def load_model(args):
if args['model'] == 'GCN':
from dgllife.model import GCNPredictor
model = GCNPredictor(
in_feats=args['in_feats'],
hidden_feats=args['gcn_hidden_feats'],
classifier_hidden_feats=args['classifier_hidden_feats'],
n_tasks=args['n_tasks'])
if args['model'] == 'GAT':
from dgllife.model import GATPredictor
model = GATPredictor(
in_feats=args['in_feats'],
hidden_feats=args['gat_hidden_feats'],
num_heads=args['num_heads'],
classifier_hidden_feats=args['classifier_hidden_feats'],
n_tasks=args['n_tasks'])
if args['model'] == 'AttentiveFP':
from dgllife.model import AttentiveFPPredictor
model = AttentiveFPPredictor(node_feat_size=args['node_feat_size'],
edge_feat_size=args['edge_feat_size'],
num_layers=args['num_layers'],
num_timesteps=args['num_timesteps'],
graph_feat_size=args['graph_feat_size'],
n_tasks=args['n_tasks'],
dropout=args['dropout'])
if args['model'] == 'SchNet':
from dgllife.model import SchNetPredictor
model = SchNetPredictor(
node_feats=args['node_feats'],
hidden_feats=args['hidden_feats'],
classifier_hidden_feats=args['classifier_hidden_feats'],
n_tasks=args['n_tasks'])
if args['model'] == 'MGCN':
from dgllife.model import MGCNPredictor
model = MGCNPredictor(
feats=args['feats'],
n_layers=args['n_layers'],
classifier_hidden_feats=args['classifier_hidden_feats'],
n_tasks=args['n_tasks'])
if args['model'] == 'MPNN':
from dgllife.model import MPNNPredictor
model = MPNNPredictor(node_in_feats=args['node_in_feats'],
edge_in_feats=args['edge_in_feats'],
node_out_feats=args['node_out_feats'],
edge_hidden_feats=args['edge_hidden_feats'],
n_tasks=args['n_tasks'])
return model
def load_model(args):
if args['model'] == 'SchNet':
from dgllife.model import SchNetPredictor
model = SchNetPredictor(node_feats=args['node_feats'],
hidden_feats=args['hidden_feats'],
classifier_hidden_feats=args['classifier_hidden_feats'],
n_tasks=args['n_tasks'])
if args['model'] == 'MGCN':
from dgllife.model import MGCNPredictor
model = MGCNPredictor(feats=args['feats'],
n_layers=args['n_layers'],
classifier_hidden_feats=args['classifier_hidden_feats'],
n_tasks=args['n_tasks'])
if args['model'] == 'MPNN':
from dgllife.model import MPNNPredictor
model = MPNNPredictor(node_in_feats=args['node_in_feats'],
edge_in_feats=args['edge_in_feats'],
node_out_feats=args['node_out_feats'],
edge_hidden_feats=args['edge_hidden_feats'],
n_tasks=args['n_tasks'])
return model
def load_model(exp_configure):
if exp_configure['model'] == 'GCN':
from dgllife.model import GCNPredictor
model = GCNPredictor(
in_feats=exp_configure['in_node_feats'],
hidden_feats=[exp_configure['gnn_hidden_feats']] *
exp_configure['num_gnn_layers'],
activation=[F.relu] * exp_configure['num_gnn_layers'],
residual=[exp_configure['residual']] *
exp_configure['num_gnn_layers'],
batchnorm=[exp_configure['batchnorm']] *
exp_configure['num_gnn_layers'],
dropout=[exp_configure['dropout']] *
exp_configure['num_gnn_layers'],
predictor_hidden_feats=exp_configure['predictor_hidden_feats'],
predictor_dropout=exp_configure['dropout'],
n_tasks=exp_configure['n_tasks'])
elif exp_configure['model'] == 'GAT':
from dgllife.model import GATPredictor
model = GATPredictor(
in_feats=exp_configure['in_node_feats'],
hidden_feats=[exp_configure['gnn_hidden_feats']] *
exp_configure['num_gnn_layers'],
num_heads=[exp_configure['num_heads']] *
exp_configure['num_gnn_layers'],
