Source code for pykeen.models.unimodal.toruse

# -*- coding: utf-8 -*-

"""Implementation of TorusE."""

from typing import Any, ClassVar, Mapping, Optional

from ..nbase import ERModel
from ...nn.modules import TorusEInteraction
from ...typing import Hint, Initializer, Normalizer

__all__ = [

[docs]class TorusE(ERModel): r"""An implementation of TorusE from [ebisu2018]_. --- citation: author: Ebisu year: 2018 link: arxiv: 1711.05435 github: TakumaE/TorusE """ #: The default strategy for optimizing the model's hyper-parameters hpo_default: ClassVar[Mapping[str, Any]] = dict( embedding_dim=DEFAULT_EMBEDDING_HPO_EMBEDDING_DIM_RANGE, p=dict(type=int, low=1, high=2), ) def __init__( self, embedding_dim: int = 256, p: int = 2, power_norm: bool = False, entity_initializer: Hint[Initializer] = None, entity_initializer_kwargs: Optional[Mapping[str, Any]] = None, entity_normalizer: Hint[Normalizer] = None, entity_normalizer_kwargs: Optional[Mapping[str, Any]] = None, relation_initializer: Hint[Initializer] = None, relation_initializer_kwargs: Optional[Mapping[str, Any]] = None, **kwargs, ) -> None: r"""Initialize TorusE via the :class:`pykeen.nn.modules.TorusEInteraction` interaction. :param embedding_dim: The entity embedding dimension $d$. :param p: The p for the norm. :param power_norm: Whether to use the p-th power of the L_p norm instead. :param entity_initializer: Entity initializer function. Defaults to None :param entity_initializer_kwargs: Keyword arguments to be used when calling the entity initializer :param entity_normalizer: Entity normalizer function. Defaults to None :param entity_normalizer_kwargs: Keyword arguments to be used when calling the entity normalizer :param relation_initializer: Relation initializer function. Defaults to None :param relation_initializer_kwargs: Keyword arguments to be used when calling the relation initializer :param kwargs: Remaining keyword arguments passed through to :class:`pykeen.models.ERModel`. """ super().__init__( interaction=TorusEInteraction, interaction_kwargs=dict(p=p, power_norm=power_norm), entity_representations_kwargs=dict( shape=embedding_dim, initializer=entity_initializer, initializer_kwargs=entity_initializer_kwargs, normalizer=entity_normalizer, normalizer_kwargs=entity_normalizer_kwargs, ), relation_representations_kwargs=dict( shape=embedding_dim, initializer=relation_initializer, initializer_kwargs=relation_initializer_kwargs, ), **kwargs, )