def model_fingerprint(model: MoleculeModel,
data_loader: MoleculeDataLoader,
fingerprint_type: str = 'MPN',
disable_progress_bar: bool = False) -> List[List[float]]:
"""
Encodes the provided molecules into the latent fingerprint vectors, according to the provided model.
:param model: A :class:`~chemprop.models.model.MoleculeModel`.
:param data_loader: A :class:`~chemprop.data.data.MoleculeDataLoader`.
:param disable_progress_bar: Whether to disable the progress bar.
:return: A list of fingerprint vector lists.
"""
model.eval()
fingerprints = []
for batch in tqdm(data_loader, disable=disable_progress_bar, leave=False):
# Prepare batch
batch: MoleculeDataset
mol_batch, features_batch, atom_descriptors_batch, atom_features_batch, bond_features_batch = \
batch.batch_graph(), batch.features(), batch.atom_descriptors(), batch.atom_features(), batch.bond_features()
# Make predictions
with torch.no_grad():
batch_fp = model.fingerprint(mol_batch, features_batch,
atom_descriptors_batch,
atom_features_batch,
bond_features_batch, fingerprint_type)
# Collect vectors
batch_fp = batch_fp.data.cpu().tolist()
fingerprints.extend(batch_fp)
return fingerprints
def prepare_model(args):
args.output_size = args.num_tasks
inv_model = MoleculeModel(
classification=args.dataset_type == 'classification',
multiclass=args.dataset_type == 'multiclass')
inv_model.create_encoder(
args) # phi(x), shared across source and target domain
inv_model.create_ffn(args) # source function
inv_model.src_ffn = inv_model.ffn
inv_model.create_ffn(args) # target function
initialize_weights(inv_model)
return inv_model.cuda()
def save_checkpoint(path: str,
model: MoleculeModel,
scaler: StandardScaler = None,
features_scaler: StandardScaler = None,
args: TrainArgs = None) -> None:
"""
Saves a model checkpoint.
:param model: A :class:`~chemprop.models.model.MoleculeModel`.
:param scaler: A :class:`~chemprop.data.scaler.StandardScaler` fitted on the data.
:param features_scaler: A :class:`~chemprop.data.scaler.StandardScaler` fitted on the features.
:param args: The :class:`~chemprop.args.TrainArgs` object containing the arguments the model was trained with.
:param path: Path where checkpoint will be saved.
"""
# Convert args to namespace for backwards compatibility
if args is not None:
args = Namespace(**args.as_dict())
state = {
'args': args,
'state_dict': model.state_dict(),
'data_scaler': {
'means': scaler.means,
'stds': scaler.stds
} if scaler is not None else None,
'features_scaler': {
'means': features_scaler.means,
'stds': features_scaler.stds
} if features_scaler is not None else None
}
torch.save(state, path)
def save_checkpoint(path: str,
model: MoleculeModel,
scaler: StandardScaler = None,
features_scaler: StandardScaler = None,
args: Namespace = None):
"""
Saves a model checkpoint.
:param model: A MoleculeModel.
:param scaler: A StandardScaler fitted on the data.
:param features_scaler: A StandardScaler fitted on the features.
:param args: Arguments namespace.
:param path: Path where checkpoint will be saved.
"""
state = {
'args': args,
'state_dict': model.state_dict(),
'data_scaler': {
'means': scaler.means,
'stds': scaler.stds
} if scaler is not None else None,
'features_scaler': {
'means': features_scaler.means,
'stds': features_scaler.stds
} if features_scaler is not None else None
}
torch.save(state, path)
def save_checkpoint(path: str,
model: MoleculeModel,
scaler: StandardScaler = None,
features_scaler: StandardScaler = None,
args: TrainArgs = None):
"""
Saves a model checkpoint.
:param model: A MoleculeModel.
:param scaler: A StandardScaler fitted on the data.
:param features_scaler: A StandardScaler fitted on the features.
:param args: Arguments.
:param path: Path where checkpoint will be saved.
"""
# Convert args to namespace for backwards compatibility
if args is not None:
args = Namespace(**args.as_dict())
state = {
'args': args,
'state_dict': model.state_dict(),
'data_scaler': {
'means': scaler.means,
'stds': scaler.stds
} if scaler is not None else None,
'features_scaler': {
'means': features_scaler.means,
'stds': features_scaler.stds
} if features_scaler is not None else None
}
torch.save(state, path)
def save_checkpoint(
path: str,
model: MoleculeModel,
scaler: StandardScaler = None,
features_scaler: StandardScaler = None,
atom_descriptor_scaler: StandardScaler = None,
bond_feature_scaler: StandardScaler = None,
args: TrainArgs = None,
) -> None:
"""
Saves a model checkpoint.
:param model: A :class:`~chemprop.models.model.MoleculeModel`.
:param scaler: A :class:`~chemprop.data.scaler.StandardScaler` fitted on the data.
:param features_scaler: A :class:`~chemprop.data.scaler.StandardScaler` fitted on the features.
:param atom_descriptor_scaler: A :class:`~chemprop.data.scaler.StandardScaler` fitted on the atom descriptors.
:param bond_feature_scaler: A :class:`~chemprop.data.scaler.StandardScaler` fitted on the bond_fetaures.
:param args: The :class:`~chemprop.args.TrainArgs` object containing the arguments the model was trained with.
:param path: Path where checkpoint will be saved.
"""
# Convert args to namespace for backwards compatibility
if args is not None:
args = Namespace(**args.as_dict())
data_scaler = {
"means": scaler.means,
"stds": scaler.stds
} if scaler is not None else None
if features_scaler is not None:
features_scaler = {
"means": features_scaler.means,
"stds": features_scaler.stds
}
if atom_descriptor_scaler is not None:
atom_descriptor_scaler = {
"means": atom_descriptor_scaler.means,
"stds": atom_descriptor_scaler.stds,
}
if bond_feature_scaler is not None:
bond_feature_scaler = {
"means": bond_feature_scaler.means,
"stds": bond_feature_scaler.stds
}
state = {
"args": args,
"state_dict": model.state_dict(),
"data_scaler": data_scaler,
"features_scaler": features_scaler,
"atom_descriptor_scaler": atom_descriptor_scaler,
"bond_feature_scaler": bond_feature_scaler,
}
torch.save(state, path)
def predict(model: MoleculeModel,
data_loader: MoleculeDataLoader,
disable_progress_bar: bool = False,
scaler: StandardScaler = None) -> List[List[float]]:
"""
Makes predictions on a dataset using an ensemble of models.
:param model: A :class:`~chemprop.models.model.MoleculeModel`.
:param data_loader: A :class:`~chemprop.data.data.MoleculeDataLoader`.
:param disable_progress_bar: Whether to disable the progress bar.
:param scaler: A :class:`~chemprop.features.scaler.StandardScaler` object fit on the training targets.
:return: A list of lists of predictions. The outer list is molecules while the inner list is tasks.
"""
model.eval()
preds = []
for batch in tqdm(data_loader, disable=disable_progress_bar, leave=False):
# Prepare batch
batch: MoleculeDataset
mol_batch, features_batch, target_batch, atom_descriptors_batch, atom_features_batch, bond_features_batch, smiles_batch = \
batch.batch_graph(), batch.features(), batch.targets(), batch.atom_descriptors(), \
batch.atom_features(), batch.bond_features(), batch.smiles_one_hot_encoding()
# Make predictions
with torch.no_grad():
batch_preds = model(mol_batch, features_batch,
atom_descriptors_batch, atom_features_batch,
bond_features_batch, smiles_batch)
batch_preds = batch_preds.data.cpu().numpy()
# Inverse scale if regression
if scaler is not None:
batch_preds = scaler.inverse_transform(batch_preds)
# Collect vectors
batch_preds = batch_preds.tolist()
preds.extend(batch_preds)
return preds
def objective(hyperparams: Dict[str, Union[int, float]],
seed: int) -> Dict:
# Convert hyperparams from float to int when necessary
for key in INT_KEYS:
hyperparams[key] = int(hyperparams[key])
# Copy args
hyper_args = deepcopy(args)
# Update args with hyperparams
if args.save_dir is not None:
folder_name = '_'.join(f'{key}_{value}'
for key, value in hyperparams.items())
hyper_args.save_dir = os.path.join(hyper_args.save_dir,
folder_name)
for key, value in hyperparams.items():
setattr(hyper_args, key, value)
hyper_args.ffn_hidden_size = hyper_args.hidden_size
# Cross validate
mean_score, std_score = cross_validate(args=hyper_args,
train_func=run_training)
# Record results
temp_model = MoleculeModel(hyper_args)
num_params = param_count(temp_model)
logger.info(f'Trial results with seed {seed}')
logger.info(hyperparams)
logger.info(f'num params: {num_params:,}')
logger.info(f'{mean_score} +/- {std_score} {hyper_args.metric}')
# Deal with nan
if np.isnan(mean_score):
if hyper_args.dataset_type == 'classification':
mean_score = 0
else:
raise ValueError(
'Can\'t handle nan score for non-classification dataset.')
loss = (1 if hyper_args.minimize_score else -1) * mean_score
return {
'loss': loss,
'status': 'ok',
'mean_score': mean_score,
'std_score': std_score,
'hyperparams': hyperparams,
'num_params': num_params,
'seed': seed,
}
def load_checkpoint(path: str,
device: torch.device = None,
logger: logging.Logger = None) -> MoleculeModel:
"""
Loads a model checkpoint.
:param path: Path where checkpoint is saved.
:param device: Device where the model will be moved.
:param logger: A logger for recording output.
:return: The loaded :class:`~chemprop.models.model.MoleculeModel`.
"""
if logger is not None:
debug, info = logger.debug, logger.info
else:
debug = info = print
# Load model and args
state = torch.load(path, map_location=lambda storage, loc: storage)
args = TrainArgs()
args.from_dict(vars(state['args']), skip_unsettable=True)
loaded_state_dict = state['state_dict']
if device is not None:
args.device = device
# Build model
model = MoleculeModel(args)
model_state_dict = model.state_dict()
# Skip missing parameters and parameters of mismatched size
pretrained_state_dict = {}
for loaded_param_name in loaded_state_dict.keys():
# Backward compatibility for parameter names
if re.match(r'(encoder\.encoder\.)([Wc])', loaded_param_name):
param_name = loaded_param_name.replace('encoder.encoder', 'encoder.encoder.0')
else:
param_name = loaded_param_name
# Load pretrained parameter, skipping unmatched parameters
if param_name not in model_state_dict:
info(f'Warning: Pretrained parameter "{loaded_param_name}" cannot be found in model parameters.')
elif model_state_dict[param_name].shape != loaded_state_dict[loaded_param_name].shape:
info(f'Warning: Pretrained parameter "{loaded_param_name}" '
f'of shape {loaded_state_dict[loaded_param_name].shape} does not match corresponding '
f'model parameter of shape {model_state_dict[param_name].shape}.')
else:
debug(f'Loading pretrained parameter "{loaded_param_name}".')
pretrained_state_dict[param_name] = loaded_state_dict[loaded_param_name]
# Load pretrained weights
model_state_dict.update(pretrained_state_dict)
model.load_state_dict(model_state_dict)
if args.cuda:
debug('Moving model to cuda')
model = model.to(args.device)
return model
def objective(hyperparams: Dict[str, Union[int, float]]) -> float:
# Convert hyperparams from float to int when necessary
for key in INT_KEYS:
hyperparams[key] = int(hyperparams[key])
# Copy args
hyper_args = deepcopy(args)
# Update args with hyperparams
if args.save_dir is not None:
folder_name = "_".join(f"{key}_{value}"
for key, value in hyperparams.items())
hyper_args.save_dir = os.path.join(hyper_args.save_dir,
folder_name)
for key, value in hyperparams.items():
setattr(hyper_args, key, value)
hyper_args.ffn_hidden_size = hyper_args.hidden_size
# Record hyperparameters
logger.info(hyperparams)
# Cross validate
mean_score, std_score = cross_validate(args=hyper_args,
train_func=run_training)
# Record results
temp_model = MoleculeModel(hyper_args)
num_params = param_count(temp_model)
logger.info(f"num params: {num_params:,}")
logger.info(f"{mean_score} +/- {std_score} {hyper_args.metric}")
results.append({
"mean_score": mean_score,
"std_score": std_score,
"hyperparams": hyperparams,
"num_params": num_params,
})
# Deal with nan
if np.isnan(mean_score):
if hyper_args.dataset_type == "classification":
mean_score = 0
else:
raise ValueError(
"Can't handle nan score for non-classification dataset.")
return (1 if hyper_args.minimize_score else -1) * mean_score
def predict(
model: MoleculeModel,
data_loader: MoleculeDataLoader,
disable_progress_bar: bool = False,
scaler: StandardScaler = None,
return_unc_parameters: bool = False,
dropout_prob: float = 0.0,
) -> List[List[float]]:
"""
Makes predictions on a dataset using an ensemble of models.
:param model: A :class:`~chemprop.models.model.MoleculeModel`.
:param data_loader: A :class:`~chemprop.data.data.MoleculeDataLoader`.
:param disable_progress_bar: Whether to disable the progress bar.
:param scaler: A :class:`~chemprop.features.scaler.StandardScaler` object fit on the training targets.
