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.HumanPPI")
@utils.copy_args(data.ProteinPairDataset.load_lmdbs, ignore=("sequence_field", "target_fields"))
class HumanPPI(data.ProteinPairDataset):
"""
Binary labels indicating whether two human proteins interact or not.
Statistics:
- #Train: 6,844
- #Valid: 277
- #Test: 227
Parameters:
path (str): the path to store the dataset
verbose (int, optional): output verbose level
**kwargs
"""
url = "https://miladeepgraphlearningproteindata.s3.us-east-2.amazonaws.com/ppidata/human_ppi.zip"
md5 = "89885545ebc2c11d774c342910230e20"
splits = ["train", "valid", "test", "cross_species_test"]
target_fields = ["interaction"]
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, "human_ppi/human_ppi_%s.lmdb" % split)
for split in self.splits]
self.load_lmdbs(lmdb_files, sequence_field=["primary_1", "primary_2"], 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
def get_item(self, index):
if self.lazy:
graph1 = data.Protein.from_sequence(self.sequences[index][0], **self.kwargs)
graph2 = data.Protein.from_sequence(self.sequences[index][1], **self.kwargs)
else:
graph1 = self.data[index][0]
graph2 = self.data[index][1]
item = {"graph1": graph1, "graph2": graph2}
item.update({k: v[index] for k, v in self.targets.items()})
if self.transform:
item = self.transform(item)
return item