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search_gccn_cell.sh
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python -m topobenchmark \
dataset=graph/NCI109 \
model=cell/topotune,cell/topotune_onehasse \
model.feature_encoder.out_channels=32 \
model.tune_gnn=GCN,GIN,GAT,GraphSAGE \
model.backbone.GNN.num_layers=1,2 \
model.backbone.neighborhoods=\[1-up_laplacian-0,1-down_laplacian-1,1-up_laplacian-1,1-down_laplacian-2\],\[1-up_laplacian-0,1-up_incidence-0,1-down_incidence-1,1-down_laplacian-1,1-up_laplacian-1,1-up_incidence-1,1-down_incidence-2,1-down_laplacian-2\],\[1-up_laplacian-0,1-down_incidence-1,1-up_laplacian-1,1-down_incidence-2\],\[1-up_laplacian-0,1-up_incidence-0,1-up_laplacian-1,1-up_incidence-1\],\[1-up_laplacian-0,1-down_incidence-1,1-down_laplacian-1,1-up_laplacian-1,1-down_incidence-2,1-down_laplacian-2\] \
model.backbone.layers=2,4,8 \
model.feature_encoder.proj_dropout=0.3 \
dataset.split_params.data_seed=1,3,5,7,9 \
model.readout.readout_name=PropagateSignalDown \
logger.wandb.project=TopoTune_cell \
trainer.max_epochs=1000 \
trainer.min_epochs=50 \
trainer.devices=\[0\] \
trainer.check_val_every_n_epoch=1 \
callbacks.early_stopping.patience=50 \
tags="[FirstExperiments]" \
--multirun &
python -m topobenchmark \
dataset=graph/cocitation_cora \
model=cell/topotune,cell/topotune_onehasse \
model.feature_encoder.out_channels=32 \
model.tune_gnn=GCN,GIN,GAT,GraphSAGE \
model.backbone.GNN.num_layers=1,2 \
model.backbone.neighborhoods=\[1-up_laplacian-0,1-down_laplacian-1,1-up_laplacian-1,1-down_laplacian-2\],\[1-up_laplacian-0,1-up_incidence-0,1-down_incidence-1,1-down_laplacian-1,1-up_laplacian-1,1-up_incidence-1,1-down_incidence-2,1-down_laplacian-2\],\[1-up_laplacian-0,1-down_incidence-1,1-up_laplacian-1,1-down_incidence-2\],\[1-up_laplacian-0,1-up_incidence-0,1-up_laplacian-1,1-up_incidence-1\],\[1-up_laplacian-0,1-down_incidence-1,1-down_laplacian-1,1-up_laplacian-1,1-down_incidence-2,1-down_laplacian-2\] \
model.backbone.layers=2,4,8 \
model.feature_encoder.proj_dropout=0.3 \
dataset.split_params.data_seed=1,3,5,7,9 \
model.readout.readout_name=PropagateSignalDown \
logger.wandb.project=TopoTune_cell \
trainer.max_epochs=1000 \
trainer.min_epochs=50 \
trainer.devices=\[1\] \
trainer.check_val_every_n_epoch=1 \
callbacks.early_stopping.patience=50 \
tags="[FirstExperiments]" \
--multirun &
python -m topobenchmark \
dataset=graph/PROTEINS \
model=cell/topotune,cell/topotune_onehasse \
model.feature_encoder.out_channels=32 \
model.tune_gnn=GCN,GIN,GAT,GraphSAGE \
model.backbone.GNN.num_layers=1,2 \
model.backbone.neighborhoods=\[1-up_laplacian-0,1-down_laplacian-1,1-up_laplacian-1,1-down_laplacian-2\],\[1-up_laplacian-0,1-up_incidence-0,1-down_incidence-1,1-down_laplacian-1,1-up_laplacian-1,1-up_incidence-1,1-down_incidence-2,1-down_laplacian-2\],\[1-up_laplacian-0,1-down_incidence-1,1-up_laplacian-1,1-down_incidence-2\],\[1-up_laplacian-0,1-up_incidence-0,1-up_laplacian-1,1-up_incidence-1\],\[1-up_laplacian-0,1-down_incidence-1,1-down_laplacian-1,1-up_laplacian-1,1-down_incidence-2,1-down_laplacian-2\] \
model.backbone.layers=2,4,8 \
