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[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 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=0.37199 val_adapt_rocauc=0.37199 adapt_steps=8.00 halt=0.12 train_steps=4.49
ep20 val_rocauc=0.52284 val_adapt_rocauc=0.52284 adapt_steps=8.00 halt=0.12 train_steps=4.50
ep30 val_rocauc=0.50000 val_adapt_rocauc=0.50000 adapt_steps=8.00 halt=0.12 train_steps=4.51
ep40 val_rocauc=0.73429 val_adapt_rocauc=0.74357 adapt_steps=2.21 halt=0.46 train_steps=1.66
ep50 val_rocauc=0.74990 val_adapt_rocauc=0.74007 adapt_steps=2.03 halt=0.45 train_steps=1.79
ep60 val_rocauc=0.73691 val_adapt_rocauc=0.72381 adapt_steps=2.07 halt=0.45 train_steps=1.77
ep70 val_rocauc=0.77428 val_adapt_rocauc=0.77133 adapt_steps=2.06 halt=0.45 train_steps=1.83
ep80 val_rocauc=0.76305 val_adapt_rocauc=0.78219 adapt_steps=2.08 halt=0.45 train_steps=1.83
ep90 val_rocauc=0.75803 val_adapt_rocauc=0.74196 adapt_steps=2.08 halt=0.45 train_steps=1.81
ep100 val_rocauc=0.75552 val_adapt_rocauc=0.75433 adapt_steps=2.07 halt=0.45 train_steps=1.83
[ogbg-molhiv_gin_rrog-act_T1_ns3_stream_hm8_hmin2_loss0.2_lq0.1_hex0.1_qw30_h128_e100_s0] best_ep=70 val={'rocauc': 0.774284122085048} test={'rocauc': 0.6994457212383399} adaptive={'rocauc': 0.6907394889820196} steps=2.0816921954777534
wrote /home/yurenh2/rrog-gnn-runner/runs/ogbg-molhiv_gin_rrog-act_T1_ns3_stream_hm8_hmin2_loss0.2_lq0.1_hex0.1_qw30_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 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=0.61849 val_adapt_rocauc=0.61849 adapt_steps=8.00 halt=0.12 train_steps=4.49
ep20 val_rocauc=0.67236 val_adapt_rocauc=0.67236 adapt_steps=8.00 halt=0.12 train_steps=4.50
ep30 val_rocauc=0.55443 val_adapt_rocauc=0.55443 adapt_steps=8.00 halt=0.12 train_steps=4.51
ep40 val_rocauc=0.67669 val_adapt_rocauc=0.68572 adapt_steps=2.04 halt=0.45 train_steps=1.71
ep50 val_rocauc=0.71379 val_adapt_rocauc=0.74036 adapt_steps=2.05 halt=0.45 train_steps=1.81
ep60 val_rocauc=0.68323 val_adapt_rocauc=0.73792 adapt_steps=2.07 halt=0.44 train_steps=1.83
ep70 val_rocauc=0.68205 val_adapt_rocauc=0.70493 adapt_steps=2.07 halt=0.44 train_steps=1.87
ep80 val_rocauc=0.74377 val_adapt_rocauc=0.75284 adapt_steps=2.09 halt=0.44 train_steps=1.90
ep90 val_rocauc=0.70841 val_adapt_rocauc=0.73329 adapt_steps=2.08 halt=0.44 train_steps=1.88
ep100 val_rocauc=0.72274 val_adapt_rocauc=0.73310 adapt_steps=2.10 halt=0.44 train_steps=1.90
[ogbg-molhiv_gcn_rrog-act_T1_ns3_stream_hm8_hmin2_loss0.2_lq0.1_hex0.1_qw30_h128_e100_s0] best_ep=80 val={'rocauc': 0.7437720458553793} test={'rocauc': 0.7537553834566137} adaptive={'rocauc': 0.7804708472546787} steps=2.135910527595429
