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 = "https://media.githubusercontent.com/media/molecularsets/moses/master/data/dataset_v1.csv"
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 = utils.download(self.url, 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