def main(args, exp_config, train_set, val_set, test_set):
if args['featurizer_type'] != 'pre_train':
exp_config['in_node_feats'] = args['node_featurizer'].feat_size()
if args['edge_featurizer'] is not None:
exp_config['in_edge_feats'] = args['edge_featurizer'].feat_size()
exp_config.update({
'n_tasks': args['n_tasks'],
'model': args['model']
})
train_loader = DataLoader(dataset=train_set, batch_size=exp_config['batch_size'], shuffle=True,
collate_fn=collate_molgraphs, num_workers=args['num_workers'])
val_loader = DataLoader(dataset=val_set, batch_size=exp_config['batch_size'],
collate_fn=collate_molgraphs, num_workers=args['num_workers'])
test_loader = DataLoader(dataset=test_set, batch_size=exp_config['batch_size'],
collate_fn=collate_molgraphs, num_workers=args['num_workers'])
if args['pretrain']:
args['num_epochs'] = 0
if args['featurizer_type'] == 'pre_train':
model = load_pretrained('{}_{}'.format(
args['model'], args['dataset'])).to(args['device'])
else:
model = load_pretrained('{}_{}_{}'.format(
args['model'], args['featurizer_type'], args['dataset'])).to(args['device'])
else:
model = load_model(exp_config).to(args['device'])
loss_criterion = nn.SmoothL1Loss(reduction='none')
optimizer = Adam(model.parameters(), lr=exp_config['lr'],
weight_decay=exp_config['weight_decay'])
stopper = EarlyStopping(patience=exp_config['patience'],
filename=args['result_path'] + '/model.pth',
metric=args['metric'])
for epoch in range(args['num_epochs']):
# Train
run_a_train_epoch(args, epoch, model, train_loader, loss_criterion, optimizer)
# Validation and early stop
val_score = run_an_eval_epoch(args, model, val_loader)
early_stop = stopper.step(val_score, model)
print('epoch {:d}/{:d}, validation {} {:.4f}, best validation {} {:.4f}'.format(
epoch + 1, args['num_epochs'], args['metric'],
val_score, args['metric'], stopper.best_score))
if early_stop:
break
if not args['pretrain']:
stopper.load_checkpoint(model)
val_score = run_an_eval_epoch(args, model, val_loader)
test_score = run_an_eval_epoch(args, model, test_loader)
print('val {} {:.4f}'.format(args['metric'], val_score))
print('test {} {:.4f}'.format(args['metric'], test_score))
with open(args['result_path'] + '/eval.txt', 'w') as f:
if not args['pretrain']:
f.write('Best val {}: {}\n'.format(args['metric'], stopper.best_score))
f.write('Val {}: {}\n'.format(args['metric'], val_score))
f.write('Test {}: {}\n'.format(args['metric'], test_score))
def test_dgmg():
model = load_pretrained('DGMG_ZINC_canonical')
run_dgmg_ZINC(model)
model = load_pretrained('DGMG_ZINC_random')
run_dgmg_ZINC(model)
model = load_pretrained('DGMG_ChEMBL_canonical')
run_dgmg_ChEMBL(model)
model = load_pretrained('DGMG_ChEMBL_random')
run_dgmg_ChEMBL(model)
remove_file('DGMG_ChEMBL_canonical_pre_trained.pth')
remove_file('DGMG_ChEMBL_random_pre_trained.pth')
remove_file('DGMG_ZINC_canonical_pre_trained.pth')
remove_file('DGMG_ZINC_random_pre_trained.pth')
def prepare_for_evaluation(rank, args):
worker_seed = args['seed'] + rank * 10000
set_random_seed(worker_seed)
torch.set_num_threads(1)
# Setup dataset and data loader
dataset = MoleculeDataset(args['dataset'],
subset_id=rank,
n_subsets=args['num_processes'])
# Initialize model
if not args['pretrained']:
model = DGMG(atom_types=dataset.atom_types,
bond_types=dataset.bond_types,
node_hidden_size=args['node_hidden_size'],
num_prop_rounds=args['num_propagation_rounds'],
dropout=args['dropout'])
model.load_state_dict(
torch.load(args['model_path'])['model_state_dict'])
else:
model = load_pretrained('_'.join(
['DGMG', args['dataset'], args['order']]),
log=False)
model.eval()
worker_num_samples = args['num_samples'] // args['num_processes']
if rank == args['num_processes'] - 1:
worker_num_samples += args['num_samples'] % args['num_processes']
worker_log_dir = os.path.join(args['log_dir'], str(rank))
mkdir_p(worker_log_dir, log=False)
generate_and_save(worker_log_dir, worker_num_samples,
args['max_num_steps'], model)
def test_attentivefp_aromaticity():
if torch.cuda.is_available():
device = torch.device('cuda:0')
else:
device = torch.device('cpu')
node_featurizer = BaseAtomFeaturizer(
featurizer_funcs={'hv': ConcatFeaturizer([
partial(atom_type_one_hot, allowable_set=[
