def run_training(args: TrainArgs, logger: Logger = None) -> 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 logger: A logger to record output.
:return: A list of model 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')
data = get_data(path=args.data_path, args=args, logger=logger)
validate_dataset_type(data, dataset_type=args.dataset_type)
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,
smiles_column=args.smiles_column)
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
# 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}'
)
return ensemble_scores
def cross_validate(
args: TrainArgs, train_func: Callable[[TrainArgs, MoleculeDataset, Logger],
Dict[str, List[float]]]
) -> Tuple[float, float]:
"""
Runs k-fold cross-validation.
For each of k splits (folds) of the data, trains and tests a model on that split
and aggregates the performance across folds.
:param args: A :class:`~chemprop.args.TrainArgs` object containing arguments for
loading data and training the Chemprop model.
:param train_func: Function which runs training.
:return: A tuple containing the mean and standard deviation performance across folds.
"""
logger = create_logger(name=TRAIN_LOGGER_NAME,
save_dir=args.save_dir,
quiet=args.quiet)
if logger is not None:
debug, info = logger.debug, logger.info
else:
debug = info = print
# Initialize relevant variables
init_seed = args.seed
save_dir = args.save_dir
args.task_names = get_task_names(path=args.data_path,
smiles_column=args.smiles_column,
target_columns=args.target_columns,
ignore_columns=args.ignore_columns)
# 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'))
# Get data
debug('Loading data')
data = get_data(path=args.data_path,
args=args,
logger=logger,
skip_none_targets=True)
validate_dataset_type(data, dataset_type=args.dataset_type)
args.features_size = data.features_size()
debug(f'Number of tasks = {args.num_tasks}')
# Run training on different random seeds for each fold
all_scores = defaultdict(list)
for fold_num in range(args.num_folds):
info(f'Fold {fold_num}')
args.seed = init_seed + fold_num
args.save_dir = os.path.join(save_dir, f'fold_{fold_num}')
makedirs(args.save_dir)
model_scores = train_func(
args, deepcopy(data),
logger) # deepcopy since data may be modified
for metric, scores in model_scores.items():
all_scores[metric].append(scores)
all_scores = dict(all_scores)
# Convert scores to numpy arrays
for metric, scores in all_scores.items():
all_scores[metric] = np.array(scores)
# Report results
info(f'{args.num_folds}-fold cross validation')
# Report scores for each fold
for fold_num in range(args.num_folds):
for metric, scores in all_scores.items():
info(
f'\tSeed {init_seed + fold_num} ==> test {metric} = {np.nanmean(scores[fold_num]):.6f}'
)
if args.show_individual_scores:
for task_name, score in zip(args.task_names, scores[fold_num]):
info(
f'\t\tSeed {init_seed + fold_num} ==> test {task_name} {metric} = {score:.6f}'
)
# Report scores across folds
for metric, scores in all_scores.items():
avg_scores = np.nanmean(
scores, axis=1) # average score for each model across tasks
mean_score, std_score = np.nanmean(avg_scores), np.nanstd(avg_scores)
info(f'Overall test {metric} = {mean_score:.6f} +/- {std_score:.6f}')
if args.show_individual_scores:
for task_num, task_name in enumerate(args.task_names):
info(
f'\tOverall test {task_name} {metric} = '
f'{np.nanmean(scores[:, task_num]):.6f} +/- {np.nanstd(scores[:, task_num]):.6f}'
)
# Save scores
with open(os.path.join(save_dir, TEST_SCORES_FILE_NAME), 'w') as f:
writer = csv.writer(f)
header = ['Task']
for metric in args.metrics:
header += [f'Mean {metric}', f'Standard deviation {metric}'] + \
[f'Fold {i} {metric}' for i in range(args.num_folds)]
writer.writerow(header)
for task_num, task_name in enumerate(args.task_names):
row = [task_name]
for metric, scores in all_scores.items():
task_scores = scores[:, task_num]
mean, std = np.nanmean(task_scores), np.nanstd(task_scores)
row += [mean, std] + task_scores.tolist()
writer.writerow(row)
# Determine mean and std score of main metric
avg_scores = np.nanmean(all_scores[args.metric], axis=1)
mean_score, std_score = np.nanmean(avg_scores), np.nanstd(avg_scores)
# Optionally merge and save test preds
if args.save_preds:
all_preds = pd.concat([
pd.read_csv(
os.path.join(save_dir, f'fold_{fold_num}', 'test_preds.csv'))
for fold_num in range(args.num_folds)
])
all_preds.to_csv(os.path.join(save_dir, 'test_preds.csv'), index=False)
return mean_score, std_score
def cross_validate(args: TrainArgs,
train_func: Callable[[TrainArgs, MoleculeDataset, Logger], Dict[str, List[float]]]
) -> Tuple[float, float]:
"""
Runs k-fold cross-validation.
For each of k splits (folds) of the data, trains and tests a model on that split
and aggregates the performance across folds.
:param args: A :class:`~chemprop.args.TrainArgs` object containing arguments for
loading data and training the Chemprop model.
:param train_func: Function which runs training.
:return: A tuple containing the mean and standard deviation performance across folds.
