def load_pretrained_paccmann(self, params_file: str, lang_file: str,
weights_file: str, batch_size: int,
batch_mode: str):
params = dict()
with open(params_file, 'r') as f:
params.update(json.load(f))
params['batch_mode'] = batch_mode
params['batch_size'] = batch_size
self.selfies = params.get('selfies', False)
self.device = get_device()
self.smiles_language = SMILESLanguage.load(lang_file)
self.gru_encoder = StackGRUEncoder(params).to(self.device)
self.gru_decoder = StackGRUDecoder(params).to(self.device)
self.gru_vae = TeacherVAE(self.gru_encoder,
self.gru_decoder).to(self.device)
self.gru_vae.load_state_dict(
torch.load(weights_file, map_location=self.device))
self.gru_vae.eval()
transforms = []
if self.selfies:
transforms += [Selfies()]
transforms += [
SMILESToTokenIndexes(smiles_language=self.smiles_language)
]
transforms += [ToTensor(device=self.device)]
self.transform = Compose(transforms)
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, filename, smiles_language):
super().__init__()
self.filename = filename
with open(self.filename, 'r') as f:
self.data = [line.strip().split('\t') for line in f.readlines()]
self.data = [[(x1, x2), int(y)] for x1, x2, y in self.data]
if isinstance(smiles_language, str):
smiles_language = SMILESLanguage.load(smiles_language)
self.smiles_language = smiles_language
def create_smiles_language(smi_path: str, pretrained_path: str) -> None:
"""
Create a SMILESLanguage object and save it to disk.
Args:
smi_path (str): path to a folder containing .smi files.
pretrained_path (str): directory to store the language as text files.
"""
os.makedirs(pretrained_path, exist_ok=True)
smiles_language = SMILESLanguage()
smiles_language.add_smis([
os.path.join(smi_path, smi_filename)
for smi_filename in os.listdir(smi_path)
if smi_filename.endswith('.smi')
])
smiles_language.save_pretrained(pretrained_path)
def main(train_affinity_filepath, test_affinity_filepath, protein_filepath,
smi_filepath, smiles_language_filepath, protein_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...")
device = get_device()
# Load languages
smiles_language = SMILESLanguage.load(smiles_language_filepath)
protein_language = ProteinLanguage.load(protein_language_filepath)
# Assemble datasets
train_dataset = DrugAffinityDataset(
drug_affinity_filepath=train_affinity_filepath,
smi_filepath=smi_filepath,
protein_filepath=protein_filepath,
smiles_language=smiles_language,
protein_language=protein_language,
smiles_padding=params.get('smiles_padding', True),
smiles_padding_length=params.get('smiles_padding_length', None),
smiles_add_start_and_stop=params.get('smiles_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('protein_padding', True),
protein_padding_length=params.get('protein_padding_length', None),
protein_add_start_and_stop=params.get('protein_add_start_stop', True),
protein_augment_by_revert=params.get('protein_augment', False),
device=device,
drug_affinity_dtype=torch.float,
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 = DrugAffinityDataset(
drug_affinity_filepath=test_affinity_filepath,
smi_filepath=smi_filepath,
protein_filepath=protein_filepath,
smiles_language=smiles_language,
protein_language=protein_language,
smiles_padding=params.get('smiles_padding', True),
smiles_padding_length=params.get('smiles_padding_length', None),
smiles_add_start_and_stop=params.get('smiles_add_start_stop', True),
smiles_augment=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('protein_padding', True),
protein_padding_length=params.get('protein_padding_length', None),
protein_add_start_and_stop=params.get('protein_add_start_stop', True),
protein_augment_by_revert=False,
device=device,
drug_affinity_dtype=torch.float,
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)}.')
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({
'smiles_vocabulary_size': smiles_language.number_of_tokens,
'protein_vocabulary_size': protein_language.number_of_tokens
}) # yapf: disable
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, (smiles, proteins, y) in enumerate(train_loader):
if ind % 100 == 0:
logger.info(f'Batch {ind}/{len(train_loader)}')
y_hat, pred_dict = model(smiles, proteins)
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, proteins, y) in enumerate(test_loader):
y_hat, pred_dict = model(smiles.to(device),
proteins.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 load_data(
filename: str,
smiles_language: Union[SMILESLanguage, str],
max_close_neighbors: int = 5,
max_total_bundle: int = 20,
) -> List[Data]:
"""Loads the data from a pre-processed JSON file and parses it into
a list of torch_geometric.data.Data objects.
Each element of the Data object is obtained by expanding a node up
to 2 levels of depth. The sampling for this is controlled by
`max_close_neighbors` and `max_total_bundle` (i.e. maximum total
number of elements). This will be applied to all the nodes of the
dataset, resulting in a returned list of `len` equal to the number
of SMILES (i.e. total nodes).