'B', 'C', 'N', 'O', 'F', 'Si', 'P', 'S', 'Cl', 'As', 'Se', 'Br', 'Te', 'I', 'At'],
encode_unknown=True),
partial(atom_degree_one_hot, allowable_set=list(range(6))),
atom_formal_charge, atom_num_radical_electrons,
partial(atom_hybridization_one_hot, encode_unknown=True),
lambda atom: [0], # A placeholder for aromatic information,
atom_total_num_H_one_hot, chirality
],
)}
)
edge_featurizer = BaseBondFeaturizer({
'he': lambda bond: [0 for _ in range(10)]
})
g1 = smiles_to_bigraph('CO', node_featurizer=node_featurizer,
edge_featurizer=edge_featurizer)
g2 = smiles_to_bigraph('CCO', node_featurizer=node_featurizer,
edge_featurizer=edge_featurizer)
bg = dgl.batch([g1, g2])
model = load_pretrained('AttentiveFP_Aromaticity').to(device)
model(bg.to(device), bg.ndata.pop('hv').to(device), bg.edata.pop('he').to(device))
model.eval()
model(g1.to(device), g1.ndata.pop('hv').to(device), g1.edata.pop('he').to(device))
remove_file('AttentiveFP_Aromaticity_pre_trained.pth')
def main(args, dataset):
data_loader = DataLoader(dataset,
batch_size=args['batch_size'],
collate_fn=collate,
shuffle=False)
model = load_pretrained(args['model']).to(args['device'])
model.eval()
readout = AvgPooling()
mol_emb = []
for batch_id, bg in enumerate(data_loader):
print('Processing batch {:d}/{:d}'.format(batch_id + 1,
len(data_loader)))
bg = bg.to(args['device'])
nfeats = [
bg.ndata.pop('atomic_number').to(args['device']),
bg.ndata.pop('chirality_type').to(args['device'])
]
efeats = [
bg.edata.pop('bond_type').to(args['device']),
bg.edata.pop('bond_direction_type').to(args['device'])
]
with torch.no_grad():
node_repr = model(bg, nfeats, efeats)
mol_emb.append(readout(bg, node_repr))
mol_emb = torch.cat(mol_emb, dim=0).detach().cpu().numpy()
np.save(args['out_dir'] + '/mol_emb.npy', mol_emb)
def test_weave_tox21():
if torch.cuda.is_available():
device = torch.device('cuda:0')
else:
device = torch.device('cpu')
node_featurizer = WeaveAtomFeaturizer()
edge_featurizer = WeaveEdgeFeaturizer(max_distance=2)
g1 = smiles_to_complete_graph('CO',
node_featurizer=node_featurizer,
edge_featurizer=edge_featurizer,
add_self_loop=True)
g2 = smiles_to_complete_graph('CCO',
node_featurizer=node_featurizer,
edge_featurizer=edge_featurizer,
add_self_loop=True)
bg = dgl.batch([g1, g2])
model = load_pretrained('Weave_Tox21').to(device)
model(bg.to(device),
bg.ndata.pop('h').to(device),
bg.edata.pop('e').to(device))
model.eval()
model(g1.to(device),
g1.ndata.pop('h').to(device),
g1.edata.pop('e').to(device))
remove_file('Weave_Tox21_pre_trained.pth')
def test_jtnn():
if torch.cuda.is_available():
device = torch.device('cuda:0')
else:
device = torch.device('cpu')
model = load_pretrained('JTNN_ZINC_no_kl').to(device)
def main(args):
args['device'] = torch.device(
"cuda: 0") if torch.cuda.is_available() else torch.device("cpu")
set_random_seed(args['random_seed'])
dataset = PubChemBioAssayAromaticity(
smiles_to_graph=args['smiles_to_graph'],
node_featurizer=args.get('node_featurizer', None),
edge_featurizer=args.get('edge_featurizer', None))
train_set, val_set, test_set = RandomSplitter.train_val_test_split(
dataset,
frac_train=args['frac_train'],
frac_val=args['frac_val'],
frac_test=args['frac_test'],
random_state=args['random_seed'])
train_loader = DataLoader(dataset=train_set,
batch_size=args['batch_size'],
shuffle=True,
collate_fn=collate_molgraphs)
val_loader = DataLoader(dataset=val_set,
batch_size=args['batch_size'],
collate_fn=collate_molgraphs)
test_loader = DataLoader(dataset=test_set,
batch_size=args['batch_size'],
collate_fn=collate_molgraphs)
if args['pre_trained']:
args['num_epochs'] = 0
model = load_pretrained(args['exp'])
else:
model = load_model(args)
loss_fn = nn.MSELoss(reduction='none')
optimizer = torch.optim.Adam(model.parameters(),
lr=args['lr'],
weight_decay=args['weight_decay'])
stopper = EarlyStopping(mode=args['mode'], patience=args['patience'])
model.to(args['device'])
for epoch in range(args['num_epochs']):
# Train
run_a_train_epoch(args, epoch, model, train_loader, loss_fn, optimizer)
# Validation and early stop
val_score = run_an_eval_epoch(args, model, val_loader)
early_stop = stopper.step(val_score, model)
print(
'epoch {:d}/{:d}, validation {} {:.4f}, best validation {} {:.4f}'.
