[run] ogbg-molbbbp view=gin compute=rrog-act mode=stream T=1 ns=3 seed=0 device=cuda:3
python3 rrog/train_ogb_graphprop.py --dataset ogbg-molbbbp --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=0.44429 val_adapt_rocauc=0.44429 adapt_steps=8.00 halt=0.17 train_steps=5.17
ep20 val_rocauc=0.86896 val_adapt_rocauc=0.86896 adapt_steps=8.00 halt=0.17 train_steps=5.17
ep30 val_rocauc=0.85911 val_adapt_rocauc=0.85911 adapt_steps=8.00 halt=0.17 train_steps=5.17
ep40 val_rocauc=0.90103 val_adapt_rocauc=0.93627 adapt_steps=3.02 halt=0.35 train_steps=2.59
ep50 val_rocauc=0.85224 val_adapt_rocauc=0.91536 adapt_steps=3.34 halt=0.30 train_steps=2.99
ep60 val_rocauc=0.94265 val_adapt_rocauc=0.94404 adapt_steps=3.14 halt=0.36 train_steps=2.40
ep70 val_rocauc=0.93976 val_adapt_rocauc=0.93936 adapt_steps=2.47 halt=0.36 train_steps=2.46
ep80 val_rocauc=0.91945 val_adapt_rocauc=0.91546 adapt_steps=2.47 halt=0.37 train_steps=2.42
ep90 val_rocauc=0.92034 val_adapt_rocauc=0.92194 adapt_steps=2.34 halt=0.38 train_steps=2.37
ep100 val_rocauc=0.91835 val_adapt_rocauc=0.91855 adapt_steps=2.29 halt=0.37 train_steps=2.31
[ogbg-molbbbp_gin_rrog-act_T1_ns3_stream_hm8_hmin2_loss0.2_lq0.1_hex0.1_qw30_h128_e100_s0] best_ep=60 val={'rocauc': 0.9426466195359953} test={'rocauc': 0.6331018518518519} adaptive={'rocauc': 0.638406635802469} steps=3.2058823529411766
  wrote /home/yurenh2/rrog-gnn-runner/runs/ogbg-molbbbp_gin_rrog-act_T1_ns3_stream_hm8_hmin2_loss0.2_lq0.1_hex0.1_qw30_h128_e100_s0.json
[run] ogbg-molbbbp view=gcn compute=rrog-act mode=stream T=1 ns=3 seed=0 device=cuda:3
python3 rrog/train_ogb_graphprop.py --dataset ogbg-molbbbp --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=0.66982 val_adapt_rocauc=0.66982 adapt_steps=8.00 halt=0.17 train_steps=5.17
ep20 val_rocauc=0.83013 val_adapt_rocauc=0.83013 adapt_steps=8.00 halt=0.17 train_steps=5.17
ep30 val_rocauc=0.91039 val_adapt_rocauc=0.91039 adapt_steps=8.00 halt=0.17 train_steps=5.17
ep40 val_rocauc=0.91805 val_adapt_rocauc=0.92472 adapt_steps=3.05 halt=0.33 train_steps=2.69
ep50 val_rocauc=0.90451 val_adapt_rocauc=0.92761 adapt_steps=2.40 halt=0.33 train_steps=2.70
ep60 val_rocauc=0.86080 val_adapt_rocauc=0.91168 adapt_steps=2.52 halt=0.33 train_steps=2.74
ep70 val_rocauc=0.93438 val_adapt_rocauc=0.93637 adapt_steps=2.38 halt=0.39 train_steps=2.22
ep80 val_rocauc=0.92761 val_adapt_rocauc=0.93229 adapt_steps=2.22 halt=0.39 train_steps=2.17
ep90 val_rocauc=0.92462 val_adapt_rocauc=0.92731 adapt_steps=2.14 halt=0.42 train_steps=1.99
ep100 val_rocauc=0.92881 val_adapt_rocauc=0.93189 adapt_steps=2.14 halt=0.42 train_steps=1.97
[ogbg-molbbbp_gcn_rrog-act_T1_ns3_stream_hm8_hmin2_loss0.2_lq0.1_hex0.1_qw30_h128_e100_s0] best_ep=70 val={'rocauc': 0.9343821567260779} test={'rocauc': 0.6946373456790124} adaptive={'rocauc': 0.6956983024691358} steps=3.0049019607843137
  wrote /home/yurenh2/rrog-gnn-runner/runs/ogbg-molbbbp_gcn_rrog-act_T1_ns3_stream_hm8_hmin2_loss0.2_lq0.1_hex0.1_qw30_h128_e100_s0.json