:param return_unc_parameters: A bool indicating whether additional uncertainty parameters would be returned alongside the mean predictions.
:param dropout_prob: For use during uncertainty prediction only. The propout probability used in generating a dropout ensemble.
:return: A list of lists of predictions. The outer list is molecules while the inner list is tasks. If returning uncertainty parameters as well,
it is a tuple of lists of lists, of a length depending on how many uncertainty parameters are appropriate for the loss function.
"""
model.eval()
# Activate dropout layers to work during inference for uncertainty estimation
if dropout_prob > 0.0:
def activate_dropout_(model):
return activate_dropout(model, dropout_prob)
model.apply(activate_dropout_)
preds = []
var, lambdas, alphas, betas = [], [], [], [
] # only used if returning uncertainty parameters
for batch in tqdm(data_loader, disable=disable_progress_bar, leave=False):
# Prepare batch
batch: MoleculeDataset
mol_batch = batch.batch_graph()
features_batch = batch.features()
atom_descriptors_batch = batch.atom_descriptors()
atom_features_batch = batch.atom_features()
bond_features_batch = batch.bond_features()
# Make predictions
with torch.no_grad():
batch_preds = model(
mol_batch,
features_batch,
atom_descriptors_batch,
atom_features_batch,
bond_features_batch,
)
batch_preds = batch_preds.data.cpu().numpy()
if model.loss_function == "mve":
batch_preds, batch_var = np.split(batch_preds, 2, axis=1)
elif model.loss_function == "dirichlet":
if model.classification:
batch_alphas = np.reshape(
batch_preds,
[batch_preds.shape[0], batch_preds.shape[1] // 2, 2])
batch_preds = batch_alphas[:, :, 1] / np.sum(
batch_alphas, axis=2) # shape(data, tasks, 2)
elif model.multiclass:
batch_alphas = batch_preds
batch_preds = batch_preds / np.sum(
batch_alphas, axis=2,
keepdims=True) # shape(data, tasks, num_classes)
elif model.loss_function == 'evidential': # regression
batch_preds, batch_lambdas, batch_alphas, batch_betas = np.split(
batch_preds, 4, axis=1)
# Inverse scale if regression
if scaler is not None:
batch_preds = scaler.inverse_transform(batch_preds)
if model.loss_function == "mve":
batch_var = batch_var * scaler.stds**2
elif model.loss_function == "evidential":
batch_betas = batch_betas * scaler.stds**2
# Collect vectors
batch_preds = batch_preds.tolist()
preds.extend(batch_preds)
if model.loss_function == "mve":
var.extend(batch_var.tolist())
elif model.loss_function == "dirichlet" and model.classification:
alphas.extend(batch_alphas.tolist())
elif model.loss_function == "evidential": # regression
lambdas.extend(batch_lambdas.tolist())
alphas.extend(batch_alphas.tolist())
betas.extend(batch_betas.tolist())
if return_unc_parameters:
if model.loss_function == "mve":
return preds, var
elif model.loss_function == "dirichlet":
return preds, alphas
elif model.loss_function == "evidential":
return preds, lambdas, alphas, betas
return preds
def run_training(args: TrainArgs,
data: MoleculeDataset,
logger: Logger = None) -> Dict[str, List[float]]:
"""
Loads data, trains a Chemprop model, and returns test scores for the model checkpoint with the highest validation score.
:param args: A :class:`~chemprop.args.TrainArgs` object containing arguments for
loading data and training the Chemprop model.
:param data: A :class:`~chemprop.data.MoleculeDataset` containing the data.
:param logger: A logger to record output.
:return: A dictionary mapping each metric in :code:`args.metrics` to a list of values for each task.
"""
if logger is not None:
debug, info = logger.debug, logger.info
else:
debug = info = print
# Set pytorch seed for random initial weights
torch.manual_seed(args.pytorch_seed)
# Split data
debug(f'Splitting data with seed {args.seed}')
if args.separate_test_path:
test_data = get_data(
path=args.separate_test_path,
args=args,
features_path=args.separate_test_features_path,
atom_descriptors_path=args.separate_test_atom_descriptors_path,
bond_features_path=args.separate_test_bond_features_path,
phase_features_path=args.separate_test_phase_features_path,
smiles_columns=args.smiles_columns,
logger=logger)
if args.separate_val_path:
val_data = get_data(
path=args.separate_val_path,
args=args,
features_path=args.separate_val_features_path,
atom_descriptors_path=args.separate_val_atom_descriptors_path,
bond_features_path=args.separate_val_bond_features_path,
phase_features_path=args.separate_val_phase_features_path,
smiles_columns=args.smiles_columns,
logger=logger)
if args.separate_val_path and args.separate_test_path:
train_data = data
elif args.separate_val_path:
train_data, _, test_data = split_data(data=data,
split_type=args.split_type,
sizes=(0.8, 0.0, 0.2),
seed=args.seed,
num_folds=args.num_folds,
args=args,
logger=logger)
elif args.separate_test_path:
train_data, val_data, _ = split_data(data=data,
split_type=args.split_type,
sizes=(0.8, 0.2, 0.0),
seed=args.seed,
num_folds=args.num_folds,
args=args,
logger=logger)
else:
train_data, val_data, test_data = split_data(
data=data,
split_type=args.split_type,
sizes=args.split_sizes,
seed=args.seed,
num_folds=args.num_folds,
args=args,
logger=logger)
if args.dataset_type == 'classification':
class_sizes = get_class_sizes(data)
debug('Class sizes')
for i, task_class_sizes in enumerate(class_sizes):
debug(
f'{args.task_names[i]} '
f'{", ".join(f"{cls}: {size * 100:.2f}%" for cls, size in enumerate(task_class_sizes))}'
)
if args.save_smiles_splits:
save_smiles_splits(
data_path=args.data_path,
save_dir=args.save_dir,
task_names=args.task_names,
features_path=args.features_path,
train_data=train_data,
val_data=val_data,
test_data=test_data,
smiles_columns=args.smiles_columns,
logger=logger,
)
if args.features_scaling:
features_scaler = train_data.normalize_features(replace_nan_token=0)
val_data.normalize_features(features_scaler)
test_data.normalize_features(features_scaler)
else:
features_scaler = None
if args.atom_descriptor_scaling and args.atom_descriptors is not None:
atom_descriptor_scaler = train_data.normalize_features(
replace_nan_token=0, scale_atom_descriptors=True)
val_data.normalize_features(atom_descriptor_scaler,
scale_atom_descriptors=True)
test_data.normalize_features(atom_descriptor_scaler,
scale_atom_descriptors=True)
else:
atom_descriptor_scaler = None
if args.bond_feature_scaling and args.bond_features_size > 0:
bond_feature_scaler = train_data.normalize_features(
replace_nan_token=0, scale_bond_features=True)
val_data.normalize_features(bond_feature_scaler,
scale_bond_features=True)
test_data.normalize_features(bond_feature_scaler,
scale_bond_features=True)
else:
bond_feature_scaler = None
args.train_data_size = len(train_data)
debug(
f'Total size = {len(data):,} | '
f'train size = {len(train_data):,} | val size = {len(val_data):,} | test size = {len(test_data):,}'
)
# Initialize scaler and scale training targets by subtracting mean and dividing standard deviation (regression only)
if args.dataset_type == 'regression':
debug('Fitting scaler')
scaler = train_data.normalize_targets()
elif args.dataset_type == 'spectra':
debug(
'Normalizing spectra and excluding spectra regions based on phase')
args.spectra_phase_mask = load_phase_mask(args.spectra_phase_mask_path)
for dataset in [train_data, test_data, val_data]:
data_targets = normalize_spectra(
spectra=dataset.targets(),
phase_features=dataset.phase_features(),
phase_mask=args.spectra_phase_mask,
excluded_sub_value=None,
threshold=args.spectra_target_floor,
)
dataset.set_targets(data_targets)
scaler = None
else:
scaler = None
# Get loss function
loss_func = get_loss_func(args)
# Set up test set evaluation
test_smiles, test_targets = test_data.smiles(), test_data.targets()
if args.dataset_type == 'multiclass':
sum_test_preds = np.zeros(
(len(test_smiles), args.num_tasks, args.multiclass_num_classes))
else:
sum_test_preds = np.zeros((len(test_smiles), args.num_tasks))
# Automatically determine whether to cache
if len(data) <= args.cache_cutoff:
set_cache_graph(True)
num_workers = 0
else:
set_cache_graph(False)
num_workers = args.num_workers
# Create data loaders
train_data_loader = MoleculeDataLoader(dataset=train_data,
batch_size=args.batch_size,
num_workers=num_workers,
class_balance=args.class_balance,
shuffle=True,
seed=args.seed)
val_data_loader = MoleculeDataLoader(dataset=val_data,
batch_size=args.batch_size,
num_workers=num_workers)
test_data_loader = MoleculeDataLoader(dataset=test_data,
batch_size=args.batch_size,
num_workers=num_workers)
if args.class_balance:
debug(
f'With class_balance, effective train size = {train_data_loader.iter_size:,}'
)
# Train ensemble of models
for model_idx in range(args.ensemble_size):
# Tensorboard writer
save_dir = os.path.join(args.save_dir, f'model_{model_idx}')
makedirs(save_dir)
try:
writer = SummaryWriter(log_dir=save_dir)
except:
writer = SummaryWriter(logdir=save_dir)
# Load/build model
if args.checkpoint_paths is not None:
debug(
f'Loading model {model_idx} from {args.checkpoint_paths[model_idx]}'
)
model = load_checkpoint(args.checkpoint_paths[model_idx],
logger=logger)
else:
debug(f'Building model {model_idx}')
model = MoleculeModel(args)
# Optionally, overwrite weights:
if args.checkpoint_frzn is not None:
debug(
f'Loading and freezing parameters from {args.checkpoint_frzn}.'
