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[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 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/bbbp.zip
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Processing...
Extracting /home/yurenh2/rrog-gnn-runner/data/ogb/bbbp.zip
Loading necessary files...
This might take a while.
Processing graphs...
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100%|██████████| 2039/2039 [00:00<00:00, 167890.73it/s]
Converting graphs into PyG objects...
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100%|██████████| 2039/2039 [00:00<00:00, 85245.66it/s]
/home/yurenh2/miniconda3/lib/python3.13/site-packages/ogb/graphproppred/dataset_pyg.py:156: UserWarning: The given NumPy array is not writable, and PyTorch does not support non-writable tensors. This means writing to this tensor will result in undefined behavior. You may want to copy the array to protect its data or make it writable before converting it to a tensor. This type of warning will be suppressed for the rest of this program. (Triggered internally at /pytorch/torch/csrc/utils/tensor_numpy.cpp:213.)
g.y = torch.from_numpy(graph_label[i]).view(1,-1).to(torch.long)
Done!
Saving...
ep10 val_rocauc=0.89356 val_adapt_rocauc=0.92960 adapt_steps=2.14 halt=0.41 train_steps=1.93
ep20 val_rocauc=0.84945 val_adapt_rocauc=0.83680 adapt_steps=2.08 halt=0.46 train_steps=1.71
ep30 val_rocauc=0.84815 val_adapt_rocauc=0.92940 adapt_steps=2.03 halt=0.46 train_steps=1.68
ep40 val_rocauc=0.91307 val_adapt_rocauc=0.92452 adapt_steps=2.04 halt=0.46 train_steps=1.64
ep50 val_rocauc=0.92821 val_adapt_rocauc=0.93647 adapt_steps=2.09 halt=0.47 train_steps=1.60
ep60 val_rocauc=0.88709 val_adapt_rocauc=0.91686 adapt_steps=2.06 halt=0.47 train_steps=1.60
ep70 val_rocauc=0.82187 val_adapt_rocauc=0.90979 adapt_steps=2.01 halt=0.47 train_steps=1.60
ep80 val_rocauc=0.86588 val_adapt_rocauc=0.91317 adapt_steps=2.02 halt=0.48 train_steps=1.57
ep90 val_rocauc=0.91586 val_adapt_rocauc=0.91596 adapt_steps=2.02 halt=0.48 train_steps=1.58
ep100 val_rocauc=0.90780 val_adapt_rocauc=0.91327 adapt_steps=2.01 halt=0.47 train_steps=1.60
[ogbg-molbbbp_gin_rrog-act_T1_ns3_stream_hm8_hmin2_exact_lq0.1_hex0.1_qw0_h128_e100_s0] best_ep=50 val={'rocauc': 0.9282087025789106} test={'rocauc': 0.6619405864197531} adaptive={'rocauc': 0.6692708333333333} steps=2.0833333333333335
wrote /home/yurenh2/rrog-gnn-runner/runs/ogbg-molbbbp_gin_rrog-act_T1_ns3_stream_hm8_hmin2_exact_lq0.1_hex0.1_qw0_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 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=0.70039 val_adapt_rocauc=0.92542 adapt_steps=2.07 halt=0.46 train_steps=1.65
ep20 val_rocauc=0.88380 val_adapt_rocauc=0.92642 adapt_steps=2.02 halt=0.47 train_steps=1.62
ep30 val_rocauc=0.79638 val_adapt_rocauc=0.91417 adapt_steps=2.01 halt=0.47 train_steps=1.60
ep40 val_rocauc=0.94295 val_adapt_rocauc=0.95201 adapt_steps=2.01 halt=0.48 train_steps=1.58
ep50 val_rocauc=0.91327 val_adapt_rocauc=0.92164 adapt_steps=2.00 halt=0.48 train_steps=1.56
ep60 val_rocauc=0.91407 val_adapt_rocauc=0.93030 adapt_steps=2.00 halt=0.47 train_steps=1.58
