def set_features(args: PredictArgs, train_args: TrainArgs):
"""
Function to set extra options.
:param args: A :class:`~chemprop.args.PredictArgs` object containing arguments for
loading data and a model and making predictions.
:param train_args: A :class:`~chemprop.args.TrainArgs` object containing arguments for training the model.
"""
reset_featurization_parameters()
if args.atom_descriptors == 'feature':
set_extra_atom_fdim(train_args.atom_features_size)
if args.bond_features_path is not None:
set_extra_bond_fdim(train_args.bond_features_size)
#set explicit H option and reaction option
set_explicit_h(train_args.explicit_h)
set_adding_hs(args.adding_h)
set_reaction(train_args.reaction, train_args.reaction_mode)
def make_predictions(
args: PredictArgs,
smiles: List[List[str]] = None) -> List[List[Optional[float]]]:
"""
Loads data and a trained model and uses the model to make predictions on the data.
If SMILES are provided, then makes predictions on smiles.
Otherwise makes predictions on :code:`args.test_data`.
:param args: A :class:`~chemprop.args.PredictArgs` object containing arguments for
loading data and a model and making predictions.
:param smiles: List of list of SMILES to make predictions on.
:return: A list of lists of target predictions.
"""
print("Loading training args")
train_args = load_args(args.checkpoint_paths[0])
num_tasks, task_names = train_args.num_tasks, train_args.task_names
update_prediction_args(predict_args=args, train_args=train_args)
args: Union[PredictArgs, TrainArgs]
if args.atom_descriptors == "feature":
set_extra_atom_fdim(train_args.atom_features_size)
if args.bond_features_path is not None:
set_extra_bond_fdim(train_args.bond_features_size)
# set explicit H option and reaction option
set_explicit_h(train_args.explicit_h)
set_reaction(train_args.reaction, train_args.reaction_mode)
print("Loading data")
if smiles is not None:
full_data = get_data_from_smiles(
smiles=smiles,
skip_invalid_smiles=False,
features_generator=args.features_generator,
)
else:
full_data = get_data(
path=args.test_path,
smiles_columns=args.smiles_columns,
target_columns=[],
ignore_columns=[],
skip_invalid_smiles=False,
args=args,
store_row=not args.drop_extra_columns,
)
print("Validating SMILES")
full_to_valid_indices = {}
valid_index = 0
for full_index in range(len(full_data)):
if all(mol is not None for mol in full_data[full_index].mol):
full_to_valid_indices[full_index] = valid_index
valid_index += 1
test_data = MoleculeDataset(
[full_data[i] for i in sorted(full_to_valid_indices.keys())])
# Edge case if empty list of smiles is provided
if len(test_data) == 0:
return [None] * len(full_data)
print(f"Test size = {len(test_data):,}")
# Predict with each model individually and sum predictions
if args.dataset_type == "multiclass":
sum_preds = np.zeros(
(len(test_data), num_tasks, args.multiclass_num_classes))
else:
sum_preds = np.zeros((len(test_data), num_tasks))
# Create data loader
test_data_loader = MoleculeDataLoader(
dataset=test_data,
batch_size=args.batch_size,
num_workers=0 if sys.platform == "darwin" else args.num_workers,
)
