import os
from torch.utils import data as torch_data
from torchdrug import data, utils
from torchdrug.core import Registry as R
[docs]@R.register("datasets.Fold")
@utils.copy_args(data.ProteinDataset.load_lmdbs, ignore=("target_fields",))
class Fold(data.ProteinDataset):
"""
Fold labels for a set of proteins determined by the global structural topology.
Statistics:
- #Train: 12,312
- #Valid: 736
- #Test: 718
Parameters:
path (str): the path to store the dataset
verbose (int, optional): output verbose level
**kwargs
"""
url = "http://s3.amazonaws.com/songlabdata/proteindata/data_pytorch/remote_homology.tar.gz"
md5 = "1d687bdeb9e3866f77504d6079eed00a"
splits = ["train", "valid", "test_fold_holdout", "test_family_holdout", "test_superfamily_holdout"]
target_fields = ["fold_label"]
def __init__(self, path, verbose=1, **kwargs):
path = os.path.expanduser(path)
if not os.path.exists(path):
os.makedirs(path)
self.path = path
zip_file = utils.download(self.url, path, md5=self.md5)
data_path = utils.extract(zip_file)
lmdb_files = [os.path.join(data_path, "remote_homology/remote_homology_%s.lmdb" % split)
for split in self.splits]
self.load_lmdbs(lmdb_files, target_fields=self.target_fields, verbose=verbose, **kwargs)
def split(self, keys=None):
keys = keys or self.splits
offset = 0
splits = []
for split_name, num_sample in zip(self.splits, self.num_samples):
if split_name in keys:
split = torch_data.Subset(self, range(offset, offset + num_sample))
splits.append(split)
offset += num_sample
return splits