NOTE: Reason for this implementation is that ClusterData is
failing when running it on a Data object with the enire dataset:
```
# smiles = [nodes2smiles[i] for i in range(len(nodes2smiles))]
# return Data(
# x=torch.tensor(list(range(len(smiles)))).view(-1, 1),
# edge_index=torch.tensor(edgelist).T,
# # num_nodes=len(smiles)
# )
# number_of_nodes = 10
# cluster_data = ClusterData(
# data, num_parts=data.num_nodes // number_of_nodes, log=True
# )
# --> This is causing: SIGSEGV (Address boundary error)
```
Args:
filename (str): Path to the JSON file. It must have the fields
`smiles2nodes` and `edgelist`.
max_close_neighbors (int, optional): Number of close (1 jump
away) nodes that the algorith sample. Defaults to 5.
max_total_bundle (int, optional): Maximum total number of
elements in each Data object. Defaults to 20.
Returns:
List[Data]
"""
with open(filename, 'r') as f:
raw = json.load(f)
nodes2smiles = {v: k for k, v in raw['smiles2nodes'].items()}
edgelist = raw['edgelist']
if isinstance(smiles_language, str):
smiles_language = SMILESLanguage.load(smiles_language)
smiles_language = smiles_language
data = []
# Custom sampling
G = nx.from_edgelist(edgelist)
for root in range(len(G)):
# FIXME This could have better been a DFS-like approach with fixed
# depth but I had this half way there and it was easier to first sample
# the closer hood and then expand to one more step from there. Not even
# sure if DFS is what I want tho. I can see pros and cons.
shortlist = []
close_neighbors = np.random.permutation(list(
G.neighbors(root)))[:max_close_neighbors]
total_far_neighbors = sum(
[len(list(G.neighbors(x))) for x in close_neighbors])
shortlist += [[root, x] for x in close_neighbors]
counter = 0
while (len(shortlist) < max_total_bundle
and counter < total_far_neighbors):
# TODO Random sampling probability inversely proportional to the
# degree?
current_node = close_neighbors[counter % len(close_neighbors)]
far_node = np.random.choice(list(G.neighbors(current_node)))
shortlist.append([current_node, far_node])
counter += 1
# We need to relabel the nodes, but keep the original location in order
# to retrieve the corresponding smiles
sub_graph = nx.convert_node_labels_to_integers(
nx.from_edgelist(shortlist), label_attribute='original')
x = [
torch.tensor(
smiles_language.smiles_to_token_indexes(
nodes2smiles[sub_graph.nodes[i]['original']]))
for i in range(len(sub_graph))
]
edge_index = torch.tensor(nx.to_pandas_edgelist(sub_graph).values).T
data.append(Data(x=x, edge_index=edge_index, num_nodes=len(x)))
return data
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 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(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 main(parser_namespace):
# model loading
disable_rdkit_logging()
affinity_path = parser_namespace.affinity_path
svae_path = parser_namespace.svae_path
svae_weights_path = os.path.join(svae_path, "weights", "best_rec.pt")
results_file_name = parser_namespace.optimisation_name
logger.add(results_file_name + ".log", rotation="10 MB")
svae_params = dict()
with open(os.path.join(svae_path, "model_params.json"), "r") as f:
svae_params.update(json.load(f))
smiles_language = SMILESLanguage.load(
os.path.join(svae_path, "selfies_language.pkl"))
# initialize encoder, decoder, testVAE, and GP_generator_MW
gru_encoder = StackGRUEncoder(svae_params)
gru_decoder = StackGRUDecoder(svae_params)
gru_vae = TeacherVAE(gru_encoder, gru_decoder)
gru_vae.load_state_dict(
torch.load(svae_weights_path, map_location=get_device()))
gru_vae._associate_language(smiles_language)
gru_vae.eval()
