"""Implementation of the HolE model."""
from collections.abc import Mapping
from typing import Any, ClassVar, 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, FloatTensor, Initializer
from ...utils import clamp_norm
__all__ = [
"HolE",
]
[docs]
class HolE(ERModel[FloatTensor, FloatTensor, FloatTensor]):
r"""The HolE model as described in [nickel2016]_.
This model represents both entities and relations as $d$-dimensional vectors stored in an
:class:`~pykeen.nn.representation.Embedding` matrix.
The representations are then passed to the :class:`~pykeen.nn.modules.HolEInteraction` function to obtain
scores.
.. note ::
The original paper describes modelling the probability as $\sigma(f(h, r, t))$, however, since the margin
ranking loss is used for all experiments, the implementation of the score function does not include the
$\sigma$.
.. 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/64c4ec8157abfc39112772e9825a349091da45f1/openke/module/model/HolE.py>`_
---
citation:
author: Nickel
year: 2016
link: https://www.aaai.org/ocs/index.php/AAAI/AAAI16/paper/viewFile/12484/11828
github: mnick/holographic-embeddings
arxiv: 1510.04935
"""
#: 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,
)