def load_mca(self, model_path: str):
"""
Restores pretrained MCA
Arguments:
model_path {String} -- Path to the model
"""
# Restore model
self.model_path = model_path
with open(os.path.join(model_path, 'model_params.json')) as f:
params = json.load(f)
# Set up language and transforms
self.smiles_language = SMILESLanguage.load(
os.path.join(model_path, 'smiles_language.pkl')
)
self.transforms = self.compose_smiles_transforms(params)
# Initialize and restore model weights
self.model = MODEL_FACTORY['mca'](params)
self.model.load(
os.path.join(model_path, 'weights', 'best_ROC-AUC_mca.pt'),
map_location=get_device()
)
self.model.eval()
def __init__(self, generator, encoder, params, model_name, logger):
"""
Constructor for the Reinforcement object.
Args:
generator (nn.Module): SMILES generator object.
encoder (nn.Module): An encoder object.
params (dict): dict with hyperparameter.
model_name (str): name of the model.
logger: a logger.
Returns:
object of type REINFORCE used for biasing the properties
estimated by the predictor of trajectories produced by the
generator to maximize the custom reward function get_reward.
"""
super(Reinforce, self).__init__()
self.generator = generator
self.generator.eval()
self.encoder = encoder
self.encoder.eval()
self.logger = logger
self.device = get_device()
self.optimizer = torch.optim.Adam(
list(self.generator.decoder.parameters()),
lr=params.get('learning_rate', 0.0001),
eps=params.get('eps', 0.0001),
weight_decay=params.get('weight_decay', 0.00001))
self.model_name = model_name
self.model_path = os.path.join(
params.get('model_folder', 'biased_models'), model_name)
self.weights_path = os.path.join(self.model_path, 'weights/{}')
self.smiles_to_tensor = ToTensor(self.device)
# If model does not yet exist, create it.
if not os.path.isdir(self.model_path):
os.makedirs(os.path.join(self.model_path, 'weights'),
exist_ok=True)
os.makedirs(os.path.join(self.model_path, 'results'),
exist_ok=True)
# Save untrained models
self.save('generator_epoch_0.pt', 'encoder_epoch_0.pt')
with open(os.path.join(self.model_path, 'model_params.json'),
'w') as f:
json.dump(params, f)
else:
self.logger.warning(
'Model exists already. Call model.load() to restore weights.')
def __init__(self, params, *args, **kwargs):
"""Constructor.
Args:
params (dict): A dictionary containing the parameter to built the
dense Decoder.
Items in params:
dense_sizes (list[int]): Number of neurons in the hidden layers.
num_drug_features (int, optional): Number of features for molecule.
Defaults to 512 (bits fingerprint).
activation_fn (string, optional): Activation function used in all
layers for specification in ACTIVATION_FN_FACTORY.
Defaults to 'relu'.
batch_norm (bool, optional): Whether batch normalization is
applied. Defaults to False.
dropout (float, optional): Dropout probability in all
except parametric layer. Defaults to 0.0.
*args, **kwargs: positional and keyword arguments are ignored.
"""
super(Dense, self).__init__(*args, **kwargs)
self.device = get_device()
self.params = params
self.num_drug_features = params.get('num_drug_features', 512)
self.num_tasks = params.get('num_tasks', 12)
self.hidden_sizes = params.get(
'stacked_dense_hidden_sizes',
[self.num_drug_features, 5000, 1000, 500]
)
self.dropout = params.get('dropout', 0.0)
self.act_fn = ACTIVATION_FN_FACTORY[
params.get('activation_fn', 'relu')]
self.dense_layers = nn.ModuleList(
[
dense_layer(
self.hidden_sizes[ind],
self.hidden_sizes[ind + 1],
act_fn=self.act_fn,
dropout=self.dropout,
batch_norm=self.params.get('batch_norm', True)
).to(self.device) for ind in range(len(self.hidden_sizes) - 1)
]
)
self.final_dense = EnsembleLayer(
typ=params.get('ensemble', 'score'),
input_size=self.hidden_sizes[-1],
output_size=self.num_tasks,
ensemble_size=params.get('ensemble_size', 5),
fn=ACTIVATION_FN_FACTORY['sigmoid']
).to(self.device)
self.loss_fn = LOSS_FN_FACTORY[
params.get('loss_fn', 'binary_cross_entropy_ignore_nan_and_sum')]
def main(train_affinity_filepath, test_affinity_filepath, receptor_filepath,
ligand_filepath, model_path, params_filepath, training_name,
smiles_language_filepath):
logger = logging.getLogger(f'{training_name}')
# Process parameter file:
params = {}
with open(params_filepath) as fp:
params.update(json.load(fp))
# Create model directory and dump files
model_dir = os.path.join(model_path, training_name)
os.makedirs(os.path.join(model_dir, 'weights'), exist_ok=True)
os.makedirs(os.path.join(model_dir, 'results'), exist_ok=True)
with open(os.path.join(model_dir, 'model_params.json'), 'w') as fp:
json.dump(params, fp, indent=4)
# Prepare the dataset
logger.info("Start data preprocessing...")
device = get_device()
# Load languages
if smiles_language_filepath == '':
smiles_language_filepath = os.path.join(
os.sep,
*metadata.__file__.split(os.sep)[:-1], 'smiles_language')
smiles_language = SMILESTokenizer.from_pretrained(smiles_language_filepath)
smiles_language.set_encoding_transforms(
randomize=None,
add_start_and_stop=params.get('ligand_start_stop_token', True),
padding=params.get('ligand_padding', True),
padding_length=params.get('ligand_padding_length', True),
device=device,
)
smiles_language.set_smiles_transforms(
augment=params.get('augment_smiles', False),
canonical=params.get('smiles_canonical', False),
kekulize=params.get('smiles_kekulize', False),
all_bonds_explicit=params.get('smiles_bonds_explicit', False),
all_hs_explicit=params.get('smiles_all_hs_explicit', False),
remove_bonddir=params.get('smiles_remove_bonddir', False),
remove_chirality=params.get('smiles_remove_chirality', False),
selfies=params.get('selfies', False),
sanitize=params.get('sanitize', False))
if params.get('receptor_embedding', 'learned') == 'predefined':
protein_language = ProteinFeatureLanguage(
features=params.get('predefined_embedding', 'blosum'))
else:
protein_language = ProteinLanguage()
if params.get('ligand_embedding', 'learned') == 'one_hot':
logger.warning(
'ligand_embedding_size parameter in param file is ignored in '
'one_hot embedding setting, ligand_vocabulary_size used instead.')
if params.get('receptor_embedding', 'learned') == 'one_hot':
logger.warning(
'receptor_embedding_size parameter in param file is ignored in '
'one_hot embedding setting, receptor_vocabulary_size used instead.'
)
# Assemble datasets
train_dataset = DrugAffinityDataset(
drug_affinity_filepath=train_affinity_filepath,
smi_filepath=ligand_filepath,
protein_filepath=receptor_filepath,
protein_language=protein_language,
smiles_language=smiles_language,
smiles_padding=params.get('ligand_padding', True),
smiles_padding_length=params.get('ligand_padding_length', None),
smiles_add_start_and_stop=params.get('ligand_add_start_stop', True),
smiles_augment=params.get('augment_smiles', False),
smiles_canonical=params.get('smiles_canonical', False),
smiles_kekulize=params.get('smiles_kekulize', False),
smiles_all_bonds_explicit=params.get('smiles_bonds_explicit', False),
smiles_all_hs_explicit=params.get('smiles_all_hs_explicit', False),
smiles_remove_bonddir=params.get('smiles_remove_bonddir', False),
smiles_remove_chirality=params.get('smiles_remove_chirality', False),
smiles_selfies=params.get('selfies', False),
protein_amino_acid_dict=params.get('protein_amino_acid_dict', 'iupac'),
protein_padding=params.get('receptor_padding', True),
protein_padding_length=params.get('receptor_padding_length', None),
protein_add_start_and_stop=params.get('receptor_add_start_stop', True),
protein_augment_by_revert=params.get('protein_augment', False),
device=device,
drug_affinity_dtype=torch.float,
backend='eager',
iterate_dataset=params.get('iterate_dataset', False),
)
train_loader = torch.utils.data.DataLoader(
dataset=train_dataset,
batch_size=params['batch_size'],
shuffle=True,
drop_last=True,
num_workers=params.get('num_workers', 0),
)
test_dataset = DrugAffinityDataset(
drug_affinity_filepath=test_affinity_filepath,
smi_filepath=ligand_filepath,
protein_filepath=receptor_filepath,
protein_language=protein_language,
smiles_language=smiles_language,
smiles_padding=params.get('ligand_padding', True),
smiles_padding_length=params.get('ligand_padding_length', None),
smiles_add_start_and_stop=params.get('ligand_add_start_stop', True),
smiles_augment=False,
smiles_canonical=params.get('smiles_test_canonical', False),
smiles_kekulize=params.get('smiles_kekulize', False),
smiles_all_bonds_explicit=params.get('smiles_bonds_explicit', False),
smiles_all_hs_explicit=params.get('smiles_all_hs_explicit', False),
smiles_remove_bonddir=params.get('smiles_remove_bonddir', False),
smiles_remove_chirality=params.get('smiles_remove_chirality', False),
smiles_selfies=params.get('selfies', False),
protein_amino_acid_dict=params.get('protein_amino_acid_dict', 'iupac'),
protein_padding=params.get('receptor_padding', True),
protein_padding_length=params.get('receptor_padding_length', None),
protein_add_start_and_stop=params.get('receptor_add_start_stop', True),
protein_augment_by_revert=False,
device=device,
drug_affinity_dtype=torch.float,
backend='eager',
iterate_dataset=params.get('iterate_dataset', False),
)
test_loader = torch.utils.data.DataLoader(
dataset=test_dataset,
batch_size=params['batch_size'],
shuffle=True,
drop_last=True,
num_workers=params.get('num_workers', 0),
)
logger.info(
f'Training dataset has {len(train_dataset)} samples, test set has '
f'{len(test_dataset)}.')
