn-runner/runs/ogbg-molsider_appnp_rrog-act_T1_ns3_stream_hm8_hmin2_loss0.2_lq0.1_hex0.1_qw30_h128_e100_s0.json
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