"""Implementation of the DistMultLiteral model."""
from collections.abc import Mapping
from typing import Any, ClassVar
import torch.nn as nn
from .base import LiteralModel
from ...constants import DEFAULT_DROPOUT_HPO_RANGE, DEFAULT_EMBEDDING_HPO_EMBEDDING_DIM_RANGE
from ...nn import ConcatProjectionCombination
from ...nn.modules import DistMultInteraction, Interaction
from ...triples import TriplesNumericLiteralsFactory
__all__ = [
"DistMultLiteral",
]
[docs]
class DistMultLiteral(LiteralModel):
"""An implementation of the LiteralE model with the DistMult interaction from [kristiadi2018]_.
---
name: DistMult Literal
citation:
author: Kristiadi
year: 2018
link: https://arxiv.org/abs/1802.00934
"""
#: The default strategy for optimizing the model's hyper-parameters
hpo_default: ClassVar[Mapping[str, Any]] = {
"embedding_dim": DEFAULT_EMBEDDING_HPO_EMBEDDING_DIM_RANGE,
"input_dropout": DEFAULT_DROPOUT_HPO_RANGE,
}
#: The default parameters for the default loss function class
loss_default_kwargs: ClassVar[Mapping[str, Any]] = {"margin": 0.0}
interaction_cls: ClassVar[type[Interaction]] = DistMultInteraction
def __init__(
self,
triples_factory: TriplesNumericLiteralsFactory,
embedding_dim: int = 50,
input_dropout: float = 0.0,
**kwargs,
) -> None:
"""
Initialize the model.
:param triples_factory:
the (training) triples factory
:param embedding_dim:
the embedding dimension
:param input_dropout:
the input dropout, cf. :meth:`DistMultCombination.__init__`
:param kwargs:
additional keyword-based parameters passed to :meth:`LiteralModel.__init__`
"""
super().__init__(
triples_factory=triples_factory,
interaction=self.interaction_cls,
combination=ConcatProjectionCombination,
combination_kwargs={
"input_dims": [embedding_dim, triples_factory.literal_shape[0]],
"output_dim": embedding_dim,
"bias": True,
"dropout": input_dropout,
# no activation
"activation": nn.Identity,
"activation_kwargs": None,
},
entity_representations_kwargs=[
{
"shape": embedding_dim,
"initializer": nn.init.xavier_normal_,
},
],
relation_representations_kwargs=[
{
"shape": embedding_dim,
"initializer": nn.init.xavier_normal_,
},
],
**kwargs,
)