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[Example] Update Hyperparameters (#160)
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* Update

* Update
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@mufeili
mufeili authored Nov 19, 2021
1 parent d7522cc commit 0149145
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25 changes: 25 additions & 0 deletions examples/property_prediction/MTL/README.md
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Original file line number Diff line number Diff line change
Expand Up @@ -39,3 +39,28 @@ python main.py -c syn_data.csv -m GCN --mode parallel -p results -s smiles -t lo

Once the experiment is completed, `results/model.pth` is the trained model checkpoint
and `results/results.txt` is the evaluation result.

## Hyperparameters

The hyperparameters for all experiments are included in `configure.py`:

| Configure | Multi-task | Model | Architecture | Dataset |
| ------------ | ----------- | ----- | ------------ | ------- |
| GAT_parallel | Yes | GAT | Parallel | All |
| GAT_bypass | Yes | GAT | Bypass | All |
| GAT_LogD | No | GAT | Parallel | LogD |
| GAT_HLM | No | GAT | Parallel | HLM |
| GAT_HH | No | GAT | Parallel | HH |
| GAT_KinSol | No | GAT | Parallel | KinSol |
| GCN_LogD | No | GCN | Parallel | LogD |
| GCN_HLM | No | GCN | Parallel | HLM |
| GCN_HH | No | GCN | Parallel | HH |
| GCN_KinSol | No | GCN | Parallel | KinSol |
| MPNN_LogD | No | MPNN | Parallel | LogD |
| MPNN_HLM | No | MPNN | Parallel | HLM |
| MPNN_HH | No | MPNN | Parallel | HH |
| MPNN_KinSol | No | MPNN | Parallel | KinSol |
| AttentiveFP_LogD | No | AttentiveFP | Parallel | LogD |
| AttentiveFP_HLM | No | AttentiveFP | Parallel | HLM |
| AttentiveFP_HH | No | AttentiveFP | Parallel | HH |
| AttentiveFP_KinSol | No | AttentiveFP | Parallel | KinSol |
218 changes: 207 additions & 11 deletions examples/property_prediction/MTL/configure.py
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Expand Up @@ -21,27 +21,27 @@
}

GAT_parallel = {
'gnn_hidden_feats': 32,
'num_gnn_layers': 2,
'gnn_hidden_feats': 128,
'num_gnn_layers': 3,
'num_heads': 6,
'regressor_hidden_feats': 32,
'lr': 3e-3,
'weight_decay': 3e-5,
'dropout': 0.01,
'lr': 0.002,
'weight_decay': 0.000013,
'dropout': 0.069,
'patience': 100,
'batch_size': 128
'batch_size': 256
}

GAT_bypass = {
'gnn_hidden_feats': 32,
'gnn_hidden_feats': 64,
'num_gnn_layers': 3,
'num_heads': 8,
'regressor_hidden_feats': 32,
'lr': 1e-3,
'weight_decay': 3e-6,
'dropout': 0.1,
'lr': 0.00089,
'weight_decay': 0.000039,
'dropout': 0.039,
'patience': 30,
'batch_size': 128
'batch_size': 256
}

MPNN_parallel = {
Expand Down Expand Up @@ -106,3 +106,199 @@
'AttentiveFP_parallel': AttentiveFP_parallel,
'AttentiveFP_bypass': AttentiveFP_bypass
}

GAT_LogD = {
'gnn_hidden_feats': 16,
'num_gnn_layers': 3,
'num_heads': 8,
'regressor_hidden_feats': 32,
'lr': 0.0031,
'weight_decay': 0.0000064,
'dropout': 0.087,
'patience': 100,
'batch_size': 128
}

GAT_HLM = {
'gnn_hidden_feats': 64,
'num_gnn_layers': 2,
'num_heads': 4,
'regressor_hidden_feats': 16,
'lr': 0.0032,
'weight_decay': 0.000058,
'dropout': 0.04,
'patience': 100,
'batch_size': 256
}

GAT_HH = {
'gnn_hidden_feats': 64,
'num_gnn_layers': 3,
'num_heads': 6,
'regressor_hidden_feats': 32,
'lr': 0.00057,
'weight_decay': 0.000042,
'dropout': 0.063,
'patience': 100,
'batch_size': 256
}

