Source code for torchdrug.datasets.moses

import os
from collections import defaultdict

from torch.utils import data as torch_data

from torchdrug import data, utils
from torchdrug.core import Registry as R

[docs]@R.register("datasets.MOSES") @utils.copy_args(data.MoleculeDataset.load_csv, ignore=("smiles_field", "target_fields")) class MOSES(data.MoleculeDataset): """ Subset of ZINC database for molecule generation. This dataset doesn't contain any label information. Statistics: - #Molecule: 1,936,963 Parameters: path (str): path for the CSV dataset verbose (int, optional): output verbose level **kwargs """ url = "" md5 = "6bdb0d9526ddf5fdeb87d6aa541df213" target_fields = ["SPLIT"] def __init__(self, path, verbose=1, **kwargs): path = os.path.expanduser(path) if not os.path.exists(path): os.makedirs(path) self.path = path file_name =, path, md5=self.md5) self.load_csv(file_name, smiles_field="SMILES", target_fields=self.target_fields, lazy=True, verbose=verbose, **kwargs) def split(self): indexes = defaultdict(list) for i, split in enumerate(self.targets["SPLIT"]): indexes[split].append(i) train_set = torch_data.Subset(self, indexes["train"]) valid_set = torch_data.Subset(self, indexes["valid"]) test_set = torch_data.Subset(self, indexes["test"]) return train_set, valid_set, test_set