)
model = load_frzn_model(model=model,
path=args.checkpoint_frzn,
current_args=args,
logger=logger)
debug(model)
if args.checkpoint_frzn is not None:
debug(f'Number of unfrozen parameters = {param_count(model):,}')
debug(f'Total number of parameters = {param_count_all(model):,}')
else:
debug(f'Number of parameters = {param_count_all(model):,}')
if args.cuda:
debug('Moving model to cuda')
model = model.to(args.device)
# Ensure that model is saved in correct location for evaluation if 0 epochs
save_checkpoint(os.path.join(save_dir, MODEL_FILE_NAME), model, scaler,
features_scaler, atom_descriptor_scaler,
bond_feature_scaler, args)
# Optimizers
optimizer = build_optimizer(model, args)
# Learning rate schedulers
scheduler = build_lr_scheduler(optimizer, args)
# Run training
best_score = float('inf') if args.minimize_score else -float('inf')
best_epoch, n_iter = 0, 0
for epoch in trange(args.epochs):
debug(f'Epoch {epoch}')
n_iter = train(model=model,
data_loader=train_data_loader,
loss_func=loss_func,
optimizer=optimizer,
scheduler=scheduler,
args=args,
n_iter=n_iter,
logger=logger,
writer=writer)
if isinstance(scheduler, ExponentialLR):
scheduler.step()
val_scores = evaluate(model=model,
data_loader=val_data_loader,
num_tasks=args.num_tasks,
metrics=args.metrics,
dataset_type=args.dataset_type,
scaler=scaler,
logger=logger)
for metric, scores in val_scores.items():
# Average validation score
avg_val_score = np.nanmean(scores)
debug(f'Validation {metric} = {avg_val_score:.6f}')
writer.add_scalar(f'validation_{metric}', avg_val_score,
n_iter)
if args.show_individual_scores:
# Individual validation scores
for task_name, val_score in zip(args.task_names, scores):
debug(
f'Validation {task_name} {metric} = {val_score:.6f}'
)
writer.add_scalar(f'validation_{task_name}_{metric}',
val_score, n_iter)
# Save model checkpoint if improved validation score
avg_val_score = np.nanmean(val_scores[args.metric])
if args.minimize_score and avg_val_score < best_score or \
not args.minimize_score and avg_val_score > best_score:
best_score, best_epoch = avg_val_score, epoch
save_checkpoint(os.path.join(save_dir,
MODEL_FILE_NAME), model, scaler,
features_scaler, atom_descriptor_scaler,
bond_feature_scaler, args)
# Evaluate on test set using model with best validation score
info(
f'Model {model_idx} best validation {args.metric} = {best_score:.6f} on epoch {best_epoch}'
)
model = load_checkpoint(os.path.join(save_dir, MODEL_FILE_NAME),
device=args.device,
logger=logger)
test_preds = predict(model=model,
data_loader=test_data_loader,
scaler=scaler)
test_scores = evaluate_predictions(preds=test_preds,
targets=test_targets,
num_tasks=args.num_tasks,
metrics=args.metrics,
dataset_type=args.dataset_type,
logger=logger)
if len(test_preds) != 0:
sum_test_preds += np.array(test_preds)
# Average test score
for metric, scores in test_scores.items():
avg_test_score = np.nanmean(scores)
info(f'Model {model_idx} test {metric} = {avg_test_score:.6f}')
writer.add_scalar(f'test_{metric}', avg_test_score, 0)
if args.show_individual_scores and args.dataset_type != 'spectra':
# Individual test scores
for task_name, test_score in zip(args.task_names, scores):
info(
f'Model {model_idx} test {task_name} {metric} = {test_score:.6f}'
)
writer.add_scalar(f'test_{task_name}_{metric}', test_score,
n_iter)
writer.close()
# Evaluate ensemble on test set
avg_test_preds = (sum_test_preds / args.ensemble_size).tolist()
ensemble_scores = evaluate_predictions(preds=avg_test_preds,
targets=test_targets,
num_tasks=args.num_tasks,
metrics=args.metrics,
dataset_type=args.dataset_type,
logger=logger)
for metric, scores in ensemble_scores.items():
# Average ensemble score
avg_ensemble_test_score = np.nanmean(scores)
info(f'Ensemble test {metric} = {avg_ensemble_test_score:.6f}')
# Individual ensemble scores
if args.show_individual_scores:
for task_name, ensemble_score in zip(args.task_names, scores):
info(
f'Ensemble test {task_name} {metric} = {ensemble_score:.6f}'
)
# Save scores
with open(os.path.join(args.save_dir, 'test_scores.json'), 'w') as f:
json.dump(ensemble_scores, f, indent=4, sort_keys=True)
# Optionally save test preds
if args.save_preds:
test_preds_dataframe = pd.DataFrame(
data={'smiles': test_data.smiles()})
for i, task_name in enumerate(args.task_names):
test_preds_dataframe[task_name] = [
pred[i] for pred in avg_test_preds
]
test_preds_dataframe.to_csv(os.path.join(args.save_dir,
'test_preds.csv'),
index=False)
return ensemble_scores
def train_gp(
model,
train_data,
val_data,
num_workers,
cache,
metric_func,
scaler,
features_scaler,
args,
save_dir):
# create data loaders for gp (allows different batch size)
train_data_loader = MoleculeDataLoader(
dataset=train_data,
batch_size=args.batch_size_gp,
num_workers=num_workers,
cache=cache,
class_balance=args.class_balance,
shuffle=True,
seed=args.seed
)
val_data_loader = MoleculeDataLoader(
dataset=val_data,
batch_size=args.batch_size_gp,
num_workers=num_workers,
cache=cache
)
# feature_extractor
model.featurizer = True
feature_extractor = model
# inducing points
inducing_points = initial_inducing_points(
train_data_loader,
feature_extractor,
args
)
# GP layer
gp_layer = GPLayer(inducing_points, args.num_tasks)
# full DKL model
model = copy.deepcopy(DKLMoleculeModel(feature_extractor, gp_layer))
# likelihood
# rank 0 restricts to diagonal matrix
likelihood = gpytorch.likelihoods.MultitaskGaussianLikelihood(num_tasks=12, rank=0)
# model and likelihood to CUDA
if args.cuda:
model.cuda()
likelihood.cuda()
# loss object
mll = gpytorch.mlls.VariationalELBO(likelihood, model.gp_layer, num_data=args.train_data_size)
# optimizer
params_list = [
{'params': model.feature_extractor.parameters(), 'weight_decay': args.weight_decay_gp},
{'params': model.gp_layer.hyperparameters()},
{'params': model.gp_layer.variational_parameters()},
{'params': likelihood.parameters()},
]
optimizer = torch.optim.Adam(params_list, lr = args.init_lr_gp)
# scheduler
num_params = len(params_list)
scheduler = NoamLR(
optimizer=optimizer,
warmup_epochs=[args.warmup_epochs_gp]*num_params,
total_epochs=[args.noam_epochs_gp]*num_params,
steps_per_epoch=args.train_data_size // args.batch_size_gp,
init_lr=[args.init_lr_gp]*num_params,
max_lr=[args.max_lr_gp]*num_params,
final_lr=[args.final_lr_gp]*num_params)
print("----------GP training----------")
# training loop
best_score = float('inf') if args.minimize_score else -float('inf')
best_epoch, n_iter = 0, 0
for epoch in range(args.epochs_gp):
print(f'GP epoch {epoch}')
if epoch == args.noam_epochs_gp:
scheduler = scheduler_const([args.final_lr_gp])
n_iter = train(
model=model,
data_loader=train_data_loader,
loss_func=mll,
optimizer=optimizer,
scheduler=scheduler,
args=args,
n_iter=n_iter,
gp_switch=True,
likelihood = likelihood
)
val_scores = evaluate(
model=model,
data_loader=val_data_loader,
args=args,
num_tasks=args.num_tasks,
metric_func=metric_func,
dataset_type=args.dataset_type,
scaler=scaler
)
# Average validation score
avg_val_score = np.nanmean(val_scores)
print(f'Validation {args.metric} = {avg_val_score:.6f}')
wandb.log({"Validation MAE": avg_val_score})
# Save model AND LIKELIHOOD checkpoint if improved validation score
if args.minimize_score and avg_val_score < best_score or \
not args.minimize_score and avg_val_score > best_score:
best_score, best_epoch = avg_val_score, epoch
save_checkpoint(os.path.join(save_dir, 'DKN_model.pt'), model, scaler, features_scaler, args)
best_likelihood = copy.deepcopy(likelihood)
# load model with best validation score
# NOTE: TEMPLATE MUST BE NEWLY INSTANTIATED MODEL
print(f'Loading model with best validation {args.metric} = {best_score:.6f} on epoch {best_epoch}')
model = load_checkpoint(os.path.join(save_dir, 'DKN_model.pt'), device=args.device, logger=None,
template = DKLMoleculeModel(MoleculeModel(args, featurizer=True), gp_layer))
return model, best_likelihood
def load_checkpoint(path: str,
device: torch.device = None,
logger: logging.Logger = None,
template=None) -> MoleculeModel:
"""
Loads a model checkpoint.
:param path: Path where checkpoint is saved.
:param device: Device where the model will be moved.
:param logger: A logger.
:return: The loaded MoleculeModel.
"""
if logger is not None:
debug, info = logger.debug, logger.info
else:
debug = info = print
# Load model and args
state = torch.load(path, map_location=lambda storage, loc: storage)
args = TrainArgs()
args.from_dict(vars(state['args']), skip_unsettable=True)
loaded_state_dict = state['state_dict']
if device is not None:
args.device = device
# Build model
if template is not None:
model = template
else:
model = MoleculeModel(args)
model_state_dict = model.state_dict()
# Skip missing parameters and parameters of mismatched size
pretrained_state_dict = {}
for param_name in loaded_state_dict.keys():
if param_name not in model_state_dict:
info(
f'Warning: Pretrained parameter "{param_name}" cannot be found in model parameters.'
)
elif model_state_dict[param_name].shape != loaded_state_dict[
param_name].shape:
info(
f'Warning: Pretrained parameter "{param_name}" '
f'of shape {loaded_state_dict[param_name].shape} does not match corresponding '
f'model parameter of shape {model_state_dict[param_name].shape}.'
)
else:
#debug(f'Loading pretrained parameter "{param_name}".')
pretrained_state_dict[param_name] = loaded_state_dict[param_name]
# Load pretrained weights
model_state_dict.update(pretrained_state_dict)
model.load_state_dict(model_state_dict)
if args.cuda:
debug('Moving model to cuda')
model = model.to(args.device)
return model
def run_meta_training(args: TrainArgs, logger: Logger = None) -> List[float]:
"""
Trains a model and returns test scores on the model checkpoint with the highest validation score.
:param args: Arguments.
:param logger: Logger.
:return: A list of ensemble scores for each task.
"""
if logger is not None:
debug, info = logger.debug, logger.info
else:
debug = info = print
# Print command line
debug('Command line')
debug(f'python {" ".join(sys.argv)}')
# Print args
debug('Args')
debug(args)
# Save args
args.save(os.path.join(args.save_dir, 'args.json'))
# Set pytorch seed for random initial weights
torch.manual_seed(args.pytorch_seed)
# Get data
debug('Loading data')
args.task_names = args.target_columns or get_task_names(args.data_path)
data = get_data(path=args.data_path, args=args, logger=logger)
args.num_tasks = data.num_tasks()
args.features_size = data.features_size()
debug(f'Number of tasks = {args.num_tasks}')
# Split data
# debug(f'Splitting data with seed {args.seed}')
# if args.separate_test_path:
# test_data = get_data(path=args.separate_test_path, args=args, features_path=args.separate_test_features_path, logger=logger)
# if args.separate_val_path:
# val_data = get_data(path=args.separate_val_path, args=args, features_path=args.separate_val_features_path, logger=logger)
# if args.separate_val_path and args.separate_test_path:
# train_data = data
# elif args.separate_val_path:
# train_data, _, test_data = split_data(data=data, split_type=args.split_type, sizes=(0.8, 0.0, 0.2), seed=args.seed, args=args, logger=logger)
# elif args.separate_test_path:
# train_data, val_data, _ = split_data(data=data, split_type=args.split_type, sizes=(0.8, 0.2, 0.0), seed=args.seed, args=args, logger=logger)
# else:
# train_data, val_data, test_data = split_data(data=data, split_type=args.split_type, sizes=args.split_sizes, seed=args.seed, args=args, logger=logger)
if args.dataset_type == 'classification':
class_sizes = get_class_sizes(data)
debug('Class sizes')
for i, task_class_sizes in enumerate(class_sizes):
debug(f'{args.task_names[i]} '
f'{", ".join(f"{cls}: {size * 100:.2f}%" for cls, size in enumerate(task_class_sizes))}')
# if args.save_smiles_splits:
# save_smiles_splits(
# train_data=train_data,
# val_data=val_data,
# test_data=test_data,
# data_path=args.data_path,
# save_dir=args.save_dir
# )
# If this happens, then need to move this logic into the task data loader
# when it creates the datasets!
# if args.features_scaling:
# features_scaler = train_data.normalize_features(replace_nan_token=0)
# val_data.normalize_features(features_scaler)
# test_data.normalize_features(features_scaler)
# else:
# features_scaler = None
# args.train_data_size = len(train_data)
# debug(f'Total size = {len(data):,} | '
# f'train size = {len(train_data):,} | val size = {len(val_data):,} | test size = {len(test_data):,}')
# Initialize scaler and scale training targets by subtracting mean and dividing standard deviation (regression only)
# if args.dataset_type == 'regression':
# debug('Fitting scaler')
# train_smiles, train_targets = train_data.smiles(), train_data.targets()
# scaler = StandardScaler().fit(train_targets)
# scaled_targets = scaler.transform(train_targets).tolist()
# train_data.set_targets(scaled_targets)
# else:
# scaler = None
# Get loss and metric functions
loss_func = get_loss_func(args)
metric_func = get_metric_func(metric=args.metric)
# Set up test set evaluation
# test_smiles, test_targets = test_data.smiles(), test_data.targets()
# if args.dataset_type == 'multiclass':
# sum_test_preds = np.zeros((len(test_smiles), args.num_tasks, args.multiclass_num_classes))
# else:
# sum_test_preds = np.zeros((len(test_smiles), args.num_tasks))
# Automatically determine whether to cache
if len(data) <= args.cache_cutoff:
cache = True
num_workers = 0
else:
cache = False
num_workers = args.num_workers
# Set up MetaTaskDataLoaders, which takes care of task splits under the hood
# Set up task splits into T_tr, T_val, T_test
assert args.chembl_assay_metadata_pickle_path is not None
with open(args.chembl_assay_metadata_pickle_path +
'chembl_128_assay_type_to_names.pickle', 'rb') as handle:
chembl_128_assay_type_to_names = pickle.load(handle)
with open(args.chembl_assay_metadata_pickle_path +
'chembl_128_assay_name_to_type.pickle', 'rb') as handle:
chembl_128_assay_name_to_type = pickle.load(handle)
"""
Copy GSK implementation of task split
We have 5 Task types remaining
ADME (A)
Toxicity (T)
Unassigned (U)
Binding (B)
Functional (F)
resulting in 902 tasks.
For T_val, randomly select 10 B and F tasks
For T_test, select another 10 B and F tasks and allocate all A, T, and U
tasks to the test split.
For T_train, allocate the remaining B and F tasks.