feat_drops=[exp_configure['dropout']] *
exp_configure['num_gnn_layers'],
attn_drops=[exp_configure['dropout']] *
exp_configure['num_gnn_layers'],
alphas=[exp_configure['alpha']] * exp_configure['num_gnn_layers'],
residuals=[exp_configure['residual']] *
exp_configure['num_gnn_layers'],
predictor_hidden_feats=exp_configure['predictor_hidden_feats'],
predictor_dropout=exp_configure['dropout'],
n_tasks=exp_configure['n_tasks'])
elif exp_configure['model'] == 'Weave':
from dgllife.model import WeavePredictor
model = WeavePredictor(
node_in_feats=exp_configure['in_node_feats'],
edge_in_feats=exp_configure['in_edge_feats'],
num_gnn_layers=exp_configure['num_gnn_layers'],
gnn_hidden_feats=exp_configure['gnn_hidden_feats'],
graph_feats=exp_configure['graph_feats'],
gaussian_expand=exp_configure['gaussian_expand'],
n_tasks=exp_configure['n_tasks'])
elif exp_configure['model'] == 'MPNN':
from dgllife.model import MPNNPredictor
model = MPNNPredictor(
node_in_feats=exp_configure['in_node_feats'],
edge_in_feats=exp_configure['in_edge_feats'],
node_out_feats=exp_configure['node_out_feats'],
edge_hidden_feats=exp_configure['edge_hidden_feats'],
num_step_message_passing=exp_configure['num_step_message_passing'],
num_step_set2set=exp_configure['num_step_set2set'],
num_layer_set2set=exp_configure['num_layer_set2set'],
n_tasks=exp_configure['n_tasks'])
elif exp_configure['model'] == 'AttentiveFP':
from dgllife.model import AttentiveFPPredictor
model = AttentiveFPPredictor(
node_feat_size=exp_configure['in_node_feats'],
edge_feat_size=exp_configure['in_edge_feats'],
num_layers=exp_configure['num_layers'],
num_timesteps=exp_configure['num_timesteps'],
graph_feat_size=exp_configure['graph_feat_size'],
dropout=exp_configure['dropout'],
n_tasks=exp_configure['n_tasks'])
elif exp_configure['model'] in [
'gin_supervised_contextpred', 'gin_supervised_infomax',
'gin_supervised_edgepred', 'gin_supervised_masking'
]:
from dgllife.model import GINPredictor
from dgllife.model import load_pretrained
model = GINPredictor(num_node_emb_list=[120, 3],
num_edge_emb_list=[6, 3],
num_layers=5,
emb_dim=300,
JK=exp_configure['jk'],
dropout=0.5,
readout=exp_configure['readout'],
n_tasks=exp_configure['n_tasks'])
model.gnn = load_pretrained(exp_configure['model'])
model.gnn.JK = exp_configure['jk']
else:
return ValueError(
"Expect model to be from ['GCN', 'GAT', 'Weave', 'MPNN', 'AttentiveFP', "
"'gin_supervised_contextpred', 'gin_supervised_infomax', "
"'gin_supervised_edgepred', 'gin_supervised_masking'], "
"got {}".format(exp_configure['model']))
return model
# loss_fn = nn.MSELoss(reduction='none')
loss_fn = CrossEntropyLoss()
optimizer = torch.optim.Adam(
model.parameters(),
lr=10**(-2.5),
weight_decay=10**(-5.0),
)
train_roc_auc, test_roc_auc, train_prc_auc, test_prc_auc = train_one(
0, model, loss_fn, optimizer, show_acc=True, EDGE=True)
#%%
model = MPNNPredictor(node_in_feats=n_feats,
edge_in_feats=e_feats,
node_out_feats=64,
edge_hidden_feats=128,
n_tasks=2,
num_step_message_passing=6,
num_step_set2set=6,
num_layer_set2set=3)
model = model.to(device)
# loss_fn = nn.MSELoss(reduction='none')
loss_fn = CrossEntropyLoss()
optimizer = torch.optim.Adam(
model.parameters(),
lr=10**(-2.5),
weight_decay=10**(-5.0),
)
train_roc_auc, test_roc_auc, train_prc_auc, test_prc_auc = train_one(
0, model, loss_fn, optimizer, show_acc=True, EDGE=True)