# Partial results for variance robust calculation.
if args.ensemble_variance:
all_preds = np.zeros(
(len(test_data), num_tasks, len(args.checkpoint_paths)))
print(
f"Predicting with an ensemble of {len(args.checkpoint_paths)} models")
for index, checkpoint_path in enumerate(
tqdm(args.checkpoint_paths, total=len(args.checkpoint_paths))):
# Load model and scalers
model = load_checkpoint(checkpoint_path, device=args.device)
(
scaler,
features_scaler,
atom_descriptor_scaler,
bond_feature_scaler,
) = load_scalers(checkpoint_path)
# Normalize features
if (args.features_scaling or train_args.atom_descriptor_scaling
or train_args.bond_feature_scaling):
test_data.reset_features_and_targets()
if args.features_scaling:
test_data.normalize_features(features_scaler)
if (train_args.atom_descriptor_scaling
and args.atom_descriptors is not None):
test_data.normalize_features(atom_descriptor_scaler,
scale_atom_descriptors=True)
if train_args.bond_feature_scaling and args.bond_features_size > 0:
test_data.normalize_features(bond_feature_scaler,
scale_bond_features=True)
# Make predictions
model_preds = predict(model=model,
data_loader=test_data_loader,
scaler=scaler)
sum_preds += np.array(model_preds)
if args.ensemble_variance:
all_preds[:, :, index] = model_preds
# Ensemble predictions
avg_preds = sum_preds / len(args.checkpoint_paths)
avg_preds = avg_preds.tolist()
if args.ensemble_variance:
all_epi_uncs = np.var(all_preds, axis=2)
all_epi_uncs = all_epi_uncs.tolist()
# Save predictions
print(f"Saving predictions to {args.preds_path}")
assert len(test_data) == len(avg_preds)
if args.ensemble_variance:
assert len(test_data) == len(all_epi_uncs)
makedirs(args.preds_path, isfile=True)
# Get prediction column names
if args.dataset_type == "multiclass":
task_names = [
f"{name}_class_{i}" for name in task_names
for i in range(args.multiclass_num_classes)
]
else:
task_names = task_names
# Copy predictions over to full_data
for full_index, datapoint in enumerate(full_data):
valid_index = full_to_valid_indices.get(full_index, None)
preds = (avg_preds[valid_index] if valid_index is not None else
["Invalid SMILES"] * len(task_names))
if args.ensemble_variance:
epi_uncs = (all_epi_uncs[valid_index] if valid_index is not None
else ["Invalid SMILES"] * len(task_names))
# If extra columns have been dropped, add back in SMILES columns
if args.drop_extra_columns:
datapoint.row = OrderedDict()
smiles_columns = args.smiles_columns
for column, smiles in zip(smiles_columns, datapoint.smiles):
datapoint.row[column] = smiles
# Add predictions columns
if args.ensemble_variance:
for pred_name, pred, epi_unc in zip(task_names, preds, epi_uncs):
datapoint.row[pred_name] = pred
datapoint.row[pred_name + "_epi_unc"] = epi_unc
else:
for pred_name, pred in zip(task_names, preds):
datapoint.row[pred_name] = pred
# Save
with open(args.preds_path, "w") as f:
writer = csv.DictWriter(f, fieldnames=full_data[0].row.keys())
writer.writeheader()
for datapoint in full_data:
writer.writerow(datapoint.row)
return avg_preds
def molecule_fingerprint(
args: FingerprintArgs,
smiles: List[List[str]] = None) -> List[List[Optional[float]]]:
"""
Loads data and a trained model and uses the model to encode fingerprint vectors for the data.
:param args: A :class:`~chemprop.args.PredictArgs` object containing arguments for
loading data and a model and making predictions.
:param smiles: List of list of SMILES to make predictions on.
:return: A list of fingerprint vectors (list of floats)
"""
print('Loading training args')
train_args = load_args(args.checkpoint_paths[0])
# Update args with training arguments
if args.fingerprint_type == 'MPN': # only need to supply input features if using FFN latent representation and if model calls for them.
validate_feature_sources = False
else:
validate_feature_sources = True
update_prediction_args(predict_args=args,
train_args=train_args,
validate_feature_sources=validate_feature_sources)
args: Union[FingerprintArgs, TrainArgs]
#set explicit H option and reaction option
reset_featurization_parameters()
if args.atom_descriptors == 'feature':
set_extra_atom_fdim(train_args.atom_features_size)
if args.bond_features_path is not None:
set_extra_bond_fdim(train_args.bond_features_size)
set_explicit_h(train_args.explicit_h)
set_adding_hs(args.adding_h)
if train_args.reaction:
set_reaction(train_args.reaction, train_args.reaction_mode)
elif train_args.reaction_solvent:
set_reaction(True, train_args.reaction_mode)
print('Loading data')
if smiles is not None:
full_data = get_data_from_smiles(
smiles=smiles,
skip_invalid_smiles=False,
features_generator=args.features_generator)
else:
full_data = get_data(path=args.test_path,
smiles_columns=args.smiles_columns,
target_columns=[],
ignore_columns=[],
skip_invalid_smiles=False,
args=args,
store_row=True)
print('Validating SMILES')
full_to_valid_indices = {}
valid_index = 0
for full_index in range(len(full_data)):
if all(mol is not None for mol in full_data[full_index].mol):
full_to_valid_indices[full_index] = valid_index
valid_index += 1
test_data = MoleculeDataset(
[full_data[i] for i in sorted(full_to_valid_indices.keys())])
# Edge case if empty list of smiles is provided
if len(test_data) == 0:
return [None] * len(full_data)
print(f'Test size = {len(test_data):,}')
# Create data loader
test_data_loader = MoleculeDataLoader(dataset=test_data,
batch_size=args.batch_size,
num_workers=args.num_workers)
# Set fingerprint size
if args.fingerprint_type == 'MPN':
if args.atom_descriptors == "descriptor": # special case when we have 'descriptor' extra dimensions need to be added
total_fp_size = (
args.hidden_size +
test_data.atom_descriptors_size()) * args.number_of_molecules
else:
if args.reaction_solvent:
total_fp_size = args.hidden_size + args.hidden_size_solvent
else:
total_fp_size = args.hidden_size * args.number_of_molecules
if args.features_only:
raise ValueError(
'With features_only models, there is no latent MPN representation. Use last_FFN fingerprint type instead.'