format(epoch + 1, args['num_epochs'], args['metric_name'],
val_score, args['metric_name'], stopper.best_score))
if early_stop:
break
if not args['pre_trained']:
stopper.load_checkpoint(model)
test_score = run_an_eval_epoch(args, model, test_loader)
print('test {} {:.4f}'.format(args['metric_name'], test_score))
def test_jtnn():
if torch.cuda.is_available():
device = torch.device('cuda:0')
else:
device = torch.device('cpu')
model = load_pretrained('JTNN_ZINC').to(device)
remove_file('JTNN_ZINC_pre_trained.pth')
def __init__(self, predictor_dim=None):
super(DGL_GIN_ContextPred, self).__init__()
from dgllife.model import load_pretrained
from dgl.nn.pytorch.glob import AvgPooling
## this is fixed hyperparameters as it is a pretrained model
self.gnn = load_pretrained('gin_supervised_contextpred')
self.readout = AvgPooling()
self.transform = nn.Linear(300, predictor_dim)
def main(args):
args['device'] = torch.device(
"cuda") if torch.cuda.is_available() else torch.device("cpu")
set_random_seed(args['random_seed'])
train_set, val_set, test_set = load_dataset_for_regression(args)
train_loader = DataLoader(dataset=train_set,
batch_size=args['batch_size'],
shuffle=True,
collate_fn=collate_molgraphs)
val_loader = DataLoader(dataset=val_set,
batch_size=args['batch_size'],
shuffle=True,
collate_fn=collate_molgraphs)
if test_set is not None:
test_loader = DataLoader(dataset=test_set,
batch_size=args['batch_size'],
collate_fn=collate_molgraphs)
if args['pre_trained']:
args['num_epochs'] = 0
model = load_pretrained(args['exp'])
else:
model = load_model(args)
loss_fn = nn.MSELoss(reduction='none')
optimizer = torch.optim.Adam(model.parameters(),
lr=args['lr'],
weight_decay=args['weight_decay'])
stopper = EarlyStopping(mode='lower', patience=args['patience'])
model.to(args['device'])
for epoch in range(args['num_epochs']):
# Train
run_a_train_epoch(args, epoch, model, train_loader, loss_fn, optimizer)
# Validation and early stop
val_score = run_an_eval_epoch(args, model, val_loader)
early_stop = stopper.step(val_score, model)
print(
'epoch {:d}/{:d}, validation {} {:.4f}, best validation {} {:.4f}'.
format(epoch + 1, args['num_epochs'], args['metric_name'],
val_score, args['metric_name'], stopper.best_score))
if early_stop:
break
if test_set is not None:
if not args['pre_trained']:
stopper.load_checkpoint(model)
test_score = run_an_eval_epoch(args, model, test_loader)
print('test {} {:.4f}'.format(args['metric_name'], test_score))
def main(args):
args['device'] = torch.device(
"cuda") if torch.cuda.is_available() else torch.device("cpu")
set_random_seed(args['random_seed'])
# Interchangeable with other datasets
dataset, train_set, val_set, test_set = load_dataset_for_classification(
args)
train_loader = DataLoader(train_set,
batch_size=args['batch_size'],
collate_fn=collate_molgraphs,
shuffle=True)
val_loader = DataLoader(val_set,
batch_size=args['batch_size'],
collate_fn=collate_molgraphs)
test_loader = DataLoader(test_set,
batch_size=args['batch_size'],
collate_fn=collate_molgraphs)
if args['pre_trained']:
args['num_epochs'] = 0
model = load_pretrained(args['exp'])
else:
args['n_tasks'] = dataset.n_tasks
model = load_model(args)
loss_criterion = BCEWithLogitsLoss(pos_weight=dataset.task_pos_weights(
torch.tensor(train_set.indices)).to(args['device']),
reduction='none')
optimizer = Adam(model.parameters(), lr=args['lr'])
stopper = EarlyStopping(patience=args['patience'])
model.to(args['device'])
for epoch in range(args['num_epochs']):
# Train
run_a_train_epoch(args, epoch, model, train_loader, loss_criterion,
optimizer)
# Validation and early stop
val_score = run_an_eval_epoch(args, model, val_loader)
early_stop = stopper.step(val_score, model)
print(
'epoch {:d}/{:d}, validation {} {:.4f}, best validation {} {:.4f}'.