model.feature_encoder.proj_dropout=0.3 \
dataset.split_params.data_seed=1,3,5,7,9 \
model.readout.readout_name=PropagateSignalDown \
logger.wandb.project=TopoTune_cell \
trainer.max_epochs=1000 \
trainer.min_epochs=50 \
trainer.devices=\[2\] \
trainer.check_val_every_n_epoch=1 \
callbacks.early_stopping.patience=50 \
tags="[FirstExperiments]" \
--multirun &
python -m topobenchmark \
dataset=graph/MUTAG \
model=cell/topotune,cell/topotune_onehasse \
model.feature_encoder.out_channels=32 \
model.tune_gnn=GCN,GIN,GAT,GraphSAGE \
model.backbone.GNN.num_layers=1,2 \
model.backbone.neighborhoods=\[1-up_laplacian-0,1-down_laplacian-1,1-up_laplacian-1,1-down_laplacian-2\],\[1-up_laplacian-0,1-up_incidence-0,1-down_incidence-1,1-down_laplacian-1,1-up_laplacian-1,1-up_incidence-1,1-down_incidence-2,1-down_laplacian-2\],\[1-up_laplacian-0,1-down_incidence-1,1-up_laplacian-1,1-down_incidence-2\],\[1-up_laplacian-0,1-up_incidence-0,1-up_laplacian-1,1-up_incidence-1\],\[1-up_laplacian-0,1-down_incidence-1,1-down_laplacian-1,1-up_laplacian-1,1-down_incidence-2,1-down_laplacian-2\] \
model.backbone.layers=2,4,8 \
model.feature_encoder.proj_dropout=0.3 \
dataset.split_params.data_seed=1,3,5,7,9 \
model.readout.readout_name=PropagateSignalDown \
logger.wandb.project=TopoTune_cell \
trainer.max_epochs=1000 \
trainer.min_epochs=50 \
trainer.devices=\[3\] \
trainer.check_val_every_n_epoch=1 \
callbacks.early_stopping.patience=50 \
tags="[FirstExperiments]" \
--multirun &
python -m topobenchmark \
dataset=graph/ZINC \
model=cell/topotune,cell/topotune_onehasse \
model.feature_encoder.out_channels=32 \
model.tune_gnn=GCN,GIN,GAT,GraphSAGE \
model.backbone.GNN.num_layers=1,2 \
model.backbone.neighborhoods=\[1-up_laplacian-0,1-down_laplacian-1,1-up_laplacian-1,1-down_laplacian-2\],\[1-up_laplacian-0,1-up_incidence-0,1-down_incidence-1,1-down_laplacian-1,1-up_laplacian-1,1-up_incidence-1,1-down_incidence-2,1-down_laplacian-2\],\[1-up_laplacian-0,1-down_incidence-1,1-up_laplacian-1,1-down_incidence-2\],\[1-up_laplacian-0,1-up_incidence-0,1-up_laplacian-1,1-up_incidence-1\],\[1-up_laplacian-0,1-down_incidence-1,1-down_laplacian-1,1-up_laplacian-1,1-down_incidence-2,1-down_laplacian-2\] \
model.backbone.layers=2,4,8 \
model.feature_encoder.proj_dropout=0.3 \
dataset.split_params.data_seed=1,3,5,7,9 \
model.readout.readout_name=PropagateSignalDown \
transforms.one_hot_node_degree_features.degrees_fields=x \
logger.wandb.project=TopoTune_cell \
trainer.max_epochs=1000 \
trainer.min_epochs=50 \
trainer.devices=\[3\] \
trainer.check_val_every_n_epoch=1 \
callbacks.early_stopping.patience=50 \
tags="[FirstExperiments]" \
--multirun &
python -m topobenchmark \
dataset=graph/cocitation_citeseer \
model=cell/topotune,cell/topotune_onehasse \
model.feature_encoder.out_channels=32 \
model.tune_gnn=GCN,GIN,GAT,GraphSAGE \
model.backbone.GNN.num_layers=1,2 \
