# -*- coding: utf-8 -*-
"""The Hetionet dataset.
Get a summary with ``python -m pykeen.datasets.hetionet``
"""
import click
from docdata import parse_docdata
from more_click import verbose_option
from .base import SingleTabbedDataset
from ..typing import TorchRandomHint
__all__ = [
"Hetionet",
]
URL = "https://github.com/hetio/hetionet/raw/master/hetnet/tsv/hetionet-v1.0-edges.sif.gz"
[docs]@parse_docdata
class Hetionet(SingleTabbedDataset):
"""The Hetionet dataset from [himmelstein2017]_.
In its publication [himmelstein2017]_, it is demonstrated to be useful for link prediction in drug repositioning
and made publicly available through its `GitHub repository <https://github.com/hetio/hetionet>`_ in several formats.
The link prediction algorithm showcased does not rely on embeddings, which leaves room for interesting comparison.
One such comparison was made during the master's thesis of Lingling Xu [xu2019]_.
---
name: Hetionet
citation:
author: Himmelstein
year: 2017
link: https://doi.org/10.7554/eLife.26726
github: hetio/hetionet
single: true
statistics:
entities: 45158
relations: 24
triples: 2250197
training: 1800157
testing: 225020
validation: 225020
"""
def __init__(
self,
random_state: TorchRandomHint = 0,
**kwargs,
):
"""Initialize the `Hetionet <https://github.com/hetio/hetionet>`_ dataset from [himmelstein2017]_.
:param random_state: The random seed to use in splitting the dataset. Defaults to 0.
:param kwargs: keyword arguments passed to :class:`pykeen.datasets.base.SingleTabbedDataset`.
"""
super().__init__(
url=URL,
random_state=random_state,
**kwargs,
)
@click.command()
@verbose_option
def _main():
from pykeen.datasets import get_dataset
ds = get_dataset(dataset=Hetionet)
ds.summarize()
if __name__ == "__main__":
_main()