import os
import os.path
from ggfm.data import graph as gg
from typing import Callable, List, Optional
from ggfm.data import download_url, extract_zip
[docs]class PubMed:
r"""
The PubMed dataset is a comprehensive collection of biomedical literature,
including research articles, clinical studies, and reviews. It serves as a
valuable resource for various applications, such as literature mining,
biomedical text mining, and information retrieval.
"""
url = 'todo'
def __init__(self, root: str = None, transform: Optional[Callable] = None,
pre_transform: Optional[Callable] = None, force_reload: bool = False):
super().__init__(root, transform, pre_transform, force_reload=force_reload)
self.data, self.slices = self.load_data(self.processed_paths[0])
@property
def raw_file_names(self) -> List[str]:
pass
@property
def processed_file_names(self) -> str:
return 'PubMed_pre_data.pt'
def download(self):
path = download_url(self.url, self.raw_dir)
extract_zip(path, self.raw_dir)
os.remove(path)
def process(self):
data = gg()
pass
# todo
def __repr__(self) -> str:
return f'{self.__class__.__name__}()'