format(epoch + 1, args['num_epochs'], args['metric_name'],
val_score, args['metric_name'], stopper.best_score))
if early_stop:
break
if not args['pre_trained']:
stopper.load_checkpoint(model)
test_score = run_an_eval_epoch(args, model, test_loader)
print('test {} {:.4f}'.format(args['metric_name'], test_score))
def test_gat_tox21():
if torch.cuda.is_available():
device = torch.device('cuda:0')
else:
device = torch.device('cpu')
node_featurizer = CanonicalAtomFeaturizer()
g1 = smiles_to_bigraph('CO', node_featurizer=node_featurizer)
g2 = smiles_to_bigraph('CCO', node_featurizer=node_featurizer)
bg = dgl.batch([g1, g2])
model = load_pretrained('GAT_Tox21').to(device)
model(bg.to(device), bg.ndata.pop('h').to(device))
model.eval()
model(g1.to(device), g1.ndata.pop('h').to(device))
remove_file('GAT_Tox21_pre_trained.pth')
def main(args):
set_seed()
if torch.cuda.is_available():
args['device'] = torch.device('cuda:0')
else:
args['device'] = torch.device('cpu')
# Set current device
torch.cuda.set_device(args['device'])
if args['test_path'] is None:
test_set = USPTOCenter('test',
num_processes=args['num_processes'],
load=args['load'])
else:
test_set = WLNCenterDataset(raw_file_path=args['test_path'],
mol_graph_path=args['test_path'] + '.bin',
num_processes=args['num_processes'],
load=args['load'],
reaction_validity_result_prefix='test')
test_loader = DataLoader(test_set,
batch_size=args['batch_size'],
collate_fn=collate_center,
shuffle=False)
if args['model_path'] is None:
model = load_pretrained('wln_center_uspto')
else:
model = WLNReactionCenter(
node_in_feats=args['node_in_feats'],
edge_in_feats=args['edge_in_feats'],
node_pair_in_feats=args['node_pair_in_feats'],
node_out_feats=args['node_out_feats'],
n_layers=args['n_layers'],
n_tasks=args['n_tasks'])
model.load_state_dict(
torch.load(args['model_path'],
map_location='cpu')['model_state_dict'])
model = model.to(args['device'])
print('Evaluation on the test set.')
test_result = reaction_center_final_eval(args, args['top_ks_test'], model,
test_loader, args['easy'])
print(test_result)
with open(args['result_path'] + '/test_eval.txt', 'w') as f:
f.write(test_result)
def main(args, path_to_candidate_bonds):
if args['test_path'] is None:
test_set = USPTORank(
subset='test',
candidate_bond_path=path_to_candidate_bonds['test'],
max_num_change_combos_per_reaction=args[
'max_num_change_combos_per_reaction_eval'],
num_processes=args['num_processes'])
else:
test_set = WLNRankDataset(
path_to_reaction_file=args['test_path'],
candidate_bond_path=path_to_candidate_bonds['test'],
mode='test',
max_num_change_combos_per_reaction=args[
'max_num_change_combos_per_reaction_eval'],
num_processes=args['num_processes'])
test_loader = DataLoader(test_set,
batch_size=1,
collate_fn=collate_rank_eval,
shuffle=False,
num_workers=args['num_workers'])
if args['num_workers'] > 1:
torch.multiprocessing.set_sharing_strategy('file_system')
if args['model_path'] is None:
model = load_pretrained('wln_rank_uspto')
else:
model = WLNReactionRanking(
node_in_feats=args['node_in_feats'],
edge_in_feats=args['edge_in_feats'],
node_hidden_feats=args['hidden_size'],
num_encode_gnn_layers=args['num_encode_gnn_layers'])
model.load_state_dict(
torch.load(args['model_path'],
map_location='cpu')['model_state_dict'])
model = model.to(args['device'])
prediction_summary = candidate_ranking_eval(args, model, test_loader)
with open(args['result_path'] + '/test_eval.txt', 'w') as f:
f.write(prediction_summary)
def main(args):
lg = rdkit.RDLogger.logger()
lg.setLevel(rdkit.RDLogger.CRITICAL)
if args.use_cpu or not torch.cuda.is_available():
device = torch.device('cpu')
