Source code for pykeen.models.unimodal.rescal

# -*- coding: utf-8 -*-

"""Implementation of RESCAL."""

from typing import Any, ClassVar, Mapping, Type

from class_resolver import HintOrType, OptionalKwargs
from torch.nn.init import uniform_

from ..nbase import ERModel
from ...constants import DEFAULT_EMBEDDING_HPO_EMBEDDING_DIM_RANGE
from ...nn import RESCALInteraction
from ...regularizers import LpRegularizer, Regularizer
from ...typing import Hint, Initializer

__all__ = [
"RESCAL",
]

[docs]class RESCAL(ERModel): r"""An implementation of RESCAL from [nickel2011]_. This model represents relations as matrices and models interactions between latent features. RESCAL is a bilinear model that models entities as vectors and relations as matrices. The relation matrices $\textbf{W}_{r} \in \mathbb{R}^{d \times d}$ contain weights $w_{i,j}$ that capture the amount of interaction between the $i$-th latent factor of $\textbf{e}_h \in \mathbb{R}^{d}$ and the $j$-th latent factor of $\textbf{e}_t \in \mathbb{R}^{d}$. Thus, the plausibility score of $(h,r,t) \in \mathbb{K}$ is given by: .. math:: f(h,r,t) = \textbf{e}_h^{T} \textbf{W}_{r} \textbf{e}_t = \sum_{i=1}^{d}\sum_{j=1}^{d} w_{ij}^{(r)} (\textbf{e}_h)_{i} (\textbf{e}_t)_{j} --- citation: author: Nickel year: 2011 link: http://www.cip.ifi.lmu.de/~nickel/data/paper-icml2011.pdf """ #: 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, ) #: The regularizer used by [nickel2011]_ for for RESCAL #: According to https://github.com/mnick/rescal.py/blob/master/examples/kinships.py #: a normalized weight of 10 is used. regularizer_default: ClassVar[Type[Regularizer]] = LpRegularizer #: The LP settings used by [nickel2011]_ for for RESCAL regularizer_default_kwargs: ClassVar[Mapping[str, Any]] = dict( weight=10, p=2.0, normalize=True, ) def __init__( self, *, embedding_dim: int = 50, entity_initializer: Hint[Initializer] = uniform_, relation_initializer: Hint[Initializer] = uniform_, regularizer: HintOrType[Regularizer] = None, regularizer_kwargs: OptionalKwargs = None, **kwargs, ) -> None: r"""Initialize RESCAL. :param embedding_dim: the entity embedding dimension $d$. Is usually $d \in [50, 300]$. :param entity_initializer: entity initializer function. Defaults to :func:torch.nn.init.uniform_ :param relation_initializer: relation initializer function. Defaults to :func:torch.nn.init.uniform_ :param regularizer: the regularizer. Default to :attr:RESCAL.defaul_regularizer :param regularizer_kwargs: additional keyword-based parameters for the regularizer :param kwargs: remaining keyword arguments to forward to :meth:pykeen.models.ERModel.__init__ .. seealso:: - OpenKE implementation of RESCAL <https://github.com/thunlp/OpenKE/blob/master/models/RESCAL.py>_ """ regularizer = self._instantiate_regularizer(regularizer=regularizer, regularizer_kwargs=regularizer_kwargs) super().__init__( interaction=RESCALInteraction, entity_representations_kwargs=dict( shape=embedding_dim, initializer=entity_initializer, regularizer=regularizer, ), relation_representations_kwargs=dict( shape=(embedding_dim, embedding_dim), # d x d matrices initializer=relation_initializer, regularizer=regularizer, ), **kwargs, )