logger.info(f'Device for data loader is {train_dataset.device} and for '
f'model is {device}')
save_top_model = os.path.join(model_dir, 'weights/{}_{}_{}.pt')
params.update({
'ligand_vocabulary_size':
(train_dataset.smiles_dataset.smiles_language.number_of_tokens),
'receptor_vocabulary_size':
protein_language.number_of_tokens,
})
logger.info(
f'Receptor vocabulary size is {protein_language.number_of_tokens} and '
f'ligand vocabulary size is {train_dataset.smiles_dataset.smiles_language.number_of_tokens}'
)
model_fn = params.get('model_fn', 'bimodal_mca')
model = MODEL_FACTORY[model_fn](params).to(device)
model._associate_language(smiles_language)
model._associate_language(protein_language)
if os.path.isfile(os.path.join(model_dir, 'weights', 'best_mca.pt')):
logger.info('Found existing model, restoring now...')
try:
model.load(os.path.join(model_dir, 'weights', 'best_mca.pt'))
with open(os.path.join(model_dir, 'results', 'mse.json'),
'r') as f:
info = json.load(f)
max_roc_auc = info['best_roc_auc']
min_loss = info['test_loss']
except Exception:
min_loss, max_roc_auc = 100, 0
else:
min_loss, max_roc_auc = 100, 0
# Define optimizer
optimizer = OPTIMIZER_FACTORY[params.get('optimizer',
'adam')](model.parameters(),
lr=params.get(
'lr', 0.001))
num_params = sum(p.numel() for p in model.parameters() if p.requires_grad)
params.update({'number_of_parameters': num_params})
logger.info(f'Number of parameters: {num_params}')
logger.info(f'Model: {model}')
# Overwrite params.json file with updated parameters.
with open(os.path.join(model_dir, 'model_params.json'), 'w') as fp:
json.dump(params, fp)
# Start training
logger.info('Training about to start...\n')
t = time()
model.save(save_top_model.format('epoch', '0', model_fn))
for epoch in range(params['epochs']):
model.train()
logger.info(f"== Epoch [{epoch}/{params['epochs']}] ==")
train_loss = 0
for ind, (ligands, receptors, y) in enumerate(train_loader):
if ind % 100 == 0:
logger.info(f'Batch {ind}/{len(train_loader)}')
y_hat, pred_dict = model(ligands, receptors)
loss = model.loss(y_hat, y.to(device))
optimizer.zero_grad()
loss.backward()
# Apply gradient clipping
# torch.nn.utils.clip_grad_norm_(model.parameters(),1e-6)
optimizer.step()
train_loss += loss.item()
logger.info("\t **** TRAINING **** "
f"Epoch [{epoch + 1}/{params['epochs']}], "
f"loss: {train_loss / len(train_loader):.5f}. "
f"This took {time() - t:.1f} secs.")
t = time()
# Measure validation performance
model.eval()
with torch.no_grad():
test_loss = 0
predictions = []
labels = []
for ind, (ligands, receptors, y) in enumerate(test_loader):
y_hat, pred_dict = model(ligands.to(device),
receptors.to(device))
predictions.append(y_hat)
labels.append(y.clone())
loss = model.loss(y_hat, y.to(device))
test_loss += loss.item()
predictions = torch.cat(predictions, dim=0).flatten().cpu().numpy()
labels = torch.cat(labels, dim=0).flatten().cpu().numpy()
test_loss = test_loss / len(test_loader)
fpr, tpr, _ = roc_curve(labels, predictions)
test_roc_auc = auc(fpr, tpr)
# calculations for visualization plot
precision, recall, _ = precision_recall_curve(labels, predictions)
avg_precision = average_precision_score(labels, predictions)
test_loss = test_loss / len(test_loader)
logger.info(
f"\t **** TESTING **** Epoch [{epoch + 1}/{params['epochs']}], "
f"loss: {test_loss:.5f}, ROC-AUC: {test_roc_auc:.3f}, "
f"Average precision: {avg_precision:.3f}.")
def save(path, metric, typ, val=None):
model.save(path.format(typ, metric, model_fn))
info = {
'best_roc_auc': str(max_roc_auc),
'test_loss': str(min_loss),
}
with open(os.path.join(model_dir, 'results', metric + '.json'),
'w') as f:
json.dump(info, f)
np.save(
os.path.join(model_dir, 'results', metric + '_preds.npy'),
np.vstack([predictions, labels]),
)
if typ == 'best':
logger.info(f'\t New best performance in "{metric}"'
f' with value : {val:.7f} in epoch: {epoch}')
if test_roc_auc > max_roc_auc:
max_roc_auc = test_roc_auc
save(save_top_model, 'ROC-AUC', 'best', max_roc_auc)
ep_roc = epoch
roc_auc_loss = test_loss
if test_loss < min_loss:
min_loss = test_loss
save(save_top_model, 'loss', 'best', min_loss)
ep_loss = epoch
loss_roc_auc = test_roc_auc
if (epoch + 1) % params.get('save_model', 100) == 0:
save(save_top_model, 'epoch', str(epoch))
logger.info('Overall best performances are: \n \t'
f'Loss = {min_loss:.4f} in epoch {ep_loss} '
f'\t (ROC-AUC was {loss_roc_auc:4f}) \n \t'
f'ROC-AUC = {max_roc_auc:.4f} in epoch {ep_roc} '
f'\t (Loss was {roc_auc_loss:4f})')
save(save_top_model, 'training', 'done')
logger.info('Done with training, models saved, shutting down.')
def main(
train_sensitivity_filepath, test_sensitivity_filepath, gep_filepath,
smi_filepath, gene_filepath, smiles_language_filepath, model_path,
params_filepath, training_name
):
logger = logging.getLogger(f'{training_name}')
# Process parameter file:
params = {}
with open(params_filepath) as fp:
params.update(json.load(fp))
# Create model directory and dump files
model_dir = os.path.join(model_path, training_name)
os.makedirs(os.path.join(model_dir, 'weights'), exist_ok=True)
os.makedirs(os.path.join(model_dir, 'results'), exist_ok=True)
with open(os.path.join(model_dir, 'model_params.json'), 'w') as fp:
json.dump(params, fp, indent=4)
# Prepare the dataset
logger.info("Start data preprocessing...")
# Load SMILES language
smiles_language = SMILESLanguage.load(smiles_language_filepath)
# Load the gene list
with open(gene_filepath, 'rb') as f:
gene_list = pickle.load(f)
# Assemble datasets
train_dataset = DrugSensitivityDataset(
drug_sensitivity_filepath=train_sensitivity_filepath,
smi_filepath=smi_filepath,
gene_expression_filepath=gep_filepath,
smiles_language=smiles_language,
gene_list=gene_list,
drug_sensitivity_min_max=params.get('drug_sensitivity_min_max', True),
drug_sensitivity_processing_parameters=params.get(
'drug_sensitivity_processing_parameters', {}
),
augment=params.get('augment_smiles', True),
canonical=params.get('canonical', False),
kekulize=params.get('kekulize', False),
all_bonds_explicit=params.get('all_bonds_explicit', False),
all_hs_explicit=params.get('all_hs_explicit', False),
randomize=params.get('randomize', False),
remove_bonddir=params.get('remove_bonddir', False),
remove_chirality=params.get('remove_chirality', False),
selfies=params.get('selfies', False),
add_start_and_stop=params.get('smiles_start_stop_token', True),
padding_length=params.get('smiles_padding_length', None),
gene_expression_standardize=params.get(
'gene_expression_standardize', True
),
gene_expression_min_max=params.get('gene_expression_min_max', False),
gene_expression_processing_parameters=params.get(
'gene_expression_processing_parameters', {}
),
device=torch.device(params.get('dataset_device', 'cpu')),
backend='eager'
)
train_loader = torch.utils.data.DataLoader(
dataset=train_dataset,
batch_size=params['batch_size'],
shuffle=True,
drop_last=True,
num_workers=params.get('num_workers', 0)
)
test_dataset = DrugSensitivityDataset(
drug_sensitivity_filepath=test_sensitivity_filepath,
smi_filepath=smi_filepath,
gene_expression_filepath=gep_filepath,
smiles_language=smiles_language,
gene_list=gene_list,
drug_sensitivity_min_max=params.get('drug_sensitivity_min_max', True),
drug_sensitivity_processing_parameters=params.get(
'drug_sensitivity_processing_parameters',
train_dataset.drug_sensitivity_processing_parameters
),
augment=params.get('augment_test_smiles', False),
canonical=params.get('canonical', False),
kekulize=params.get('kekulize', False),
all_bonds_explicit=params.get('all_bonds_explicit', False),
all_hs_explicit=params.get('all_hs_explicit', False),
randomize=params.get('randomize', False),
remove_bonddir=params.get('remove_bonddir', False),
remove_chirality=params.get('remove_chirality', False),
selfies=params.get('selfies', False),
add_start_and_stop=params.get('smiles_start_stop_token', True),
padding_length=params.get('smiles_padding_length', None),
gene_expression_standardize=params.get(
'gene_expression_standardize', True
),
gene_expression_min_max=params.get('gene_expression_min_max', False),
gene_expression_processing_parameters=params.get(
'gene_expression_processing_parameters',
train_dataset.gene_expression_dataset.processing
),
device=torch.device(params.get('dataset_device', 'cpu')),
backend='eager'
)
test_loader = torch.utils.data.DataLoader(
dataset=test_dataset,
batch_size=params['batch_size'],
shuffle=True,
drop_last=True,
num_workers=params.get('num_workers', 0)
)
logger.info(
f'Training dataset has {len(train_dataset)} samples, test set has '
f'{len(test_dataset)}.'