model.backbone.neighborhoods=\[1-up_laplacian-0,1-down_laplacian-1,1-up_laplacian-1,1-down_laplacian-2\],\[1-up_laplacian-0,1-up_incidence-0,1-down_incidence-1,1-down_laplacian-1,1-up_laplacian-1,1-up_incidence-1,1-down_incidence-2,1-down_laplacian-2\],\[1-up_laplacian-0,1-down_incidence-1,1-up_laplacian-1,1-down_incidence-2\],\[1-up_laplacian-0,1-up_incidence-0,1-up_laplacian-1,1-up_incidence-1\],\[1-up_laplacian-0,1-down_incidence-1,1-down_laplacian-1,1-up_laplacian-1,1-down_incidence-2,1-down_laplacian-2\] \
model.backbone.layers=2,4,8 \
model.feature_encoder.proj_dropout=0.3 \
dataset.split_params.data_seed=1,3,5,7,9 \
model.readout.readout_name=PropagateSignalDown \
logger.wandb.project=TopoTune_cell \
trainer.max_epochs=1000 \
trainer.min_epochs=50 \
trainer.devices=\[4\] \
trainer.check_val_every_n_epoch=1 \
callbacks.early_stopping.patience=50 \
tags="[FirstExperiments]" \
--multirun &
python -m topobenchmark \
dataset=graph/NCI1 \
model=cell/topotune,cell/topotune_onehasse \
model.feature_encoder.out_channels=32 \
model.tune_gnn=GCN,GIN,GAT,GraphSAGE \
model.backbone.GNN.num_layers=1,2 \
model.backbone.neighborhoods=\[1-up_laplacian-0,1-down_laplacian-1,1-up_laplacian-1,1-down_laplacian-2\],\[1-up_laplacian-0,1-up_incidence-0,1-down_incidence-1,1-down_laplacian-1,1-up_laplacian-1,1-up_incidence-1,1-down_incidence-2,1-down_laplacian-2\],\[1-up_laplacian-0,1-down_incidence-1,1-up_laplacian-1,1-down_incidence-2\],\[1-up_laplacian-0,1-up_incidence-0,1-up_laplacian-1,1-up_incidence-1\],\[1-up_laplacian-0,1-down_incidence-1,1-down_laplacian-1,1-up_laplacian-1,1-down_incidence-2,1-down_laplacian-2\] \
model.backbone.layers=2,4,8 \
model.feature_encoder.proj_dropout=0.3 \
dataset.split_params.data_seed=1,3,5,7,9 \
model.readout.readout_name=PropagateSignalDown \
logger.wandb.project=TopoTune_cell \
trainer.max_epochs=1000 \
trainer.min_epochs=50 \
trainer.devices=\[6\] \
trainer.check_val_every_n_epoch=1 \
callbacks.early_stopping.patience=50 \
tags="[FirstExperiments]" \
--multirun &
python -m topobenchmark \
dataset=graph/cocitation_pubmed \
model=cell/topotune,cell/topotune_onehasse \
model.feature_encoder.out_channels=32 \
model.tune_gnn=GCN,GIN,GAT,GraphSAGE \
model.backbone.GNN.num_layers=1,2 \
model.backbone.neighborhoods=\[1-up_laplacian-0,1-down_laplacian-1,1-up_laplacian-1,1-down_laplacian-2\],\[1-up_laplacian-0,1-up_incidence-0,1-down_incidence-1,1-down_laplacian-1,1-up_laplacian-1,1-up_incidence-1,1-down_incidence-2,1-down_laplacian-2\],\[1-up_laplacian-0,1-down_incidence-1,1-up_laplacian-1,1-down_incidence-2\],\[1-up_laplacian-0,1-up_incidence-0,1-up_laplacian-1,1-up_incidence-1\],\[1-up_laplacian-0,1-down_incidence-1,1-down_laplacian-1,1-up_laplacian-1,1-down_incidence-2,1-down_laplacian-2\] \
model.backbone.layers=2,4,8 \
model.feature_encoder.proj_dropout=0.3 \
dataset.split_params.data_seed=1,3,5,7,9 \
model.readout.readout_name=PropagateSignalDown \
logger.wandb.project=TopoTune_cell \
trainer.max_epochs=1000 \
trainer.min_epochs=50 \
trainer.devices=\[7\] \
trainer.check_val_every_n_epoch=1 \
callbacks.early_stopping.patience=50 \
tags="[FirstExperiments]" \
--multirun &
python -m topobenchmark \
dataset=graph/NCI109 \
model=cell/topotune,cell/topotune_onehasse \
model.feature_encoder.out_channels=32 \
model.tune_gnn=GCN,GIN,GAT,GraphSAGE \
model.backbone.GNN.num_layers=1,2 \
