def split_dataset(args, dataset):
"""Split the dataset
Parameters
----------
args : dict
Settings
dataset
Dataset instance
Returns
-------
train_set
Training subset
val_set
Validation subset
test_set
Test subset
"""
train_ratio, val_ratio, test_ratio = map(float, args['split_ratio'].split(','))
if args['split'] == 'scaffold':
train_set, val_set, test_set = ScaffoldSplitter.train_val_test_split(
dataset, frac_train=train_ratio, frac_val=val_ratio, frac_test=test_ratio,
scaffold_func='smiles')
elif args['split'] == 'random':
train_set, val_set, test_set = RandomSplitter.train_val_test_split(
dataset, frac_train=train_ratio, frac_val=val_ratio, frac_test=test_ratio)
else:
return ValueError("Expect the splitting method to be 'scaffold', got {}".format(args['split']))
return train_set, val_set, test_set
def load_dataset(args):
"""Load the dataset.
Parameters
----------
args : dict
Input arguments.
Returns
-------
dataset
Full dataset.
train_set
Train subset of the dataset.
val_set
Validation subset of the dataset.
"""
assert args['dataset'] in ['PDBBind'], 'Unexpected dataset {}'.format(args['dataset'])
if args['dataset'] == 'PDBBind':
if args['model'] == 'PotentialNet':
from functools import partial
from dgllife.utils import potentialNet_graph_construction_featurization
dataset = PDBBind(subset=args['subset'], pdb_version=args['version'],
load_binding_pocket=args['load_binding_pocket'],
construct_graph_and_featurize = partial(potentialNet_graph_construction_featurization,
distance_bins=args['distance_bins'],
max_num_neighbors=args['max_num_neighbors'])
)
elif args['model'] =='ACNN':
dataset = PDBBind(subset=args['subset'], pdb_version=args['version'],
load_binding_pocket=args['load_binding_pocket'],
)
if args['split'] == 'random':
train_set, val_set, test_set = RandomSplitter.train_val_test_split(
dataset,
frac_train=args['frac_train'],
frac_val=args['frac_val'],
frac_test=args['frac_test'],
random_state=args['random_seed'])
elif args['split'] == 'scaffold':
train_set, val_set, test_set = ScaffoldSplitter.train_val_test_split(
dataset,
mols=dataset.ligand_mols,
sanitize=False,
frac_train=args['frac_train'],
frac_val=args['frac_val'],
frac_test=args['frac_test'])
elif args['split'] == 'stratified':
train_set, val_set, test_set = SingleTaskStratifiedSplitter.train_val_test_split(
dataset,
labels=dataset.labels,
task_id=0,
frac_train=args['frac_train'],
frac_val=args['frac_val'],
frac_test=args['frac_test'],
random_state=args['random_seed'])
elif args['split'] == 'temporal':
years = dataset.df['release_year'].values.astype(np.float32)
indices = np.argsort(years).tolist()
frac_list = np.array([args['frac_train'], args['frac_val'], args['frac_test']])
num_data = len(dataset)
lengths = (num_data * frac_list).astype(int)
lengths[-1] = num_data - np.sum(lengths[:-1])
train_set, val_set, test_set = [
Subset(dataset, list(indices[offset - length:offset]))
for offset, length in zip(accumulate(lengths), lengths)]
else:
raise ValueError('Expect the splitting method '
'to be "random", "scaffold", "stratified" or "temporal", got {}'.format(args['split']))
if args['frac_train'] > 0:
train_labels = torch.stack([train_set.dataset.labels[i] for i in train_set.indices])
train_set.labels_mean = train_labels.mean(dim=0)
train_set.labels_std = train_labels.std(dim=0)
return dataset, train_set, val_set, test_set
def load_dataset(args):
"""Load the dataset.
Parameters
----------
args : dict
Input arguments.
Returns
-------
dataset
Full dataset.
train_set
Train subset of the dataset.
val_set
Validation subset of the dataset.
"""
assert args['dataset'] in ['PDBBind'], 'Unexpected dataset {}'.format(
args['dataset'])
if args['dataset'] == 'PDBBind':
dataset = PDBBind(subset=args['subset'],
load_binding_pocket=args['load_binding_pocket'],
zero_padding=True)
# No validation set is used and frac_val = 0.
if args['split'] == 'random':
train_set, _, test_set = RandomSplitter.train_val_test_split(
dataset,
frac_train=args['frac_train'],
frac_val=args['frac_val'],
frac_test=args['frac_test'],
random_state=args['random_seed'])
elif args['split'] == 'scaffold':
train_set, _, test_set = ScaffoldSplitter.train_val_test_split(
dataset,
mols=dataset.ligand_mols,
sanitize=False,
frac_train=args['frac_train'],
frac_val=args['frac_val'],
frac_test=args['frac_test'])
elif args['split'] == 'stratified':
train_set, _, test_set = SingleTaskStratifiedSplitter.train_val_test_split(
dataset,
labels=dataset.labels,
task_id=0,
frac_train=args['frac_train'],
frac_val=args['frac_val'],
frac_test=args['frac_test'],
random_state=args['random_seed'])
elif args['split'] == 'temporal':
years = dataset.df['release_year'].values.astype(np.float32)
indices = np.argsort(years).tolist()
frac_list = np.array(
[args['frac_train'], args['frac_val'], args['frac_test']])
num_data = len(dataset)
lengths = (num_data * frac_list).astype(int)
lengths[-1] = num_data - np.sum(lengths[:-1])
train_set, val_set, test_set = [
Subset(dataset, list(indices[offset - length:offset]))
for offset, length in zip(accumulate(lengths), lengths)
]
else:
raise ValueError('Expect the splitting method '
'to be "random" or "scaffold", got {}'.format(
args['split']))
train_labels = torch.stack(
[train_set.dataset.labels[i] for i in train_set.indices])
train_set.labels_mean = train_labels.mean(dim=0)
train_set.labels_std = train_labels.std(dim=0)
return dataset, train_set, test_set