# -*- coding: utf-8 -*-
"""Implementation of the DistMultLiteral model."""
from typing import Any, ClassVar, Mapping
import torch.nn as nn
from .base import LiteralModel
from ...constants import DEFAULT_DROPOUT_HPO_RANGE, DEFAULT_EMBEDDING_HPO_EMBEDDING_DIM_RANGE
from ...nn.combinations import DistMultCombination
from ...nn.emb import EmbeddingSpecification
from ...nn.modules import DistMultInteraction, LiteralInteraction
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]] = dict(
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]] = dict(margin=0.0)
def __init__(
self,
triples_factory: TriplesNumericLiteralsFactory,
embedding_dim: int = 50,
input_dropout: float = 0.0,
**kwargs,
) -> None:
super().__init__(
triples_factory=triples_factory,
interaction=LiteralInteraction(
base=DistMultInteraction(),
combination=DistMultCombination(
entity_embedding_dim=embedding_dim,
literal_embedding_dim=triples_factory.numeric_literals.shape[1],
input_dropout=input_dropout,
),
),
entity_representations=[
EmbeddingSpecification(
embedding_dim=embedding_dim,
initializer=nn.init.xavier_normal_,
),
],
relation_representations=[
EmbeddingSpecification(
embedding_dim=embedding_dim,
initializer=nn.init.xavier_normal_,
),
],
**kwargs,
)