"""Implementation of SimplE."""
from __future__ import annotations
from collections.abc import Mapping
from typing import Any, ClassVar
from class_resolver import OptionalKwargs
from ..nbase import ERModel
from ...constants import DEFAULT_EMBEDDING_HPO_EMBEDDING_DIM_RANGE
from ...losses import Loss, SoftplusLoss
from ...nn.modules import Clamp, ClampedInteraction, SimplEInteraction
from ...regularizers import PowerSumRegularizer, Regularizer, regularizer_resolver
from ...typing import FloatTensor, Hint, Initializer
__all__ = [
"SimplE",
]
[docs]
class SimplE(
ERModel[tuple[FloatTensor, FloatTensor], tuple[FloatTensor, FloatTensor], tuple[FloatTensor, FloatTensor]]
):
r"""An implementation of SimplE [kazemi2018]_.
SimplE learns two $d$-dimensional vectors for each entity and each relation, stored in
:class:`~pykeen.nn.representation.Embedding`, and applies the :class:`~pykeen.nn.modules.SimplEInteraction` on top.
.. note ::
In the code in their repository, the score is clamped to $[-20, 20]$.
That is not mentioned in the paper, so it is made optional.
.. seealso::
- Official implementation: https://github.com/Mehran-k/SimplE
- Improved implementation in pytorch: https://github.com/baharefatemi/SimplE
---
citation:
author: Kazemi
year: 2018
link: https://papers.nips.cc/paper/7682-simple-embedding-for-link-prediction-in-knowledge-graphs
github: Mehran-k/SimplE
"""
#: The default strategy for optimizing the model's hyper-parameters
hpo_default: ClassVar[Mapping[str, Any]] = {
"embedding_dim": DEFAULT_EMBEDDING_HPO_EMBEDDING_DIM_RANGE,
}
#: The default loss function class
loss_default: ClassVar[type[Loss]] = SoftplusLoss
#: The default parameters for the default loss function class
loss_default_kwargs: ClassVar[Mapping[str, Any]] = {}
#: The regularizer used by [trouillon2016]_ for SimplE
#: In the paper, they use weight of 0.1, and do not normalize the
#: regularization term by the number of elements, which is 200.
regularizer_default: ClassVar[type[Regularizer]] = PowerSumRegularizer
#: The power sum settings used by [trouillon2016]_ for SimplE
regularizer_default_kwargs: ClassVar[Mapping[str, Any]] = {
"weight": 20,
"p": 2.0,
"normalize": True,
}
def __init__(
self,
*,
embedding_dim: int = 200,
clamp_score: Clamp | float | None = None,
entity_initializer: Hint[Initializer] = None,
relation_initializer: Hint[Initializer] = None,
regularizer: Hint[Regularizer] = None,
regularizer_kwargs: OptionalKwargs = None,
**kwargs,
) -> None:
"""
Initialize the model.
:param embedding_dim:
the embedding dimension
:param clamp_score:
whether to clamp scores, cf. :class:`~pykeen.nn.modules.ClampedInteraction`
:param entity_initializer:
the entity representation initializer
:param relation_initializer:
the relation representation initializer
:param regularizer:
the regularizer, defaults to :attr:`SimplE.regularizer_default`
:param regularizer_kwargs:
additional keyword-based parameters passed to the regularizer, defaults to
:attr:`SimplE.regularizer_default_kwargs`
:param kwargs:
additional keyword-based parameters passed to :meth:`ERModel.__init__`
"""
# TODO: what about using the default regularizer?
regularizer = regularizer_resolver.make_safe(regularizer, pos_kwargs=regularizer_kwargs)
# Note: In the code in their repository, the score is clamped to [-20, 20].
# That is not mentioned in the paper, so it is made optional here.
super().__init__(
interaction=ClampedInteraction,
interaction_kwargs={"base": SimplEInteraction, "clamp_score": clamp_score},
entity_representations_kwargs=[
# (head) entity
{
"shape": embedding_dim,
"initializer": entity_initializer,
"regularizer": regularizer,
},
# tail entity
{
"shape": embedding_dim,
"initializer": entity_initializer,
"regularizer": regularizer,
},
],
relation_representations_kwargs=[
# relations
{
"shape": embedding_dim,
"initializer": relation_initializer,
"regularizer": regularizer,
},
# inverse relations
{
"shape": embedding_dim,
"initializer": relation_initializer,
"regularizer": regularizer,
},
],
**kwargs,
)