"""
import pdb; pdb.set_trace()
T_val_num_BF_tasks = args.meta_split_sizes_BF[0]
T_test_num_BF_tasks = args.meta_split_sizes_BF[1]
T_val_idx = T_val_num_BF_tasks
T_test_idx = T_val_num_BF_tasks + T_test_num_BF_tasks
chembl_id_to_idx = {chembl_id: idx for idx, chembl_id in enumerate(args.task_names)}
# Shuffle B and F tasks
randomized_B_tasks = np.copy(chembl_128_assay_type_to_names['B'])
np.random.shuffle(randomized_B_tasks)
randomized_B_task_indices = [chembl_id_to_idx[assay] for assay in
randomized_B_tasks]
randomized_F_tasks = np.copy(chembl_128_assay_type_to_names['F'])
np.random.shuffle(randomized_F_tasks)
randomized_F_task_indices = [chembl_id_to_idx[assay] for assay in
randomized_F_tasks]
# Grab B and F indices for T_val
T_val_B_task_indices = randomized_B_task_indices[:T_val_idx]
T_val_F_task_indices = randomized_F_task_indices[:T_val_idx]
# Grab B and F indices for T_test
T_test_B_task_indices = randomized_B_task_indices[T_val_idx:T_test_idx]
T_test_F_task_indices = randomized_F_task_indices[T_val_idx:T_test_idx]
# Grab all A, T and U indices for T_test
T_test_A_task_indices = [chembl_id_to_idx[assay] for assay in chembl_128_assay_type_to_names['A']]
T_test_T_task_indices = [chembl_id_to_idx[assay] for assay in chembl_128_assay_type_to_names['T']]
T_test_U_task_indices = [chembl_id_to_idx[assay] for assay in chembl_128_assay_type_to_names['U']]
# Slot remaining BF tasks into T_tr
T_tr_B_task_indices = randomized_B_task_indices[T_test_idx:]
T_tr_F_task_indices = randomized_F_task_indices[T_test_idx:]
T_tr = [0] * len(args.task_names)
T_val = [0] * len(args.task_names)
T_test = [0] * len(args.task_names)
# Now make task bit vectors
for idx_list in (T_tr_B_task_indices, T_tr_F_task_indices):
for idx in idx_list:
T_tr[idx] = 1
for idx_list in (T_val_B_task_indices, T_val_F_task_indices):
for idx in idx_list:
T_val[idx] = 1
for idx_list in (T_test_B_task_indices, T_test_F_task_indices, T_test_A_task_indices, T_test_T_task_indices, T_test_U_task_indices):
for idx in idx_list:
T_test[idx] = 1
"""
Random task split for testing
task_indices = list(range(len(args.task_names)))
np.random.shuffle(task_indices)
train_task_split, val_task_split, test_task_split = 0.9, 0, 0.1
train_task_cutoff = int(len(task_indices) * train_task_split)
train_task_idxs, test_task_idxs = [0] * len(task_indices), [0] * len(task_indices)
for idx in task_indices[:train_task_cutoff]:
train_task_idxs[idx] = 1
for idx in task_indices[train_task_cutoff:]:
test_task_idxs[idx] = 1
"""
train_meta_task_data_loader = MetaTaskDataLoader(
dataset=data,
tasks=T_tr,
sizes=args.meta_train_split_sizes,
args=args,
logger=logger)
val_meta_task_data_loader = MetaTaskDataLoader(
dataset=data,
tasks=T_val,
sizes=args.meta_test_split_sizes,
args=args,
logger=logger)
test_meta_task_data_loader = MetaTaskDataLoader(
dataset=data,
tasks=T_test,
sizes=args.meta_test_split_sizes,
args=args,
logger=logger)
import pdb; pdb.set_trace()
for meta_train_batch in train_meta_task_data_loader.tasks():
for train_task in meta_train_batch:
print('In inner loop')
continue
# Train ensemble of models
for model_idx in range(args.ensemble_size):
# Tensorboard writer
save_dir = os.path.join(args.save_dir, f'model_{model_idx}')
makedirs(save_dir)
try:
writer = SummaryWriter(log_dir=save_dir)
except:
writer = SummaryWriter(logdir=save_dir)
# Load/build model
if args.checkpoint_paths is not None:
debug(f'Loading model {model_idx} from {args.checkpoint_paths[model_idx]}')
model = load_checkpoint(args.checkpoint_paths[model_idx], logger=logger)
else:
debug(f'Building model {model_idx}')
model = MoleculeModel(args)
debug(model)
debug(f'Number of parameters = {param_count(model):,}')
if args.cuda:
debug('Moving model to cuda')
model = model.to(args.device)
# Ensure that model is saved in correct location for evaluation if 0 epochs
save_checkpoint(os.path.join(save_dir, 'model.pt'), model, scaler, features_scaler, args)
# Optimizers
optimizer = build_optimizer(model, args)
# Learning rate schedulers
scheduler = build_lr_scheduler(optimizer, args)
# Run training
best_score = float('inf') if args.minimize_score else -float('inf')
best_epoch, n_iter = 0, 0
for epoch in trange(args.epochs):
debug(f'Epoch {epoch}')
n_iter = train(
model=model,
data_loader=train_data_loader,
loss_func=loss_func,
optimizer=optimizer,
scheduler=scheduler,
args=args,
n_iter=n_iter,
logger=logger,
writer=writer
)
if isinstance(scheduler, ExponentialLR):
scheduler.step()
val_scores = evaluate(
model=model,
data_loader=val_data_loader,
num_tasks=args.num_tasks,
metric_func=metric_func,
dataset_type=args.dataset_type,
scaler=scaler,
logger=logger
)
# Average validation score
avg_val_score = np.nanmean(val_scores)
debug(f'Validation {args.metric} = {avg_val_score:.6f}')
writer.add_scalar(f'validation_{args.metric}', avg_val_score, n_iter)
if args.show_individual_scores:
# Individual validation scores
for task_name, val_score in zip(args.task_names, val_scores):
debug(f'Validation {task_name} {args.metric} = {val_score:.6f}')
writer.add_scalar(f'validation_{task_name}_{args.metric}', val_score, n_iter)
# Save model checkpoint if improved validation score
if args.minimize_score and avg_val_score < best_score or \
not args.minimize_score and avg_val_score > best_score:
best_score, best_epoch = avg_val_score, epoch
save_checkpoint(os.path.join(save_dir, 'model.pt'), model, scaler, features_scaler, args)
# Evaluate on test set using model with best validation score
info(f'Model {model_idx} best validation {args.metric} = {best_score:.6f} on epoch {best_epoch}')
model = load_checkpoint(os.path.join(save_dir, 'model.pt'), device=args.device, logger=logger)
test_preds = predict(
model=model,
data_loader=test_data_loader,
scaler=scaler
)
test_scores = evaluate_predictions(
preds=test_preds,
targets=test_targets,
num_tasks=args.num_tasks,
metric_func=metric_func,
dataset_type=args.dataset_type,
logger=logger
)
if len(test_preds) != 0:
sum_test_preds += np.array(test_preds)
# Average test score
avg_test_score = np.nanmean(test_scores)
info(f'Model {model_idx} test {args.metric} = {avg_test_score:.6f}')
writer.add_scalar(f'test_{args.metric}', avg_test_score, 0)
if args.show_individual_scores:
# Individual test scores
for task_name, test_score in zip(args.task_names, test_scores):
info(f'Model {model_idx} test {task_name} {args.metric} = {test_score:.6f}')
writer.add_scalar(f'test_{task_name}_{args.metric}', test_score, n_iter)
writer.close()
# Evaluate ensemble on test set
avg_test_preds = (sum_test_preds / args.ensemble_size).tolist()
ensemble_scores = evaluate_predictions(
preds=avg_test_preds,
targets=test_targets,
num_tasks=args.num_tasks,
metric_func=metric_func,
dataset_type=args.dataset_type,
logger=logger
)
# Average ensemble score
avg_ensemble_test_score = np.nanmean(ensemble_scores)
info(f'Ensemble test {args.metric} = {avg_ensemble_test_score:.6f}')
# Individual ensemble scores
if args.show_individual_scores:
for task_name, ensemble_score in zip(args.task_names, ensemble_scores):
info(f'Ensemble test {task_name} {args.metric} = {ensemble_score:.6f}')
return ensemble_scores
def pdts(args: TrainArgs, model_idx):
"""
preliminary experiment with PDTS (approximate BO)
we use a data set size of 50k and run until we have trained with 15k data points
our batch size is 50
we initialise with 1000 data points
"""
######## set up all logging ########
logger = None
# make save_dir
save_dir = os.path.join(args.save_dir, f'model_{model_idx}')
makedirs(save_dir)
# make results_dir
results_dir = args.results_dir
makedirs(results_dir)
# initialise wandb
#os.environ['WANDB_MODE'] = 'dryrun'
wandb.init(name=args.wandb_name + '_' + str(model_idx),
project=args.wandb_proj,
reinit=True)
#print('WANDB directory is:')
#print(wandb.run.dir)
####################################
########## get data
args.task_names = args.target_columns or get_task_names(args.data_path)
data = get_data(path=args.data_path, args=args, logger=logger)
args.num_tasks = data.num_tasks()
args.features_size = data.features_size()
########## SMILES of top 1%
top1p = np.array(MoleculeDataset(data).targets())
top1p_idx = np.argsort(-top1p[:, 0])[:int(args.max_data_size * 0.01)]
SMILES = np.array(MoleculeDataset(data).smiles())[top1p_idx]
########## initial data splits
args.seed = args.data_seeds[model_idx]
data.shuffle(seed=args.seed)
sizes = args.split_sizes
train_size = int(sizes[0] * len(data))
train_orig = data[:train_size]
test_orig = data[train_size:]
train_data, test_data = copy.deepcopy(
MoleculeDataset(train_orig)), copy.deepcopy(MoleculeDataset(test_orig))
args.train_data_size = len(train_data)
########## standardising
# features (train and test)
features_scaler = train_data.normalize_features(replace_nan_token=0)
test_data.normalize_features(features_scaler)
# targets (train)
train_targets = train_data.targets()
test_targets = test_data.targets()
scaler = StandardScaler().fit(train_targets)
scaled_targets = scaler.transform(train_targets).tolist()
train_data.set_targets(scaled_targets)
########## loss, metric functions
loss_func = neg_log_like
metric_func = get_metric_func(metric=args.metric)
########## data loaders
if len(data) <= args.cache_cutoff:
cache = True
num_workers = 0
else:
cache = False
num_workers = args.num_workers
train_data_loader = MoleculeDataLoader(dataset=train_data,
batch_size=args.batch_size,
num_workers=num_workers,
cache=cache,
class_balance=args.class_balance,
shuffle=True,
seed=args.seed)
test_data_loader = MoleculeDataLoader(dataset=test_data,
batch_size=args.batch_size,
num_workers=num_workers,
cache=cache)
########## instantiating model, optimiser, scheduler (MAP)
# set pytorch seed for random initial weights
torch.manual_seed(args.pytorch_seeds[model_idx])
# build model
print(f'Building model {model_idx}')
model = MoleculeModel(args)
print(model)
print(f'Number of parameters = {param_count(model):,}')
if args.cuda:
print('Moving model to cuda')
model = model.to(args.device)
# optimizer
optimizer = Adam([{
'params': model.encoder.parameters()
}, {
'params': model.ffn.parameters()
}, {
'params': model.log_noise,
'weight_decay': 0
}],
lr=args.lr,
weight_decay=args.weight_decay)
# learning rate scheduler
scheduler = scheduler_const([args.lr])
####################################################################
####################################################################
# FIRST THOMPSON ITERATION
### scores array
ptds_scores = np.ones(args.pdts_batches + 1)
batch_no = 0
### fill for batch 0
SMILES_train = np.array(train_data.smiles())
SMILES_stack = np.hstack((SMILES, SMILES_train))
overlap = len(SMILES_stack) - len(np.unique(SMILES_stack))
prop = overlap / len(SMILES)
ptds_scores[batch_no] = prop
wandb.log({
"Proportion of top 1%": prop,
"batch_no": batch_no
},
commit=False)
### train MAP posterior
gp_switch = False
likelihood = None
bbp_switch = None
n_iter = 0
for epoch in range(args.epochs_init_map):
n_iter = train(model=model,
data_loader=train_data_loader,
loss_func=loss_func,
optimizer=optimizer,
scheduler=scheduler,
args=args,
n_iter=n_iter,
bbp_switch=bbp_switch)
# save to save_dir
#if epoch == args.epochs_init_map - 1:
#save_checkpoint(os.path.join(save_dir, f'model_{batch_no}.pt'), model, scaler, features_scaler, args)
# if X load from checkpoint path
if args.bbp or args.gp or args.swag or args.sgld:
model = load_checkpoint(args.checkpoint_path +
f'/model_{model_idx}/model_{batch_no}.pt',
device=args.device,
logger=None)
########## BBP
if args.bbp:
model_bbp = MoleculeModelBBP(
args) # instantiate with bayesian linear layers
for (_, param_bbp), (_, param_pre) in zip(model_bbp.named_parameters(),
model.named_parameters()):
param_bbp.data = copy.deepcopy(
param_pre.data.T) # copy over parameters
# instantiate rhos
for layer in model_bbp.children():
if isinstance(layer, BayesLinear):
layer.init_rho(args.rho_min_bbp, args.rho_max_bbp)
for layer in model_bbp.encoder.encoder.children():
if isinstance(layer, BayesLinear):
layer.init_rho(args.rho_min_bbp, args.rho_max_bbp)
model = model_bbp # name back
# move to cuda
if args.cuda:
print('Moving bbp model to cuda')
model = model.to(args.device)
# optimiser and scheduler
optimizer = torch.optim.Adam(model.parameters(), lr=args.lr)
scheduler = scheduler_const([args.lr])
bbp_switch = 2
n_iter = 0
for epoch in range(args.epochs_init):
n_iter = train(model=model,
data_loader=train_data_loader,
loss_func=loss_func,
optimizer=optimizer,
scheduler=scheduler,
args=args,
n_iter=n_iter,
bbp_switch=bbp_switch)
########## GP
if args.gp:
# feature_extractor
model.featurizer = True
feature_extractor = model
# inducing points
inducing_points = initial_inducing_points(train_data_loader,
feature_extractor, args)
# GP layer
gp_layer = GPLayer(inducing_points, args.num_tasks)
# full DKL model
model = copy.deepcopy(DKLMoleculeModel(feature_extractor, gp_layer))