model.backbone.neighborhoods=\[1-up_laplacian-0,1-up_incidence-0,1-down_laplacian-1,1-up_laplacian-1,1-up_incidence-1,1-down_laplacian-2\],\[1-up_laplacian-0,1-down_laplacian-1\],\[1-up_laplacian-0,1-up_laplacian-1\],\[1-up_laplacian-0,1-up_laplacian-1,1-down_laplacian-1\],\[1-up_laplacian-0,1-down_incidence-2\] \
model.backbone.layers=2,4,8 \
model.feature_encoder.proj_dropout=0.3 \
dataset.split_params.data_seed=1,3,5,7,9 \
model.readout.readout_name=PropagateSignalDown \
logger.wandb.project=TopoTune_cell \
trainer.max_epochs=1000 \
trainer.min_epochs=50 \
trainer.devices=\[0\] \
trainer.check_val_every_n_epoch=1 \
callbacks.early_stopping.patience=50 \
tags="[FirstExperiments]" \
--multirun &
python -m topobenchmark \
dataset=graph/cocitation_cora \
model=cell/topotune,cell/topotune_onehasse \
model.feature_encoder.out_channels=32 \
model.tune_gnn=GCN,GIN,GAT,GraphSAGE \
model.backbone.GNN.num_layers=1,2 \
model.backbone.neighborhoods=\[1-up_laplacian-0,1-up_incidence-0,1-down_laplacian-1,1-up_laplacian-1,1-up_incidence-1,1-down_laplacian-2\],\[1-up_laplacian-0,1-down_laplacian-1\],\[1-up_laplacian-0,1-up_laplacian-1\],\[1-up_laplacian-0,1-up_laplacian-1,1-down_laplacian-1\],\[1-up_laplacian-0,1-down_incidence-2\] \
model.backbone.layers=2,4,8 \
model.feature_encoder.proj_dropout=0.3 \
dataset.split_params.data_seed=1,3,5,7,9 \
model.readout.readout_name=PropagateSignalDown \
logger.wandb.project=TopoTune_cell \
trainer.max_epochs=1000 \
trainer.min_epochs=50 \
trainer.devices=\[1\] \
trainer.check_val_every_n_epoch=1 \
callbacks.early_stopping.patience=50 \
tags="[FirstExperiments]" \
--multirun &
python -m topobenchmark \
dataset=graph/PROTEINS \
model=cell/topotune,cell/topotune_onehasse \
model.feature_encoder.out_channels=32 \
model.tune_gnn=GCN,GIN,GAT,GraphSAGE \
model.backbone.GNN.num_layers=1,2 \
model.backbone.neighborhoods=\[1-up_laplacian-0,1-up_incidence-0,1-down_laplacian-1,1-up_laplacian-1,1-up_incidence-1,1-down_laplacian-2\],\[1-up_laplacian-0,1-down_laplacian-1\],\[1-up_laplacian-0,1-up_laplacian-1\],\[1-up_laplacian-0,1-up_laplacian-1,1-down_laplacian-1\],\[1-up_laplacian-0,1-down_incidence-2\] \
model.backbone.layers=2,4,8 \
model.feature_encoder.proj_dropout=0.3 \
dataset.split_params.data_seed=1,3,5,7,9 \
model.readout.readout_name=PropagateSignalDown \
logger.wandb.project=TopoTune_cell \
trainer.max_epochs=1000 \
trainer.min_epochs=50 \
trainer.devices=\[2\] \
trainer.check_val_every_n_epoch=1 \
callbacks.early_stopping.patience=50 \
tags="[FirstExperiments]" \
--multirun &
python -m topobenchmark \
dataset=graph/MUTAG \
model=cell/topotune,cell/topotune_onehasse \
model.feature_encoder.out_channels=32 \
model.tune_gnn=GCN,GIN,GAT,GraphSAGE \
model.backbone.GNN.num_layers=1,2 \
model.backbone.neighborhoods=\[1-up_laplacian-0,1-up_incidence-0,1-down_laplacian-1,1-up_laplacian-1,1-up_incidence-1,1-down_laplacian-2\],\[1-up_laplacian-0,1-down_laplacian-1\],\[1-up_laplacian-0,1-up_laplacian-1\],\[1-up_laplacian-0,1-up_laplacian-1,1-down_laplacian-1\],\[1-up_laplacian-0,1-down_incidence-2\] \
model.backbone.layers=2,4,8 \
