# -*- coding: utf-8 -*-
"""An implementation of the extension to ERMLP."""
from typing import Any, ClassVar, Mapping, Optional, Type
from torch.nn.init import uniform_
from ..nbase import ERModel
from ...constants import DEFAULT_DROPOUT_HPO_RANGE, DEFAULT_EMBEDDING_HPO_EMBEDDING_DIM_RANGE
from ...losses import BCEAfterSigmoidLoss, Loss
from ...nn.modules import ERMLPEInteraction
from ...typing import Hint, Initializer
__all__ = [
"ERMLPE",
]
[docs]class ERMLPE(ERModel):
r"""An extension of :class:`pykeen.models.ERMLP` proposed by [sharifzadeh2019]_.
This model uses a neural network-based approach similar to ER-MLP and with slight modifications.
In ER-MLP, the model is:
.. math::
f(h, r, t) = \textbf{w}^{T} g(\textbf{W} [\textbf{h}; \textbf{r}; \textbf{t}])
whereas in ER-MLP (E) the model is:
.. math::
f(h, r, t) = \textbf{t}^{T} f(\textbf{W} (g(\textbf{W} [\textbf{h}; \textbf{r}]))
including dropouts and batch-norms between each two hidden layers.
ConvE can be seen as a special case of ER-MLP (E) that contains the unnecessary inductive bias of convolutional
filters. The aim of this model is to show that lifting this bias from :class:`pykeen.models.ConvE` (which simply
leaves us with a modified ER-MLP model), not only reduces the number of parameters but also improves performance.
---
name: ER-MLP (E)
citation:
author: Sharifzadeh
year: 2019
link: https://github.com/pykeen/pykeen
github: pykeen/pykeen
"""
#: 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,
hidden_dim=dict(type=int, low=5, high=9, scale="power_two"),
input_dropout=DEFAULT_DROPOUT_HPO_RANGE,
hidden_dropout=DEFAULT_DROPOUT_HPO_RANGE,
)
#: The default loss function class
loss_default: ClassVar[Type[Loss]] = BCEAfterSigmoidLoss
#: The default parameters for the default loss function class
loss_default_kwargs: ClassVar[Mapping[str, Any]] = {}
def __init__(
self,
*,
embedding_dim: int = 256,
hidden_dim: Optional[int] = None,
input_dropout: float = 0.2,
hidden_dropout: Optional[float] = None,
entity_initializer: Hint[Initializer] = uniform_,
relation_initializer: Hint[Initializer] = None,
**kwargs,
) -> None:
"""
Initialize the model.
:param embedding_dim:
the embedding dimension (for both, entities and relations)
:param hidden_dim:
the hidden dimension of the MLP; defaults to ``embedding_dim``.
:param input_dropout:
the input dropout of the MLP
:param hidden_dropout:
the hidden dropout of the MLP; defaults to ``input_dropout``.
:param entity_initializer:
the entity embedding initializer
:param relation_initializer:
the relation embedding initializer; defaults to ``entity_initializer``.
:param kwargs:
additional keyword-based parameters passed to :meth:`ERModel.__init__`
"""
super().__init__(
interaction=ERMLPEInteraction,
interaction_kwargs=dict(
embedding_dim=embedding_dim,
hidden_dim=hidden_dim,
input_dropout=input_dropout,
hidden_dropout=hidden_dropout,
),
entity_representations_kwargs=dict(
shape=embedding_dim,
initializer=entity_initializer,
),
relation_representations_kwargs=dict(
shape=embedding_dim,
initializer=relation_initializer or entity_initializer,
),
**kwargs,
)