)
device = get_device()
logger.info(
f'Device for data loader is {train_dataset.device} and for '
f'model is {device}'
)
save_top_model = os.path.join(model_dir, 'weights/{}_{}_{}.pt')
params.update({ # yapf: disable
'number_of_genes': len(gene_list), # yapf: disable
'smiles_vocabulary_size': smiles_language.number_of_tokens,
'drug_sensitivity_processing_parameters':
train_dataset.drug_sensitivity_processing_parameters,
'gene_expression_processing_parameters':
train_dataset.gene_expression_dataset.processing
})
model = MODEL_FACTORY[params.get('model_fn', 'mca')](params).to(device)
model._associate_language(smiles_language)
if os.path.isfile(os.path.join(model_dir, 'weights', 'best_mse_mca.pt')):
logger.info('Found existing model, restoring now...')
model.load(os.path.join(model_dir, 'weights', 'mse_best_mca.pt'))
with open(os.path.join(model_dir, 'results', 'mse.json'), 'r') as f:
info = json.load(f)
min_rmse = info['best_rmse']
max_pearson = info['best_pearson']
min_loss = info['test_loss']
else:
min_loss, min_rmse, max_pearson = 100, 1000, 0
# Define optimizer
optimizer = (
OPTIMIZER_FACTORY[params.get('optimizer', 'Adam')]
(model.parameters(), lr=params.get('lr', 0.01))
)
num_params = sum(p.numel() for p in model.parameters() if p.requires_grad)
params.update({'number_of_parameters': num_params})
logger.info(f'Number of parameters {num_params}')
# Overwrite params.json file with updated parameters.
with open(os.path.join(model_dir, 'model_params.json'), 'w') as fp:
json.dump(params, fp)
# Start training
logger.info('Training about to start...\n')
t = time()
model.save(
save_top_model.format('epoch', '0', params.get('model_fn', 'mca'))
)
for epoch in range(params['epochs']):
model.train()
logger.info(params_filepath.split('/')[-1])
logger.info(f"== Epoch [{epoch}/{params['epochs']}] ==")
train_loss = 0
for ind, (smiles, gep, y) in enumerate(train_loader):
y_hat, pred_dict = model(
torch.squeeze(smiles.to(device)), gep.to(device)
)
loss = model.loss(y_hat, y.to(device))
optimizer.zero_grad()
loss.backward()
# Apply gradient clipping
# torch.nn.utils.clip_grad_norm_(model.parameters(),1e-6)
optimizer.step()
train_loss += loss.item()
logger.info(
"\t **** TRAINING **** "
f"Epoch [{epoch + 1}/{params['epochs']}], "
f"loss: {train_loss / len(train_loader):.5f}. "
f"This took {time() - t:.1f} secs."
)
t = time()
# Measure validation performance
model.eval()
with torch.no_grad():
test_loss = 0
predictions = []
labels = []
for ind, (smiles, gep, y) in enumerate(test_loader):
y_hat, pred_dict = model(
torch.squeeze(smiles.to(device)), gep.to(device)
)
predictions.append(y_hat)
labels.append(y)
loss = model.loss(y_hat, y.to(device))
test_loss += loss.item()
predictions = np.array(
[p.cpu() for preds in predictions for p in preds]
)
labels = np.array([l.cpu() for label in labels for l in label])
test_pearson_a = pearsonr(
torch.Tensor(predictions), torch.Tensor(labels)
)
test_rmse_a = np.sqrt(np.mean((predictions - labels)**2))
test_loss_a = test_loss / len(test_loader)
logger.info(
f"\t **** TESTING **** Epoch [{epoch + 1}/{params['epochs']}], "
f"loss: {test_loss_a:.5f}, "
f"Pearson: {test_pearson_a:.3f}, "
f"RMSE: {test_rmse_a:.3f}"
)
def save(path, metric, typ, val=None):
model.save(path.format(typ, metric, params.get('model_fn', 'mca')))
with open(
os.path.join(model_dir, 'results', metric + '.json'), 'w'
) as f:
json.dump(info, f)
np.save(
os.path.join(model_dir, 'results', metric + '_preds.npy'),
np.vstack([predictions, labels])
)
if typ == 'best':
logger.info(
f'\t New best performance in "{metric}"'
f' with value : {val:.7f} in epoch: {epoch}'
)
def update_info():
return {
'best_rmse': str(min_rmse),
'best_pearson': str(float(max_pearson)),
'test_loss': str(min_loss),
'predictions': [float(p) for p in predictions]
}
if test_loss_a < min_loss:
min_rmse = test_rmse_a
min_loss = test_loss_a
min_loss_pearson = test_pearson_a
info = update_info()
save(save_top_model, 'mse', 'best', min_loss)
ep_loss = epoch
if test_pearson_a > max_pearson:
max_pearson = test_pearson_a
max_pearson_loss = test_loss_a
info = update_info()
save(save_top_model, 'pearson', 'best', max_pearson)
ep_pearson = epoch
if (epoch + 1) % params.get('save_model', 100) == 0:
save(save_top_model, 'epoch', str(epoch))
logger.info(
'Overall best performances are: \n \t'
f'Loss = {min_loss:.4f} in epoch {ep_loss} '
f'\t (Pearson was {min_loss_pearson:4f}) \n \t'
f'Pearson = {max_pearson:.4f} in epoch {ep_pearson} '
f'\t (Loss was {max_pearson_loss:2f})'
)
save(save_top_model, 'training', 'done')
logger.info('Done with training, models saved, shutting down.')
def __init__(self):
self.device = get_device()
def main(
train_scores_filepath, test_scores_filepath, smi_filepath, model_path,
params_filepath, training_name
):
logging.basicConfig(level=logging.INFO, format='%(message)s')
logger = logging.getLogger(f'{training_name}')
logger.setLevel(logging.INFO)
disable_rdkit_logging()
device = get_device()
# Restore pretrained model
logger.info('Start model restoring.')
try:
with open(os.path.join(model_path, 'model_params.json'), 'r') as fp:
params = json.load(fp)
smiles_language = SMILESLanguage.load(
os.path.join(model_path, 'smiles_language.pkl')
)
model = MODEL_FACTORY[params.get('model_fn', 'mca')](params).to(device)
# Try weight restoring
try:
weight_path = os.path.join(
model_path, 'weights',
params.get('weights_name', 'best_ROC-AUC_mca.pt')
)
model.load(weight_path)
except Exception:
try:
wp = os.listdir(os.path.join(model_path, 'weights'))[0]
logger.info(
f"Weights {weight_path} not found. Try restore {wp}"
)
model.load(os.path.join(model_path, 'weights', wp))
except Exception:
raise Exception('Error in weight loading.')
except Exception:
raise Exception(
'Error in model restoring. model_path should point to the model root '
'folder that contains a model_params.json, a smiles_language.pkl and a '
'weights folder.'
)
logger.info('Model restored. Now starting to craft it for the task')
# Process parameter file:
model_params = {}
with open(params_filepath) as fp:
model_params.update(json.load(fp))
model = update_mca_model(model, model_params)
logger.info('Model set up.')
for idx, (name, param) in enumerate(model.named_parameters()):
logger.info(
(idx, name, param.shape, f'Gradients: {param.requires_grad}')
)
ft_model_path = os.path.join(model_path, 'finetuned')
os.makedirs(os.path.join(ft_model_path, 'weights'), exist_ok=True)
os.makedirs(os.path.join(ft_model_path, 'results'), exist_ok=True)
logger.info('Now start data preprocessing...')