model.feature_encoder.proj_dropout=0.3 \
dataset.split_params.data_seed=1,3,5,7,9 \
model.readout.readout_name=PropagateSignalDown \
logger.wandb.project=TopoTune_cell \
trainer.max_epochs=1000 \
trainer.min_epochs=50 \
trainer.devices=\[3\] \
trainer.check_val_every_n_epoch=1 \
callbacks.early_stopping.patience=50 \
tags="[FirstExperiments]" \
--multirun &
python -m topobenchmark \
dataset=graph/cocitation_citeseer \
model=cell/topotune,cell/topotune_onehasse \
model.feature_encoder.out_channels=32 \
model.tune_gnn=GCN,GIN,GAT,GraphSAGE \
model.backbone.GNN.num_layers=1,2 \
model.backbone.neighborhoods=\[1-up_laplacian-0,1-up_incidence-0,1-down_laplacian-1,1-up_laplacian-1,1-up_incidence-1,1-down_laplacian-2\],\[1-up_laplacian-0,1-down_laplacian-1\],\[1-up_laplacian-0,1-up_laplacian-1\],\[1-up_laplacian-0,1-up_laplacian-1,1-down_laplacian-1\],\[1-up_laplacian-0,1-down_incidence-2\] \
model.backbone.layers=2,4,8 \
model.feature_encoder.proj_dropout=0.3 \
dataset.split_params.data_seed=1,3,5,7,9 \
model.readout.readout_name=PropagateSignalDown \
logger.wandb.project=TopoTune_cell \
trainer.max_epochs=1000 \
trainer.min_epochs=50 \
trainer.devices=\[4\] \
trainer.check_val_every_n_epoch=1 \
callbacks.early_stopping.patience=50 \
tags="[FirstExperiments]" \
--multirun &
python -m topobenchmark \
dataset=graph/NCI1 \
model=cell/topotune,cell/topotune_onehasse \
model.feature_encoder.out_channels=32 \
model.tune_gnn=GCN,GIN,GAT,GraphSAGE \
model.backbone.GNN.num_layers=1,2 \
model.backbone.neighborhoods=\[1-up_laplacian-0,1-up_incidence-0,1-down_laplacian-1,1-up_laplacian-1,1-up_incidence-1,1-down_laplacian-2\],\[1-up_laplacian-0,1-down_laplacian-1\],\[1-up_laplacian-0,1-up_laplacian-1\],\[1-up_laplacian-0,1-up_laplacian-1,1-down_laplacian-1\],\[1-up_laplacian-0,1-down_incidence-2\] \
model.backbone.layers=2,4,8 \
model.feature_encoder.proj_dropout=0.3 \
dataset.split_params.data_seed=1,3,5,7,9 \
model.readout.readout_name=PropagateSignalDown \
logger.wandb.project=TopoTune_cell \
trainer.max_epochs=1000 \
trainer.min_epochs=50 \
trainer.devices=\[6\] \
trainer.check_val_every_n_epoch=1 \
callbacks.early_stopping.patience=50 \
tags="[FirstExperiments]" \
--multirun &
python -m topobenchmark \
dataset=graph/cocitation_pubmed \
model=cell/topotune,cell/topotune_onehasse \
model.feature_encoder.out_channels=32 \
model.tune_gnn=GCN,GIN,GAT,GraphSAGE \
model.backbone.GNN.num_layers=1,2 \
model.backbone.neighborhoods=\[1-up_laplacian-0,1-up_incidence-0,1-down_laplacian-1,1-up_laplacian-1,1-up_incidence-1,1-down_laplacian-2\],\[1-up_laplacian-0,1-down_laplacian-1\],\[1-up_laplacian-0,1-up_laplacian-1\],\[1-up_laplacian-0,1-up_laplacian-1,1-down_laplacian-1\],\[1-up_laplacian-0,1-down_incidence-2\] \
model.backbone.layers=2,4,8 \
model.feature_encoder.proj_dropout=0.3 \
dataset.split_params.data_seed=1,3,5,7,9 \
model.readout.readout_name=PropagateSignalDown \
logger.wandb.project=TopoTune_cell \
trainer.max_epochs=1000 \
trainer.min_epochs=50 \
trainer.devices=\[7\] \
trainer.check_val_every_n_epoch=1 \
callbacks.early_stopping.patience=50 \
tags="[FirstExperiments]" \
--multirun