ep70 val_rocauc=0.91716 val_adapt_rocauc=0.92801 adapt_steps=2.00 halt=0.47 train_steps=1.59
ep80 val_rocauc=0.91407 val_adapt_rocauc=0.91825 adapt_steps=2.00 halt=0.48 train_steps=1.57
ep90 val_rocauc=0.90959 val_adapt_rocauc=0.91138 adapt_steps=2.00 halt=0.48 train_steps=1.58
ep100 val_rocauc=0.91228 val_adapt_rocauc=0.91566 adapt_steps=2.00 halt=0.48 train_steps=1.58
[ogbg-molbbbp_gcn_rrog-act_T1_ns3_stream_hm8_hmin2_exact_lq0.1_hex0.1_qw0_h128_e100_s0] best_ep=40 val={'rocauc': 0.9429453350592453} test={'rocauc': 0.6137152777777778} adaptive={'rocauc': 0.675829475308642} steps=2.0
wrote /home/yurenh2/rrog-gnn-runner/runs/ogbg-molbbbp_gcn_rrog-act_T1_ns3_stream_hm8_hmin2_exact_lq0.1_hex0.1_qw0_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 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=0.87613 val_adapt_rocauc=0.92184 adapt_steps=2.00 halt=0.42 train_steps=1.95
ep20 val_rocauc=0.87285 val_adapt_rocauc=0.92134 adapt_steps=2.01 halt=0.46 train_steps=1.74
ep30 val_rocauc=0.87892 val_adapt_rocauc=0.93090 adapt_steps=2.02 halt=0.46 train_steps=1.66
ep40 val_rocauc=0.87315 val_adapt_rocauc=0.92920 adapt_steps=2.05 halt=0.47 train_steps=1.60
ep50 val_rocauc=0.87922 val_adapt_rocauc=0.92104 adapt_steps=2.00 halt=0.48 train_steps=1.57
ep60 val_rocauc=0.89366 val_adapt_rocauc=0.92223 adapt_steps=2.00 halt=0.47 train_steps=1.58
ep70 val_rocauc=0.93468 val_adapt_rocauc=0.94484 adapt_steps=2.00 halt=0.47 train_steps=1.57
ep80 val_rocauc=0.91945 val_adapt_rocauc=0.92811 adapt_steps=2.00 halt=0.48 train_steps=1.56
ep90 val_rocauc=0.92253 val_adapt_rocauc=0.93060 adapt_steps=2.00 halt=0.48 train_steps=1.58
ep100 val_rocauc=0.91925 val_adapt_rocauc=0.92930 adapt_steps=2.00 halt=0.48 train_steps=1.58
[ogbg-molbbbp_sgc_rrog-act_T1_ns3_stream_hm8_hmin2_exact_lq0.1_hex0.1_qw0_h128_e100_s0] best_ep=70 val={'rocauc': 0.9346808722493279} test={'rocauc': 0.6516203703703703} adaptive={'rocauc': 0.6385030864197532} steps=2.0
wrote /home/yurenh2/rrog-gnn-runner/runs/ogbg-molbbbp_sgc_rrog-act_T1_ns3_stream_hm8_hmin2_exact_lq0.1_hex0.1_qw0_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 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=0.78522 val_adapt_rocauc=0.83192 adapt_steps=2.08 halt=0.40 train_steps=2.07
ep20 val_rocauc=0.92472 val_adapt_rocauc=0.93388 adapt_steps=2.22 halt=0.44 train_steps=1.81
ep30 val_rocauc=0.94056 val_adapt_rocauc=0.94852 adapt_steps=2.01 halt=0.46 train_steps=1.66
ep40 val_rocauc=0.89077 val_adapt_rocauc=0.92691 adapt_steps=2.01 halt=0.47 train_steps=1.61
ep50 val_rocauc=0.89605 val_adapt_rocauc=0.92423 adapt_steps=2.03 halt=0.48 train_steps=1.57
ep60 val_rocauc=0.90571 val_adapt_rocauc=0.93647 adapt_steps=2.03 halt=0.47 train_steps=1.60
ep70 val_rocauc=0.91337 val_adapt_rocauc=0.93179 adapt_steps=2.00 halt=0.47 train_steps=1.58
ep80 val_rocauc=0.92512 val_adapt_rocauc=0.93866 adapt_steps=2.01 halt=0.48 train_steps=1.56
ep90 val_rocauc=0.92194 val_adapt_rocauc=0.93418 adapt_steps=2.01 halt=0.48 train_steps=1.58
ep100 val_rocauc=0.92134 val_adapt_rocauc=0.93259 adapt_steps=2.01 halt=0.47 train_steps=1.58
[ogbg-molbbbp_tag_rrog-act_T1_ns3_stream_hm8_hmin2_exact_lq0.1_hex0.1_qw0_h128_e100_s0] best_ep=30 val={'rocauc': 0.9405556108732449} test={'rocauc': 0.635030864197531} adaptive={'rocauc': 0.6146797839506173} steps=2.014705882352941
