Source code for pykeen.models.unimodal.ermlpe

# -*- 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 ...losses import BCEAfterSigmoidLoss, Loss
from ...nn.modules import ERMLPEInteraction
from ...typing import Hint, Initializer

__all__ = [

[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: 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, )