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train.py
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train.py
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import torch
from torch import optim
from torch.utils.data import DataLoader
import gnn.summation_mpnn_implementations
import gnn.aggregation_mpnn_implementations
import gnn.emn_implementations
from losses import LOSS_FUNCTIONS
from train_logging import LOG_FUNCTIONS
from gnn.molgraph_data import MolGraphDataset, molgraph_collate_fn
import argparse
MODEL_CONSTRUCTOR_DICTS = {
'ENNS2V': {
'constructor': gnn.summation_mpnn_implementations.ENNS2V,
'hyperparameters': {
'message-passes': {'type': int, 'default': 5},
'message-size': {'type': int, 'default': 50},
'enn-depth': {'type': int, 'default': 3},
'enn-hidden-dim': {'type': int, 'default': 100},
'enn-dropout-p': {'type': float, 'default': 0.0},
's2v-lstm-computations': {'type': int, 'default': 7},
's2v-memory-size': {'type': int, 'default': 50},
'out-depth': {'type': int, 'default': 2},
'out-hidden-dim': {'type': int, 'default': 300},
'out-dropout-p': {'type': float, 'default': 0.0}
}
},
'GGNN': {
'constructor': gnn.summation_mpnn_implementations.GGNN,
'hyperparameters': { # the below, batch size 50, learn rate 1.176e-5 and 1200 epochs is good for ESOL
'message-passes': {'type': int, 'default': 1},
'message-size': {'type': int, 'default': 25},
'msg-depth': {'type': int, 'default': 2},
'msg-hidden-dim': {'type': int, 'default': 50},
'msg-dropout-p': {'type': float, 'default': 0.0},
'gather-width': {'type': int, 'default': 45},
'gather-att-depth': {'type': int, 'default': 2},
'gather-att-hidden-dim': {'type': int, 'default': 26},
'gather-att-dropout-p': {'type': float, 'default': 0.0},
'gather-emb-depth': {'type': int, 'default': 2},
'gather-emb-hidden-dim': {'type': int, 'default': 26},
'gather-emb-dropout-p': {'type': float, 'default': 0.0},
'out-depth': {'type': int, 'default': 2},
'out-hidden-dim': {'type': int, 'default': 450},
'out-dropout-p': {'type': float, 'default': 0.00463},
'out-layer-shrinkage': {'type': float, 'default': 0.5028}
}
},
'AttentionGGNN': { # the below, batch size 50, learn rate 1.560e-5 and 600 epochs is good for BBBP
'constructor': gnn.aggregation_mpnn_implementations.AttentionGGNN,
'hyperparameters': {
'message-passes': {'type': int, 'default': 8},
'message-size': {'type': int, 'default': 25},
'msg-depth': {'type': int, 'default': 2},
'msg-hidden-dim': {'type': int, 'default': 50},
'msg-dropout-p': {'type': float, 'default': 0.0},
'att-depth': {'type': int, 'default': 2},
'att-hidden-dim': {'type': int, 'default': 50},
'att-dropout-p': {'type': float, 'default': 0.0},
'gather-width': {'type': int, 'default': 45},
'gather-att-depth': {'type': int, 'default': 2},
'gather-att-hidden-dim': {'type': int, 'default': 45},
'gather-att-dropout-p': {'type': float, 'default': 0.0},
'gather-emb-depth': {'type': int, 'default': 2},
'gather-emb-hidden-dim': {'type': int, 'default': 26},
'gather-emb-dropout-p': {'type': float, 'default': 0.0},
'out-depth': {'type': int, 'default': 2},
'out-hidden-dim': {'type': int, 'default': 560},
'out-dropout-p': {'type': float, 'default': 0.1},
'out-layer-shrinkage': {'type': float, 'default': 0.6}
}
},
'EMN': { # the below, batch size 50, learn rate 1e-4 and 1000 epochs is good for SIDER
'constructor': gnn.emn_implementations.EMNImplementation,
'hyperparameters': {
'message-passes': {'type': int, 'default': 8},
'edge-embedding-size': {'type': int, 'default': 50},
'edge-emb-depth': {'type': int, 'default': 2},
'edge-emb-hidden-dim': {'type': int, 'default': 105},
'edge-emb-dropout-p': {'type': float, 'default': 0.0},
'att-depth': {'type': int, 'default': 2},
'att-hidden-dim': {'type': int, 'default': 85},
'att-dropout-p': {'type': float, 'default': 0.0},
'msg-depth': {'type': int, 'default': 2},
'msg-hidden-dim': {'type': int, 'default': 150},
'msg-dropout-p': {'type': float, 'default': 0.0},
'gather-width': {'type': int, 'default': 45},
'gather-att-depth': {'type': int, 'default': 2},
'gather-att-hidden-dim': {'type': int, 'default': 45},
'gather-att-dropout-p': {'type': float, 'default': 0.0},
