class_docstr: The Precision Medicine Knowledge Graph (PrimeKG) dataset from [chandak2022]_.
  
  ---
  name: PrimeKG
  citation:
      author: Chandak
      year: 2022
      link: https://doi.org/10.1101/2022.05.01.489928
      github: mims-harvard/PrimeKG
  single: true
  statistics:
      entities: 129375
      relations: 30
      triples: 8100498
      training: 6479992
      testing: 809999
      validation: 810000
docstr: Initialize the PrimeKG dataset from [chandak2022]_.
  
  :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`.
code: def __init__(
      self,
      random_state: TorchRandomHint = 0,
      **kwargs,
  ):
      """Initialize the PrimeKG dataset from [chandak2022]_.
  
      :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,
          name="primekg.csv",
          random_state=random_state,
          download_kwargs=dict(
              backend="requests",
          ),
          read_csv_kwargs=dict(
              usecols=["x_name", "relation", "y_name"],
              sep=",",
          ),
          **kwargs,
      )
  
file_path: pykeen/src/pykeen/datasets/primekg.py
imports: import click
  from docdata import parse_docdata
  from more_click import verbose_option
  from .base import SingleTabbedDataset
  from ..typing import TorchRandomHint
label_desc: Use image node_id 1 to create a new PrimeKG object from inherited base classes: SingleTabbedDataset with example: obj = PrimeKG(random_state)