smiles_generator = SmilesGenerator(gru_vae)
with open(os.path.join(affinity_path, "model_params.json")) as f:
predictor_params = json.load(f)
affinity_predictor = MODEL_FACTORY["bimodal_mca"](predictor_params)
affinity_predictor.load(
os.path.join(
affinity_path,
f"weights/best_{predictor_params.get('p_metric', 'ROC-AUC')}_bimodal_mca.pt",
),
map_location=get_device(),
)
affinity_protein_language = ProteinLanguage.load(
os.path.join(affinity_path, "protein_language.pkl"))
affinity_smiles_language = SMILESLanguage.load(
os.path.join(affinity_path, "smiles_language.pkl"))
affinity_predictor._associate_language(affinity_smiles_language)
affinity_predictor._associate_language(affinity_protein_language)
affinity_predictor.eval()
erg_protein = "MASTIKEALSVVSEDQSLFECAYGTPHLAKTEMTASSSSDYGQTSKMSPRVPQQDWLSQPPARVTIKMECNPSQVNGSRNSPDECSVAKGGKMVGSPDTVGMNYGSYMEEKHMPPPNMTTNERRVIVPADPTLWSTDHVRQWLEWAVKEYGLPDVNILLFQNIDGKELCKMTKDDFQRLTPSYNADILLSHLHYLRETPLPHLTSDDVDKALQNSPRLMHARNTGGAAFIFPNTSVYPEATQRITTRPDLPYEPPRRSAWTGHGHPTPQSKAAQPSPSTVPKTEDQRPQLDPYQILGPTSSRLANPGSGQIQLWQFLLELLSDSSNSSCITWEGTNGEFKMTDPDEVARRWGERKSKPNMNYDKLSRALRYYYDKNIMTKVHGKRYAYKFDFHGIAQALQPHPPESSLYKYPSDLPYMGSYHAHPQKMNFVAPHPPALPVTSSSFFAAPNPYWNSPTGGIYPNTRLPTSHMPSHLGTYY"
target_minimization_function = AffinityMinimization(
smiles_generator, 30, affinity_predictor, erg_protein)
qed_function = QEDMinimization(smiles_generator, 30)
sa_function = SAMinimization(smiles_generator, 30)
combined_minimization = CombinedMinimization(
[target_minimization_function, qed_function, sa_function], 1,
[0.75, 1, 0.5])
target_optimizer = GPOptimizer(combined_minimization.evaluate)
params = dict(
dimensions=[(-5.0, 5.0)] * 256,
acq_func="EI",
n_calls=20,
n_initial_points=19,
initial_point_generator="random",
random_state=1234,
)
logger.info("Optimisation parameters: {params}", params=params)
# optimisation
for j in range(5):
res = target_optimizer.optimize(params)
latent_point = torch.tensor([[res.x]])
with open(results_file_name + "_LP" + str(j + 1) + ".pkl", "wb") as f:
pickle.dump(latent_point, f, protocol=2)
smile_set = set()
while len(smile_set) < 20:
smiles = smiles_generator.generate_smiles(
latent_point.repeat(1, 30, 1))
smile_set.update(set(smiles))
smile_set = list(smile_set)
pad_smiles_predictor = LeftPadding(
affinity_predictor.smiles_padding_length,
affinity_predictor.smiles_language.padding_index,
)
to_tensor = ToTensor(get_device())
smiles_num = [
torch.unsqueeze(
to_tensor(
pad_smiles_predictor(
affinity_predictor.smiles_language.
smiles_to_token_indexes(smile))),
0,
) for smile in smile_set
]
smiles_tensor = torch.cat(smiles_num, dim=0)
pad_protein_predictor = LeftPadding(
affinity_predictor.protein_padding_length,
affinity_predictor.protein_language.padding_index,
)
protein_num = torch.unsqueeze(
to_tensor(
pad_protein_predictor(
affinity_predictor.protein_language.
sequence_to_token_indexes([erg_protein]))),
0,
)
protein_num = protein_num.repeat(len(smile_set), 1)
with torch.no_grad():
pred, _ = affinity_predictor(smiles_tensor, protein_num)
affinities = torch.squeeze(pred, 1).numpy()
sas = SAS()
sa_scores = [sas(smile) for smile in smile_set]
qed_scores = [qed(Chem.MolFromSmiles(smile)) for smile in smile_set]
# save to file
file = results_file_name + str(j + 1) + ".txt"
logger.info("creating {file}", file=file)
with open(file, "w") as f:
f.write(
f'{"point":<10}{"Affinity":<10}{"QED":<10}{"SA":<10}{"smiles":<15}\n'
)
for i in range(20):
dat = [
i + 1, affinities[i], qed_scores[i], sa_scores[i],
smile_set[i]
]
f.write(
f'{dat[0]:<10}{"%.3f"%dat[1]:<10}{"%.3f"%dat[2]:<10}{"%.3f"%dat[3]:<10}{dat[4]:<15}\n'
)