else:
device = torch.device('cuda:0')
vocab = JTVAEVocab(file_path=args.train_path)
if args.test_path is None:
dataset = JTVAEZINC('test', vocab)
else:
dataset = JTVAEDataset(args.test_path, vocab, training=False)
dataloader = DataLoader(dataset,
batch_size=1,
collate_fn=JTVAECollator(training=False))
if args.model_path is None:
model = load_pretrained('JTVAE_ZINC_no_kl')
else:
model = JTNNVAE(vocab, args.hidden_size, args.latent_size, args.depth)
model.load_state_dict(torch.load(args.model_path, map_location='cpu'))
model = model.to(device)
acc = 0.0
for it, (tree, tree_graph, mol_graph) in enumerate(dataloader):
tot = it + 1
smiles = tree.smiles
tree_graph = tree_graph.to(device)
mol_graph = mol_graph.to(device)
dec_smiles = model.reconstruct(tree_graph, mol_graph)
if dec_smiles == smiles:
acc += 1
if tot % args.print_iter == 0:
print('Iter {:d}/{:d} | Acc {:.4f}'.format(
tot // args.print_iter, len(dataloader) // args.print_iter, acc / tot))
print('Final acc: {:.4f}'.format(acc / tot))
dataset = JTNNDataset(data=args.train, vocab=args.vocab, training=False)
vocab_file = dataset.vocab_file
hidden_size = int(args.hidden_size)
latent_size = int(args.latent_size)
depth = int(args.depth)
model = DGLJTNNVAE(vocab_file=vocab_file,
hidden_size=hidden_size,
latent_size=latent_size,
depth=depth)
if args.model_path is not None:
model.load_state_dict(torch.load(args.model_path))
else:
model = load_pretrained("JTNN_ZINC")
model = cuda(model)
model.eval()
print("Model #Params: %dK" %
(sum([x.nelement() for x in model.parameters()]) / 1000,))
MAX_EPOCH = 100
PRINT_ITER = 20
def reconstruct():
dataset.training = False
dataloader = DataLoader(
dataset,
batch_size=1,
shuffle=False,
def prepare_reaction_center(args, reaction_center_config):
"""Use a trained model for reaction center prediction to prepare candidate bonds.
Parameters
----------
args : dict
Configuration for the experiment.
reaction_center_config : dict
Configuration for the experiment on reaction center prediction.
Returns
-------
path_to_candidate_bonds : dict
Mapping 'train', 'val', 'test' to the corresponding files for candidate bonds.
"""
if args['center_model_path'] is None:
reaction_center_model = load_pretrained('wln_center_uspto').to(
args['device'])
else:
reaction_center_model = WLNReactionCenter(
node_in_feats=reaction_center_config['node_in_feats'],
edge_in_feats=reaction_center_config['edge_in_feats'],
node_pair_in_feats=reaction_center_config['node_pair_in_feats'],
node_out_feats=reaction_center_config['node_out_feats'],
n_layers=reaction_center_config['n_layers'],
n_tasks=reaction_center_config['n_tasks'])
reaction_center_model.load_state_dict(
torch.load(args['center_model_path'])['model_state_dict'])
reaction_center_model = reaction_center_model.to(args['device'])
reaction_center_model.eval()
path_to_candidate_bonds = dict()
for subset in ['train', 'val', 'test']:
if '{}_path'.format(subset) not in args:
continue
path_to_candidate_bonds[subset] = args['result_path'] + \
'/{}_candidate_bonds.txt'.format(subset)
if os.path.isfile(path_to_candidate_bonds[subset]):
continue
print('Processing subset {}...'.format(subset))
print('Stage 1/3: Loading dataset...')
if args['{}_path'.format(subset)] is None:
dataset = USPTOCenter(subset, num_processes=args['num_processes'])
else:
dataset = WLNCenterDataset(
raw_file_path=args['{}_path'.format(subset)],
mol_graph_path='{}.bin'.format(subset),
num_processes=args['num_processes'])
dataloader = DataLoader(dataset,
batch_size=args['reaction_center_batch_size'],
collate_fn=collate_center,
shuffle=False)
print('Stage 2/3: Performing model prediction...')