# likelihood (rank 0 restricts to diagonal matrix)
likelihood = gpytorch.likelihoods.MultitaskGaussianLikelihood(
num_tasks=12, rank=0)
# model and likelihood to CUDA
if args.cuda:
model.cuda()
likelihood.cuda()
# loss object
loss_func = gpytorch.mlls.VariationalELBO(
likelihood, model.gp_layer, num_data=args.train_data_size)
# optimiser and scheduler
params_list = [
{
'params': model.feature_extractor.parameters(),
'weight_decay': args.weight_decay_gp
},
{
'params': model.gp_layer.hyperparameters()
},
{
'params': model.gp_layer.variational_parameters()
},
{
'params': likelihood.parameters()
},
]
optimizer = torch.optim.Adam(params_list, lr=args.lr)
scheduler = scheduler_const([args.lr])
gp_switch = True
n_iter = 0
for epoch in range(args.epochs_init):
n_iter = train(model=model,
data_loader=train_data_loader,
loss_func=loss_func,
optimizer=optimizer,
scheduler=scheduler,
args=args,
n_iter=n_iter,
gp_switch=gp_switch,
likelihood=likelihood)
########## SWAG
if args.swag:
model_core = copy.deepcopy(model)
model = train_swag_pdts(model_core, train_data_loader, loss_func,
scaler, features_scaler, args, save_dir,
batch_no)
########## SGLD
if args.sgld:
model = train_sgld_pdts(model, train_data_loader, loss_func, scaler,
features_scaler, args, save_dir, batch_no)
### find top_idx
top_idx = [] # need for thom
sum_test_preds = np.zeros(
(len(test_orig), args.num_tasks)) # need for greedy
for sample in range(args.samples):
# draw model from SWAG posterior
if args.swag:
model.sample(scale=1.0, cov=args.cov_mat, block=args.block)
# retrieve sgld sample
if args.sgld:
model = load_checkpoint(
args.save_dir +
f'/model_{model_idx}/model_{batch_no}/model_{sample}.pt',
device=args.device,
logger=logger)
test_preds = predict(model=model,
data_loader=test_data_loader,
args=args,
scaler=scaler,
test_data=True,
gp_sample=args.thompson,
bbp_sample=True)
test_preds = np.array(test_preds)
# thompson bit
rank = 0
# base length
if args.sgld:
base_length = 5 * sample + 4
else:
base_length = sample
while args.thompson and (len(top_idx) <= base_length):
top_unique_molecule = np.argsort(-test_preds[:, 0])[rank]
rank += 1
if top_unique_molecule not in top_idx:
top_idx.append(top_unique_molecule)
# add to sum_test_preds
sum_test_preds += test_preds
# print
print('done sample ' + str(sample))
# final top_idx
if args.thompson:
top_idx = np.array(top_idx)
else:
sum_test_preds /= args.samples
top_idx = np.argsort(-sum_test_preds[:, 0])[:50]
### transfer from test to train
top_idx = -np.sort(-top_idx)
for idx in top_idx:
train_orig.append(test_orig.pop(idx))
train_data, test_data = copy.deepcopy(
MoleculeDataset(train_orig)), copy.deepcopy(MoleculeDataset(test_orig))
args.train_data_size = len(train_data)
if args.gp:
loss_func = gpytorch.mlls.VariationalELBO(
likelihood, model.gp_layer, num_data=args.train_data_size)
print(args.train_data_size)
### standardise features (train and test; using original features_scaler)
train_data.normalize_features(features_scaler)
test_data.normalize_features(features_scaler)
### standardise targets (train only; using original scaler)
train_targets = train_data.targets()
scaled_targets_tr = scaler.transform(train_targets).tolist()
train_data.set_targets(scaled_targets_tr)
### create data loaders
train_data_loader = MoleculeDataLoader(dataset=train_data,
batch_size=args.batch_size,
num_workers=num_workers,
cache=cache,
class_balance=args.class_balance,
shuffle=True,
seed=args.seed)
test_data_loader = MoleculeDataLoader(dataset=test_data,
batch_size=args.batch_size,
num_workers=num_workers,
cache=cache)
####################################################################
####################################################################
##################################
########## thompson sampling loop
##################################
for batch_no in range(1, args.pdts_batches + 1):
### fill in ptds_scores
SMILES_train = np.array(train_data.smiles())
SMILES_stack = np.hstack((SMILES, SMILES_train))
overlap = len(SMILES_stack) - len(np.unique(SMILES_stack))
prop = overlap / len(SMILES)
ptds_scores[batch_no] = prop
wandb.log({
"Proportion of top 1%": prop,
"batch_no": batch_no
},
commit=False)
### train posterior
n_iter = 0
for epoch in range(args.epochs):
n_iter = train(model=model,
data_loader=train_data_loader,
loss_func=loss_func,
optimizer=optimizer,
scheduler=scheduler,
args=args,
n_iter=n_iter,
gp_switch=gp_switch,
likelihood=likelihood,
bbp_switch=bbp_switch)
# save to save_dir
#if epoch == args.epochs - 1:
#save_checkpoint(os.path.join(save_dir, f'model_{batch_no}.pt'), model, scaler, features_scaler, args)
# if swag, load checkpoint
if args.swag:
model_core = load_checkpoint(
args.checkpoint_path +
f'/model_{model_idx}/model_{batch_no}.pt',
device=args.device,
logger=None)
########## SWAG
if args.swag:
model = train_swag_pdts(model_core, train_data_loader, loss_func,
scaler, features_scaler, args, save_dir,
batch_no)
########## SGLD
if args.sgld:
model = train_sgld_pdts(model, train_data_loader, loss_func,
scaler, features_scaler, args, save_dir,
batch_no)
### find top_idx
top_idx = [] # need for thom
sum_test_preds = np.zeros(
(len(test_orig), args.num_tasks)) # need for greedy
for sample in range(args.samples):
# draw model from SWAG posterior
if args.swag:
model.sample(scale=1.0, cov=args.cov_mat, block=args.block)
# retrieve sgld sample
if args.sgld:
model = load_checkpoint(
args.save_dir +
f'/model_{model_idx}/model_{batch_no}/model_{sample}.pt',
device=args.device,
logger=logger)
test_preds = predict(model=model,
data_loader=test_data_loader,
args=args,
scaler=scaler,
test_data=True,
gp_sample=args.thompson,
bbp_sample=True)
test_preds = np.array(test_preds)
# thompson bit
rank = 0
# base length
if args.sgld:
base_length = 5 * sample + 4
else:
base_length = sample
while args.thompson and (len(top_idx) <= base_length):
top_unique_molecule = np.argsort(-test_preds[:, 0])[rank]
rank += 1
if top_unique_molecule not in top_idx:
top_idx.append(top_unique_molecule)
# add to sum_test_preds
sum_test_preds += test_preds
# print
print('done sample ' + str(sample))
# final top_idx
if args.thompson:
top_idx = np.array(top_idx)
else:
sum_test_preds /= args.samples
top_idx = np.argsort(-sum_test_preds[:, 0])[:50]
### transfer from test to train
top_idx = -np.sort(-top_idx)
for idx in top_idx:
train_orig.append(test_orig.pop(idx))
train_data, test_data = copy.deepcopy(
MoleculeDataset(train_orig)), copy.deepcopy(
MoleculeDataset(test_orig))
args.train_data_size = len(train_data)
if args.gp:
loss_func = gpytorch.mlls.VariationalELBO(
likelihood, model.gp_layer, num_data=args.train_data_size)
print(args.train_data_size)
### standardise features (train and test; using original features_scaler)
train_data.normalize_features(features_scaler)
test_data.normalize_features(features_scaler)
### standardise targets (train only; using original scaler)
train_targets = train_data.targets()
scaled_targets_tr = scaler.transform(train_targets).tolist()
train_data.set_targets(scaled_targets_tr)
### create data loaders
train_data_loader = MoleculeDataLoader(
dataset=train_data,
batch_size=args.batch_size,
num_workers=num_workers,
cache=cache,
class_balance=args.class_balance,
shuffle=True,
seed=args.seed)
test_data_loader = MoleculeDataLoader(dataset=test_data,
batch_size=args.batch_size,
num_workers=num_workers,
cache=cache)
# save scores
np.savez(os.path.join(results_dir, f'ptds_{model_idx}'), ptds_scores)
def new_noise(args: TrainArgs, logger: Logger = None) -> List[float]:
"""
Trains a model and returns test scores on the model checkpoint with the highest validation score.
:param args: Arguments.
:param logger: Logger.
:return: A list of ensemble scores for each task.
"""
debug = info = print
# Get data
args.task_names = args.target_columns or get_task_names(args.data_path)
data = get_data(path=args.data_path, args=args, logger=logger)
args.num_tasks = data.num_tasks()
args.features_size = data.features_size()
# Split data
debug(f'Splitting data with seed {args.seed}')
train_data, val_data, test_data = split_data(data=data,
split_type=args.split_type,
sizes=args.split_sizes,
seed=args.seed,
args=args,
logger=logger)
if args.features_scaling:
features_scaler = train_data.normalize_features(replace_nan_token=0)
val_data.normalize_features(features_scaler)
test_data.normalize_features(features_scaler)
else:
features_scaler = None
args.train_data_size = len(train_data)
# Initialize scaler and scale training targets by subtracting mean and dividing standard deviation (regression only)
if args.dataset_type == 'regression':
debug('Fitting scaler')
train_smiles, train_targets = train_data.smiles(), train_data.targets()
scaler = StandardScaler().fit(train_targets)
scaled_targets = scaler.transform(train_targets).tolist()
train_data.set_targets(scaled_targets)
else:
scaler = None
# Get loss and metric functions
loss_func = neg_log_like
metric_func = get_metric_func(metric=args.metric)
# Set up test set evaluation
test_smiles, test_targets = test_data.smiles(), test_data.targets()
sum_test_preds = np.zeros((len(test_smiles), args.num_tasks))
# Automatically determine whether to cache
if len(data) <= args.cache_cutoff:
cache = True
num_workers = 0
else:
cache = False
num_workers = args.num_workers
# Create data loaders
train_data_loader = MoleculeDataLoader(dataset=train_data,
batch_size=args.batch_size,
num_workers=num_workers,
cache=cache)
val_data_loader = MoleculeDataLoader(dataset=val_data,
batch_size=args.batch_size,
num_workers=num_workers,
cache=cache)
test_data_loader = MoleculeDataLoader(dataset=test_data,
batch_size=args.batch_size,
num_workers=num_workers,
cache=cache)
###########################################
########## Outer loop over ensemble members
###########################################
for model_idx in range(args.ensemble_start_idx,
args.ensemble_start_idx + args.ensemble_size):
# load the model
if (args.method == 'map') or (args.method == 'swag') or (args.method
== 'sgld'):
model = load_checkpoint(args.checkpoint_path +
f'/model_{model_idx}/model.pt',
device=args.device,
logger=logger)
if args.method == 'gp':
args.num_inducing_points = 1200
fake_model = MoleculeModel(args)
fake_model.featurizer = True
feature_extractor = fake_model
inducing_points = initial_inducing_points(train_data_loader,
feature_extractor, args)
gp_layer = GPLayer(inducing_points, args.num_tasks)
model = load_checkpoint(
args.checkpoint_path + f'/model_{model_idx}/DKN_model.pt',
device=args.device,
logger=None,
template=DKLMoleculeModel(MoleculeModel(args, featurizer=True),
gp_layer))
if args.method == 'dropR' or args.method == 'dropA':
model = load_checkpoint(args.checkpoint_path +
f'/model_{model_idx}/model.pt',
device=args.device,
logger=logger)
if args.method == 'bbp':
template = MoleculeModelBBP(args)
for layer in template.children():
if isinstance(layer, BayesLinear):
layer.init_rho(args.rho_min_bbp, args.rho_max_bbp)
for layer in template.encoder.encoder.children():
if isinstance(layer, BayesLinear):
layer.init_rho(args.rho_min_bbp, args.rho_max_bbp)
model = load_checkpoint(args.checkpoint_path +
f'/model_{model_idx}/model_bbp.pt',
device=args.device,
logger=None,
template=template)
if args.method == 'dun':
args.prior_sig_dun = 0.05
args.depth_min = 1
args.depth_max = 5
args.rho_min_dun = -5.5
args.rho_max_dun = -5
args.log_cat_init = 0
template = MoleculeModelDUN(args)
for layer in template.children():
if isinstance(layer, BayesLinear):
layer.init_rho(args.rho_min_dun, args.rho_max_dun)
for layer in template.encoder.encoder.children():
if isinstance(layer, BayesLinear):
layer.init_rho(args.rho_min_dun, args.rho_max_dun)
template.create_log_cat(args)
model = load_checkpoint(args.checkpoint_path +
f'/model_{model_idx}/model_dun.pt',
device=args.device,
logger=None,
template=template)
# make results_dir
results_dir = os.path.join(args.results_dir, f'model_{model_idx}')
makedirs(results_dir)
# train_preds, train_targets
train_preds = predict(model=model,
data_loader=train_data_loader,
args=args,
scaler=scaler,
test_data=False,
bbp_sample=False)
train_preds = np.array(train_preds)
train_targets = np.array(train_targets)
# compute tstats
tstats = np.ones((12, 3))
for task in range(12):
resid = train_preds[:, task] - train_targets[:, task]
tstats[task] = np.array(stats.t.fit(resid, floc=0.0))
##################################
########## Inner loop over samples
##################################
for sample_idx in range(args.samples):
# save down
np.savez(os.path.join(results_dir, f'tstats_{sample_idx}'), tstats)
print('done one')
def train(model: MoleculeModel,
data_loader: MoleculeDataLoader,
loss_func: Callable,
optimizer: Optimizer,
scheduler: _LRScheduler,
args: TrainArgs,
n_iter: int = 0,
logger: logging.Logger = None,
writer: SummaryWriter = None) -> int:
"""
Trains a model for an epoch.