[run] ogbg-molbbbp view=sgc compute=rrog-act mode=stream T=1 ns=3 seed=0 device=cuda:3
python3 rrog/train_ogb_graphprop.py --dataset ogbg-molbbbp --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=0.73623 val_adapt_rocauc=0.73623 adapt_steps=8.00 halt=0.17 train_steps=5.17
ep20 val_rocauc=0.90551 val_adapt_rocauc=0.90551 adapt_steps=8.00 halt=0.17 train_steps=5.17
ep30 val_rocauc=0.93329 val_adapt_rocauc=0.93329 adapt_steps=8.00 halt=0.17 train_steps=5.17
ep40 val_rocauc=0.95221 val_adapt_rocauc=0.94633 adapt_steps=2.30 halt=0.36 train_steps=2.50
ep50 val_rocauc=0.83879 val_adapt_rocauc=0.89077 adapt_steps=2.45 halt=0.38 train_steps=2.43
ep60 val_rocauc=0.93149 val_adapt_rocauc=0.93657 adapt_steps=2.44 halt=0.38 train_steps=2.33
ep70 val_rocauc=0.94842 val_adapt_rocauc=0.94982 adapt_steps=2.18 halt=0.41 train_steps=1.99
ep80 val_rocauc=0.92452 val_adapt_rocauc=0.92960 adapt_steps=2.18 halt=0.43 train_steps=1.93
ep90 val_rocauc=0.93100 val_adapt_rocauc=0.93269 adapt_steps=2.17 halt=0.44 train_steps=1.85
ep100 val_rocauc=0.92502 val_adapt_rocauc=0.92522 adapt_steps=2.18 halt=0.44 train_steps=1.84
[ogbg-molbbbp_sgc_rrog-act_T1_ns3_stream_hm8_hmin2_loss0.2_lq0.1_hex0.1_qw30_h128_e100_s0] best_ep=40 val={'rocauc': 0.9522055162799961} test={'rocauc': 0.6266396604938272} adaptive={'rocauc': 0.6264467592592592} steps=2.7892156862745097
  wrote /home/yurenh2/rrog-gnn-runner/runs/ogbg-molbbbp_sgc_rrog-act_T1_ns3_stream_hm8_hmin2_loss0.2_lq0.1_hex0.1_qw30_h128_e100_s0.json
[run] ogbg-molbbbp view=tag compute=rrog-act mode=stream T=1 ns=3 seed=0 device=cuda:3
python3 rrog/train_ogb_graphprop.py --dataset ogbg-molbbbp --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=0.35647 val_adapt_rocauc=0.35647 adapt_steps=8.00 halt=0.17 train_steps=5.17
ep20 val_rocauc=0.80783 val_adapt_rocauc=0.80783 adapt_steps=8.00 halt=0.17 train_steps=5.17
ep30 val_rocauc=0.83362 val_adapt_rocauc=0.83362 adapt_steps=8.00 halt=0.17 train_steps=5.17
ep40 val_rocauc=0.90670 val_adapt_rocauc=0.90869 adapt_steps=3.12 halt=0.30 train_steps=2.90
ep50 val_rocauc=0.91915 val_adapt_rocauc=0.92751 adapt_steps=2.87 halt=0.34 train_steps=2.55
ep60 val_rocauc=0.93627 val_adapt_rocauc=0.93707 adapt_steps=2.55 halt=0.37 train_steps=2.44
ep70 val_rocauc=0.95350 val_adapt_rocauc=0.94932 adapt_steps=2.43 halt=0.37 train_steps=2.44
ep80 val_rocauc=0.92582 val_adapt_rocauc=0.92721 adapt_steps=2.32 halt=0.39 train_steps=2.31
ep90 val_rocauc=0.92662 val_adapt_rocauc=0.92662 adapt_steps=2.21 halt=0.41 train_steps=2.10
ep100 val_rocauc=0.94155 val_adapt_rocauc=0.94175 adapt_steps=2.19 halt=0.41 train_steps=2.06
[ogbg-molbbbp_tag_rrog-act_T1_ns3_stream_hm8_hmin2_loss0.2_lq0.1_hex0.1_qw30_h128_e100_s0] best_ep=70 val={'rocauc': 0.9534999502140795} test={'rocauc': 0.6597222222222222} adaptive={'rocauc': 0.6678240740740741} steps=3.2450980392156863
  wrote /home/yurenh2/rrog-gnn-runner/runs/ogbg-molbbbp_tag_rrog-act_T1_ns3_stream_hm8_hmin2_loss0.2_lq0.1_hex0.1_qw30_h128_e100_s0.json