output_strings = []
for batch_id, batch_data in enumerate(dataloader):
print('Computing candidate bonds for batch {:d}/{:d}'.format(
batch_id + 1, len(dataloader)))
batch_reactions, batch_graph_edits, batch_mol_graphs, \
batch_complete_graphs, batch_atom_pair_labels = batch_data
with torch.no_grad():
pred, biased_pred = reaction_center_prediction(
args['device'], reaction_center_model, batch_mol_graphs,
batch_complete_graphs)
batch_size = len(batch_reactions)
start = 0
for i in range(batch_size):
end = start + batch_complete_graphs.batch_num_edges[i]
output_strings.append(
output_candidate_bonds_for_a_reaction(
(batch_reactions[i],
biased_pred[start:end, :].flatten(),
batch_complete_graphs.batch_num_nodes[i]),
reaction_center_config['max_k']))
start = end
print('Stage 3/3: Output candidate bonds...')
with open(path_to_candidate_bonds[subset], 'w') as f:
for candidate_string in output_strings:
f.write(candidate_string)
del dataset
del dataloader
del reaction_center_model
return path_to_candidate_bonds
def main(args):
worker_init_fn(None)
if torch.cuda.is_available():
device = torch.device('cuda:0')
else:
device = torch.device('cpu')
if args.model_path is not None:
model = DGLJTNNVAE(vocab_file=get_vocab_file(args.vocab),
hidden_size=args.hidden_size,
latent_size=args.latent_size,
depth=args.depth)
model.load_state_dict(torch.load(args.model_path))
else:
model = load_pretrained("JTNN_ZINC")
print("# model parameters: {:d}K".format(
sum([x.nelement() for x in model.parameters()]) // 1000))
dataset = JTVAEDataset(data=args.data, vocab=model.vocab, training=False)
dataloader = DataLoader(
dataset,
batch_size=1,
shuffle=False,
num_workers=0,
collate_fn=JTVAECollator(False),
worker_init_fn=worker_init_fn)
# Just an example of molecule decoding; in reality you may want to sample
# tree and molecule vectors.
acc = 0.0
tot = 0
model = model.to(device)
model.eval()
for it, batch in enumerate(tqdm(dataloader)):
gt_smiles = batch['mol_trees'][0].smiles
batch = dataset.move_to_device(batch, device)
try:
_, tree_vec, mol_vec = model.encode(batch)
tree_mean = model.T_mean(tree_vec)
# Following Mueller et al.
tree_log_var = -torch.abs(model.T_var(tree_vec))
epsilon = torch.randn(1, model.latent_size // 2).to(device)
tree_vec = tree_mean + torch.exp(tree_log_var // 2) * epsilon
mol_mean = model.G_mean(mol_vec)
# Following Mueller et al.
mol_log_var = -torch.abs(model.G_var(mol_vec))
epsilon = torch.randn(1, model.latent_size // 2).to(device)
mol_vec = mol_mean + torch.exp(mol_log_var // 2) * epsilon
dec_smiles = model.decode(tree_vec, mol_vec)
if dec_smiles == gt_smiles:
acc += 1
tot += 1
except Exception as e:
print("Failed to encode: {}".format(gt_smiles))
print(e)
if it % 20 == 1:
print("Progress {}/{}; Current Reconstruction Accuracy: {:.4f}".format(
it, len(dataloader), acc / tot))
print("Reconstruction Accuracy: {}".format(acc / tot))
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
def main(args):
setup(args)
if args['train_path'] is None:
train_set = USPTO('train')
else:
train_set = WLNReactionDataset(raw_file_path=args['train_path'],
mol_graph_path='train.bin')
if args['val_path'] is None:
val_set = USPTO('val')
else:
val_set = WLNReactionDataset(raw_file_path=args['val_path'],
mol_graph_path='val.bin')
if args['test_path'] is None:
test_set = USPTO('test')
else:
test_set = WLNReactionDataset(raw_file_path=args['test_path'],
mol_graph_path='test.bin')
train_loader = DataLoader(train_set,
batch_size=args['batch_size'],
collate_fn=collate,
shuffle=True)
val_loader = DataLoader(val_set,
batch_size=args['batch_size'],
collate_fn=collate,
shuffle=False)
test_loader = DataLoader(test_set,
batch_size=args['batch_size'],
collate_fn=collate,
shuffle=False)
if args['pre_trained']:
model = load_pretrained('wln_center_uspto').to(args['device'])
args['num_epochs'] = 0
else:
model = WLNReactionCenter(
node_in_feats=args['node_in_feats'],
edge_in_feats=args['edge_in_feats'],
node_pair_in_feats=args['node_pair_in_feats'],
node_out_feats=args['node_out_feats'],
n_layers=args['n_layers'],
n_tasks=args['n_tasks']).to(args['device'])
criterion = BCEWithLogitsLoss(reduction='sum')
optimizer = Adam(model.parameters(), lr=args['lr'])
scheduler = StepLR(optimizer,
step_size=args['decay_every'],
gamma=args['lr_decay_factor'])
total_iter = 0
grad_norm_sum = 0
loss_sum = 0
dur = []
for epoch in range(args['num_epochs']):
t0 = time.time()
for batch_id, batch_data in enumerate(train_loader):
total_iter += 1
batch_reactions, batch_graph_edits, batch_mols, batch_mol_graphs, \
batch_complete_graphs, batch_atom_pair_labels = batch_data
labels = batch_atom_pair_labels.to(args['device'])
pred, biased_pred = reaction_center_prediction(
args['device'], model, batch_mol_graphs, batch_complete_graphs)
loss = criterion(pred, labels) / len(batch_reactions)
loss_sum += loss.cpu().detach().data.item()
optimizer.zero_grad()
loss.backward()
grad_norm = clip_grad_norm_(model.parameters(), args['max_norm'])
grad_norm_sum += grad_norm
optimizer.step()
scheduler.step()
if total_iter % args['print_every'] == 0:
progress = 'Epoch {:d}/{:d}, iter {:d}/{:d} | time/minibatch {:.4f} | ' \
'loss {:.4f} | grad norm {:.4f}'.format(
epoch + 1, args['num_epochs'], batch_id + 1, len(train_loader),
(np.sum(dur) + time.time() - t0) / total_iter, loss_sum / args['print_every'],
grad_norm_sum / args['print_every'])
grad_norm_sum = 0
loss_sum = 0
print(progress)
if total_iter % args['decay_every'] == 0:
torch.save(model.state_dict(),
args['result_path'] + '/model.pkl')
dur.append(time.time() - t0)
print('Epoch {:d}/{:d}, validation '.format(epoch + 1, args['num_epochs']) + \
rough_eval_on_a_loader(args, model, val_loader))
del train_loader
del val_loader
del train_set
del val_set
print('Evaluation on the test set.')
test_result = reaction_center_final_eval(args, model, test_loader,
args['easy'])
print(test_result)
with open(args['result_path'] + '/results.txt', 'w') as f:
f.write(test_result)
def main(args):
torch.cuda.set_device(args['gpu'])
set_random_seed(args['random_seed'])
dataset, train_set, val_set, test_set = load_dataset_for_classification(
args) # 6264, 783, 784
train_loader = DataLoader(train_set,
batch_size=args['batch_size'],
collate_fn=collate_molgraphs,
shuffle=True)
val_loader = DataLoader(val_set,
batch_size=args['batch_size'],
collate_fn=collate_molgraphs)
test_loader = DataLoader(test_set,
batch_size=args['batch_size'],
collate_fn=collate_molgraphs)
if args['pre_trained']:
args['num_epochs'] = 0
model = load_pretrained(args['exp'])
else:
args['n_tasks'] = dataset.n_tasks
if args['method'] == 'twp':
model = load_mymodel(args)
print(model)
else:
model = load_model(args)
for name, parameters in model.named_parameters():
print(name, ':', parameters.size())
method = args['method']
life_model = importlib.import_module(f'LifeModel.{method}_model')
life_model_ins = life_model.NET(model, args)
data_loader = DataLoader(train_set,
batch_size=len(train_set),
collate_fn=collate_molgraphs,
shuffle=True)
life_model_ins.data_loader = data_loader
loss_criterion = BCEWithLogitsLoss(
pos_weight=dataset.task_pos_weights.cuda(), reduction='none')
model.cuda()
score_mean = []
score_matrix = np.zeros([args['n_tasks'], args['n_tasks']])
prev_model = None
for task_i in range(12):
print('\n********' + str(task_i))
stopper = EarlyStopping(patience=args['patience'])
for epoch in range(args['num_epochs']):
# Train
if args['method'] == 'lwf':
life_model_ins.observe(train_loader, loss_criterion, task_i,