wrote /home/yurenh2/rrog-gnn-runner/runs/ogbg-molbbbp_tag_rrog-act_T1_ns3_stream_hm8_hmin2_exact_lq0.1_hex0.1_qw0_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 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=0.72269 val_adapt_rocauc=0.93777 adapt_steps=2.29 halt=0.42 train_steps=2.02
ep20 val_rocauc=0.86110 val_adapt_rocauc=0.88460 adapt_steps=2.14 halt=0.47 train_steps=1.61
ep30 val_rocauc=0.88181 val_adapt_rocauc=0.91278 adapt_steps=2.00 halt=0.47 train_steps=1.63
ep40 val_rocauc=0.82027 val_adapt_rocauc=0.88151 adapt_steps=2.06 halt=0.46 train_steps=1.67
ep50 val_rocauc=0.90561 val_adapt_rocauc=0.92393 adapt_steps=2.00 halt=0.47 train_steps=1.57
ep60 val_rocauc=0.86339 val_adapt_rocauc=0.89804 adapt_steps=2.00 halt=0.47 train_steps=1.58
ep70 val_rocauc=0.84397 val_adapt_rocauc=0.90242 adapt_steps=2.00 halt=0.47 train_steps=1.57
ep80 val_rocauc=0.86578 val_adapt_rocauc=0.91955 adapt_steps=2.00 halt=0.48 train_steps=1.56
ep90 val_rocauc=0.84049 val_adapt_rocauc=0.91019 adapt_steps=2.00 halt=0.48 train_steps=1.58
ep100 val_rocauc=0.84556 val_adapt_rocauc=0.91248 adapt_steps=2.00 halt=0.47 train_steps=1.59
[ogbg-molbbbp_graphconv_rrog-act_T1_ns3_stream_hm8_hmin2_exact_lq0.1_hex0.1_qw0_h128_e100_s0] best_ep=50 val={'rocauc': 0.9056058946529921} test={'rocauc': 0.6368634259259259} adaptive={'rocauc': 0.6415895061728395} steps=2.1176470588235294
wrote /home/yurenh2/rrog-gnn-runner/runs/ogbg-molbbbp_graphconv_rrog-act_T1_ns3_stream_hm8_hmin2_exact_lq0.1_hex0.1_qw0_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 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=0.76730 val_adapt_rocauc=0.88778 adapt_steps=2.00 halt=0.47 train_steps=1.59
ep20 val_rocauc=0.87454 val_adapt_rocauc=0.90780 adapt_steps=2.00 halt=0.48 train_steps=1.57
ep30 val_rocauc=0.80006 val_adapt_rocauc=0.87334 adapt_steps=2.00 halt=0.47 train_steps=1.60
ep40 val_rocauc=0.86010 val_adapt_rocauc=0.90411 adapt_steps=2.00 halt=0.48 train_steps=1.58
ep50 val_rocauc=0.87972 val_adapt_rocauc=0.90810 adapt_steps=2.00 halt=0.47 train_steps=1.58
ep60 val_rocauc=0.83292 val_adapt_rocauc=0.88918 adapt_steps=2.01 halt=0.47 train_steps=1.59
ep70 val_rocauc=0.84706 val_adapt_rocauc=0.89893 adapt_steps=2.00 halt=0.47 train_steps=1.59
ep80 val_rocauc=0.83740 val_adapt_rocauc=0.90232 adapt_steps=2.00 halt=0.48 train_steps=1.57
ep90 val_rocauc=0.83670 val_adapt_rocauc=0.90222 adapt_steps=2.00 halt=0.48 train_steps=1.59
ep100 val_rocauc=0.84357 val_adapt_rocauc=0.90252 adapt_steps=2.00 halt=0.47 train_steps=1.60
[ogbg-molbbbp_appnp_rrog-act_T1_ns3_stream_hm8_hmin2_exact_lq0.1_hex0.1_qw0_h128_e100_s0] best_ep=50 val={'rocauc': 0.8797172159713232} test={'rocauc': 0.5456211419753086} adaptive={'rocauc': 0.5880594135802469} steps=2.0098039215686274
wrote /home/yurenh2/rrog-gnn-runner/runs/ogbg-molbbbp_appnp_rrog-act_T1_ns3_stream_hm8_hmin2_exact_lq0.1_hex0.1_qw0_h128_e100_s0.json
[run] ogbg-molbbbp view=pna compute=rrog-act mode=stream T=1 ns=3 seed=0 device=cuda:3