# Assemble datasets
smiles_dataset = SMILESDataset(
smi_filepath,
smiles_language=smiles_language,
padding_length=params.get('smiles_padding_length', None),
padding=params.get('padd_smiles', True),
add_start_and_stop=params.get('add_start_stop_token', True),
augment=params.get('augment_smiles', False),
canonical=params.get('canonical', False),
kekulize=params.get('kekulize', False),
all_bonds_explicit=params.get('all_bonds_explicit', False),
all_hs_explicit=params.get('all_hs_explicit', False),
randomize=params.get('randomize', False),
remove_bonddir=params.get('remove_bonddir', False),
remove_chirality=params.get('remove_chirality', False),
selfies=params.get('selfies', False),
sanitize=params.get('sanitize', True)
)
train_dataset = AnnotatedDataset(
annotations_filepath=train_scores_filepath,
dataset=smiles_dataset,
device=get_device()
)
train_loader = torch.utils.data.DataLoader(
dataset=train_dataset,
batch_size=params['batch_size'],
shuffle=True,
drop_last=True,
num_workers=params.get('num_workers', 0)
)
# Generally, if sanitize is True molecules are de-kekulized. Augmentation
# preserves the "kekulization", so if it is used, test data should be
# sanitized or canonicalized.
smiles_test_dataset = SMILESDataset(
smi_filepath,
smiles_language=smiles_language,
padding_length=params.get('smiles_padding_length', None),
padding=params.get('padd_smiles', True),
add_start_and_stop=params.get('add_start_stop_token', True),
augment=params.get('augment_test_smiles', False),
canonical=params.get('test_canonical', False),
kekulize=params.get('test_kekulize', False),
all_bonds_explicit=params.get('test_all_bonds_explicit', False),
all_hs_explicit=params.get('test_all_hs_explicit', False),
randomize=False,
remove_bonddir=params.get('test_remove_bonddir', False),
remove_chirality=params.get('test_remove_chirality', False),
selfies=params.get('selfies', False),
sanitize=params.get('test_sanitize', False)
)
# Dump eventually modified SMILES Language
smiles_language.save(os.path.join(ft_model_path, 'smiles_language.pkl'))
test_dataset = AnnotatedDataset(
annotations_filepath=test_scores_filepath,
dataset=smiles_test_dataset,
device=get_device()
)
test_loader = torch.utils.data.DataLoader(
dataset=test_dataset,
batch_size=params['batch_size'],
shuffle=False,
drop_last=False,
num_workers=params.get('num_workers', 0)
)
save_top_model = os.path.join(ft_model_path, 'weights/{}_{}_{}.pt')
num_t_params = sum(
p.numel() for p in model.parameters() if p.requires_grad
)
num_params = sum(p.numel() for p in model.parameters())
params.update({'params': num_params, 'trainable_params': num_t_params})
logger.info(
f'Number of parameters: {num_params} (trainable {num_t_params}).'
)
# Define optimizer only for those layers which require gradients
optimizer = (
OPTIMIZER_FACTORY[params.get('optimizer', 'adam')](
filter(lambda p: p.requires_grad, model.parameters()),
lr=params.get('lr', 0.00001)
)
)
# Dump params.json file with updated parameters.
with open(os.path.join(ft_model_path, 'model_params.json'), 'w') as fp:
json.dump(params, fp)
# Start training
logger.info('Training about to start...\n')
t = time()
min_loss, max_roc_auc = 1000000, 0
max_precision_recall_score = 0
for epoch in range(params['epochs']):
model.train()
logger.info(params_filepath.split('/')[-1])
logger.info(f"== Epoch [{epoch}/{params['epochs']}] ==")
train_loss = 0
for ind, (smiles, y) in enumerate(train_loader):
smiles = torch.squeeze(smiles.to(device))
y_hat, pred_dict = model(smiles)
loss = model.loss(y_hat, y.to(device))
optimizer.zero_grad()
loss.backward()
optimizer.step()
train_loss += loss.item()
logger.info(
'\t **** TRAINING **** '
f"Epoch [{epoch + 1}/{params['epochs']}], "
f'loss: {train_loss / len(train_loader):.5f}. '
f'This took {time() - t:.1f} secs.'
)
t = time()
# Measure validation performance
model.eval()
with torch.no_grad():
test_loss = 0
predictions = []
labels = []
for ind, (smiles, y) in enumerate(test_loader):
smiles = torch.squeeze(smiles.to(device))
y_hat, pred_dict = model(smiles)
predictions.append(y_hat)
# Copy y tensor since loss function applies downstream modification
labels.append(y.clone())
loss = model.loss(y_hat, y.to(device))
test_loss += loss.item()
predictions = torch.cat(predictions, dim=0).flatten().cpu().numpy()
labels = torch.cat(labels, dim=0).flatten().cpu().numpy()
# Remove NaNs from labels to compute scores
predictions = predictions[~np.isnan(labels)]
labels = labels[~np.isnan(labels)]
test_loss_a = test_loss / len(test_loader)
fpr, tpr, _ = roc_curve(labels, predictions)
test_roc_auc_a = auc(fpr, tpr)
# calculations for visualization plot
precision, recall, _ = precision_recall_curve(labels, predictions)
# score for precision vs accuracy
test_precision_recall_score = average_precision_score(
labels, predictions
)
logger.info(
f"\t **** TEST **** Epoch [{epoch + 1}/{params['epochs']}], "
f'loss: {test_loss_a:.5f}, , roc_auc: {test_roc_auc_a:.5f}, '
f'avg precision-recall score: {test_precision_recall_score:.5f}'
)
info = {
'test_auc': test_roc_auc_a,
'train_loss': train_loss / len(train_loader),
'test_loss': test_loss_a,
'test_auc': test_roc_auc_a,
'best_test_auc': max_roc_auc,
'test_precision_recall_score': test_precision_recall_score,
'best_precision_recall_score': max_precision_recall_score,
}
def save(path, metric, typ, val=None):
model.save(path.format(typ, metric, params.get('model_fn', 'mca')))
if typ == 'best':
logger.info(
f'\t New best performance in {metric}'
f' with value : {val:.7f} in epoch: {epoch+1}'
)
if test_roc_auc_a > max_roc_auc:
max_roc_auc = test_roc_auc_a
info.update({'best_test_auc': max_roc_auc})
save(save_top_model, 'ROC-AUC', 'best', max_roc_auc)
np.save(
os.path.join(ft_model_path, 'results', 'best_predictions.npy'),
predictions
)
with open(
os.path.join(ft_model_path, 'results', 'metrics.json'), 'w'
) as f:
json.dump(info, f)
if test_precision_recall_score > max_precision_recall_score:
max_precision_recall_score = test_precision_recall_score
info.update(
{'best_precision_recall_score': max_precision_recall_score}
)
save(
save_top_model, 'precision-recall score', 'best',
max_precision_recall_score
)
if test_loss_a < min_loss:
min_loss = test_loss_a
save(save_top_model, 'loss', 'best', min_loss)
ep_loss = epoch
if (epoch + 1) % params.get('save_model', 100) == 0:
save(save_top_model, 'epoch', str(epoch))
logger.info(
'Overall best performances are: \n \t'
f'Loss = {min_loss:.4f} in epoch {ep_loss} '
)
save(save_top_model, 'training', 'done')
logger.info('Done with training, models saved, shutting down.')
def update_mca_model(model: MCAMultiTask, params: dict) -> MCAMultiTask:
"""
Receives a pretrained model (instance of MCAMultiTask), modifies it and returns
the updated object
Args:
model (MCAMultiTask): Pretrained model to be modified.
params (dict): Hyperparameter file for the modifications. Needs to include:
- number_of_tunable_layers (how many layers should not be frozen. If
number exceeds number of existing layers, all layers are tuned.)
- fresh_dense_sizes (a list of fresh dense layers to be plugged in at
the end).
- num_tasks (number of classfication tasks being performed).
Returns:
MCAMultiTask: Modified model for finetune
"""
if not isinstance(model, MCAMultiTask):
raise TypeError(
f'Wrong model type, was {type(model)}, not MCAMultiTask.'
)
# Freeze the correct layers and add new ones
# Not strictly speaking all layers, but all param matrices, gradient-req or not.
num_layers = len(['' for p in model.parameters()])
num_to_tune = params['number_of_tunable_layers']
if num_to_tune > num_layers:
logger.warning(
f'Model has {num_layers} tunable layers. Given # is larger: {num_to_tune}.'
)
num_to_tune = num_layers
fresh_sizes = params['fresh_dense_sizes']
logger.info(
f'Model has {num_layers} layers. {num_to_tune} will be finetuned, '
f'{len(fresh_sizes)} fresh ones will be added (sizes: {fresh_sizes}).'
