"""Implementation of TransR."""
from collections.abc import Mapping
from typing import Any, ClassVar
import torch
import torch.autograd
import torch.nn.init
from class_resolver import OptionalKwargs
from ..nbase import ERModel
from ...constants import DEFAULT_EMBEDDING_HPO_EMBEDDING_DIM_RANGE
from ...nn import TransRInteraction
from ...nn.init import xavier_uniform_, xavier_uniform_norm_
from ...typing import Constrainer, FloatTensor, Hint, Initializer
from ...utils import clamp_norm
__all__ = [
"TransR",
]
[docs]
class TransR(ERModel[FloatTensor, tuple[FloatTensor, FloatTensor], FloatTensor]):
r"""An implementation of TransR from [lin2015]_.
This model represents entities as $d$-dimensional vectors, and relations as $k$-dimensional vectors.
To bring them into the same vector space, a relation-specific projection is learned, too.
All representations are stored in :class:`~pykeen.nn.representation.Embedding` matrices.
The representations are then passed to the :class:`~pykeen.nn.modules.TransRInteraction` function to obtain scores.
The following constraints are applied:
- $\|\textbf{e}_h\|_2 \leq 1$
- $\|\textbf{r}_r\|_2 \leq 1$
- $\|\textbf{e}_t\|_2 \leq 1$
as well as inside the :class:`~pykeen.nn.modules.TransRInteraction`
- $\|\textbf{M}_{r}\textbf{e}_h\|_2 \leq 1$
- $\|\textbf{M}_{r}\textbf{e}_t\|_2 \leq 1$
.. seealso::
- OpenKE `TensorFlow implementation of TransR
<https://github.com/thunlp/OpenKE/blob/master/models/TransR.py>`_
- OpenKE `PyTorch implementation of TransR
<https://github.com/thunlp/OpenKE/blob/OpenKE-PyTorch/models/TransR.py>`_
---
citation:
author: Lin
year: 2015
link: http://www.aaai.org/ocs/index.php/AAAI/AAAI15/paper/download/9571/9523/
"""
#: 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,
relation_dim=DEFAULT_EMBEDDING_HPO_EMBEDDING_DIM_RANGE,
scoring_fct_norm=dict(type=int, low=1, high=2),
)
def __init__(
self,
*,
embedding_dim: int = 50,
relation_dim: int = 30,
max_projection_norm: float = 1.0,
# interaction function kwargs
scoring_fct_norm: int = 1,
power_norm: bool = False,
# entity embedding
entity_initializer: Hint[Initializer] = xavier_uniform_,
entity_initializer_kwargs: OptionalKwargs = None,
entity_constrainer: Hint[Constrainer] = clamp_norm, # type: ignore
# relation embedding
relation_initializer: Hint[Initializer] = xavier_uniform_norm_,
relation_initializer_kwargs: OptionalKwargs = None,
relation_constrainer: Hint[Constrainer] = clamp_norm, # type: ignore
# relation projection
relation_projection_initializer: Hint[Initializer] = torch.nn.init.xavier_uniform_,
relation_projection_initializer_kwargs: OptionalKwargs = None,
**kwargs,
) -> None:
"""Initialize the model.
:param embedding_dim: The entity embedding dimension $d$.
:param relation_dim: The relation embedding dimension $k$.
:param max_projection_norm:
The maximum norm to be clamped after projection.
:param scoring_fct_norm:
The norm used with :func:`torch.linalg.vector_norm`. Typically is 1 or 2.
:param power_norm:
Whether to use the p-th power of the $L_p$ norm. It has the advantage of being differentiable around 0,
and numerically more stable.
:param entity_initializer: Entity initializer function. Defaults to :func:`pykeen.nn.init.xavier_uniform_`.
:param entity_initializer_kwargs: Keyword arguments to be used when calling the entity initializer.
:param entity_constrainer: The entity constrainer. Defaults to :func:`pykeen.utils.clamp_norm`.
:param relation_initializer:
Relation initializer function. Defaults to :func:`pykeen.nn.init.xavier_uniform_norm_`.
:param relation_initializer_kwargs: Keyword arguments to be used when calling the relation initializer.
:param relation_constrainer: The relation constrainer. Defaults to :func:`pykeen.utils.clamp_norm`.
:param relation_projection_initializer:
Relation projection initializer function. Defaults to :func:`torch.nn.init.xavier_uniform_`.
:param relation_projection_initializer_kwargs:
Keyword arguments to be used when calling the relation projection initializer.
:param kwargs: Remaining keyword arguments passed through to :class:`~pykeen.models.ERModel`.
"""
# TODO: Initialize from TransE
super().__init__(
interaction=TransRInteraction,
interaction_kwargs=dict(p=scoring_fct_norm, power_norm=power_norm),
entity_representations_kwargs=dict(
shape=embedding_dim,
initializer=entity_initializer,
initializer_kwargs=entity_initializer_kwargs,
constrainer=entity_constrainer,
constrainer_kwargs=dict(maxnorm=max_projection_norm, p=scoring_fct_norm, dim=-1),
),
relation_representations_kwargs=[
# relation embedding
dict(
shape=(relation_dim,),
initializer=relation_initializer,
initializer_kwargs=relation_initializer_kwargs,
constrainer=relation_constrainer,
constrainer_kwargs=dict(maxnorm=max_projection_norm, p=scoring_fct_norm, dim=-1),
),
# relation projection
dict(
shape=(embedding_dim, relation_dim),
initializer=relation_projection_initializer,
initializer_kwargs=relation_projection_initializer_kwargs,
),
],
**kwargs,
)