in python/dgllife/model/pretrain/moleculenet/bace.py [0:0]
def create_bace_model(model_name):
"""Create a model.
Parameters
----------
model_name : str
Name for the model.
Returns
-------
Created model
"""
n_tasks = 1
if model_name == 'GCN_canonical_BACE':
dropout = 0.022033656211803594
return GCNPredictor(in_feats=74,
hidden_feats=[128],
activation=[F.relu],
residual=[True],
batchnorm=[False],
dropout=[dropout],
predictor_hidden_feats=16,
predictor_dropout=dropout,
n_tasks=n_tasks)
elif model_name == 'GCN_attentivefp_BACE':
dropout = 0.009923177126280991
num_gnn_layers = 2
return GCNPredictor(in_feats=39,
hidden_feats=[64] * num_gnn_layers,
activation=[F.relu] * num_gnn_layers,
residual=[False] * num_gnn_layers,
batchnorm=[False] * num_gnn_layers,
dropout=[dropout] * num_gnn_layers,
predictor_hidden_feats=256,
predictor_dropout=dropout,
n_tasks=n_tasks)
elif model_name == 'GAT_canonical_BACE':
dropout = 0.012993892934328621
return GATPredictor(in_feats=74,
hidden_feats=[64],
num_heads=[8],
feat_drops=[dropout],
attn_drops=[dropout],
alphas=[0.2547844032722401],
residuals=[False],
biases=[False],
predictor_hidden_feats=128,
predictor_dropout=dropout,
n_tasks=n_tasks)
elif model_name == 'GAT_attentivefp_BACE':
dropout = 0.09842987062340869
num_gnn_layers = 2
return GATPredictor(in_feats=39,
hidden_feats=[256] * num_gnn_layers,
num_heads=[8] * num_gnn_layers,
feat_drops=[dropout] * num_gnn_layers,
attn_drops=[dropout] * num_gnn_layers,
alphas=[0.6702823790658061] * num_gnn_layers,
residuals=[False] * num_gnn_layers,
biases=[False] * num_gnn_layers,
predictor_hidden_feats=128,
predictor_dropout=dropout,
n_tasks=n_tasks)
elif model_name == 'Weave_canonical_BACE':
return WeavePredictor(node_in_feats=74,
edge_in_feats=13,
num_gnn_layers=2,
gnn_hidden_feats=32,
graph_feats=256,
gaussian_expand=False,
n_tasks=n_tasks)
elif model_name == 'Weave_attentivefp_BACE':
return WeavePredictor(node_in_feats=39,
edge_in_feats=11,
num_gnn_layers=1,
gnn_hidden_feats=32,
graph_feats=32,
gaussian_expand=False,
n_tasks=n_tasks)
elif model_name == 'MPNN_canonical_BACE':
return MPNNPredictor(node_in_feats=74,
edge_in_feats=13,
node_out_feats=64,
edge_hidden_feats=64,
num_step_message_passing=1,
num_step_set2set=3,
num_layer_set2set=1,
n_tasks=n_tasks)
elif model_name == 'MPNN_attentivefp_BACE':
return MPNNPredictor(node_in_feats=39,
edge_in_feats=11,
node_out_feats=64,
edge_hidden_feats=32,
num_step_message_passing=1,
num_step_set2set=1,
num_layer_set2set=1,
n_tasks=n_tasks)
elif model_name == 'AttentiveFP_canonical_BACE':
return AttentiveFPPredictor(node_feat_size=74,
edge_feat_size=13,
num_layers=2,
num_timesteps=4,
graph_feat_size=16,
dropout=0.39078446228187624,
n_tasks=n_tasks)
elif model_name == 'AttentiveFP_attentivefp_BACE':
return AttentiveFPPredictor(node_feat_size=39,
edge_feat_size=11,
num_layers=1,
num_timesteps=4,
graph_feat_size=32,
dropout=0.12249297382460408,
n_tasks=n_tasks)
elif model_name == 'gin_supervised_contextpred_BACE':
jk = 'concat'
model = GINPredictor(
num_node_emb_list=[120, 3],
num_edge_emb_list=[6, 3],
num_layers=5,
emb_dim=300,
JK=jk,
dropout=0.5,
readout='max',
n_tasks=n_tasks
)
model.gnn.JK = jk
return model
elif model_name == 'gin_supervised_infomax_BACE':
jk = 'sum'
model = GINPredictor(
num_node_emb_list=[120, 3],
num_edge_emb_list=[6, 3],
num_layers=5,
emb_dim=300,
JK=jk,
dropout=0.5,
readout='attention',
n_tasks=n_tasks
)
model.gnn.JK = jk
return model
elif model_name == 'gin_supervised_edgepred_BACE':
jk = 'last'
model = GINPredictor(
num_node_emb_list=[120, 3],
num_edge_emb_list=[6, 3],
num_layers=5,
emb_dim=300,
JK=jk,
dropout=0.5,
readout='max',
n_tasks=n_tasks
)
model.gnn.JK = jk
return model
elif model_name == 'gin_supervised_masking_BACE':
jk = 'sum'
model = GINPredictor(
num_node_emb_list=[120, 3],
num_edge_emb_list=[6, 3],
num_layers=5,
emb_dim=300,
JK=jk,
dropout=0.5,
readout='attention',
n_tasks=n_tasks
)
model.gnn.JK = jk
return model
elif model_name == 'NF_canonical_BACE':
num_gnn_layers = 1
dropout = 0.14096514656248904
return NFPredictor(in_feats=74,
n_tasks=n_tasks,
hidden_feats=[32] * num_gnn_layers,
batchnorm=[True] * num_gnn_layers,
dropout=[dropout] * num_gnn_layers,
predictor_hidden_size=1024,
predictor_batchnorm=True,
predictor_dropout=dropout)
else:
return None