# in the thesis we used 40k molecules extracted
# from the PubChem database
# The model is separated in 3 files, PARAMS, WEIGHTS, LANGUAGE.
# See: https://github.com/PaccMann/paccmann_chemistry for more info.
DATA_DIR = # Directory with the model and the data
PARAM_FILE = os.path.join(DATA_DIR, 'model_params.json')
WEIGHT_FILE = os.path.join(DATA_DIR, 'weights', 'best_loss.pt')
LANG_FILE = os.path.join(DATA_DIR, 'selfies_language.pkl')
MODEL_DIR = './finetuned_model' # Directory for the finetuned model
# START LOADING
paccmann_vae = Encoder(PARAM_FILE, LANG_FILE, WEIGHT_FILE, 1)
smiles_language = SMILESLanguage.load(LANG_FILE)
SAVE_FOLDER = './'
LATENTS_FILE = latents_file if latents_file is not None \
else os.path.join(SAVE_FOLDER, f'latents.{model}.npy')
CATALYST_LATENTS_FILE = os.path.join(
SAVE_FOLDER, f'catalyst_latents.{model}.npy'
)
df = pd.read_csv(COMPOUNDS_PATH, header=None, sep='\t', names=['SMILES', 'id'])
db_catalysts = pd.read_csv(CATALYSTS_PATH)
latents, failed_smiles = calculate_latents(
paccmann_vae, df.SMILES, LATENTS_FILE
)
def main(parser_namespace):
disable_rdkit_logging()
model_path = parser_namespace.model_path
data_path = parser_namespace.data_path
weights_path = os.path.join(model_path, 'weights', 'best_loss.pt')
device = get_device()
# read the params json
params = dict()
with open(os.path.join(model_path, 'model_params.json'), 'r') as f:
params.update(json.load(f))
params['batch_size'] = 1
# Load SMILES language
smiles_language = SMILESLanguage.load(
os.path.join(model_path, 'selfies_language.pkl'))
data_preparation = get_data_preparation(params.get('batch_mode'))
device = get_device()
dataset = SMILESDataset(
data_path,
smiles_language=smiles_language,
padding=False,
selfies=params.get('selfies', False),
add_start_and_stop=params.get('add_start_stop_token', True),
augment=False, #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),
remove_bonddir=params.get('remove_bonddir', False),
remove_chirality=params.get('remove_chirality', False),
backend='lazy',
device=device)
dataloader = torch.utils.data.DataLoader(
dataset,
batch_size=params.get('batch_size', 64),
collate_fn=collate_fn,
drop_last=True,
shuffle=True,
pin_memory=params.get('pin_memory', True),
num_workers=params.get('num_workers', 8))
# initialize encoder and decoder
gru_encoder = StackGRUEncoder(params).to(device)
gru_decoder = StackGRUDecoder(params).to(device)
gru_vae = TeacherVAE(gru_encoder, gru_decoder).to(device)
logger.info('\n****MODEL SUMMARY***\n')
for name, parameter in gru_vae.named_parameters():
logger.info(f'Param {name}, shape:\t{parameter.shape}')
total_params = sum(p.numel() for p in gru_vae.parameters())
logger.info(f'Total # params: {total_params}')
gru_vae.load_state_dict(torch.load(weights_path, map_location=device))
# Updating the vocab size will break the model
params.update({
# 'vocab_size': smiles_language.number_of_tokens,
'pad_index': smiles_language.padding_index
}) # yapf:disable
# if params.get('embedding', 'learned') == 'one_hot':
# params.update({'embedding_size': params['vocab_size']})
# train for n_epoch epochs
logger.info(
'Model creation, loading and data processing done. Evaluation starts.')
gru_vae.eval()
gru_vae.to(device)
counter = 0
with torch.no_grad():
latent_code = []
from tqdm import tqdm
for batch in tqdm(dataloader, total=len(dataloader)):
(encoder_seq, _, _) = data_preparation(batch,
input_keep=0.,
start_index=2,
end_index=3,
device=device)
try:
mu, logvar = gru_vae.encode(encoder_seq)
except RuntimeError:
# Substitute any new tokens by "<UNK>" tokens
new_seq = []
padd_encoder_seq, lenghts = (
torch.nn.utils.rnn.pad_packed_sequence(encoder_seq,
batch_first=True))
for seq, _len in zip(padd_encoder_seq, lenghts):
seq = seq[:_len]
if any([x >= params['vocab_size'] for x in seq]):
seq = torch.tensor([
x if x < params['vocab_size'] else
smiles_language.unknown_index
for x in seq.tolist()
]).short()
failed_smiles = smiles_language.selfies_to_smiles(
smiles_language.token_indexes_to_smiles(
seq.tolist()))
logger.warning(
f'Out of bounds sample: ~{counter}\t{failed_smiles}'
)
new_seq.append(seq)
if new_seq:
for _ in range(params['batch_size'] - len(new_seq)):
new_seq.append(torch.ones_like(new_seq[-1]))
(encoder_seq, _, _) = data_preparation(new_seq,
input_keep=0.,
start_index=2,
end_index=3,
device=device)
mu, logvar = gru_vae.encode(encoder_seq)
for _mu in mu.tolist():
latent_code.append([counter, _mu])
counter += 1
LATENT_CODE_PATH = os.path.join(os.path.dirname(data_path),
'samples_latent_code.tsv')
with open(LATENT_CODE_PATH, 'w') as f:
for i, mu in latent_code:
f.write(f'{i}\t{",".join([str(x) for x in mu[0]])}\n')
def main(parser_namespace):
try:
device = get_device()
disable_rdkit_logging()
# read the params json
params = dict()
with open(parser_namespace.params_filepath) as f:
params.update(json.load(f))
# get params
train_smiles_filepath = parser_namespace.train_smiles_filepath
test_smiles_filepath = parser_namespace.test_smiles_filepath
smiles_language_filepath = (
parser_namespace.smiles_language_filepath
if parser_namespace.smiles_language_filepath.lower() != 'none' else
None)
model_path = parser_namespace.model_path
training_name = parser_namespace.training_name
writer = SummaryWriter(f'logs/{training_name}')
logger.info(f'Model with name {training_name} starts.')