"""
logger = create_logger(name=TRAIN_LOGGER_NAME, save_dir=args.save_dir, quiet=args.quiet)
if logger is not None:
debug, info = logger.debug, logger.info
else:
debug = info = print
# Initialize relevant variables
init_seed = args.seed
save_dir = args.save_dir
args.task_names = get_task_names(path=args.data_path, smiles_columns=args.smiles_columns,
target_columns=args.target_columns, ignore_columns=args.ignore_columns)
# Print command line
debug('Command line')
debug(f'python {" ".join(sys.argv)}')
# Print args
debug('Args')
debug(args)
# Save args
makedirs(args.save_dir)
try:
args.save(os.path.join(args.save_dir, 'args.json'))
except subprocess.CalledProcessError:
debug('Could not write the reproducibility section of the arguments to file, thus omitting this section.')
args.save(os.path.join(args.save_dir, 'args.json'), with_reproducibility=False)
# set explicit H option and reaction option
reset_featurization_parameters(logger=logger)
set_explicit_h(args.explicit_h)
set_adding_hs(args.adding_h)
if args.reaction:
set_reaction(args.reaction, args.reaction_mode)
elif args.reaction_solvent:
set_reaction(True, args.reaction_mode)
# Get data
debug('Loading data')
data = get_data(
path=args.data_path,
args=args,
logger=logger,
skip_none_targets=True,
data_weights_path=args.data_weights_path
)
validate_dataset_type(data, dataset_type=args.dataset_type)
args.features_size = data.features_size()
if args.atom_descriptors == 'descriptor':
args.atom_descriptors_size = data.atom_descriptors_size()
args.ffn_hidden_size += args.atom_descriptors_size
elif args.atom_descriptors == 'feature':
args.atom_features_size = data.atom_features_size()
set_extra_atom_fdim(args.atom_features_size)
if args.bond_features_path is not None:
args.bond_features_size = data.bond_features_size()
set_extra_bond_fdim(args.bond_features_size)
debug(f'Number of tasks = {args.num_tasks}')
if args.target_weights is not None and len(args.target_weights) != args.num_tasks:
raise ValueError('The number of provided target weights must match the number and order of the prediction tasks')
# Run training on different random seeds for each fold
all_scores = defaultdict(list)
for fold_num in range(args.num_folds):
info(f'Fold {fold_num}')
args.seed = init_seed + fold_num
args.save_dir = os.path.join(save_dir, f'fold_{fold_num}')
makedirs(args.save_dir)
data.reset_features_and_targets()
# If resuming experiment, load results from trained models
test_scores_path = os.path.join(args.save_dir, 'test_scores.json')
if args.resume_experiment and os.path.exists(test_scores_path):
print('Loading scores')
with open(test_scores_path) as f:
model_scores = json.load(f)
# Otherwise, train the models
else:
model_scores = train_func(args, data, logger)
for metric, scores in model_scores.items():
all_scores[metric].append(scores)
all_scores = dict(all_scores)
# Convert scores to numpy arrays
for metric, scores in all_scores.items():
all_scores[metric] = np.array(scores)
# Report results
info(f'{args.num_folds}-fold cross validation')
# Report scores for each fold
contains_nan_scores = False
for fold_num in range(args.num_folds):
for metric, scores in all_scores.items():
info(f'\tSeed {init_seed + fold_num} ==> test {metric} = {multitask_mean(scores[fold_num], metric):.6f}')
if args.show_individual_scores:
for task_name, score in zip(args.task_names, scores[fold_num]):
info(f'\t\tSeed {init_seed + fold_num} ==> test {task_name} {metric} = {score:.6f}')
if np.isnan(score):
contains_nan_scores = True
# Report scores across folds
for metric, scores in all_scores.items():
avg_scores = multitask_mean(scores, axis=1, metric=metric) # average score for each model across tasks
mean_score, std_score = np.mean(avg_scores), np.std(avg_scores)
info(f'Overall test {metric} = {mean_score:.6f} +/- {std_score:.6f}')
if args.show_individual_scores:
for task_num, task_name in enumerate(args.task_names):
info(f'\tOverall test {task_name} {metric} = '
f'{np.mean(scores[:, task_num]):.6f} +/- {np.std(scores[:, task_num]):.6f}')
if contains_nan_scores:
info("The metric scores observed for some fold test splits contain 'nan' values. \
This can occur when the test set does not meet the requirements \
for a particular metric, such as having no valid instances of one \
task in the test set or not having positive examples for some classification metrics. \
Before v1.5.1, the default behavior was to ignore nan values in individual folds or tasks \
and still return an overall average for the remaining folds or tasks. The behavior now \
is to include them in the average, converting overall average metrics to 'nan' as well.")
# Save scores
with open(os.path.join(save_dir, TEST_SCORES_FILE_NAME), 'w') as f:
writer = csv.writer(f)
header = ['Task']
for metric in args.metrics:
header += [f'Mean {metric}', f'Standard deviation {metric}'] + \
[f'Fold {i} {metric}' for i in range(args.num_folds)]
writer.writerow(header)
if args.dataset_type == 'spectra': # spectra data type has only one score to report
row = ['spectra']
for metric, scores in all_scores.items():
task_scores = scores[:,0]
mean, std = np.mean(task_scores), np.std(task_scores)
row += [mean, std] + task_scores.tolist()
writer.writerow(row)
else: # all other data types, separate scores by task
for task_num, task_name in enumerate(args.task_names):
row = [task_name]
for metric, scores in all_scores.items():
task_scores = scores[:, task_num]
mean, std = np.mean(task_scores), np.std(task_scores)
row += [mean, std] + task_scores.tolist()
writer.writerow(row)
# Determine mean and std score of main metric
avg_scores = multitask_mean(all_scores[args.metric], metric=args.metric, axis=1)
mean_score, std_score = np.mean(avg_scores), np.std(avg_scores)
# Optionally merge and save test preds
if args.save_preds:
all_preds = pd.concat([pd.read_csv(os.path.join(save_dir, f'fold_{fold_num}', 'test_preds.csv'))
for fold_num in range(args.num_folds)])
all_preds.to_csv(os.path.join(save_dir, 'test_preds.csv'), index=False)
return mean_score, std_score