)
# Count the ensemble layers (will be replaced anyways)
num_ensemble_layers = len(
list(
filter(lambda tpl: 'ensemble' in tpl[0], model.named_parameters())
)
)
# Freeze the right layers
for idx, (name, param) in enumerate(model.named_parameters()):
if idx < num_layers - num_to_tune - num_ensemble_layers:
param.requires_grad = False
# Add more dense layers
fresh_sizes.insert(0, model.hidden_sizes[-1])
model.dense_layers = nn.Sequential(
model.dense_layers,
nn.Sequential(
OrderedDict(
[
(
'fresh_dense_{}'.format(ind),
dense_layer(
fresh_sizes[ind],
fresh_sizes[ind + 1],
act_fn=ACTIVATION_FN_FACTORY[
params.get('activation_fn', 'relu')],
dropout=params.get('dropout', 0.5),
batch_norm=params.get('batch_norm', True)
).to(get_device())
) for ind in range(len(fresh_sizes) - 1)
]
)
)
)
# Replace final layer
model.num_tasks = params['num_tasks']
model.final_dense = EnsembleLayer(
typ=params.get('ensemble', 'score'),
input_size=fresh_sizes[-1],
output_size=model.num_tasks,
ensemble_size=params.get('ensemble_size', 5),
fn=ACTIVATION_FN_FACTORY['sigmoid']
)
return model
def main(train_sensitivity_filepath, test_sensitivity_filepath, gep_filepath,
smi_filepath, gene_filepath, smiles_language_filepath, model_path,
params_filepath, training_name):
logger = logging.getLogger(f'{training_name}')
# Process parameter file:
params = {}
with open(params_filepath) as fp:
params.update(json.load(fp))
# Create model directory and dump files
model_dir = os.path.join(model_path, training_name)
os.makedirs(os.path.join(model_dir, 'weights'), exist_ok=True)
os.makedirs(os.path.join(model_dir, 'results'), exist_ok=True)
with open(os.path.join(model_dir, 'model_params.json'), 'w') as fp:
json.dump(params, fp, indent=4)
# Prepare the dataset
logger.info("Start data preprocessing...")
# Load SMILES language
smiles_language = SMILESLanguage.load(smiles_language_filepath)
# Load the gene list
with open(gene_filepath, 'rb') as f:
gene_list = pickle.load(f)
# Assemble datasets
train_dataset = DrugSensitivityDataset(
drug_sensitivity_filepath=train_sensitivity_filepath,
smi_filepath=smi_filepath,
gene_expression_filepath=gep_filepath,
smiles_language=smiles_language,
gene_list=gene_list,
drug_sensitivity_min_max=params.get('drug_sensitivity_min_max', True),
augment=params.get('augment_smiles', True),
add_start_and_stop=params.get('smiles_start_stop_token', True),
padding_length=params.get('smiles_padding_length', None),
device=torch.device(params.get('dataset_device', 'cpu')),
backend='eager')
train_loader = torch.utils.data.DataLoader(dataset=train_dataset,
batch_size=params['batch_size'],
shuffle=True,
drop_last=True,
num_workers=params.get(
'num_workers', 0))
test_dataset = DrugSensitivityDataset(
drug_sensitivity_filepath=test_sensitivity_filepath,
smi_filepath=smi_filepath,
gene_expression_filepath=gep_filepath,
smiles_language=smiles_language,
gene_list=gene_list,
drug_sensitivity_min_max=params.get('drug_sensitivity_min_max', True),
augment=params.get('augment_smiles', True),
add_start_and_stop=params.get('smiles_start_stop_token', True),
padding_length=params.get('smiles_padding_length', None),
device=torch.device(params.get('dataset_device', 'cpu')),
backend='eager')
test_loader = torch.utils.data.DataLoader(dataset=test_dataset,
batch_size=params['batch_size'],
shuffle=True,
drop_last=True,
num_workers=params.get(
'num_workers', 0))
params.update({
'number_of_genes': len(gene_list),
'smiles_vocabulary_size': smiles_language.number_of_tokens
})
# Set the tensorboard logger
tb_logger = Logger(os.path.join(model_dir, 'tb_logs'))
device = get_device()
logger.info(f'Device for data loader is {train_dataset.device} and for '
f'model is {device}')
save_top_model = os.path.join(model_dir, 'weights/{}_{}_{}.pt')
model = MODEL_FACTORY[params.get('model_fn', 'mca')](params).to(device)
# Define optimizer
optimizer = (OPTIMIZER_FACTORY[params.get('optimizer',
'Adam')](model.parameters(),
lr=params.get(
'lr', 0.01)))
# Overwrite params.json file with updated parameters.
with open(os.path.join(model_dir, 'model_params.json'), 'w') as fp:
json.dump(params, fp)
# Start training
logger.info('Training about to start...\n')
t = time()
min_loss, max_pearson = 100, 0
for epoch in range(params['epochs']):
model.train()
logger.info(params_filepath.split('/')[-1])
logger.info(f"== Epoch [{epoch}/{params['epochs']}] ==")
train_loss = 0
for ind, (smiles, gep, y) in enumerate(train_loader):
y_hat, pred_dict = model(torch.squeeze(smiles.to(device)),
gep.to(device))
loss = model.loss(y_hat, y.to(device))
optimizer.zero_grad()
loss.backward()
# Apply gradient clipping
# torch.nn.utils.clip_grad_norm_(model.parameters(),1e-6)
optimizer.step()
train_loss += loss.item()
logger.info("\t **** TRAINING **** "
f"Epoch [{epoch + 1}/{params['epochs']}], "
f"loss: {train_loss / len(train_loader):.5f}. "
f"This took {time() - t:.1f} secs.")
t = time()
# Measure validation performance
model.eval()
with torch.no_grad():
test_loss = 0
predictions = []
labels = []
for ind, (smiles, gep, y) in enumerate(test_loader):
y_hat, pred_dict = model(torch.squeeze(smiles.to(device)),
gep.to(device))
predictions.append(y_hat)
labels.append(y)
loss = model.loss(y_hat, y.to(device))
test_loss += loss.item()
predictions = np.array(
[p.cpu() for preds in predictions for p in preds])
labels = np.array([l.cpu() for label in labels for l in label])
test_pearson_a = pearsonr(predictions, labels)
test_rmse_a = np.sqrt(np.mean((predictions - labels)**2))
np.save(os.path.join(model_dir, 'results', 'preds.npy'),
np.hstack([predictions, labels]))
test_loss_a = test_loss / len(test_loader)
logger.info(
f"\t **** TESTING **** Epoch [{epoch + 1}/{params['epochs']}], "
f"loss: {test_loss_a:.5f}, "
f"Pearson: {test_pearson_a:.3f}, "
f"RMSE: {test_rmse_a:.3f}")
# TensorBoard logging of scalars.
info = {
'train_loss': train_loss / len(train_loader),
'test_loss': test_loss_a,
}
for tag, value in info.items():
tb_logger.scalar_summary(tag, value, epoch + 1)
def save(path, metric, typ, val=None):
model.save(path.format(typ, metric, params.get('model_fn', 'mca')))
if typ == 'best':
logger.info(f'\t New best performance in "{metric}"'
f' with value : {val:.7f} in epoch: {epoch}')
if test_loss_a < min_loss:
min_loss = test_loss_a
min_loss_pearson = test_pearson_a
save(save_top_model, 'mse', 'best', min_loss)
ep_loss = epoch
if test_pearson_a > max_pearson:
max_pearson = test_pearson_a
max_pearson_loss = test_loss_a
save(save_top_model, 'pearson', 'best', max_pearson)
ep_pearson = epoch
if (epoch + 1) % params.get('save_model', 100) == 0:
save(save_top_model, 'epoch', str(epoch))
logger.info('Overall best performances are: \n \t'
f'Loss = {min_loss:.4f} in epoch {ep_loss} '
f'\t (Pearson was {min_loss_pearson:4f}) \n \t'
f'Pearson = {max_pearson:.4f} in epoch {ep_pearson} '
f'\t (Loss was {max_pearson_loss:2f})')
save(save_top_model, 'training', 'done')
logger.info('Done with training, models saved, shutting down.')
def __init__(self, params, *args, **kwargs):
"""Constructor.
Args:
params (dict): A dictionary containing the parameter to built the
dense Decoder.
TODO params should become actual arguments (use **params).
Items in params:
filters (list[int], optional): Numbers of filters to learn per
convolutional layer.
kernel_sizes (list[list[int]], optional): Sizes of kernels per
convolutional layer. Defaults to [
[3, params['smiles_embedding_size']],
[5, params['smiles_embedding_size']],
[11, params['smiles_embedding_size']]
]
activation_fn (string, optional): Activation function used in all
layers for specification in ACTIVATION_FN_FACTORY.
Defaults to 'relu'.
batch_norm (bool, optional): Whether batch normalization is
applied. Defaults to False.
dropout (float, optional): Dropout probability in all
except parametric layer. Defaults to 0.0.
*args, **kwargs: positional and keyword arguments are ignored.