model_dir = os.path.join(model_path, training_name)
log_path = os.path.join(model_dir, 'logs')
val_dir = os.path.join(log_path, 'val_logs')
os.makedirs(os.path.join(model_dir, 'weights'), exist_ok=True)
os.makedirs(os.path.join(model_dir, 'results'), exist_ok=True)
os.makedirs(log_path, exist_ok=True)
os.makedirs(val_dir, exist_ok=True)
# Load SMILES language
smiles_language = None
if smiles_language_filepath is not None:
smiles_language = SMILESLanguage.load(smiles_language_filepath)
logger.info(f'Smiles filepath: {train_smiles_filepath}')
# create SMILES eager dataset
smiles_train_data = SMILESDataset(
train_smiles_filepath,
smiles_language=smiles_language,
padding=False,
selfies=params.get('selfies', False),
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),
remove_bonddir=params.get('remove_bonddir', False),
remove_chirality=params.get('remove_chirality', False),
backend='lazy',
device=device,
)
smiles_test_data = SMILESDataset(
test_smiles_filepath,
smiles_language=smiles_language,
padding=False,
selfies=params.get('selfies', False),
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),
remove_bonddir=params.get('remove_bonddir', False),
remove_chirality=params.get('remove_chirality', False),
backend='lazy',
device=device,
)
if smiles_language_filepath is None:
smiles_language = smiles_train_data.smiles_language
smiles_language.save(
os.path.join(model_path, f'{training_name}.lang'))
else:
smiles_language_filename = os.path.basename(
smiles_language_filepath)
smiles_language.save(
os.path.join(model_dir, smiles_language_filename))
params.update({
'vocab_size': smiles_language.number_of_tokens,
'pad_index': smiles_language.padding_index
})
vocab_dict = smiles_language.index_to_token
params.update({
'start_index':
list(vocab_dict.keys())[list(
vocab_dict.values()).index('<START>')],
'end_index':
list(vocab_dict.keys())[list(vocab_dict.values()).index('<STOP>')]
})
if params.get('embedding', 'learned') == 'one_hot':
params.update({'embedding_size': params['vocab_size']})
with open(os.path.join(model_dir, 'model_params.json'), 'w') as fp:
json.dump(params, fp)
# create DataLoaders
train_data_loader = torch.utils.data.DataLoader(
smiles_train_data,
batch_size=params.get('batch_size', 64),
collate_fn=collate_fn,
drop_last=True,
shuffle=True,
pin_memory=params.get('pin_memory', True),
num_workers=params.get('num_workers', 8))
test_data_loader = torch.utils.data.DataLoader(
smiles_test_data,
batch_size=params.get('batch_size', 64),
collate_fn=collate_fn,
drop_last=True,
shuffle=True,
pin_memory=params.get('pin_memory', True),
num_workers=params.get('num_workers', 8))
# initialize encoder and decoder
gru_encoder = StackGRUEncoder(params).to(device)
gru_decoder = StackGRUDecoder(params).to(device)
gru_vae = TeacherVAE(gru_encoder, gru_decoder).to(device)
# TODO I haven't managed to get this to work. I will leave it here
# if somewant (or future me) wants to give it a look and get the
# tensorboard graph to work
# if writer and False:
# gru_vae.set_batch_mode('padded')
# dummy_input = torch.ones(smiles_train_data[0].shape)
# dummy_input = dummy_input.unsqueeze(0).to(device)
# writer.add_graph(gru_vae, (dummy_input, dummy_input, dummy_input))
# gru_vae.set_batch_mode(params.get('batch_mode'))
logger.info('\n****MODEL SUMMARY***\n')
for name, parameter in gru_vae.named_parameters():
logger.info(f'Param {name}, shape:\t{parameter.shape}')
total_params = sum(p.numel() for p in gru_vae.parameters())
logger.info(f'Total # params: {total_params}')
loss_tracker = {
'test_loss_a': 10e4,
'test_rec_a': 10e4,
'test_kld_a': 10e4,
'ep_loss': 0,
'ep_rec': 0,
'ep_kld': 0
}
# train for n_epoch epochs
logger.info(
'Model creation and data processing done, Training starts.')
decoder_search = SEARCH_FACTORY[
params.get('decoder_search', 'sampling')
](
temperature=params.get('temperature', 1.),
beam_width=params.get('beam_width', 3),
top_tokens=params.get('top_tokens', 5)
) # yapf: disable
if writer:
pparams = params.copy()
pparams['training_file'] = train_smiles_filepath
pparams['test_file'] = test_smiles_filepath
pparams['language_file'] = smiles_language_filepath
pparams['model_path'] = model_path
pparams = {
k: v if v is not None else 'N.A.'
for k, v in params.items()
}
pparams['training_name'] = training_name
from pprint import pprint
pprint(pparams)
writer.add_hparams(hparam_dict=pparams, metric_dict={})
for epoch in range(params['epochs'] + 1):
t = time()
loss_tracker = train_vae(
epoch,
gru_vae,
train_data_loader,
test_data_loader,
smiles_language,
model_dir,
search=decoder_search,
optimizer=params.get('optimizer', 'adadelta'),
lr=params['learning_rate'],
kl_growth=params['kl_growth'],
input_keep=params['input_keep'],
test_input_keep=params['test_input_keep'],
generate_len=params['generate_len'],
log_interval=params['log_interval'],
save_interval=params['save_interval'],
eval_interval=params['eval_interval'],
loss_tracker=loss_tracker,
logger=logger,
# writer=writer,
batch_mode=params.get('batch_mode'))
logger.info(f'Epoch {epoch}, took {time() - t:.1f}.')