:param model: A :class:`~chemprop.models.model.MoleculeModel`.
:param data_loader: A :class:`~chemprop.data.data.MoleculeDataLoader`.
:param loss_func: Loss function.
:param optimizer: An optimizer.
:param scheduler: A learning rate scheduler.
:param args: A :class:`~chemprop.args.TrainArgs` object containing arguments for training the model.
:param n_iter: The number of iterations (training examples) trained on so far.
:param logger: A logger for recording output.
:param writer: A tensorboardX SummaryWriter.
:return: The total number of iterations (training examples) trained on so far.
"""
debug = logger.debug if logger is not None else print
model.train()
loss_sum = iter_count = 0
for batch in tqdm(data_loader, total=len(data_loader), leave=False):
# Prepare batch
batch: MoleculeDataset
mol_batch, features_batch, target_batch, mask_batch, atom_descriptors_batch, atom_features_batch, bond_features_batch, data_weights_batch = \
batch.batch_graph(), batch.features(), batch.targets(), batch.mask(), batch.atom_descriptors(), \
batch.atom_features(), batch.bond_features(), batch.data_weights()
mask = torch.tensor(mask_batch, dtype=torch.bool) # shape(batch, tasks)
targets = torch.tensor([[0 if x is None else x for x in tb] for tb in target_batch]) # shape(batch, tasks)
if args.target_weights is not None:
target_weights = torch.tensor(args.target_weights).unsqueeze(0) # shape(1,tasks)
else:
target_weights = torch.ones(targets.shape[1]).unsqueeze(0)
data_weights = torch.tensor(data_weights_batch).unsqueeze(1) # shape(batch,1)
if args.loss_function == 'bounded_mse':
lt_target_batch = batch.lt_targets() # shape(batch, tasks)
gt_target_batch = batch.gt_targets() # shape(batch, tasks)
lt_target_batch = torch.tensor(lt_target_batch)
gt_target_batch = torch.tensor(gt_target_batch)
# Run model
model.zero_grad()
preds = model(mol_batch, features_batch, atom_descriptors_batch, atom_features_batch, bond_features_batch)
# Move tensors to correct device
torch_device = preds.device
mask = mask.to(torch_device)
targets = targets.to(torch_device)
target_weights = target_weights.to(torch_device)
data_weights = data_weights.to(torch_device)
if args.loss_function == 'bounded_mse':
lt_target_batch = lt_target_batch.to(torch_device)
gt_target_batch = gt_target_batch.to(torch_device)
# Calculate losses
if args.loss_function == 'mcc' and args.dataset_type == 'classification':
loss = loss_func(preds, targets, data_weights, mask) *target_weights.squeeze(0)
elif args.loss_function == 'mcc': # multiclass dataset type
targets = targets.long()
target_losses = []
for target_index in range(preds.size(1)):
target_loss = loss_func(preds[:, target_index, :], targets[:, target_index], data_weights, mask[:, target_index]).unsqueeze(0)
target_losses.append(target_loss)
loss = torch.cat(target_losses).to(torch_device) * target_weights.squeeze(0)
elif args.dataset_type == 'multiclass':
targets = targets.long()
if args.loss_function == 'dirichlet':
loss = loss_func(preds, targets, args.evidential_regularization) * target_weights * data_weights * mask
else:
target_losses = []
for target_index in range(preds.size(1)):
target_loss = loss_func(preds[:, target_index, :], targets[:, target_index]).unsqueeze(1)
target_losses.append(target_loss)
loss = torch.cat(target_losses, dim=1).to(torch_device) * target_weights * data_weights * mask
elif args.dataset_type == 'spectra':
loss = loss_func(preds, targets, mask) * target_weights * data_weights * mask
elif args.loss_function == 'bounded_mse':
loss = loss_func(preds, targets, lt_target_batch, gt_target_batch) * target_weights * data_weights * mask
elif args.loss_function == 'evidential':
loss = loss_func(preds, targets, args.evidential_regularization) * target_weights * data_weights * mask
elif args.loss_function == 'dirichlet': # classification
loss = loss_func(preds, targets, args.evidential_regularization) * target_weights * data_weights * mask
else:
loss = loss_func(preds, targets) * target_weights * data_weights * mask
loss = loss.sum() / mask.sum()
loss_sum += loss.item()
iter_count += 1
loss.backward()
if args.grad_clip:
nn.utils.clip_grad_norm_(model.parameters(), args.grad_clip)
optimizer.step()
if isinstance(scheduler, NoamLR):
scheduler.step()
n_iter += len(batch)
# Log and/or add to tensorboard
if (n_iter // args.batch_size) % args.log_frequency == 0:
lrs = scheduler.get_lr()
pnorm = compute_pnorm(model)
gnorm = compute_gnorm(model)
loss_avg = loss_sum / iter_count
loss_sum = iter_count = 0
lrs_str = ', '.join(f'lr_{i} = {lr:.4e}' for i, lr in enumerate(lrs))
debug(f'Loss = {loss_avg:.4e}, PNorm = {pnorm:.4f}, GNorm = {gnorm:.4f}, {lrs_str}')
if writer is not None:
writer.add_scalar('train_loss', loss_avg, n_iter)
writer.add_scalar('param_norm', pnorm, n_iter)
writer.add_scalar('gradient_norm', gnorm, n_iter)
for i, lr in enumerate(lrs):
writer.add_scalar(f'learning_rate_{i}', lr, n_iter)
return n_iter
def train(model: MoleculeModel,
data_loader: MoleculeDataLoader,
loss_func: Callable,
optimizer: Optimizer,
scheduler: _LRScheduler,
args: TrainArgs,
n_iter: int = 0,
logger: logging.Logger = None,
writer: SummaryWriter = None) -> int:
"""
Trains a model for an epoch.
:param model: A :class:`~chemprop.models.model.MoleculeModel`.
:param data_loader: A :class:`~chemprop.data.data.MoleculeDataLoader`.
:param loss_func: Loss function.
:param optimizer: An optimizer.
:param scheduler: A learning rate scheduler.
:param args: A :class:`~chemprop.args.TrainArgs` object containing arguments for training the model.
:param n_iter: The number of iterations (training examples) trained on so far.
:param logger: A logger for recording output.
:param writer: A tensorboardX SummaryWriter.
:return: The total number of iterations (training examples) trained on so far.
"""
debug = logger.debug if logger is not None else print
model.train()
loss_sum, iter_count = 0, 0
for batch in tqdm(data_loader, total=len(data_loader)):
# Prepare batch
batch: MoleculeDataset
mol_batch, features_batch, target_batch = batch.batch_graph(
), batch.features(), batch.targets()
mask = torch.Tensor([[x is not None for x in tb]
for tb in target_batch])
targets = torch.Tensor([[0 if x is None else x for x in tb]
for tb in target_batch])
# Run model
model.zero_grad()
preds = model(mol_batch, features_batch)
# Move tensors to correct device
mask = mask.to(preds.device)
targets = targets.to(preds.device)
class_weights = torch.ones(targets.shape, device=preds.device)
if args.dataset_type == 'multiclass':
targets = targets.long()
loss = torch.cat([
loss_func(preds[:, target_index, :],
targets[:, target_index]).unsqueeze(1)
for target_index in range(preds.size(1))
],
dim=1) * class_weights * mask
else:
loss = loss_func(preds, targets) * class_weights * mask
loss = loss.sum() / mask.sum()
loss_sum += loss.item()
iter_count += len(batch)
loss.backward()
if args.grad_clip:
nn.utils.clip_grad_norm_(model.parameters(), args.grad_clip)
optimizer.step()
if isinstance(scheduler, NoamLR):
scheduler.step()
n_iter += len(batch)
# Log and/or add to tensorboard
if (n_iter // args.batch_size) % args.log_frequency == 0:
lrs = scheduler.get_lr()
pnorm = compute_pnorm(model)
gnorm = compute_gnorm(model)
loss_avg = loss_sum / iter_count
loss_sum, iter_count = 0, 0
lrs_str = ', '.join(f'lr_{i} = {lr:.4e}'
for i, lr in enumerate(lrs))
debug(
f'Loss = {loss_avg:.4e}, PNorm = {pnorm:.4f}, GNorm = {gnorm:.4f}, {lrs_str}'
)
if writer is not None:
writer.add_scalar('train_loss', loss_avg, n_iter)
writer.add_scalar('param_norm', pnorm, n_iter)
writer.add_scalar('gradient_norm', gnorm, n_iter)
for i, lr in enumerate(lrs):
writer.add_scalar(f'learning_rate_{i}', lr, n_iter)
return n_iter
def run_training(args: TrainArgs, logger: Logger = None) -> List[float]:
"""
Trains a model and returns test scores on the model checkpoint with the highest validation score.
:param args: Arguments.
:param logger: Logger.
:return: A list of ensemble scores for each task.
