Source code for pykeen.models.unimodal.trans_r

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

"""Implementation of TransR."""

from typing import Any, ClassVar, Mapping

import torch
import torch.autograd
import torch.nn.init

from ..nbase import ERModel
from ...nn import TransRInteraction
from ...nn.init import xavier_uniform_, xavier_uniform_norm_
from ...typing import Constrainer, Hint, Initializer
from ...utils import clamp_norm

__all__ = [

[docs]class TransR(ERModel): r"""An implementation of TransR from [lin2015]_. TransR is an extension of :class:`pykeen.models.TransH` that explicitly considers entities and relations as different objects and therefore represents them in different vector spaces. For a triple $(h,r,t) \in \mathbb{K}$, the entity embeddings, $\textbf{e}_h, \textbf{e}_t \in \mathbb{R}^d$, are first projected into the relation space by means of a relation-specific projection matrix $\textbf{M}_{r} \in \mathbb{R}^{k \times d}$. With relation embedding $\textbf{r}_r \in \mathbb{R}^k$, the interaction model is defined similarly to TransE with: .. math:: f(h,r,t) = -\|\textbf{M}_{r}\textbf{e}_h + \textbf{r}_r - \textbf{M}_{r}\textbf{e}_t\|_{p}^2 The following constraints are applied: * $\|\textbf{e}_h\|_2 \leq 1$ * $\|\textbf{r}_r\|_2 \leq 1$ * $\|\textbf{e}_t\|_2 \leq 1$ * $\|\textbf{M}_{r}\textbf{e}_h\|_2 \leq 1$ * $\|\textbf{M}_{r}\textbf{e}_t\|_2 \leq 1$ .. seealso:: - OpenKE `TensorFlow implementation of TransR <>`_ - OpenKE `PyTorch implementation of TransR <>`_ --- citation: author: Lin year: 2015 link: """ #: 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, relation_dim=DEFAULT_EMBEDDING_HPO_EMBEDDING_DIM_RANGE, scoring_fct_norm=dict(type=int, low=1, high=2), ) def __init__( self, *, embedding_dim: int = 50, relation_dim: int = 30, scoring_fct_norm: int = 1, entity_initializer: Hint[Initializer] = xavier_uniform_, entity_constrainer: Hint[Constrainer] = clamp_norm, # type: ignore relation_initializer: Hint[Initializer] = xavier_uniform_norm_, relation_constrainer: Hint[Constrainer] = clamp_norm, # type: ignore **kwargs, ) -> None: """Initialize the model.""" # TODO: Initialize from TransE super().__init__( interaction=TransRInteraction, interaction_kwargs=dict( p=scoring_fct_norm, ), entity_representations_kwargs=dict( shape=embedding_dim, initializer=entity_initializer, constrainer=entity_constrainer, constrainer_kwargs=dict(maxnorm=1.0, p=2, dim=-1), ), relation_representations_kwargs=[ # relation embedding dict( shape=(relation_dim,), initializer=relation_initializer, constrainer=relation_constrainer, constrainer_kwargs=dict(maxnorm=1.0, p=2, dim=-1), ), # relation projection dict( shape=(embedding_dim, relation_dim), initializer=torch.nn.init.xavier_uniform_, ), ], **kwargs, )