logger.info('OVERALL: \t Best loss = {0:.4f} in Ep {1}, '
'best Rec = {2:.4f} in Ep {3}, '
'best KLD = {4:.4f} in Ep {5}'.format(
loss_tracker['test_loss_a'], loss_tracker['ep_loss'],
loss_tracker['test_rec_a'], loss_tracker['ep_rec'],
loss_tracker['test_kld_a'], loss_tracker['ep_kld']))
logger.info('Training done, shutting down.')
except Exception:
logger.exception('Exception occurred while running train_vae.py.')
def main(*, parser_namespace):
disable_rdkit_logging()
# read the params json
params = dict()
with open(parser_namespace.params_path) as f:
params.update(json.load(f))
# get params, json args take precedence
mol_model_path = params.get('mol_model_path',
parser_namespace.mol_model_path)
protein_model_path = params.get('protein_model_path',
parser_namespace.protein_model_path)
affinity_model_path = params.get('affinity_model_path',
parser_namespace.affinity_model_path)
protein_data_path = params.get('protein_data_path',
parser_namespace.protein_data_path)
model_name = params.get(
'model_name', parser_namespace.model_name
) # yapf: disable
test_id = int(params.get(
'test_protein_id', parser_namespace.test_protein_id
)) # yapf: disable
unbiased_preds_path = params.get(
'unbiased_predictions_path', parser_namespace.unbiased_predictions_path
) # yapf: disable
model_name += '_' + str(test_id)
logger.info(f'Model with name {model_name} starts.')
# passing optional paths to params to possibly update_reward_fn
optional_reward_args = [
'tox21_path', 'organdb_path', 'site', 'clintox_path', 'sider_path'
]
for arg in optional_reward_args:
if parser_namespace.__dict__[arg]:
# json still has presedence
params[arg] = params.get(arg, parser_namespace.__dict__[arg])
# Load protein sequence data
if protein_data_path.endswith('.smi'):
protein_df = read_smi(protein_data_path, names=['Sequence'])
elif protein_data_path.endswith('.csv'):
protein_df = pd.read_csv(protein_data_path, index_col='entry_name')
else:
raise TypeError(
f"{protein_data_path.split('.')[-1]} files are not supported.")
protein_test_name = protein_df.iloc[test_id].name
logger.info(f'Test protein is {protein_test_name}')
# Restore SMILES Model
with open(os.path.join(mol_model_path, 'model_params.json')) as f:
mol_params = json.load(f)
gru_encoder = StackGRUEncoder(mol_params)
gru_decoder = StackGRUDecoder(mol_params)
generator = TeacherVAE(gru_encoder, gru_decoder)
generator.load(os.path.join(
mol_model_path,
f"weights/best_{params.get('smiles_metric', 'rec')}.pt"),
map_location=get_device())
# Load languages
generator_smiles_language = SMILESLanguage.load(
os.path.join(mol_model_path, 'selfies_language.pkl'))
generator._associate_language(generator_smiles_language)
# Restore protein model
with open(os.path.join(protein_model_path, 'model_params.json')) as f:
protein_params = json.load(f)
# Define network
protein_encoder = ENCODER_FACTORY['dense'](protein_params)
protein_encoder.load(os.path.join(
protein_model_path,
f"weights/best_{params.get('omics_metric','both')}_encoder.pt"),
map_location=get_device())
protein_encoder.eval()
# Restore affinity predictor
with open(os.path.join(affinity_model_path, 'model_params.json')) as f:
predictor_params = json.load(f)
predictor = MODEL_FACTORY['bimodal_mca'](predictor_params)
predictor.load(os.path.join(
affinity_model_path,
f"weights/best_{params.get('p_metric', 'ROC-AUC')}_bimodal_mca.pt"),
map_location=get_device())
predictor.eval()
# Load languages
affinity_smiles_language = SMILESLanguage.load(
os.path.join(affinity_model_path, 'smiles_language.pkl'))
affinity_protein_language = ProteinLanguage.load(
os.path.join(affinity_model_path, 'protein_language.pkl'))
predictor._associate_language(affinity_smiles_language)
predictor._associate_language(affinity_protein_language)
# Specifies the baseline model used for comparison
unbiased_preds = np.array(
pd.read_csv(
os.path.join(unbiased_preds_path, protein_test_name + '.csv')
)['affinity'].values
) # yapf: disable
