in python/dgllife/model/pretrain/moleculenet/sider.py [0:0]
def create_sider_model(model_name):
"""Create a model.
Parameters
----------
model_name : str
Name for the model.
Returns
-------
Created model
"""
n_tasks = 27
if model_name == 'GCN_canonical_SIDER':
dropout = 0.034959769945995006
num_gnn_layers = 3
return GCNPredictor(in_feats=74,
hidden_feats=[256] * num_gnn_layers,
activation=[F.relu] * num_gnn_layers,
residual=[True] * num_gnn_layers,
batchnorm=[True] * num_gnn_layers,
dropout=[dropout] * num_gnn_layers,
predictor_hidden_feats=512,
predictor_dropout=dropout,
n_tasks=n_tasks)
elif model_name == 'GCN_attentivefp_SIDER':
dropout = 0.08333992387843633
num_gnn_layers = 4
return GCNPredictor(in_feats=39,
hidden_feats=[256] * num_gnn_layers,
activation=[F.relu] * num_gnn_layers,
residual=[False] * num_gnn_layers,
batchnorm=[True] * num_gnn_layers,
dropout=[dropout] * num_gnn_layers,
predictor_hidden_feats=1024,
predictor_dropout=dropout,
n_tasks=n_tasks)
elif model_name == 'GAT_canonical_SIDER':
dropout = 0.026789468731402546
num_gnn_layers = 4
return GATPredictor(in_feats=74,
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=[0.7874749485670144] * num_gnn_layers,
residuals=[True] * num_gnn_layers,
biases=[False] * num_gnn_layers,
predictor_hidden_feats=64,
predictor_dropout=dropout,
n_tasks=n_tasks)
elif model_name == 'GAT_attentivefp_SIDER':
dropout = 0.5941912608794613
num_gnn_layers = 4
return GATPredictor(in_feats=39,
hidden_feats=[256] * num_gnn_layers,
num_heads=[4] * num_gnn_layers,
feat_drops=[dropout] * num_gnn_layers,
attn_drops=[dropout] * num_gnn_layers,
alphas=[0.15881060281037407] * num_gnn_layers,
residuals=[True] * num_gnn_layers,
biases=[False] * num_gnn_layers,
predictor_hidden_feats=128,
predictor_dropout=dropout,
n_tasks=n_tasks)
elif model_name == 'Weave_canonical_SIDER':
return WeavePredictor(node_in_feats=74,
edge_in_feats=13,
num_gnn_layers=1,
gnn_hidden_feats=64,
graph_feats=16,
gaussian_expand=False,
n_tasks=n_tasks)
elif model_name == 'Weave_attentivefp_SIDER':
return WeavePredictor(node_in_feats=39,
edge_in_feats=11,
num_gnn_layers=3,
gnn_hidden_feats=64,
graph_feats=64,
gaussian_expand=True,
n_tasks=n_tasks)
elif model_name == 'MPNN_canonical_SIDER':
return MPNNPredictor(node_in_feats=74,
edge_in_feats=13,
node_out_feats=64,
edge_hidden_feats=64,
num_step_message_passing=5,
num_step_set2set=2,
num_layer_set2set=2,
n_tasks=n_tasks)
elif model_name == 'MPNN_attentivefp_SIDER':
return MPNNPredictor(node_in_feats=39,
edge_in_feats=11,
node_out_feats=64,
edge_hidden_feats=32,
num_step_message_passing=5,
num_step_set2set=1,
num_layer_set2set=1,
n_tasks=n_tasks)
elif model_name == 'AttentiveFP_canonical_SIDER':
return AttentiveFPPredictor(node_feat_size=74,
edge_feat_size=13,
num_layers=5,
num_timesteps=2,
graph_feat_size=128,
dropout=0.4023383130974747,
n_tasks=n_tasks)
elif model_name == 'AttentiveFP_attentivefp_SIDER':
return AttentiveFPPredictor(node_feat_size=39,
edge_feat_size=11,
num_layers=2,
num_timesteps=1,
graph_feat_size=16,
dropout=0.4246486427806226,
n_tasks=n_tasks)
elif model_name == 'gin_supervised_contextpred_SIDER':
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_SIDER':
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='attention',
n_tasks=n_tasks
)
model.gnn.JK = jk
return model
elif model_name == 'gin_supervised_edgepred_SIDER':
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='attention',
n_tasks=n_tasks
)
model.gnn.JK = jk
return model
elif model_name == 'gin_supervised_masking_SIDER':
jk = 'max'
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 == 'NF_canonical_SIDER':
dropout = 0.3004340538386648
num_gnn_layers = 1
return NFPredictor(in_feats=74,
n_tasks=n_tasks,
hidden_feats=[64] * num_gnn_layers,
batchnorm=[True] * num_gnn_layers,
dropout=[dropout] * num_gnn_layers,
predictor_hidden_size=32,
predictor_batchnorm=True,
predictor_dropout=dropout)
else:
return None