python3 rrog/train_ogb_graphprop.py --dataset ogbg-molbbbp --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=0.92482 val_adapt_rocauc=0.93149 adapt_steps=2.24 halt=0.44 train_steps=1.79
/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=0.50592 val_adapt_rocauc=0.89495 adapt_steps=2.03 halt=0.45 train_steps=1.73
/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=0.93797 val_adapt_rocauc=0.94275 adapt_steps=2.20 halt=0.44 train_steps=1.75
/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=0.85004 val_adapt_rocauc=0.90750 adapt_steps=2.00 halt=0.44 train_steps=1.77
/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=0.88719 val_adapt_rocauc=0.89734 adapt_steps=2.01 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(
ep60 val_rocauc=0.92024 val_adapt_rocauc=0.92930 adapt_steps=2.03 halt=0.46 train_steps=1.65
/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=0.91278 val_adapt_rocauc=0.91975 adapt_steps=2.01 halt=0.47 train_steps=1.62
/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=0.88728 val_adapt_rocauc=0.89485 adapt_steps=2.01 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(
ep90 val_rocauc=0.89605 val_adapt_rocauc=0.89157 adapt_steps=2.00 halt=0.48 train_steps=1.59
/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=0.89704 val_adapt_rocauc=0.89196 adapt_steps=2.00 halt=0.47 train_steps=1.59
[ogbg-molbbbp_pna_rrog-act_T1_ns3_stream_hm8_hmin2_exact_lq0.1_hex0.1_qw0_h128_e100_s0] best_ep=30 val={'rocauc': 0.9379667430050782} test={'rocauc': 0.6539351851851851} adaptive={'rocauc': 0.6563464506172839} steps=2.3137254901960786
wrote /home/yurenh2/rrog-gnn-runner/runs/ogbg-molbbbp_pna_rrog-act_T1_ns3_stream_hm8_hmin2_exact_lq0.1_hex0.1_qw0_h128_e100_s0.json
[run] ogbg-molbbbp view=resgated compute=rrog-act mode=stream T=1 ns=3 seed=0 device=cuda:3
python3 rrog/train_ogb_graphprop.py --dataset ogbg-molbbbp --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=0.80335 val_adapt_rocauc=0.89714 adapt_steps=2.00 halt=0.43 train_steps=1.80
ep20 val_rocauc=0.94992 val_adapt_rocauc=0.95589 adapt_steps=2.15 halt=0.46 train_steps=1.65
ep30 val_rocauc=0.89844 val_adapt_rocauc=0.92413 adapt_steps=2.31 halt=0.46 train_steps=1.60
ep40 val_rocauc=0.88499 val_adapt_rocauc=0.92303 adapt_steps=2.03 halt=0.47 train_steps=1.61
ep50 val_rocauc=0.87145 val_adapt_rocauc=0.91118 adapt_steps=2.02 halt=0.47 train_steps=1.58
ep60 val_rocauc=0.83670 val_adapt_rocauc=0.89266 adapt_steps=2.00 halt=0.47 train_steps=1.58
ep70 val_rocauc=0.83142 val_adapt_rocauc=0.89963 adapt_steps=2.01 halt=0.47 train_steps=1.58
ep80 val_rocauc=0.85273 val_adapt_rocauc=0.90859 adapt_steps=2.00 halt=0.48 train_steps=1.56
ep90 val_rocauc=0.82246 val_adapt_rocauc=0.89346 adapt_steps=2.00 halt=0.48 train_steps=1.58
ep100 val_rocauc=0.83471 val_adapt_rocauc=0.90033 adapt_steps=2.00 halt=0.48 train_steps=1.58
[ogbg-molbbbp_resgated_rrog-act_T1_ns3_stream_hm8_hmin2_exact_lq0.1_hex0.1_qw0_h128_e100_s0] best_ep=20 val={'rocauc': 0.9499153639350792} test={'rocauc': 0.6603973765432098} adaptive={'rocauc': 0.6677276234567902} steps=2.343137254901961
wrote /home/yurenh2/rrog-gnn-runner/runs/ogbg-molbbbp_resgated_rrog-act_T1_ns3_stream_hm8_hmin2_exact_lq0.1_hex0.1_qw0_h128_e100_s0.json
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