wrote /home/yurenh2/rrog-gnn-runner/runs/ogbg-molhiv_gcn_rrog-act_T1_ns3_stream_hm8_hmin2_loss0.2_lq0.1_hex0.1_qw30_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 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=0.58438 val_adapt_rocauc=0.58438 adapt_steps=8.00 halt=0.12 train_steps=4.49
ep20 val_rocauc=0.61779 val_adapt_rocauc=0.61779 adapt_steps=8.00 halt=0.12 train_steps=4.50
ep30 val_rocauc=0.50074 val_adapt_rocauc=0.50074 adapt_steps=8.00 halt=0.12 train_steps=4.51
ep40 val_rocauc=0.54256 val_adapt_rocauc=0.63047 adapt_steps=2.04 halt=0.46 train_steps=1.69
ep50 val_rocauc=0.72845 val_adapt_rocauc=0.69057 adapt_steps=2.09 halt=0.45 train_steps=1.76
ep60 val_rocauc=0.65977 val_adapt_rocauc=0.67932 adapt_steps=2.10 halt=0.45 train_steps=1.79
ep70 val_rocauc=0.70633 val_adapt_rocauc=0.71830 adapt_steps=2.06 halt=0.44 train_steps=1.83
ep80 val_rocauc=0.67811 val_adapt_rocauc=0.73880 adapt_steps=2.06 halt=0.45 train_steps=1.81
ep90 val_rocauc=0.73965 val_adapt_rocauc=0.75724 adapt_steps=2.10 halt=0.45 train_steps=1.83
ep100 val_rocauc=0.72724 val_adapt_rocauc=0.75658 adapt_steps=2.08 halt=0.45 train_steps=1.82
[ogbg-molhiv_sgc_rrog-act_T1_ns3_stream_hm8_hmin2_loss0.2_lq0.1_hex0.1_qw30_h128_e100_s0] best_ep=90 val={'rocauc': 0.7396537575935725} test={'rocauc': 0.7500666293284923} adaptive={'rocauc': 0.7314046234960119} steps=2.136883053732069
wrote /home/yurenh2/rrog-gnn-runner/runs/ogbg-molhiv_sgc_rrog-act_T1_ns3_stream_hm8_hmin2_loss0.2_lq0.1_hex0.1_qw30_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 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=0.56478 val_adapt_rocauc=0.56478 adapt_steps=8.00 halt=0.12 train_steps=4.49
ep20 val_rocauc=0.54631 val_adapt_rocauc=0.54631 adapt_steps=8.00 halt=0.12 train_steps=4.50
ep30 val_rocauc=0.50000 val_adapt_rocauc=0.50000 adapt_steps=8.00 halt=0.12 train_steps=4.51
ep40 val_rocauc=0.67990 val_adapt_rocauc=0.67585 adapt_steps=2.01 halt=0.47 train_steps=1.63
ep50 val_rocauc=0.65561 val_adapt_rocauc=0.69113 adapt_steps=2.15 halt=0.44 train_steps=1.89
ep60 val_rocauc=0.72984 val_adapt_rocauc=0.71413 adapt_steps=2.29 halt=0.44 train_steps=1.90
ep70 val_rocauc=0.70342 val_adapt_rocauc=0.72345 adapt_steps=2.11 halt=0.44 train_steps=1.90
ep80 val_rocauc=0.71017 val_adapt_rocauc=0.70042 adapt_steps=2.07 halt=0.44 train_steps=1.84
ep90 val_rocauc=0.71672 val_adapt_rocauc=0.71984 adapt_steps=2.12 halt=0.44 train_steps=1.83
ep100 val_rocauc=0.71364 val_adapt_rocauc=0.71268 adapt_steps=2.08 halt=0.44 train_steps=1.84
[ogbg-molhiv_tag_rrog-act_T1_ns3_stream_hm8_hmin2_loss0.2_lq0.1_hex0.1_qw30_h128_e100_s0] best_ep=60 val={'rocauc': 0.7298433519498334} test={'rocauc': 0.6791286042604143} adaptive={'rocauc': 0.7162469340852469} steps=2.4831023583758816
wrote /home/yurenh2/rrog-gnn-runner/runs/ogbg-molhiv_tag_rrog-act_T1_ns3_stream_hm8_hmin2_loss0.2_lq0.1_hex0.1_qw30_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 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=0.67715 val_adapt_rocauc=0.67715 adapt_steps=8.00 halt=0.12 train_steps=4.49