GAT_KinSol = {
'gnn_hidden_feats': 32,
'num_gnn_layers': 3,
'num_heads': 6,
'regressor_hidden_feats': 16,
'lr': 0.00038,
'weight_decay': 0.000091,
'dropout': 0.025,
'patience': 100,
'batch_size': 128
}

GCN_logD = {
'gnn_hidden_feats': 128,
'num_gnn_layers': 3,
'regressor_hidden_feats': 128,
'lr': 0.00015,
'weight_decay': 0.00007,
'dropout': 0.0859,
'patience': 50,
'batch_size': 256
}

GCN_HLM = {
'gnn_hidden_feats': 64,
'num_gnn_layers': 3,
'regressor_hidden_feats': 64,
'lr': 0.0013,
'weight_decay': 0.000012,
'dropout': 0.14,
'patience': 50,
'batch_size': 256
}

GCN_HH = {
'gnn_hidden_feats': 128,
'num_gnn_layers': 3,
'regressor_hidden_feats': 32,
'lr': 0.00855,
'weight_decay': 0.00000086,
'dropout': 0.059,
'patience': 50,
'batch_size': 128
}

GCN_KinSol = {
'gnn_hidden_feats': 128,
'num_gnn_layers': 3,
'regressor_hidden_feats': 128,
'lr': 0.0029,
'weight_decay': 0.000017,
'dropout': 0.035,
'patience': 50,
'batch_size': 256
}

MPNN_LogD = {
'node_hidden_dim': 32,
'edge_hidden_dim': 16,
'num_step_message_passing': 3,
'num_step_set2set': 3,
'num_layer_set2set': 2,
'regressor_hidden_feats': 32,
'lr': 0.0064,
'weight_decay': 0.00006,
'dropout': 0.16,
'patience': 50,
'batch_size': 256
}

MPNN_HLM = {
'node_hidden_dim': 128,
'edge_hidden_dim': 64,
'num_step_message_passing': 2,
'num_step_set2set': 3,
'num_layer_set2set': 2,
'regressor_hidden_feats': 32,
'lr': 0.0025,
'weight_decay': 0.0000094,
'dropout': 0.11,
'patience': 50,
'batch_size': 64
}

MPNN_HH = {
'node_hidden_dim': 64,
'edge_hidden_dim': 64,
'num_step_message_passing': 3,
'num_step_set2set': 2,
'num_layer_set2set': 2,
'regressor_hidden_feats': 64,
'lr': 0.0069,
'weight_decay': 0.00004,
'dropout': 0.086,
'patience': 50,
'batch_size': 256
}

MPNN_KinSol = {
'node_hidden_dim': 128,
'edge_hidden_dim': 32,
'num_step_message_passing': 3,
'num_step_set2set': 2,
'num_layer_set2set': 1,
'regressor_hidden_feats': 32,
'lr': 0.0036,
'weight_decay': 0.000031,
'dropout': 0.032,
'patience': 50,
'batch_size': 256
}

AttentiveFP_logD = {
'num_gnn_layers': 3,
'gnn_out_feats': 16,
'num_timesteps': 3,
'regressor_hidden_feats': 16,
'lr': 0.0007,
'weight_decay': 0.000078,
'dropout': 0.086,
'patience': 50,
'batch_size': 64
}

AttentiveFP_HLM = {
'num_gnn_layers': 2,
'gnn_out_feats': 32,
'num_timesteps': 3,
'regressor_hidden_feats': 64,
'lr': 0.0025,
'weight_decay': 0.000032,
'dropout': 0.067,
'patience': 50,
'batch_size': 256
}

AttentiveFP_HH = {
'num_gnn_layers': 2,
'gnn_out_feats': 64,
'num_timesteps': 1,
'regressor_hidden_feats': 16,
'lr': 0.0029,
'weight_decay': 0.000017,
'dropout': 0.08,
'patience': 50,
'batch_size': 256
}

AttentiveFP_KinSol = {
'num_gnn_layers': 3,
'gnn_out_feats': 64,
'num_timesteps': 1,
'regressor_hidden_feats': 64,
'lr': 0.002,
'weight_decay': 0.000052,
'dropout': 0.125,
'patience': 50,
'batch_size': 64
}

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