'gather-emb-depth': {'type': int, 'default': 2},
'gather-emb-hidden-dim': {'type': int, 'default': 45},
'gather-emb-dropout-p': {'type': float, 'default': 0.0},
'out-depth': {'type': int, 'default': 2},
'out-hidden-dim': {'type': int, 'default': 450},
'out-dropout-p': {'type': float, 'default': 0.1},
'out-layer-shrinkage': {'type': float, 'default': 0.6}
}
}
}
common_args_parser = argparse.ArgumentParser(formatter_class=argparse.ArgumentDefaultsHelpFormatter, add_help=False)
common_args_parser.add_argument('--cuda', action='store_true', default=False, help='Enables CUDA training')
common_args_parser.add_argument('--train-set', type=str, default='toydata/piece-of-tox21-train.csv.gz', help='Training dataset path')
common_args_parser.add_argument('--valid-set', type=str, default='toydata/piece-of-tox21-valid.csv.gz', help='Validation dataset path')
common_args_parser.add_argument('--test-set', type=str, default='toydata/piece-of-tox21-test.csv.gz', help='Testing dataset path')
common_args_parser.add_argument('--loss', type=str, default='MaskedMultiTaskCrossEntropy', choices=[k for k, v in LOSS_FUNCTIONS.items()])
common_args_parser.add_argument('--score', type=str, default='roc-auc', help='roc-auc or MSE')
common_args_parser.add_argument('--epochs', type=int, default=500, help='Number of training epochs')
common_args_parser.add_argument('--batch-size', type=int, default=50, help='Number of graphs in a mini-batch')
common_args_parser.add_argument('--learn-rate', type=float, default=1e-5)
common_args_parser.add_argument('--savemodel', action='store_true', default=False, help='Saves model with highest validation score')
common_args_parser.add_argument('--logging', type=str, default='less', choices=[k for k, v in LOG_FUNCTIONS.items()])
main_parser = argparse.ArgumentParser(formatter_class=argparse.ArgumentDefaultsHelpFormatter)
subparsers = main_parser.add_subparsers(help=', '.join([k for k, v in MODEL_CONSTRUCTOR_DICTS.items()]), dest='model')
subparsers.required = True
model_parsers = {}
for model_name, constructor_dict in MODEL_CONSTRUCTOR_DICTS.items():
subparser = subparsers.add_parser(model_name, parents=[common_args_parser])
for hp_name, hp_kwargs in constructor_dict['hyperparameters'].items():
subparser.add_argument('--' + hp_name, **hp_kwargs, help=model_name + ' hyperparameter')
model_parsers[model_name] = subparser
def main():
global args
args = main_parser.parse_args()
args_dict = vars(args)
# dictionary of hyperparameters that are specific to the chosen model
model_hp_kwargs = {
name.replace('-', '_'): args_dict[name.replace('-', '_')] # argparse converts to "_" implicitly
for name, v in MODEL_CONSTRUCTOR_DICTS[args.model]['hyperparameters'].items()
}
train_dataset = MolGraphDataset(args.train_set)
train_dataloader = DataLoader(train_dataset, batch_size=args.batch_size, shuffle=True, collate_fn=molgraph_collate_fn)
validation_dataset = MolGraphDataset(args.valid_set)
validation_dataloader = DataLoader(validation_dataset, batch_size=args.batch_size, collate_fn=molgraph_collate_fn)
test_dataset = MolGraphDataset(args.test_set)
test_dataloader = DataLoader(test_dataset, batch_size=args.batch_size, collate_fn=molgraph_collate_fn)
((sample_adjacency, sample_nodes, sample_edges), sample_target) = train_dataset[0]
net = MODEL_CONSTRUCTOR_DICTS[args.model]['constructor'](
node_features=len(sample_nodes[0]), edge_features=len(sample_edges[0, 0]), out_features=len(sample_target),
**model_hp_kwargs
)
if args.cuda:
net = net.cuda()
optimizer = optim.Adam(net.parameters(), lr=args.learn_rate)
criterion = LOSS_FUNCTIONS[args.loss]
for epoch in range(args.epochs):
net.train()
for i_batch, batch in enumerate(train_dataloader):
if args.cuda:
batch = [tensor.cuda() for tensor in batch]
adjacency, nodes, edges, target = batch
optimizer.zero_grad()
output = net(adjacency, nodes, edges)
loss = criterion(output, target)
loss.backward()
torch.nn.utils.clip_grad_value_(net.parameters(), 5.0)
optimizer.step()
with torch.no_grad():
net.eval()
LOG_FUNCTIONS[args.logging](
net, train_dataloader, validation_dataloader, test_dataloader, criterion, epoch, args
)
if __name__ == '__main__':
main()