Source code for pykeen.models.unimodal.hole

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

"""Implementation of the HolE model."""

from typing import Any, ClassVar, Mapping, Optional

from class_resolver import Hint, OptionalKwargs

from ..nbase import ERModel
from ...constants import DEFAULT_EMBEDDING_HPO_EMBEDDING_DIM_RANGE
from ...nn import HolEInteraction
from ...nn.init import xavier_uniform_
from ...typing import Constrainer, Initializer
from ...utils import clamp_norm

__all__ = [
    "HolE",
]


[docs]class HolE(ERModel): r"""An implementation of HolE [nickel2016]_. Holographic embeddings (HolE) make use of the circular correlation operator to compute interactions between latent features of entities and relations: .. math:: f(h,r,t) = \sigma(\textbf{r}^{T}(\textbf{h} \star \textbf{t})) where the circular correlation $\star: \mathbb{R}^d \times \mathbb{R}^d \rightarrow \mathbb{R}^d$ is defined as: .. math:: [\textbf{a} \star \textbf{b}]_i = \sum_{k=0}^{d-1} \textbf{a}_{k} * \textbf{b}_{(i+k)\ mod \ d} By using the correlation operator each component $[\textbf{h} \star \textbf{t}]_i$ represents a sum over a fixed partition over pairwise interactions. This enables the model to put semantic similar interactions into the same partition and share weights through $\textbf{r}$. Similarly irrelevant interactions of features could also be placed into the same partition which could be assigned a small weight in $\textbf{r}$. .. seealso:: - `author's implementation of HolE <https://github.com/mnick/holographic-embeddings>`_ - `scikit-kge implementation of HolE <https://github.com/mnick/scikit-kge>`_ - OpenKE `implementation of HolE <https://github.com/thunlp/OpenKE/blob/OpenKE-PyTorch/models/TransE.py>`_ --- citation: author: Nickel year: 2016 link: https://www.aaai.org/ocs/index.php/AAAI/AAAI16/paper/viewFile/12484/11828 github: mnick/holographic-embeddings """ #: 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, ) #: The default settings for the entity constrainer entity_constrainer_default_kwargs = dict(maxnorm=1.0, p=2, dim=-1) def __init__( self, *, embedding_dim: int = 200, # Initialisation, cf. https://github.com/mnick/scikit-kge/blob/master/skge/param.py#L18-L27 entity_initializer: Hint[Initializer] = xavier_uniform_, entity_constrainer: Hint[Constrainer] = clamp_norm, # type: ignore entity_constrainer_kwargs: Optional[Mapping[str, Any]] = None, entity_representation_kwargs: OptionalKwargs = None, relation_initializer: Hint[Constrainer] = xavier_uniform_, relation_representation_kwargs: OptionalKwargs = None, **kwargs, ) -> None: """ Initialize the model. :param embedding_dim: the embedding dimension (for entities and relations) :param entity_initializer: the initializer for entity representations :param entity_constrainer: the constrainer for entity representations :param entity_constrainer_kwargs: keyword-based parameters passed to the constrainer. If None, use :attr:`entity_constrainer_default_kwargs` :param entity_representation_kwargs: additional keyword-based parameters passed to the entity representation :param relation_initializer: the initializer for relation representations :param relation_representation_kwargs: additional keyword-based parameters passed to the entity representation :param kwargs: additional keyword-based parameters passed to :meth:`ERModel.__init__` """ super().__init__( interaction=HolEInteraction, entity_representations_kwargs=dict( shape=embedding_dim, initializer=entity_initializer, constrainer=entity_constrainer, constrainer_kwargs=entity_constrainer_kwargs or self.entity_constrainer_default_kwargs, **(entity_representation_kwargs or {}), ), relation_representations_kwargs=dict( shape=embedding_dim, initializer=relation_initializer, **(relation_representation_kwargs or {}), ), **kwargs, )