Source code for torchdrug.datasets.binary_localization

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.BinaryLocalization") @utils.copy_args(data.ProteinDataset.load_lmdbs, ignore=("target_fields",)) class BinaryLocalization(data.ProteinDataset): """ Simpler version of the Subcellular Localization with binary labels indicating whether a protein is membrane-bound or soluble. Statistics: - #Train: 5,161 - #Valid: 1,727 - #Test: 1,746 Parameters: path (str): the path to store the dataset verbose (int, optional): output verbose level **kwargs """ url = "" md5 = "5d2309bf1c0c2aed450102578e434f4e" splits = ["train", "valid", "test"] target_fields = ["localization"] 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, "subcellular_localization_2/subcellular_localization_2_%s.lmdb" % split) for split in self.splits] self.load_lmdbs(lmdb_files, target_fields=self.target_fields, verbose=verbose, **kwargs) def split(self): offset = 0 splits = [] for num_sample in self.num_samples: split = torch_data.Subset(self, range(offset, offset + num_sample)) splits.append(split) offset += num_sample return splits