"""
super(CNN, self).__init__(*args, **kwargs)
# Model Parameter
self.device = get_device()
self.params = params
self.loss_fn = LOSS_FN_FACTORY[params.get('loss_fn', 'cnn')]
self.kernel_sizes = params.get('kernel_sizes',
[[3, params['smiles_embedding_size']],
[5, params['smiles_embedding_size']],
[11, params['smiles_embedding_size']]])
self.num_filters = [1] + params.get('num_filters', [10, 20, 50])
if len(self.filters) != len(self.kernel_sizes):
raise ValueError(
'Length of filter and kernel size lists do not match.')
self.smiles_embedding = nn.Embedding(
self.params['smiles_vocabulary_size'],
self.params['smiles_embedding_size'],
scale_grad_by_freq=params.get('embed_scale_grad', False))
self.dropout = params.get('dropout', 0.0)
self.act_fn = ACTIVATION_FN_FACTORY[params.get('activation_fn',
'relu')]
self.conv_layers = [
convolutional_layer(self.num_filters[layer],
self.num_filters[layer + 1],
self.kernel_sizes[layer])
for layer in range(len(self.channel_inputs) - 1)
]
self.final_dense = nn.Linear(
(self.num_filters[-1] * self.params['smiles_embedding_size']),
self.num_tasks)
self.final_act_fn = ACTIVATION_FN_FACTORY['sigmoid']
def main(train_scores_filepath,
test_scores_filepath,
smi_filepath,
smiles_language_filepath,
model_path,
params_filepath,
training_name,
embedding_path=None):
logging.basicConfig(level=logging.INFO, format='%(message)s')
logger = logging.getLogger(f'{training_name}')
logger.setLevel(logging.INFO)
disable_rdkit_logging()
# Process parameter file:
params = {}
with open(params_filepath) as fp:
params.update(json.load(fp))
if embedding_path:
params['embedding_path'] = embedding_path
# Create model directory and dump files
model_dir = os.path.join(model_path, training_name)
os.makedirs(os.path.join(model_dir, 'weights'), exist_ok=True)
os.makedirs(os.path.join(model_dir, 'results'), exist_ok=True)
with open(os.path.join(model_dir, 'model_params.json'), 'w') as fp:
json.dump(params, fp, indent=4)
logger.info('Start data preprocessing...')
smiles_language = SMILESLanguage.load(smiles_language_filepath)
# Prepare FP processing
if params.get('model_fn', 'mca') == 'dense':
morgan_transform = Compose([
SMILESToMorganFingerprints(radius=params.get('fp_radius', 2),
bits=params.get('num_drug_features',
512),
chirality=params.get(
'fp_chirality', True)),
ToTensor(get_device())
])
def smiles_tensor_batch_to_fp(smiles):
""" To abuse SMILES dataset for FP usage"""
out = torch.Tensor(smiles.shape[0],
params.get('num_drug_features', 256))
for ind, tensor in enumerate(smiles):
smiles = smiles_language.token_indexes_to_smiles(
tensor.tolist())
out[ind, :] = torch.squeeze(morgan_transform(smiles))
return out
# Assemble datasets
smiles_dataset = SMILESDataset(
smi_filepath,
smiles_language=smiles_language,
padding_length=params.get('smiles_padding_length', None),
padding=params.get('padd_smiles', True),
add_start_and_stop=params.get('add_start_stop_token', True),
augment=params.get('augment_smiles', False),
canonical=params.get('canonical', False),
kekulize=params.get('kekulize', False),
all_bonds_explicit=params.get('all_bonds_explicit', False),
all_hs_explicit=params.get('all_hs_explicit', False),
randomize=params.get('randomize', False),
remove_bonddir=params.get('remove_bonddir', False),
remove_chirality=params.get('remove_chirality', False),
selfies=params.get('selfies', False),
sanitize=params.get('sanitize', True))
# include arg label_columns if data file has any unwanted columns (such as index) to be ignored.
train_dataset = AnnotatedDataset(
annotations_filepath=train_scores_filepath,
dataset=smiles_dataset,
device=get_device())
train_loader = torch.utils.data.DataLoader(dataset=train_dataset,
batch_size=params['batch_size'],
shuffle=True,
drop_last=True,
num_workers=params.get(
'num_workers', 0))
if (params.get('uncertainty', True)
and params.get('augment_test_data', False)):
raise ValueError(
'Epistemic uncertainty evaluation not supported if augmentation '
'is not enabled for test data.')
# Generally, if sanitize is True molecules are de-kekulized. Augmentation
# preserves the "kekulization", so if it is used, test data should be
# sanitized or canonicalized.
smiles_test_dataset = SMILESDataset(
smi_filepath,
smiles_language=smiles_language,
padding_length=params.get('smiles_padding_length', None),
padding=params.get('padd_smiles', True),
add_start_and_stop=params.get('add_start_stop_token', True),
augment=params.get('augment_test_smiles', False),
canonical=params.get('test_canonical', False),
kekulize=params.get('test_kekulize', False),
all_bonds_explicit=params.get('test_all_bonds_explicit', False),
all_hs_explicit=params.get('test_all_hs_explicit', False),
randomize=False,
remove_bonddir=params.get('test_remove_bonddir', False),
remove_chirality=params.get('test_remove_chirality', False),
selfies=params.get('selfies', False),
sanitize=params.get('test_sanitize', False))
# Dump eventually modified SMILES Language
smiles_language.save(os.path.join(model_dir, 'smiles_language.pkl'))
logger.info(smiles_dataset._dataset.transform)
logger.info(smiles_test_dataset._dataset.transform)
# include arg label_columns if data file has any unwanted columns (such as index) to be ignored.
test_dataset = AnnotatedDataset(annotations_filepath=test_scores_filepath,
dataset=smiles_test_dataset,
device=get_device())
test_loader = torch.utils.data.DataLoader(dataset=test_dataset,
batch_size=params['batch_size'],
shuffle=False,
drop_last=False,
num_workers=params.get(
'num_workers', 0))
if params.get('confidence', False):
smiles_conf_dataset = SMILESDataset(
smi_filepath,
smiles_language=smiles_language,
padding_length=params.get('smiles_padding_length', None),
padding=params.get('padd_smiles', True),
add_start_and_stop=params.get('add_start_stop_token', True),
augment=True, # Natively true for epistemic uncertainity estimate
canonical=params.get('canonical', False),
kekulize=params.get('kekulize', False),
all_bonds_explicit=params.get('all_bonds_explicit', False),
all_hs_explicit=params.get('all_hs_explicit', False),
randomize=params.get('randomize', False),
remove_bonddir=params.get('remove_bonddir', False),
remove_chirality=params.get('remove_chirality', False),
selfies=params.get('selfies', False))
conf_dataset = AnnotatedDataset(
annotations_filepath=test_scores_filepath,
dataset=smiles_conf_dataset,
device=get_device())
conf_loader = torch.utils.data.DataLoader(
dataset=conf_dataset,
batch_size=params['batch_size'],
shuffle=False,
drop_last=False,
num_workers=params.get('num_workers', 0))
if not params.get('embedding', 'learned') == 'pretrained':
params.update(
{'smiles_vocabulary_size': smiles_language.number_of_tokens})
device = get_device()
logger.info(f'Device for data loader is {train_dataset.device} and for '
f'model is {device}')
save_top_model = os.path.join(model_dir, 'weights/{}_{}_{}.pt')
model = MODEL_FACTORY[params.get('model_fn', 'mca')](params).to(device)
logger.info(model)
logger.info(model.loss_fn.class_weights)
logger.info('Parameters follow')
for name, param in model.named_parameters():
logger.info((name, param.shape))
num_params = sum(p.numel() for p in model.parameters() if p.requires_grad)
params.update({'number_of_parameters': num_params})
logger.info(f'Number of parameters {num_params}')
# Define optimizer
optimizer = (OPTIMIZER_FACTORY[params.get('optimizer',
'adam')](model.parameters(),
lr=params.get(
'lr', 0.00001)))
# Overwrite params.json file with updated parameters.
with open(os.path.join(model_dir, 'model_params.json'), 'w') as fp:
json.dump(params, fp)
# Start training
logger.info('Training about to start...\n')
t = time()
min_loss, max_roc_auc = 1000000, 0
max_precision_recall_score = 0
for epoch in range(params['epochs']):
model.train()
logger.info(params_filepath.split('/')[-1])
logger.info(f"== Epoch [{epoch}/{params['epochs']}] ==")
train_loss = 0
for ind, (smiles, y) in enumerate(train_loader):
smiles = torch.squeeze(smiles.to(device))
# Transform smiles to FP if needed
if params.get('model_fn', 'mca') == 'dense':
smiles = smiles_tensor_batch_to_fp(smiles).to(device)
y_hat, pred_dict = model(smiles)
loss = model.loss(y_hat, y.to(device))
optimizer.zero_grad()
loss.backward()
optimizer.step()
train_loss += loss.item()
logger.info('\t **** TRAINING **** '
f"Epoch [{epoch + 1}/{params['epochs']}], "
f'loss: {train_loss / len(train_loader):.5f}. '
f'This took {time() - t:.1f} secs.')