args, prev_model)
else:
life_model_ins.observe(train_loader, loss_criterion, task_i,
args)
# Validation and early stop
val_score = run_an_eval_epoch(args, model, val_loader, task_i)
early_stop = stopper.step(val_score, model)
if early_stop:
print(epoch)
break
if not args['pre_trained']:
stopper.load_checkpoint(model)
score_matrix[task_i] = run_eval_epoch(args, model, test_loader)
prev_model = copy.deepcopy(life_model_ins).cuda()
print('AP: ', round(np.mean(score_matrix[-1, :]), 4))
backward = []
for t in range(args['n_tasks'] - 1):
b = score_matrix[args['n_tasks'] - 1][t] - score_matrix[t][t]
backward.append(round(b, 4))
mean_backward = round(np.mean(backward), 4)
print('AF: ', mean_backward)
def test_moleculenet():
if torch.cuda.is_available():
device = torch.device('cuda:0')
else:
device = torch.device('cpu')
for dataset in [
'BACE', 'BBBP', 'ClinTox', 'FreeSolv', 'HIV', 'MUV', 'SIDER',
'ToxCast', 'PCBA', 'ESOL', 'Lipophilicity', 'Tox21'
]:
for featurizer_type in ['canonical', 'attentivefp']:
if featurizer_type == 'canonical':
node_featurizer = CanonicalAtomFeaturizer(atom_data_field='hv')
edge_featurizer = CanonicalBondFeaturizer(bond_data_field='he',
self_loop=True)
else:
node_featurizer = AttentiveFPAtomFeaturizer(
atom_data_field='hv')
edge_featurizer = AttentiveFPBondFeaturizer(
bond_data_field='he', self_loop=True)
for model_type in ['GCN', 'GAT']:
g1 = smiles_to_bigraph('CO', node_featurizer=node_featurizer)
g2 = smiles_to_bigraph('CCO', node_featurizer=node_featurizer)
bg = dgl.batch([g1, g2])
model = load_pretrained('{}_{}_{}'.format(
model_type, featurizer_type, dataset)).to(device)
with torch.no_grad():
model(bg.to(device), bg.ndata.pop('hv').to(device))
model.eval()
model(g1.to(device), g1.ndata.pop('hv').to(device))
remove_file('{}_{}_{}_pre_trained.pth'.format(
model_type.lower(), featurizer_type, dataset))
for model_type in ['Weave', 'MPNN', 'AttentiveFP']:
g1 = smiles_to_bigraph('CO',
add_self_loop=True,
node_featurizer=node_featurizer,
edge_featurizer=edge_featurizer)
g2 = smiles_to_bigraph('CCO',
add_self_loop=True,
node_featurizer=node_featurizer,
edge_featurizer=edge_featurizer)
bg = dgl.batch([g1, g2])
model = load_pretrained('{}_{}_{}'.format(
model_type, featurizer_type, dataset)).to(device)
with torch.no_grad():
model(bg.to(device),
bg.ndata.pop('hv').to(device),
bg.edata.pop('he').to(device))
model.eval()
model(g1.to(device),
g1.ndata.pop('hv').to(device),
g1.edata.pop('he').to(device))
remove_file('{}_{}_{}_pre_trained.pth'.format(
model_type.lower(), featurizer_type, dataset))
if dataset == 'ClinTox':
continue
node_featurizer = PretrainAtomFeaturizer()
edge_featurizer = PretrainBondFeaturizer()
for model_type in [
'gin_supervised_contextpred', 'gin_supervised_infomax',
'gin_supervised_edgepred', 'gin_supervised_masking'
]:
g1 = smiles_to_bigraph('CO',
add_self_loop=True,
node_featurizer=node_featurizer,
edge_featurizer=edge_featurizer)
g2 = smiles_to_bigraph('CCO',
add_self_loop=True,
node_featurizer=node_featurizer,
edge_featurizer=edge_featurizer)
bg = dgl.batch([g1, g2])
model = load_pretrained('{}_{}'.format(model_type,
dataset)).to(device)
with torch.no_grad():
node_feats = [
bg.ndata.pop('atomic_number').to(device),
bg.ndata.pop('chirality_type').to(device)
]
edge_feats = [
bg.edata.pop('bond_type').to(device),
bg.edata.pop('bond_direction_type').to(device)
]
model(bg.to(device), node_feats, edge_feats)
model.eval()
node_feats = [
g1.ndata.pop('atomic_number').to(device),
g1.ndata.pop('chirality_type').to(device)
]
edge_feats = [
g1.edata.pop('bond_type').to(device),
g1.edata.pop('bond_direction_type').to(device)
]
model(g1.to(device), node_feats, edge_feats)
remove_file('{}_{}_pre_trained.pth'.format(model_type.lower(),
dataset))
