Source code for torchdrug.datasets.fold

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 = "" 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 =, 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