t = time()
# Measure validation performance
model.eval()
with torch.no_grad():
test_loss = 0
predictions = []
labels = []
for ind, (smiles, y) in enumerate(test_loader):
smiles = torch.squeeze(smiles.to(device))
# Transform smiles to FP if needed
if params.get('model_fn', 'mca') == 'dense':
smiles = smiles_tensor_batch_to_fp(smiles).to(device)
y_hat, pred_dict = model(smiles)
predictions.append(y_hat)
# Copy y tensor since loss function applies downstream
# modification
labels.append(y.clone())
loss = model.loss(y_hat, y.to(device))
test_loss += loss.item()
predictions = torch.cat(predictions, dim=0).flatten().cpu().numpy()
labels = torch.cat(labels, dim=0).flatten().cpu().numpy()
# Remove NaNs from labels to compute scores
predictions = predictions[~np.isnan(labels)]
labels = labels[~np.isnan(labels)]
test_loss_a = test_loss / len(test_loader)
fpr, tpr, _ = roc_curve(labels, predictions)
test_roc_auc_a = auc(fpr, tpr)
# calculations for visualization plot
precision, recall, _ = precision_recall_curve(labels, predictions)
# score for precision vs accuracy
test_precision_recall_score = average_precision_score(
labels, predictions)
logger.info(
f"\t **** TEST **** Epoch [{epoch + 1}/{params['epochs']}], "
f'loss: {test_loss_a:.5f}, , roc_auc: {test_roc_auc_a:.5f}, '
f'avg precision-recall score: {test_precision_recall_score:.5f}')
info = {
'test_auc': test_roc_auc_a,
'train_loss': train_loss / len(train_loader),
'test_loss': test_loss_a,
'test_auc': test_roc_auc_a,
'best_test_auc': max_roc_auc,
'test_precision_recall_score': test_precision_recall_score,
'best_precision_recall_score': max_precision_recall_score,
}
def save(path, metric, typ, val=None):
model.save(path.format(typ, metric, params.get('model_fn', 'mca')))
if typ == 'best':
logger.info(f'\t New best performance in {metric}'
f' with value : {val:.7f} in epoch: {epoch+1}')
if test_roc_auc_a > max_roc_auc:
max_roc_auc = test_roc_auc_a
info.update({'best_test_auc': max_roc_auc})
save(save_top_model, 'ROC-AUC', 'best', max_roc_auc)
np.save(os.path.join(model_dir, 'results', 'best_predictions.npy'),
predictions)
with open(os.path.join(model_dir, 'results', 'metrics.json'),
'w') as f:
json.dump(info, f)
if params.get('confidence', False):
# Compute uncertainity estimates and save them
epistemic_conf = monte_carlo_dropout(model,
regime='loader',
loader=conf_loader)
aleatoric_conf = test_time_augmentation(model,
regime='loader',
loader=conf_loader)
np.save(
os.path.join(model_dir, 'results', 'epistemic_conf.npy'),
epistemic_conf)
np.save(
os.path.join(model_dir, 'results', 'aleatoric_conf.npy'),
aleatoric_conf)
if test_precision_recall_score > max_precision_recall_score:
max_precision_recall_score = test_precision_recall_score
info.update(
{'best_precision_recall_score': max_precision_recall_score})
save(save_top_model, 'precision-recall score', 'best',
max_precision_recall_score)
if test_loss_a < min_loss:
min_loss = test_loss_a
save(save_top_model, 'loss', 'best', min_loss)
ep_loss = epoch
if (epoch + 1) % params.get('save_model', 100) == 0:
save(save_top_model, 'epoch', str(epoch))
logger.info('Overall best performances are: \n \t'
f'Loss = {min_loss:.4f} in epoch {ep_loss} ')
save(save_top_model, 'training', 'done')
logger.info('Done with training, models saved, shutting down.')
def main(model_path, smi_filepath, labels_filepath, output_folder,
smiles_language_filepath, model_id, confidence):
logging.basicConfig(level=logging.INFO, format='%(message)s')
logger = logging.getLogger('eval_toxicity')
logger.setLevel(logging.INFO)
disable_rdkit_logging()
# Process parameter file:
params = {}
with open(os.path.join(model_path, 'model_params.json'), 'r') as fp:
params.update(json.load(fp))
# Create model directory
os.makedirs(output_folder, exist_ok=True)
if model_id not in MODEL_FACTORY.keys():
raise KeyError(
f'Model ID: Pass one of {MODEL_FACTORY.keys()}, not {model_id}')
device = get_device()
weights_path = os.path.join(model_path, 'weights',
f'best_ROC-AUC_{model_id}.pt')
# Restore model
model = MODEL_FACTORY[model_id](params).to(device)
if os.path.isfile(weights_path):
try:
model.load(weights_path, map_location=device)
except Exception:
logger.error(f'Error in model restoring from {weights_path}')
else:
logger.info(f'Did not find weights at {weights_path}, '
f'name weights: "best_ROC-AUC_{model_id}.pt".')
model.eval()
logger.info('Model restored. Model specs & parameters follow')
for name, param in model.named_parameters():
logger.info((name, param.shape))
# Load language
if smiles_language_filepath == '.':
smiles_language_filepath = os.path.join(model_path,
'smiles_language.pkl')
smiles_language = SMILESLanguage.load(smiles_language_filepath)
# Assemble datasets
smiles_dataset = SMILESDataset(
smi_filepath,
smiles_language=smiles_language,
padding_length=params.get('smiles_padding_length', None),
padding=params.get('padd_smiles', True),
add_start_and_stop=params.get('add_start_stop_token', True),
augment=params.get('augment_test_smiles', False),
canonical=params.get('test_canonical', False),
kekulize=params.get('test_kekulize', False),
all_bonds_explicit=params.get('test_all_bonds_explicit', False),
all_hs_explicit=params.get('test_all_hs_explicit', False),
randomize=False,
remove_bonddir=params.get('test_remove_bonddir', False),
remove_chirality=params.get('test_remove_chirality', False),
selfies=params.get('selfies', False),
sanitize=params.get('test_sanitize', False),
)
logger.info(
f'SMILES Padding length is {smiles_dataset._dataset.padding_length}.'
'Consider setting manually if this looks wrong.')
dataset = AnnotatedDataset(annotations_filepath=labels_filepath,
dataset=smiles_dataset,
device=device)
loader = torch.utils.data.DataLoader(dataset=dataset,
batch_size=10,
shuffle=False,
drop_last=False,
num_workers=0)
if confidence:
smiles_aleatoric_dataset = SMILESDataset(
smi_filepath,
smiles_language=smiles_language,
padding_length=params.get('smiles_padding_length', None),
padding=params.get('padd_smiles', True),
add_start_and_stop=params.get('add_start_stop_token', True),
augment=True, # Natively true for aleatoric uncertainity estimate
canonical=params.get('test_canonical', False),
kekulize=params.get('test_kekulize', False),
all_bonds_explicit=params.get('test_all_bonds_explicit', False),
all_hs_explicit=params.get('test_all_hs_explicit', False),
remove_bonddir=params.get('test_remove_bonddir', False),
remove_chirality=params.get('test_remove_chirality', False),
selfies=params.get('selfies', False),
sanitize=params.get('test_sanitize', False),
)
ale_dataset = AnnotatedDataset(annotations_filepath=labels_filepath,
dataset=smiles_aleatoric_dataset,
device=device)
ale_loader = torch.utils.data.DataLoader(dataset=ale_dataset,
batch_size=10,
shuffle=False,
drop_last=False,
num_workers=0)
smiles_epi_dataset = SMILESDataset(
smi_filepath,
smiles_language=smiles_language,
padding_length=params.get('smiles_padding_length', None),
padding=params.get('padd_smiles', True),
add_start_and_stop=params.get('add_start_stop_token', True),
augment=False, # Natively false for epistemic uncertainity estimate
canonical=params.get('test_canonical', False),
kekulize=params.get('test_kekulize', False),
all_bonds_explicit=params.get('test_all_bonds_explicit', False),
all_hs_explicit=params.get('test_all_hs_explicit', False),
remove_bonddir=params.get('test_remove_bonddir', False),
remove_chirality=params.get('test_remove_chirality', False),
selfies=params.get('selfies', False),
sanitize=params.get('test_sanitize', False),
)
epi_dataset = AnnotatedDataset(annotations_filepath=labels_filepath,
dataset=smiles_epi_dataset,
device=device)
epi_loader = torch.utils.data.DataLoader(dataset=epi_dataset,
batch_size=10,
shuffle=False,
drop_last=False,
num_workers=0)
logger.info(f'Device for data loader is {dataset.device} and for '
f'model is {device}')
# Start evaluation
logger.info('Evaluation about to start...\n')
preds, labels, attention_scores, smiles = [], [], [], []
for idx, (smiles_batch, labels_batch) in enumerate(loader):
pred, pred_dict = model(smiles_batch.to(device))
preds.extend(pred.detach().squeeze().tolist())
attention_scores.extend(
torch.stack(pred_dict['smiles_attention'], dim=1).detach())
smiles.extend([
smiles_language.token_indexes_to_smiles(s.tolist())
for s in smiles_batch
])
labels.extend(labels_batch.squeeze().tolist())
# Scores are now 3D: num_samples x num_att_layers x padding_length
attention = torch.stack(attention_scores, dim=0).numpy()
if confidence:
# Compute uncertainity estimates and save them
epistemic_conf, epistemic_pred = monte_carlo_dropout(model,
regime='loader',
loader=epi_loader)
aleatoric_conf, aleatoric_pred = test_time_augmentation(
model, regime='loader', loader=ale_loader)
epi_conf_df = pd.DataFrame(
data=epistemic_conf.numpy(),
columns=[
f'epistemic_conf_{i}' for i in range(epistemic_conf.shape[1])
],
)
ale_conf_df = pd.DataFrame(
data=aleatoric_conf.numpy(),
columns=[
f'aleatoric_conf_{i}' for i in range(aleatoric_conf.shape[1])
],
)
epi_pred_df = pd.DataFrame(
data=epistemic_pred.numpy(),
columns=[
f'epistemic_pred_{i}' for i in range(epistemic_pred.shape[1])
],
)
ale_pred_df = pd.DataFrame(
data=aleatoric_pred.numpy(),
columns=[
f'aleatoric_pred_{i}' for i in range(aleatoric_pred.shape[1])
],
)
logger.info(f'Shape of attention scores {attention.shape}.')