# Create a fresh model that will be optimized
gru_encoder_rl = StackGRUEncoder(mol_params)
gru_decoder_rl = StackGRUDecoder(mol_params)
generator_rl = TeacherVAE(gru_encoder_rl, gru_decoder_rl)
generator_rl.load(os.path.join(
mol_model_path, f"weights/best_{params.get('metric', 'rec')}.pt"),
map_location=get_device())
generator_rl.eval()
# Load languages
generator_rl._associate_language(generator_smiles_language)
protein_encoder_rl = ENCODER_FACTORY['dense'](protein_params)
protein_encoder_rl.load(os.path.join(
protein_model_path,
f"weights/best_{params.get('metric', 'both')}_encoder.pt"),
map_location=get_device())
protein_encoder_rl.eval()
model_folder_name = model_name
learner = ReinforceProtein(generator_rl, protein_encoder_rl, predictor,
protein_df, params, model_folder_name, logger)
biased_ratios, tox_ratios = [], []
rewards, rl_losses = [], []
gen_mols, gen_prot, gen_affinity, mode = [], [], [], []
logger.info(f'Model stored at {learner.model_path}')
for epoch in range(1, params['epochs'] + 1):
for step in range(1, params['steps']):
# Randomly sample a protein
protein_name = np.random.choice(protein_df.index)
while protein_name == protein_test_name:
protein_name = np.random.choice(protein_df.index)
logger.info(f'Current train protein: {protein_name}')
rew, loss = learner.policy_gradient(protein_name, epoch,
params['batch_size'])
logger.info(
f"Epoch {epoch:d}/{params['epochs']:d}, step {step:d}/"
f"{params['steps']:d}\t loss={loss:.2f}, mean rew={rew:.2f}")
rewards.append(rew.item())
rl_losses.append(loss)
# Save model
if epoch % 10 == 0:
learner.save(f'gen_{epoch}.pt', f'enc_{epoch}.pt')
logger.info(f'EVAL protein: {protein_test_name}')
smiles, preds = (learner.generate_compounds_and_evaluate(
epoch, params['eval_batch_size'], protein_test_name))
gs = [s for i, s in enumerate(smiles) if preds[i] > 0.5]
gp = preds[preds > 0.5]
for p, s in zip(gp, gs):
gen_mols.append(s)
gen_prot.append(protein_test_name)
gen_affinity.append(p)
mode.append('eval')
inds = np.argsort(gp)[::-1]
for i in inds[:5]:
logger.info(f'Epoch {epoch:d}, generated {gs[i]} against '
f'{protein_test_name}.\n Predicted IC50 = {gp[i]}. ')
plot_and_compare_proteins(unbiased_preds, preds, protein_test_name,
epoch, learner.model_path, 'train',
params['eval_batch_size'])
biased_ratios.append(
np.round(100 * (np.sum(preds > 0.5) / len(preds)), 1))
all_toxes = np.array([learner.tox21(s) for s in smiles])
tox_ratios.append(
np.round(100 * (np.sum(all_toxes == 1.) / len(all_toxes)), 1))
logger.info(f'Percentage of non-toxic compounds {tox_ratios[-1]}')
toxes = [learner.tox21(s) for s in gen_mols]
# Save results (good molecules!) in DF
df = pd.DataFrame({
'protein': gen_prot,
'SMILES': gen_mols,
'Binding probability': gen_affinity,
'mode': mode,
'Tox21': toxes
})
df.to_csv(os.path.join(learner.model_path, 'results', 'generated.csv'))
# Plot loss development
loss_df = pd.DataFrame({'loss': rl_losses, 'rewards': rewards})
loss_df.to_csv(learner.model_path +
'/results/loss_reward_evolution.csv')
plot_loss(rl_losses,
rewards,
params['epochs'],
protein_name,
learner.model_path,
rolling=5)
pd.DataFrame({
'efficacy_ratio': biased_ratios,
'tox_ratio': tox_ratios
}).to_csv(learner.model_path + '/results/ratios.csv')
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 main(*, parser_namespace):
disable_rdkit_logging()
# read the params json
params = dict()
with open(parser_namespace.params_path) as f:
params.update(json.load(f))
# get params
mol_model_path = params.get('mol_model_path',
parser_namespace.mol_model_path)
omics_model_path = params.get('omics_model_path',
parser_namespace.omics_model_path)
ic50_model_path = params.get('ic50_model_path',
parser_namespace.ic50_model_path)
omics_data_path = params.get('omics_data_path',
parser_namespace.omics_data_path)
model_name = params.get(
'model_name', parser_namespace.model_name
) # yapf: disable
site = params.get(
'site', parser_namespace.site
) # yapf: disable
params['site'] = site
logger.info(f'Model with name {model_name} starts.')