[run] ogbg-molbbbp view=graphconv compute=rrog-act mode=stream T=1 ns=3 seed=0 device=cuda:3
python3 rrog/train_ogb_graphprop.py --dataset ogbg-molbbbp --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=0.84039 val_adapt_rocauc=0.84039 adapt_steps=8.00 halt=0.17 train_steps=5.17
ep20 val_rocauc=0.75326 val_adapt_rocauc=0.75326 adapt_steps=8.00 halt=0.17 train_steps=5.17
ep30 val_rocauc=0.87265 val_adapt_rocauc=0.87265 adapt_steps=8.00 halt=0.17 train_steps=5.17
ep40 val_rocauc=0.93976 val_adapt_rocauc=0.93886 adapt_steps=2.80 halt=0.32 train_steps=2.66
ep50 val_rocauc=0.91805 val_adapt_rocauc=0.93149 adapt_steps=2.47 halt=0.40 train_steps=2.15
ep60 val_rocauc=0.88619 val_adapt_rocauc=0.89923 adapt_steps=2.45 halt=0.36 train_steps=2.35
ep70 val_rocauc=0.94324 val_adapt_rocauc=0.94324 adapt_steps=2.54 halt=0.39 train_steps=2.18
ep80 val_rocauc=0.94952 val_adapt_rocauc=0.95021 adapt_steps=2.11 halt=0.42 train_steps=2.05
ep90 val_rocauc=0.94623 val_adapt_rocauc=0.95330 adapt_steps=2.09 halt=0.43 train_steps=1.95
ep100 val_rocauc=0.94524 val_adapt_rocauc=0.95081 adapt_steps=2.14 halt=0.42 train_steps=1.96
[ogbg-molbbbp_graphconv_rrog-act_T1_ns3_stream_hm8_hmin2_loss0.2_lq0.1_hex0.1_qw30_h128_e100_s0] best_ep=80 val={'rocauc': 0.9495170765707459} test={'rocauc': 0.6861496913580247} adaptive={'rocauc': 0.6808449074074074} steps=2.7058823529411766
  wrote /home/yurenh2/rrog-gnn-runner/runs/ogbg-molbbbp_graphconv_rrog-act_T1_ns3_stream_hm8_hmin2_loss0.2_lq0.1_hex0.1_qw30_h128_e100_s0.json
[run] ogbg-molbbbp view=appnp compute=rrog-act mode=stream T=1 ns=3 seed=0 device=cuda:3
python3 rrog/train_ogb_graphprop.py --dataset ogbg-molbbbp --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=0.84487 val_adapt_rocauc=0.84487 adapt_steps=8.00 halt=0.17 train_steps=5.17
ep20 val_rocauc=0.83471 val_adapt_rocauc=0.83471 adapt_steps=8.00 halt=0.17 train_steps=5.17
ep30 val_rocauc=0.89664 val_adapt_rocauc=0.89664 adapt_steps=8.00 halt=0.17 train_steps=5.17
ep40 val_rocauc=0.87255 val_adapt_rocauc=0.91128 adapt_steps=2.08 halt=0.46 train_steps=1.69
ep50 val_rocauc=0.81778 val_adapt_rocauc=0.90391 adapt_steps=2.08 halt=0.44 train_steps=1.83
ep60 val_rocauc=0.86707 val_adapt_rocauc=0.90909 adapt_steps=2.05 halt=0.45 train_steps=1.73
ep70 val_rocauc=0.86588 val_adapt_rocauc=0.90282 adapt_steps=2.05 halt=0.45 train_steps=1.71
ep80 val_rocauc=0.89664 val_adapt_rocauc=0.92423 adapt_steps=2.01 halt=0.47 train_steps=1.61
ep90 val_rocauc=0.87175 val_adapt_rocauc=0.90750 adapt_steps=2.02 halt=0.47 train_steps=1.67
ep100 val_rocauc=0.86787 val_adapt_rocauc=0.90670 adapt_steps=2.04 halt=0.47 train_steps=1.62
[ogbg-molbbbp_appnp_rrog-act_T1_ns3_stream_hm8_hmin2_loss0.2_lq0.1_hex0.1_qw30_h128_e100_s0] best_ep=80 val={'rocauc': 0.8966444289554915} test={'rocauc': 0.6170910493827161} adaptive={'rocauc': 0.6361882716049383} steps=2.0049019607843137
  wrote /home/yurenh2/rrog-gnn-runner/runs/ogbg-molbbbp_appnp_rrog-act_T1_ns3_stream_hm8_hmin2_loss0.2_lq0.1_hex0.1_qw30_h128_e100_s0.json