"""
debug = info = print
# Print command line and args
debug('Command line')
debug(f'python {" ".join(sys.argv)}')
debug('Args')
debug(args)
# Save args
args.save(os.path.join(args.save_dir, 'args.json'))
# Get data
debug('Loading data')
args.task_names = args.target_columns or get_task_names(args.data_path)
data = get_data(path=args.data_path, args=args, logger=logger)
args.num_tasks = data.num_tasks()
args.features_size = data.features_size()
debug(f'Number of tasks = {args.num_tasks}')
# Split data
debug(f'Splitting data with seed {args.seed}')
train_data, val_data, test_data = split_data(data=data,
split_type=args.split_type,
sizes=args.split_sizes,
seed=args.seed,
args=args,
logger=logger)
if args.features_scaling:
features_scaler = train_data.normalize_features(replace_nan_token=0)
val_data.normalize_features(features_scaler)
test_data.normalize_features(features_scaler)
else:
features_scaler = None
args.train_data_size = len(train_data)
debug(
f'Total size = {len(data):,} | '
f'train size = {len(train_data):,} | val size = {len(val_data):,} | test size = {len(test_data):,}'
)
# Initialize scaler and scale training targets by subtracting mean and dividing standard deviation (regression only)
if args.dataset_type == 'regression':
debug('Fitting scaler')
train_smiles, train_targets = train_data.smiles(), train_data.targets()
scaler = StandardScaler().fit(train_targets)
scaled_targets = scaler.transform(train_targets).tolist()
train_data.set_targets(scaled_targets)
else:
scaler = None
# Get loss and metric functions
loss_func = neg_log_like
metric_func = get_metric_func(metric=args.metric)
# Set up test set evaluation
test_smiles, test_targets = test_data.smiles(), test_data.targets()
sum_test_preds = np.zeros((len(test_smiles), args.num_tasks))
# Automatically determine whether to cache
if len(data) <= args.cache_cutoff:
cache = True
num_workers = 0
else:
cache = False
num_workers = args.num_workers
# Create data loaders
train_data_loader = MoleculeDataLoader(dataset=train_data,
batch_size=args.batch_size,
num_workers=num_workers,
cache=cache,
class_balance=args.class_balance,
shuffle=True,
seed=args.seed)
val_data_loader = MoleculeDataLoader(dataset=val_data,
batch_size=args.batch_size,
num_workers=num_workers,
cache=cache)
test_data_loader = MoleculeDataLoader(dataset=test_data,
batch_size=args.batch_size,
num_workers=num_workers,
cache=cache)
###########################################
########## Outer loop over ensemble members
###########################################
for model_idx in range(args.ensemble_start_idx,
args.ensemble_start_idx + args.ensemble_size):
# Set pytorch seed for random initial weights
torch.manual_seed(args.pytorch_seeds[model_idx])
######## set up all logging ########
# make save_dir
save_dir = os.path.join(args.save_dir, f'model_{model_idx}')
makedirs(save_dir)
# make results_dir
results_dir = os.path.join(args.results_dir, f'model_{model_idx}')
makedirs(results_dir)
# initialise wandb
os.environ['WANDB_MODE'] = 'dryrun'
wandb.init(name=args.wandb_name + '_' + str(model_idx),
project=args.wandb_proj,
reinit=True)
print('WANDB directory is:')
print(wandb.run.dir)
####################################
# Load/build model
if args.checkpoint_path is not None:
debug(f'Loading model {model_idx} from {args.checkpoint_path}')
model = load_checkpoint(args.checkpoint_path +
f'/model_{model_idx}/model.pt',
device=args.device,
logger=logger)
else:
debug(f'Building model {model_idx}')
model = MoleculeModel(args)
debug(model)
debug(f'Number of parameters = {param_count(model):,}')
if args.cuda:
debug('Moving model to cuda')
model = model.to(args.device)
# Ensure that model is saved in correct location for evaluation if 0 epochs
save_checkpoint(os.path.join(save_dir, 'model.pt'), model, scaler,
features_scaler, args)
# Optimizer
optimizer = Adam([{
'params': model.encoder.parameters()
}, {
'params': model.ffn.parameters()
}, {
'params': model.log_noise,
'weight_decay': 0
}],
lr=args.init_lr,
weight_decay=args.weight_decay)
# Learning rate scheduler
scheduler = build_lr_scheduler(optimizer, args)
# Run training
best_score = float('inf') if args.minimize_score else -float('inf')
best_epoch, n_iter = 0, 0
for epoch in range(args.epochs):
debug(f'Epoch {epoch}')
n_iter = train(model=model,
data_loader=train_data_loader,
loss_func=loss_func,
optimizer=optimizer,
scheduler=scheduler,
args=args,
n_iter=n_iter,
logger=logger)
val_scores = evaluate(model=model,
data_loader=val_data_loader,
args=args,
num_tasks=args.num_tasks,
metric_func=metric_func,
dataset_type=args.dataset_type,
scaler=scaler,
logger=logger)
# Average validation score
avg_val_score = np.nanmean(val_scores)
debug(f'Validation {args.metric} = {avg_val_score:.6f}')
wandb.log({"Validation MAE": avg_val_score})
# Save model checkpoint if improved validation score
if args.minimize_score and avg_val_score < best_score or \
not args.minimize_score and avg_val_score > best_score:
best_score, best_epoch = avg_val_score, epoch
save_checkpoint(os.path.join(save_dir, 'model.pt'), model,
scaler, features_scaler, args)
if epoch == args.noam_epochs - 1:
optimizer = Adam([{
'params': model.encoder.parameters()
}, {
'params': model.ffn.parameters()
}, {
'params': model.log_noise,
'weight_decay': 0
}],
lr=args.final_lr,
weight_decay=args.weight_decay)
scheduler = scheduler_const([args.final_lr])
# load model with best validation score
info(
f'Model {model_idx} best validation {args.metric} = {best_score:.6f} on epoch {best_epoch}'
)
model = load_checkpoint(os.path.join(save_dir, 'model.pt'),
device=args.device,
logger=logger)
# SWAG training loop, returns swag_model
if args.swag:
model = train_swag(model, train_data, val_data, num_workers, cache,
loss_func, metric_func, scaler, features_scaler,
args, save_dir)
# SGLD loop, which saves nets
if args.sgld:
model = train_sgld(model, train_data, val_data, num_workers, cache,
loss_func, metric_func, scaler, features_scaler,
args, save_dir)
# GP loop
if args.gp:
model, likelihood = train_gp(model, train_data, val_data,
num_workers, cache, metric_func,
scaler, features_scaler, args,
save_dir)
# BBP
if args.bbp:
model = train_bbp(model, train_data, val_data, num_workers, cache,
loss_func, metric_func, scaler, features_scaler,
args, save_dir)
# DUN
if args.dun:
model = train_dun(model, train_data, val_data, num_workers, cache,
loss_func, metric_func, scaler, features_scaler,
args, save_dir)
##################################
########## Inner loop over samples
##################################
for sample_idx in range(args.samples):
# draw model from SWAG posterior
if args.swag:
model.sample(scale=1.0, cov=args.cov_mat, block=args.block)
# draw model from collected SGLD models
if args.sgld:
model = load_checkpoint(os.path.join(save_dir,
f'model_{sample_idx}.pt'),
device=args.device,
logger=logger)
# make predictions
test_preds = predict(model=model,
data_loader=test_data_loader,
args=args,
scaler=scaler,
test_data=True,
bbp_sample=True)
#######################################################################
#######################################################################
##### SAVING STUFF DOWN
if args.gp:
# get test_preds_std (scaled back to original data)
test_preds_std = predict_std_gp(model=model,
data_loader=test_data_loader,
args=args,
scaler=scaler,
likelihood=likelihood)
# 1 - MEANS
np.savez(os.path.join(results_dir, f'preds_{sample_idx}'),
np.array(test_preds))
# 2 - STD, combined aleatoric and epistemic (we save down the stds, always)
np.savez(os.path.join(results_dir, f'predsSTDEV_{sample_idx}'),
np.array(test_preds_std))
else:
# save test_preds and aleatoric uncertainties
if args.dun:
log_cat = model.log_cat.detach().cpu().numpy()
cat = np.exp(log_cat) / np.sum(np.exp(log_cat))
np.savez(os.path.join(results_dir, f'cat_{sample_idx}'),
cat)
# samples from categorical dist and saves a depth MC sample
depth_sample = np.random.multinomial(1,
cat).nonzero()[0][0]
test_preds_MCdepth = predict_MCdepth(
model=model,
data_loader=test_data_loader,
args=args,
scaler=scaler,
d=depth_sample)
np.savez(
os.path.join(results_dir,
f'predsMCDEPTH_{sample_idx}'),
np.array(test_preds_MCdepth))
if args.swag:
log_noise = model.base.log_noise
else:
log_noise = model.log_noise
noise = np.exp(log_noise.detach().cpu().numpy()) * np.array(
scaler.stds)
np.savez(os.path.join(results_dir, f'preds_{sample_idx}'),
np.array(test_preds))
np.savez(os.path.join(results_dir, f'noise_{sample_idx}'),
noise)
#######################################################################
#######################################################################
# add predictions to sum_test_preds
if len(test_preds) != 0:
sum_test_preds += np.array(test_preds)
# evaluate predictions using metric function
test_scores = evaluate_predictions(preds=test_preds,
targets=test_targets,
num_tasks=args.num_tasks,
metric_func=metric_func,
dataset_type=args.dataset_type,
logger=logger)
# compute average test score
avg_test_score = np.nanmean(test_scores)
info(
f'Model {model_idx}, sample {sample_idx} test {args.metric} = {avg_test_score:.6f}'
)
#################################
########## Bayesian Model Average
#################################
# note: this is an average over Bayesian samples AND components in an ensemble
# compute number of prediction iterations
pred_iterations = args.ensemble_size * args.samples
# average predictions across iterations
avg_test_preds = (sum_test_preds / pred_iterations).tolist()
# evaluate
BMA_scores = evaluate_predictions(preds=avg_test_preds,
targets=test_targets,
num_tasks=args.num_tasks,
metric_func=metric_func,
dataset_type=args.dataset_type,
logger=logger)
# average scores across tasks
avg_BMA_test_score = np.nanmean(BMA_scores)
info(f'BMA test {args.metric} = {avg_BMA_test_score:.6f}')
return BMA_scores
def run_training_gnn_xgb(args: TrainArgs,
logger: Logger = None) -> List[float]:
"""
Trains a model and returns test scores on the model checkpoint with the highest validation score.
:param args: Arguments.
:param logger: Logger.
:return: A list of ensemble scores for each task.
"""
if logger is not None:
debug, info = logger.debug, logger.info
else:
debug = info = print
# Print command line
debug('Command line')
debug(f'python {" ".join(sys.argv)}')
# Print args
debug('Args')
debug(args)
# Save args
args.save(os.path.join(args.save_dir, 'args.json'))
# Set pytorch seed for random initial weights
torch.manual_seed(args.pytorch_seed)
# Get data
debug('Loading data')
args.task_names = args.target_columns or get_task_names(args.data_path)
data = get_data(path=args.data_path, args=args, logger=logger)
args.num_tasks = data.num_tasks()
args.features_size = data.features_size()
debug(f'Number of tasks = {args.num_tasks}')
# Split data
debug(f'Splitting data with seed {args.seed}')
if args.separate_test_path:
test_data = get_data(path=args.separate_test_path,
args=args,
features_path=args.separate_test_features_path,
logger=logger)
if args.separate_val_path:
val_data = get_data(path=args.separate_val_path,
args=args,
features_path=args.separate_val_features_path,
logger=logger)
if args.separate_val_path and args.separate_test_path:
train_data = data
elif args.separate_val_path:
train_data, _, test_data = split_data(data=data,
split_type=args.split_type,
sizes=(0.8, 0.0, 0.2),
seed=args.seed,
args=args,
logger=logger)
elif args.separate_test_path:
train_data, val_data, _ = split_data(data=data,
split_type=args.split_type,
sizes=(0.8, 0.2, 0.0),
seed=args.seed,
args=args,
logger=logger)
else:
train_data, val_data, test_data = split_data(
data=data,
split_type=args.split_type,
sizes=args.split_sizes,
seed=args.seed,
args=args,
logger=logger)
if args.dataset_type == 'classification':
class_sizes = get_class_sizes(data)
debug('Class sizes')
for i, task_class_sizes in enumerate(class_sizes):
debug(
f'{args.task_names[i]} '
f'{", ".join(f"{cls}: {size * 100:.2f}%" for cls, size in enumerate(task_class_sizes))}'
)
if args.save_smiles_splits:
save_smiles_splits(train_data=train_data,
val_data=val_data,
test_data=test_data,
data_path=args.data_path,
save_dir=args.save_dir)
if args.features_scaling:
features_scaler = train_data.normalize_features(replace_nan_token=0)
val_data.normalize_features(features_scaler)
test_data.normalize_features(features_scaler)
else:
features_scaler = None
args.train_data_size = len(train_data)
debug(
f'Total size = {len(data):,} | '
f'train size = {len(train_data):,} | val size = {len(val_data):,} | test size = {len(test_data):,}'
)
# Initialize scaler and scale training targets by subtracting mean and dividing standard deviation (regression only)
if args.dataset_type == 'regression':
debug('Fitting scaler')
train_smiles, train_targets = train_data.smiles(), train_data.targets()
scaler = StandardScaler().fit(train_targets)
scaled_targets = scaler.transform(train_targets).tolist()
train_data.set_targets(scaled_targets)
else:
scaler = None
# Get loss and metric functions
loss_func = get_loss_func(args)
metric_func = get_metric_func(metric=args.metric)
# Set up test set evaluation
val_smiles, val_targets = val_data.smiles(), val_data.targets()
test_smiles, test_targets = test_data.smiles(), test_data.targets()
if args.dataset_type == 'multiclass':
sum_test_preds = np.zeros(
(len(test_smiles), args.num_tasks, args.multiclass_num_classes))
else:
sum_test_preds = np.zeros((len(test_smiles), args.num_tasks))
# Automatically determine whether to cache
if len(data) <= args.cache_cutoff:
cache = True
num_workers = 0
else:
cache = False
num_workers = args.num_workers
# Create data loaders
train_data_loader = MoleculeDataLoader(dataset=train_data,
batch_size=args.batch_size,
num_workers=num_workers,
cache=cache,
class_balance=args.class_balance,
shuffle=True,
seed=args.seed)
val_data_loader = MoleculeDataLoader(dataset=val_data,
batch_size=args.batch_size,
num_workers=num_workers,
cache=cache)
test_data_loader = MoleculeDataLoader(dataset=test_data,
batch_size=args.batch_size,
num_workers=num_workers,
cache=cache)
# Train ensemble of models
for model_idx in range(args.ensemble_size):
# Tensorboard writer
save_dir = os.path.join(args.save_dir, f'model_{model_idx}')
makedirs(save_dir)
try:
writer = SummaryWriter(log_dir=save_dir)
except:
writer = SummaryWriter(logdir=save_dir)
# Load/build model
if args.checkpoint_paths is not None:
debug(
f'Loading model {model_idx} from {args.checkpoint_paths[model_idx]}'
)
model = load_checkpoint(args.checkpoint_paths[model_idx],
logger=logger)
else:
debug(f'Building model {model_idx}')
model = MoleculeModel(args)
debug(model)
debug(f'Number of parameters = {param_count(model):,}')
if args.cuda:
debug('Moving model to cuda')
model = model.to(args.device)
# Ensure that model is saved in correct location for evaluation if 0 epochs
save_checkpoint(os.path.join(save_dir, 'model.pt'), model, scaler,
features_scaler, args)
# Optimizers
optimizer = build_optimizer(model, args)
# Learning rate schedulers
scheduler = build_lr_scheduler(optimizer, args)
# Run training
best_score = float('inf') if args.minimize_score else -float('inf')
best_epoch, n_iter = 0, 0
for epoch in trange(args.epochs):
debug(f'Epoch {epoch}')
n_iter = train(model=model,
data_loader=train_data_loader,
loss_func=loss_func,
optimizer=optimizer,
scheduler=scheduler,
args=args,
n_iter=n_iter,
logger=logger,
writer=writer)
if isinstance(scheduler, ExponentialLR):
scheduler.step()
val_scores = evaluate(model=model,
data_loader=val_data_loader,
num_tasks=args.num_tasks,
metric_func=metric_func,
dataset_type=args.dataset_type,
scaler=scaler,
logger=logger)
# Average validation score
avg_val_score = np.nanmean(val_scores)
debug(f'Validation {args.metric} = {avg_val_score:.6f}')
writer.add_scalar(f'validation_{args.metric}', avg_val_score,
n_iter)
if args.show_individual_scores:
# Individual validation scores
for task_name, val_score in zip(args.task_names, val_scores):
debug(
f'Validation {task_name} {args.metric} = {val_score:.6f}'
)
writer.add_scalar(f'validation_{task_name}_{args.metric}',
val_score, n_iter)
# Save model checkpoint if improved validation score
if args.minimize_score and avg_val_score < best_score or \
not args.minimize_score and avg_val_score > best_score:
best_score, best_epoch = avg_val_score, epoch
save_checkpoint(os.path.join(save_dir, 'model.pt'), model,
scaler, features_scaler, args)
# Evaluate on test set using model with best validation score
info(
f'Model {model_idx} best validation {args.metric} = {best_score:.6f} on epoch {best_epoch}'
)
model = load_checkpoint(os.path.join(save_dir, 'model.pt'),
device=args.device,
logger=logger)
test_preds, _ = predict(model=model,
data_loader=test_data_loader,
scaler=scaler)
test_scores = evaluate_predictions(preds=test_preds,
targets=test_targets,
num_tasks=args.num_tasks,
metric_func=metric_func,
dataset_type=args.dataset_type,
logger=logger)
if len(test_preds) != 0:
sum_test_preds += np.array(test_preds)
# Average test score
avg_test_score = np.nanmean(test_scores)
info(f'Model {model_idx} test {args.metric} = {avg_test_score:.6f}')
writer.add_scalar(f'test_{args.metric}', avg_test_score, 0)
if args.show_individual_scores:
# Individual test scores
for task_name, test_score in zip(args.task_names, test_scores):
info(
f'Model {model_idx} test {task_name} {args.metric} = {test_score:.6f}'
)
writer.add_scalar(f'test_{task_name}_{args.metric}',
test_score, n_iter)
writer.close()
# Evaluate ensemble on test set
avg_test_preds = (sum_test_preds / args.ensemble_size).tolist()
ensemble_scores = evaluate_predictions(preds=avg_test_preds,
targets=test_targets,
num_tasks=args.num_tasks,
metric_func=metric_func,
dataset_type=args.dataset_type,
logger=logger)
# Average ensemble score
avg_ensemble_test_score = np.nanmean(ensemble_scores)
info(f'Ensemble test {args.metric} = {avg_ensemble_test_score:.6f}')
# Individual ensemble scores
if args.show_individual_scores:
for task_name, ensemble_score in zip(args.task_names, ensemble_scores):
info(
f'Ensemble test {task_name} {args.metric} = {ensemble_score:.6f}'
)
_, train_feature = predict(model=model,
data_loader=train_data_loader,
scaler=scaler)
_, val_feature = predict(model=model,
data_loader=val_data_loader,
scaler=scaler)
_, test_feature = predict(model=model,
data_loader=test_data_loader,
scaler=scaler)
return ensemble_scores, train_feature, val_feature, test_feature, train_targets, val_targets, test_targets
def run_training(args: TrainArgs,
data: MoleculeDataset,
logger: Logger = None) -> Dict[str, List[float]]:
"""
Loads data, trains a Chemprop model, and returns test scores for the model checkpoint with the highest validation score.