# Load omics profiles for conditional generation,
# complement with avg per site
omics_df = pd.read_pickle(omics_data_path)
omics_df = add_avg_profile(omics_df)
# Restore SMILES Model
with open(os.path.join(mol_model_path, 'model_params.json')) as f:
mol_params = json.load(f)
gru_encoder = StackGRUEncoder(mol_params)
gru_decoder = StackGRUDecoder(mol_params)
generator = TeacherVAE(gru_encoder, gru_decoder)
generator.load(os.path.join(
mol_model_path,
f"weights/best_{params.get('smiles_metric', 'rec')}.pt"),
map_location=get_device())
# Load languages
generator_smiles_language = SMILESLanguage.load(
os.path.join(mol_model_path, 'selfies_language.pkl'))
generator._associate_language(generator_smiles_language)
# Restore omics model
with open(os.path.join(omics_model_path, 'model_params.json')) as f:
cell_params = json.load(f)
# Define network
cell_encoder = ENCODER_FACTORY['dense'](cell_params)
cell_encoder.load(os.path.join(
omics_model_path,
f"weights/best_{params.get('omics_metric','both')}_encoder.pt"),
map_location=get_device())
cell_encoder.eval()
# Restore PaccMann
with open(os.path.join(ic50_model_path, 'model_params.json')) as f:
paccmann_params = json.load(f)
paccmann_predictor = MODEL_FACTORY['mca'](paccmann_params)
paccmann_predictor.load(os.path.join(
ic50_model_path,
f"weights/best_{params.get('ic50_metric', 'rmse')}_mca.pt"),
map_location=get_device())
paccmann_predictor.eval()
paccmann_smiles_language = SMILESLanguage.load(
os.path.join(ic50_model_path, 'smiles_language.pkl'))
paccmann_predictor._associate_language(paccmann_smiles_language)
# Specifies the baseline model used for comparison
baseline = ReinforceOmic(generator, cell_encoder, paccmann_predictor,
omics_df, params, 'baseline', logger)
# Create a fresh model that will be optimized
gru_encoder_rl = StackGRUEncoder(mol_params)
gru_decoder_rl = StackGRUDecoder(mol_params)
generator_rl = TeacherVAE(gru_encoder_rl, gru_decoder_rl)
generator_rl.load(os.path.join(
mol_model_path, f"weights/best_{params.get('metric', 'rec')}.pt"),
map_location=get_device())
generator_rl.eval()
generator_rl._associate_language(generator_smiles_language)
cell_encoder_rl = ENCODER_FACTORY['dense'](cell_params)
cell_encoder_rl.load(os.path.join(
omics_model_path,
f"weights/best_{params.get('metric', 'both')}_encoder.pt"),
map_location=get_device())
cell_encoder_rl.eval()
model_folder_name = site + '_' + model_name
learner = ReinforceOmic(generator_rl, cell_encoder_rl, paccmann_predictor,
omics_df, params, model_folder_name, logger)
# Split the samples for conditional generation and initialize training
train_omics, test_omics = omics_data_splitter(
omics_df, site, params.get('test_fraction', 0.2))
rewards, rl_losses = [], []
gen_mols, gen_cell, gen_ic50, modes = [], [], [], []
logger.info('Models restored, start training.')
for epoch in range(1, params['epochs'] + 1):
for step in range(1, params['steps']):
# Randomly sample a cell line:
cell_line = np.random.choice(train_omics)
rew, loss = learner.policy_gradient(cell_line, epoch,
params['batch_size'])
print(f"Epoch {epoch:d}/{params['epochs']:d}, step {step:d}/"
f"{params['steps']:d}\t loss={loss:.2f}, rew={rew:.2f}")
rewards.append(rew.item())
rl_losses.append(loss)
# Save model
learner.save(f'gen_{epoch}.pt', f'enc_{epoch}.pt')
# Compare baseline and trained model on cell line
base_smiles, base_preds = baseline.generate_compounds_and_evaluate(
epoch, params['eval_batch_size'], cell_line)
smiles, preds = learner.generate_compounds_and_evaluate(
epoch, params['eval_batch_size'], cell_line)
gs = [
s for i, s in enumerate(smiles)
if preds[i] < learner.ic50_threshold
]
gp = preds[preds < learner.ic50_threshold]
for p, s in zip(gp, gs):
gen_mols.append(s)
gen_cell.append(cell_line)
gen_ic50.append(p)
modes.append('train')
plot_and_compare(base_preds, preds, site, cell_line, epoch,
learner.model_path, 'train',
params['eval_batch_size'])
# Evaluate on a validation cell line.
eval_cell_line = np.random.choice(test_omics)
base_smiles, base_preds = baseline.generate_compounds_and_evaluate(
epoch, params['eval_batch_size'], eval_cell_line)
smiles, preds = learner.generate_compounds_and_evaluate(
epoch, params['eval_batch_size'], eval_cell_line)
plot_and_compare(base_preds, preds, site, eval_cell_line, epoch,
learner.model_path, 'test', params['eval_batch_size'])
gs = [
s for i, s in enumerate(smiles)
if preds[i] < learner.ic50_threshold
]
gp = preds[preds < learner.ic50_threshold]
for p, s in zip(gp, gs):
gen_mols.append(s)
gen_cell.append(eval_cell_line)
gen_ic50.append(p)
modes.append('test')
inds = np.argsort(preds)
for i in inds[:5]:
logger.info(f'Epoch {epoch:d}, generated {smiles[i]} against '
f'{eval_cell_line}.\n Predicted IC50 = {preds[i]}. ')
# Save results (good molecules!) in DF
df = pd.DataFrame({
'cell_line': gen_cell,
'SMILES': gen_mols,
'IC50': gen_ic50,
'mode': modes,
'tox21': [learner.tox21(s) for s in gen_mols]
})
df.to_csv(os.path.join(learner.model_path, 'results', 'generated.csv'))
# Plot loss development
loss_df = pd.DataFrame({'loss': rl_losses, 'rewards': rewards})
loss_df.to_csv(learner.model_path +
'/results/loss_reward_evolution.csv')
plot_loss(rl_losses,
rewards,
params['epochs'],
cell_line,
learner.model_path,
rolling=5,
site=site)
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.')
