class Hetionet(create_inverse_triples=False, random_state=0, **kwargs)[source]

Bases: 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 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].

Initialize the Hetionet dataset from [himmelstein2017].

  • create_inverse_triples (bool) – Should inverse triples be created? Defaults to false.

  • random_state (Union[None, int, Generator]) – The random seed to use in splitting the dataset. Defaults to 0.

  • kwargs – keyword arguments passed to pykeen.datasets.base.SingleTabbedDataset.