)
elif args.fingerprint_type == 'last_FFN':
if args.ffn_num_layers != 1:
total_fp_size = args.ffn_hidden_size
else:
raise ValueError(
'With a ffn_num_layers of 1, there is no latent FFN representation. Use MPN fingerprint type instead.'
)
else:
raise ValueError(
f'Fingerprint type {args.fingerprint_type} not supported')
all_fingerprints = np.zeros(
(len(test_data), total_fp_size, len(args.checkpoint_paths)))
# Load model
print(
f'Encoding smiles into a fingerprint vector from {len(args.checkpoint_paths)} models.'
)
for index, checkpoint_path in enumerate(
tqdm(args.checkpoint_paths, total=len(args.checkpoint_paths))):
model = load_checkpoint(checkpoint_path, device=args.device)
scaler, features_scaler, atom_descriptor_scaler, bond_feature_scaler = load_scalers(
args.checkpoint_paths[index])
# Normalize features
if args.features_scaling or train_args.atom_descriptor_scaling or train_args.bond_feature_scaling:
test_data.reset_features_and_targets()
if args.features_scaling:
test_data.normalize_features(features_scaler)
if train_args.atom_descriptor_scaling and args.atom_descriptors is not None:
test_data.normalize_features(atom_descriptor_scaler,
scale_atom_descriptors=True)
if train_args.bond_feature_scaling and args.bond_features_size > 0:
test_data.normalize_features(bond_feature_scaler,
scale_bond_features=True)
# Make fingerprints
model_fp = model_fingerprint(model=model,
data_loader=test_data_loader,
fingerprint_type=args.fingerprint_type)
if args.fingerprint_type == 'MPN' and (
args.features_path is not None or args.features_generator
): # truncate any features from MPN fingerprint
model_fp = np.array(model_fp)[:, :total_fp_size]
all_fingerprints[:, :, index] = model_fp
# Save predictions
print(f'Saving predictions to {args.preds_path}')
# assert len(test_data) == len(all_fingerprints) #TODO: add unit test for this
makedirs(args.preds_path, isfile=True)
# Set column names
fingerprint_columns = []
if args.fingerprint_type == 'MPN':
if len(args.checkpoint_paths) == 1:
for j in range(total_fp_size // args.number_of_molecules):
for k in range(args.number_of_molecules):
fingerprint_columns.append(f'fp_{j}_mol_{k}')
else:
for j in range(total_fp_size // args.number_of_molecules):
for i in range(len(args.checkpoint_paths)):
for k in range(args.number_of_molecules):
fingerprint_columns.append(f'fp_{j}_mol_{k}_model_{i}')
else: # args == 'last_FNN'
if len(args.checkpoint_paths) == 1:
for j in range(total_fp_size):
fingerprint_columns.append(f'fp_{j}')
else:
for j in range(total_fp_size):
for i in range(len(args.checkpoint_paths)):
fingerprint_columns.append(f'fp_{j}_model_{i}')
# Copy predictions over to full_data
for full_index, datapoint in enumerate(full_data):
valid_index = full_to_valid_indices.get(full_index, None)
preds = all_fingerprints[valid_index].reshape(
(len(args.checkpoint_paths) * total_fp_size
)) if valid_index is not None else ['Invalid SMILES'] * len(
args.checkpoint_paths) * total_fp_size
for i in range(len(fingerprint_columns)):
datapoint.row[fingerprint_columns[i]] = preds[i]
# Write predictions
with open(args.preds_path, 'w') as f:
writer = csv.DictWriter(f,
fieldnames=args.smiles_columns +
fingerprint_columns,
extrasaction='ignore')
writer.writeheader()
for datapoint in full_data:
writer.writerow(datapoint.row)
return all_fingerprints
def molecule_fingerprint(
args: PredictArgs,
smiles: List[List[str]] = None) -> List[List[Optional[float]]]:
"""
Loads data and a trained model and uses the model to encode fingerprint vectors for the data.