np.save(os.path.join(output_folder, 'attention_raw.npy'), attention)
attention_avg = np.mean(attention, axis=1)
att_df = pd.DataFrame(
data=attention_avg,
columns=[f'att_idx_{i}' for i in range(attention_avg.shape[1])],
)
pred_df = pd.DataFrame(
data=preds,
columns=[f'pred_{i}' for i in range(len(preds[0]))],
)
lab_df = pd.DataFrame(
data=labels,
columns=[f'label_{i}' for i in range(len(labels[0]))],
)
df = pd.concat([pred_df, lab_df], axis=1)
if confidence:
df = pd.concat(
[df, epi_conf_df, ale_conf_df, epi_pred_df, ale_pred_df], axis=1)
df = pd.concat([df, att_df], axis=1)
df.insert(0, 'SMILES', smiles)
df.to_csv(os.path.join(output_folder, 'results.csv'), index=False)
logger.info('Done, shutting down.')
import logging
from typing import Iterable
import torch
import torch.nn as nn
from paccmann_predictor.utils.utils import get_device
logger = logging.getLogger(__name__)
DEVICE = get_device()
class BCEIgnoreNaN(nn.Module):
"""Wrapper for BCE function that ignores NaNs"""
def __init__(self, reduction: str, class_weights: tuple = (1, 1)) -> None:
"""
Args:
reduction (str): Reduction applied in loss function. Either sum or mean.
class_weights (tuple, optional): Class weights for loss function.
Defaults to (1, 1), i.e. equal class weighhts.
"""
super(BCEIgnoreNaN, self).__init__()
self.loss = nn.BCELoss(reduction='none')
if reduction != 'sum' and reduction != 'mean':
raise ValueError(
f'Chose reduction type as mean or sum, not {reduction}'
)
self.reduction = reduction
def __init__(self, params: dict, *args, **kwargs):
"""Constructor.
Args:
params (dict): A dictionary containing the parameter to built the
dense encoder.
Items in params:
smiles_embedding_size (int): dimension of tokens' embedding.
smiles_vocabulary_size (int): size of the tokens vocabulary.
activation_fn (string, optional): Activation function used in all
layers for specification in ACTIVATION_FN_FACTORY.
Defaults to 'relu'.
batch_norm (bool, optional): Whether batch normalization is
applied. Defaults to True.
dropout (float, optional): Dropout probability in all
except parametric layer. Defaults to 0.5.
filters (list[int], optional): Numbers of filters to learn per
convolutional layer. Defaults to [64, 64, 64].
kernel_sizes (list[list[int]], optional): Sizes of kernels per
convolutional layer. Defaults to [
[3, params['smiles_embedding_size']],
[5, params['smiles_embedding_size']],
[11, params['smiles_embedding_size']]
]
NOTE: The kernel sizes should match the dimensionality of the
smiles_embedding_size, so if the latter is 8, the images are
t x 8, then treat the 8 embedding dimensions like channels
in an RGB image.
multiheads (list[int], optional): Amount of attentive multiheads
per SMILES embedding. Should have len(filters)+1.
Defaults to [4, 4, 4, 4].
stacked_dense_hidden_sizes (list[int], optional): Sizes of the
hidden dense layers. Defaults to [1024, 512].
smiles_attention_size (int, optional): size of the attentive layer
for the smiles sequence. Defaults to 64.
Example params:
```
{
"smiles_attention_size": 8,
"smiles_vocabulary_size": 28,
"smiles_embedding_size": 8,
"filters": [128, 128],
"kernel_sizes": [[3, 8], [5, 8]],
"multiheads":[4, 4, 4]
"stacked_dense_hidden_sizes": [1024, 512]
}
```
"""
super(MCAMultiTask, self).__init__(*args, **kwargs)
# Model Parameter
self.device = get_device()
self.params = params
self.num_tasks = params.get('num_tasks', 12)
self.smiles_attention_size = params.get('smiles_attention_size', 64)
# Model architecture (hyperparameter)
self.multiheads = params.get('multiheads', [4, 4, 4, 4])
self.filters = params.get('filters', [64, 64, 64])
self.dropout = params.get('dropout', 0.5)
self.use_batch_norm = self.params.get('batch_norm', True)
self.act_fn = ACTIVATION_FN_FACTORY[params.get('activation_fn',
'relu')]
# Build the model. First the embeddings
if params.get('embedding', 'learned') == 'learned':
self.smiles_embedding = nn.Embedding(
self.params['smiles_vocabulary_size'],
self.params['smiles_embedding_size'],
scale_grad_by_freq=params.get('embed_scale_grad', False))
elif params.get('embedding', 'learned') == 'one_hot':
self.smiles_embedding = nn.Embedding(
self.params['smiles_vocabulary_size'],
self.params['smiles_vocabulary_size'])
# Plug in one hot-vectors and freeze weights
self.smiles_embedding.load_state_dict({
'weight':
torch.nn.functional.one_hot(
torch.arange(self.params['smiles_vocabulary_size']))
})
self.smiles_embedding.weight.requires_grad = False
elif params.get('embedding', 'learned') == 'pretrained':
# Load the pretrained embeddings
try:
with open(params['embedding_path'], 'rb') as f:
embeddings = pickle.load(f)
except KeyError:
raise KeyError('Path for embeddings is missing in params.')
# Plug into layer
self.smiles_embedding = nn.Embedding(embeddings.shape[0],
embeddings.shape[1])
self.smiles_embedding.load_state_dict(
{'weight': torch.Tensor(embeddings)})
if params.get('fix_embeddings', True):
self.smiles_embedding.weight.requires_grad = False
else:
raise ValueError(f"Unknown embedding type: {params['embedding']}")
self.kernel_sizes = params.get(
'kernel_sizes', [[3, self.smiles_embedding.weight.shape[1]],
[5, self.smiles_embedding.weight.shape[1]],
[11, self.smiles_embedding.weight.shape[1]]])
self.hidden_sizes = ([
self.multiheads[0] * self.smiles_embedding.weight.shape[1] +
sum([h * f for h, f in zip(self.multiheads[1:], self.filters)])
] + params.get('stacked_hidden_sizes', [1024, 512]))
if len(self.filters) != len(self.kernel_sizes):
raise ValueError(
'Length of filter and kernel size lists do not match.')
if len(self.filters) + 1 != len(self.multiheads):
raise ValueError(
'Length of filter and multihead lists do not match')
self.convolutional_layers = nn.Sequential(
OrderedDict([(f'convolutional_{index}',
convolutional_layer(
num_kernel,
kernel_size,
act_fn=self.act_fn,
dropout=self.dropout,
batch_norm=self.use_batch_norm).to(self.device))
for index, (num_kernel, kernel_size) in enumerate(
zip(self.filters, self.kernel_sizes))]))
smiles_hidden_sizes = [self.smiles_embedding.weight.shape[1]
] + self.filters
self.smiles_projections = nn.Sequential(
OrderedDict([
(f'smiles_projection_{self.multiheads[0]*layer+index}',
smiles_projection(smiles_hidden_sizes[layer],
self.smiles_attention_size))
for layer in range(len(self.multiheads))
for index in range(self.multiheads[layer])
]))
self.alpha_projections = nn.Sequential(
OrderedDict([(f'alpha_projection_{self.multiheads[0]*layer+index}',
alpha_projection(self.smiles_attention_size))
for layer in range(len(self.multiheads))
for index in range(self.multiheads[layer])]))
if self.use_batch_norm:
self.batch_norm = nn.BatchNorm1d(self.hidden_sizes[0])
self.dense_layers = nn.Sequential(
OrderedDict([
('dense_{}'.format(ind),
dense_layer(self.hidden_sizes[ind],
self.hidden_sizes[ind + 1],
act_fn=self.act_fn,
dropout=self.dropout,
batch_norm=self.use_batch_norm).to(self.device))
for ind in range(len(self.hidden_sizes) - 1)
]))
if params.get('ensemble', 'None') not in ['score', 'prob', 'None']:
raise NotImplementedError(
"Choose ensemble type from ['score', 'prob', 'None']")
if params.get('ensemble', 'None') == 'None':
params['ensemble_size'] = 1
self.final_dense = EnsembleLayer(typ=params.get('ensemble', 'score'),
input_size=self.hidden_sizes[-1],
output_size=self.num_tasks,
ensemble_size=params.get(
'ensemble_size', 5),
fn=ACTIVATION_FN_FACTORY['sigmoid'])
self.loss_fn = LOSS_FN_FACTORY[
params.get('loss_fn', 'binary_cross_entropy_ignore_nan_and_sum')
] # yapf: disable
# Set class weights manually
if 'binary_cross_entropy_ignore_nan' in params.get(
'loss_fn', 'binary_cross_entropy_ignore_nan_and_sum'):
self.loss_fn.class_weights = params.get('class_weights', [1, 1])