ep20 val_rocauc=0.50236 val_adapt_rocauc=0.50236 adapt_steps=8.00 halt=0.12 train_steps=4.50
ep30 val_rocauc=0.50000 val_adapt_rocauc=0.50000 adapt_steps=8.00 halt=0.12 train_steps=4.51
ep40 val_rocauc=0.73049 val_adapt_rocauc=0.72163 adapt_steps=2.04 halt=0.45 train_steps=1.72
ep50 val_rocauc=0.70399 val_adapt_rocauc=0.70432 adapt_steps=2.04 halt=0.44 train_steps=1.84
ep60 val_rocauc=0.74930 val_adapt_rocauc=0.75474 adapt_steps=2.05 halt=0.44 train_steps=1.83
ep70 val_rocauc=0.73212 val_adapt_rocauc=0.76013 adapt_steps=2.09 halt=0.44 train_steps=1.83
ep80 val_rocauc=0.75377 val_adapt_rocauc=0.76654 adapt_steps=2.07 halt=0.45 train_steps=1.81
ep90 val_rocauc=0.75301 val_adapt_rocauc=0.77432 adapt_steps=2.10 halt=0.45 train_steps=1.82
ep100 val_rocauc=0.73251 val_adapt_rocauc=0.78027 adapt_steps=2.07 halt=0.45 train_steps=1.82
[ogbg-molhiv_graphconv_rrog-act_T1_ns3_stream_hm8_hmin2_loss0.2_lq0.1_hex0.1_qw30_h128_e100_s0] best_ep=80 val={'rocauc': 0.7537722908093278} test={'rocauc': 0.7703277390447865} adaptive={'rocauc': 0.764460495567701} steps=2.0678336980306344
wrote /home/yurenh2/rrog-gnn-runner/runs/ogbg-molhiv_graphconv_rrog-act_T1_ns3_stream_hm8_hmin2_loss0.2_lq0.1_hex0.1_qw30_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 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=0.67201 val_adapt_rocauc=0.67201 adapt_steps=8.00 halt=0.12 train_steps=4.49
ep20 val_rocauc=0.62233 val_adapt_rocauc=0.62233 adapt_steps=8.00 halt=0.12 train_steps=4.50
ep30 val_rocauc=0.65638 val_adapt_rocauc=0.65638 adapt_steps=8.00 halt=0.12 train_steps=4.51
ep40 val_rocauc=0.72057 val_adapt_rocauc=0.68850 adapt_steps=2.16 halt=0.44 train_steps=1.89
ep50 val_rocauc=0.70843 val_adapt_rocauc=0.70940 adapt_steps=2.15 halt=0.44 train_steps=1.85
ep60 val_rocauc=0.64904 val_adapt_rocauc=0.64008 adapt_steps=2.15 halt=0.44 train_steps=1.90
ep70 val_rocauc=0.65729 val_adapt_rocauc=0.64703 adapt_steps=2.09 halt=0.44 train_steps=1.85
ep80 val_rocauc=0.68292 val_adapt_rocauc=0.68777 adapt_steps=2.09 halt=0.44 train_steps=1.85
ep90 val_rocauc=0.69379 val_adapt_rocauc=0.68351 adapt_steps=2.09 halt=0.44 train_steps=1.84
ep100 val_rocauc=0.68502 val_adapt_rocauc=0.67506 adapt_steps=2.09 halt=0.45 train_steps=1.81
[ogbg-molhiv_appnp_rrog-act_T1_ns3_stream_hm8_hmin2_loss0.2_lq0.1_hex0.1_qw30_h128_e100_s0] best_ep=40 val={'rocauc': 0.720568783068783} test={'rocauc': 0.7274165202108962} adaptive={'rocauc': 0.747511539427181} steps=2.2569900316070997
wrote /home/yurenh2/rrog-gnn-runner/runs/ogbg-molhiv_appnp_rrog-act_T1_ns3_stream_hm8_hmin2_loss0.2_lq0.1_hex0.1_qw30_h128_e100_s0.json
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