:param args: A :class:`~chemprop.args.PredictArgs` object containing arguments for
loading data and a model and making predictions.
:param smiles: List of list of SMILES to make predictions on.
:return: A list of fingerprint vectors (list of floats)
"""
print('Loading training args')
train_args = load_args(args.checkpoint_paths[0])
# Update args with training arguments
update_prediction_args(predict_args=args,
train_args=train_args,
validate_feature_sources=False)
args: Union[PredictArgs, TrainArgs]
#set explicit H option and reaction option
set_explicit_h(train_args.explicit_h)
set_reaction(train_args.reaction, train_args.reaction_mode)
print('Loading data')
if smiles is not None:
full_data = get_data_from_smiles(
smiles=smiles,
skip_invalid_smiles=False,
features_generator=args.features_generator)
else:
full_data = get_data(path=args.test_path,
smiles_columns=args.smiles_columns,
target_columns=[],
ignore_columns=[],
skip_invalid_smiles=False,
args=args,
store_row=True)
print('Validating SMILES')
full_to_valid_indices = {}
valid_index = 0
for full_index in range(len(full_data)):
if all(mol is not None for mol in full_data[full_index].mol):
full_to_valid_indices[full_index] = valid_index
valid_index += 1
test_data = MoleculeDataset(
[full_data[i] for i in sorted(full_to_valid_indices.keys())])
# Edge case if empty list of smiles is provided
if len(test_data) == 0:
return [None] * len(full_data)
print(f'Test size = {len(test_data):,}')
# Create data loader
test_data_loader = MoleculeDataLoader(dataset=test_data,
batch_size=args.batch_size,
num_workers=args.num_workers)
# Load model
print(f'Encoding smiles into a fingerprint vector from a single model')
if len(args.checkpoint_paths) != 1:
raise ValueError(
"Fingerprint generation only supports one model, cannot use an ensemble"
)
model = load_checkpoint(args.checkpoint_paths[0], device=args.device)
scaler, features_scaler, atom_descriptor_scaler, bond_feature_scaler = load_scalers(
args.checkpoint_paths[0])
# Normalize features
if args.features_scaling or train_args.atom_descriptor_scaling or train_args.bond_feature_scaling:
test_data.reset_features_and_targets()
if args.features_scaling:
test_data.normalize_features(features_scaler)
if train_args.atom_descriptor_scaling and args.atom_descriptors is not None:
test_data.normalize_features(atom_descriptor_scaler,
scale_atom_descriptors=True)
if train_args.bond_feature_scaling and args.bond_features_size > 0:
test_data.normalize_features(bond_feature_scaler,
scale_bond_features=True)
# Make fingerprints
model_preds = model_fingerprint(model=model, data_loader=test_data_loader)
# Save predictions
print(f'Saving predictions to {args.preds_path}')
assert len(test_data) == len(model_preds)
makedirs(args.preds_path, isfile=True)
# Copy predictions over to full_data
total_hidden_size = args.hidden_size * args.number_of_molecules
for full_index, datapoint in enumerate(full_data):
valid_index = full_to_valid_indices.get(full_index, None)
preds = model_preds[valid_index] if valid_index is not None else [
'Invalid SMILES'
] * total_hidden_size
fingerprint_columns = [f'fp_{i}' for i in range(total_hidden_size)]
for i in range(len(fingerprint_columns)):
datapoint.row[fingerprint_columns[i]] = preds[i]
# Write predictions
with open(args.preds_path, 'w') as f:
writer = csv.DictWriter(f,
fieldnames=args.smiles_columns +
fingerprint_columns,
extrasaction='ignore')
writer.writeheader()
for datapoint in full_data:
writer.writerow(datapoint.row)
return model_preds
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
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)
args.save(os.path.join(args.save_dir, 'args.json'))
#set explicit H option and reaction option
set_explicit_h(args.explicit_h)
set_reaction(args.reaction, args.reaction_mode)
# Get data
debug('Loading data')
data = get_data(path=args.data_path,
args=args,
smiles_columns=args.smiles_columns,
logger=logger,
skip_none_targets=True)
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}')
# 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
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