:param args: A :class:`~chemprop.args.TrainArgs` object containing arguments for
loading data and training the Chemprop model.
:param data: A :class:`~chemprop.data.MoleculeDataset` containing the data.
:param logger: A logger to record output.
:return: A dictionary mapping each metric in :code:`args.metrics` to a list of values for each task.
"""
if logger is not None:
debug, info = logger.debug, logger.info
else:
debug = info = print
# Set pytorch seed for random initial weights
torch.manual_seed(args.pytorch_seed)
# Split data
debug(f"Splitting data with seed {args.seed}")
# if args.separate_test_path:
# test_data = get_data(
# path=args.separate_test_path,
# args=args,
# features_path=args.separate_test_features_path,
# atom_descriptors_path=args.separate_test_atom_descriptors_path,
# bond_features_path=args.separate_test_bond_features_path,
# smiles_columns=args.smiles_columns,
# logger=logger,
# )
# if args.separate_val_path:
# val_data = get_data(
# path=args.separate_val_path,
# args=args,
# features_path=args.separate_val_features_path,
# atom_descriptors_path=args.separate_val_atom_descriptors_path,
# bond_features_path=args.separate_val_bond_features_path,
# smiles_columns=args.smiles_columns,
# logger=logger,
# )
# if args.separate_val_path and args.separate_test_path:
# train_data = data
# elif args.separate_val_path:
# train_data, _, test_data = split_data(
# data=data,
# split_type=args.split_type,
# sizes=(0.8, 0.0, 0.2),
# seed=args.seed,
# num_folds=args.num_folds,
# args=args,
# logger=logger,
# )
# elif args.separate_test_path:
# train_data, val_data, _ = split_data(
# data=data,
# split_type=args.split_type,
# sizes=(0.8, 0.2, 0.0),
# seed=args.seed,
# num_folds=args.num_folds,
# args=args,
# logger=logger,
# )
# else: # Default
train_data, val_data, test_data = split_data(
data=data,
split_type=args.split_type,
sizes=args.split_sizes,
seed=args.seed,
num_folds=args.num_folds,
args=args,
logger=logger,
)
if args.dataset_type == "classification":
class_sizes = get_class_sizes(data)
debug("Class sizes")
for i, task_class_sizes in enumerate(class_sizes):
debug(
f"{args.task_names[i]} "
f'{", ".join(f"{cls}: {size * 100:.2f}%" for cls, size in enumerate(task_class_sizes))}'
)
if args.save_smiles_splits:
save_smiles_splits(
data_path=args.data_path,
save_dir=args.save_dir,
task_names=args.task_names,
features_path=args.features_path,
train_data=train_data,
val_data=val_data,
test_data=test_data,
smiles_columns=args.smiles_columns,
)
if args.features_scaling:
features_scaler = train_data.normalize_features(replace_nan_token=0)
val_data.normalize_features(features_scaler)
test_data.normalize_features(features_scaler)
else:
features_scaler = None
if args.atom_descriptor_scaling and args.atom_descriptors is not None:
atom_descriptor_scaler = train_data.normalize_features(
replace_nan_token=0, scale_atom_descriptors=True)
val_data.normalize_features(atom_descriptor_scaler,
scale_atom_descriptors=True)
test_data.normalize_features(atom_descriptor_scaler,
scale_atom_descriptors=True)
else:
atom_descriptor_scaler = None
if args.bond_feature_scaling and args.bond_features_size > 0:
bond_feature_scaler = train_data.normalize_features(
replace_nan_token=0, scale_bond_features=True)
val_data.normalize_features(bond_feature_scaler,
scale_bond_features=True)
test_data.normalize_features(bond_feature_scaler,
scale_bond_features=True)
else:
bond_feature_scaler = None
args.train_data_size = len(train_data)
debug(
f"Total size = {len(data):,} | "
f"train size = {len(train_data):,} | val size = {len(val_data):,} | test size = {len(test_data):,}"
)
# Initialize scaler and scale training targets by subtracting mean and dividing standard deviation (regression only)
if args.dataset_type == "regression":
debug("Fitting scaler")
scaler = train_data.normalize_targets()
else:
scaler = None
# Get loss function
loss_func = get_loss_func(args)
# Set up test set evaluation
test_smiles, test_targets = test_data.smiles(), test_data.targets()
if args.dataset_type == "multiclass":
sum_test_preds = np.zeros(
(len(test_smiles), args.num_tasks, args.multiclass_num_classes))
else:
sum_test_preds = np.zeros((len(test_smiles), args.num_tasks))
# Automatically determine whether to cache
if len(data) <= args.cache_cutoff:
set_cache_graph(True)
num_workers = 0
else:
set_cache_graph(False)
num_workers = args.num_workers
# Create data loaders
train_data_loader = MoleculeDataLoader(
dataset=train_data,
batch_size=args.batch_size,
num_workers=num_workers,
class_balance=args.class_balance,
shuffle=True,
seed=args.seed,
)
val_data_loader = MoleculeDataLoader(dataset=val_data,
batch_size=args.batch_size,
num_workers=num_workers)
test_data_loader = MoleculeDataLoader(dataset=test_data,
batch_size=args.batch_size,
num_workers=num_workers)
if args.class_balance:
debug(
f"With class_balance, effective train size = {train_data_loader.iter_size:,}"
)
# Train ensemble of models
for model_idx in range(args.ensemble_size):
# Tensorboard writer
save_dir = os.path.join(args.save_dir, f"model_{model_idx}")
makedirs(save_dir)
try:
writer = SummaryWriter(log_dir=save_dir)
except:
writer = SummaryWriter(logdir=save_dir)
# Load/build model
if args.checkpoint_paths is not None:
debug(
f"Loading model {model_idx} from {args.checkpoint_paths[model_idx]}"
)
model = load_checkpoint(args.checkpoint_paths[model_idx],
logger=logger)
else:
debug(f"Building model {model_idx}")
model = MoleculeModel(args)
debug(model)
debug(f"Number of parameters = {param_count(model):,}")
if args.cuda:
debug("Moving model to cuda")
model = model.to(args.device)
# Ensure that model is saved in correct location for evaluation if 0 epochs
save_checkpoint(
os.path.join(save_dir, MODEL_FILE_NAME),
model,
scaler,
features_scaler,
atom_descriptor_scaler,
bond_feature_scaler,
args,
)
# Optimizers
optimizer = build_optimizer(model, args)
# Learning rate schedulers
scheduler = build_lr_scheduler(optimizer, args)
# Run training
best_score = float("inf") if args.minimize_score else -float("inf")
best_epoch, n_iter = 0, 0
for epoch in trange(args.epochs):
debug(f"Epoch {epoch}")
n_iter = train(
model=model,
data_loader=train_data_loader,
loss_func=loss_func,
optimizer=optimizer,
scheduler=scheduler,
args=args,
n_iter=n_iter,
logger=logger,
writer=writer,
)
if isinstance(scheduler, ExponentialLR):
scheduler.step()
val_scores = evaluate(
model=model,
data_loader=val_data_loader,
num_tasks=args.num_tasks,
metrics=args.metrics,
dataset_type=args.dataset_type,
scaler=scaler,
logger=logger,
)
for metric, scores in val_scores.items():
# Average validation score
avg_val_score = np.nanmean(scores)
debug(f"Validation {metric} = {avg_val_score:.6f}")
writer.add_scalar(f"validation_{metric}", avg_val_score,
n_iter)
if args.show_individual_scores:
# Individual validation scores
for task_name, val_score in zip(args.task_names, scores):
debug(
f"Validation {task_name} {metric} = {val_score:.6f}"
)
writer.add_scalar(f"validation_{task_name}_{metric}",
val_score, n_iter)
# Save model checkpoint if improved validation score
avg_val_score = np.nanmean(val_scores[args.metric])
if (args.minimize_score and avg_val_score < best_score
or not args.minimize_score and avg_val_score > best_score):
best_score, best_epoch = avg_val_score, epoch
save_checkpoint(
os.path.join(save_dir, MODEL_FILE_NAME),
model,
scaler,
features_scaler,
atom_descriptor_scaler,
bond_feature_scaler,
args,
)
# Evaluate on test set using model with best validation score
info(
f"Model {model_idx} best validation {args.metric} = {best_score:.6f} on epoch {best_epoch}"
)
model = load_checkpoint(os.path.join(save_dir, MODEL_FILE_NAME),
device=args.device,
logger=logger)
test_preds = predict(model=model,
data_loader=test_data_loader,
scaler=scaler)
test_scores = evaluate_predictions(
preds=test_preds,
targets=test_targets,
num_tasks=args.num_tasks,
metrics=args.metrics,
dataset_type=args.dataset_type,
logger=logger,
)
if len(test_preds) != 0:
sum_test_preds += np.array(test_preds)
# Average test score
for metric, scores in test_scores.items():
avg_test_score = np.nanmean(scores)
info(f"Model {model_idx} test {metric} = {avg_test_score:.6f}")
writer.add_scalar(f"test_{metric}", avg_test_score, 0)
if args.show_individual_scores:
# Individual test scores
for task_name, test_score in zip(args.task_names, scores):
info(
f"Model {model_idx} test {task_name} {metric} = {test_score:.6f}"
)
writer.add_scalar(f"test_{task_name}_{metric}", test_score,
n_iter)
writer.close()
# Evaluate ensemble on test set
avg_test_preds = (sum_test_preds / args.ensemble_size).tolist()
ensemble_scores = evaluate_predictions(
preds=avg_test_preds,
targets=test_targets,
num_tasks=args.num_tasks,
metrics=args.metrics,
dataset_type=args.dataset_type,
logger=logger,
)
for metric, scores in ensemble_scores.items():
# Average ensemble score
avg_ensemble_test_score = np.nanmean(scores)
info(f"Ensemble test {metric} = {avg_ensemble_test_score:.6f}")
# Individual ensemble scores
if args.show_individual_scores:
for task_name, ensemble_score in zip(args.task_names, scores):
info(
f"Ensemble test {task_name} {metric} = {ensemble_score:.6f}"
)
# Optionally save test preds
if args.save_preds:
test_preds_dataframe = pd.DataFrame(
data={"smiles": test_data.smiles()})
for i, task_name in enumerate(args.task_names):
test_preds_dataframe[task_name] = [
pred[i] for pred in avg_test_preds
]
test_preds_dataframe.to_csv(os.path.join(args.save_dir,
"test_preds.csv"),
index=False)
return ensemble_scores
