import torch
import torch.nn as nn
import torch.nn.functional as F
from .model import Model
[docs]class PairRE(Model):
"""`PairRE: Knowledge Graph Embeddings via Paired Relation Vectors`_ (PairRE), which paired vectors for each relation representation to model complex patterns.
Attributes:
args: Model configuration parameters.
epsilon: Calculate embedding_range.
margin: Calculate embedding_range and loss.
embedding_range: Uniform distribution range.
ent_emb: Entity embedding, shape:[num_ent, emb_dim].
rel_emb: Relation embedding, shape:[num_rel, emb_dim * 2].
.. _PairRE: Knowledge Graph Embeddings via Paired Relation Vectors: https://arxiv.org/pdf/2011.03798.pdf
"""
def __init__(self, args):
super(PairRE, self).__init__(args)
self.args = args
self.ent_emb = None
self.rel_emb = None
self.init_emb()
[docs] def init_emb(self):
"""Initialize the entity and relation embeddings in the form of a uniform distribution.
"""
self.epsilon = 2.0
self.margin = nn.Parameter(
torch.Tensor([self.args.margin]),
requires_grad=False,
)
self.embedding_range = nn.Parameter(
torch.Tensor([(self.margin.item() + self.epsilon) / self.args.emb_dim]),
requires_grad=False,
)
self.ent_emb = nn.Embedding(self.args.num_ent, self.args.emb_dim)
nn.init.uniform_(
tensor=self.ent_emb.weight.data,
a = -self.embedding_range.item(),
b = self.embedding_range.item(),
)
self.rel_emb = nn.Embedding(self.args.num_rel, self.args.emb_dim * 2)
nn.init.uniform_(
tensor=self.rel_emb.weight.data,
a = -self.embedding_range.item(),
b = self.embedding_range.item(),
)
[docs] def score_func(self, head_emb, relation_emb, tail_emb, mode):
"""Calculating the score of triples.
The formula for calculating the score is :math:`\gamma - || h \circ r^H - t \circ r^T ||`
Args:
head_emb: The head entity embedding.
relation_emb: The relation embedding.
tail_emb: The tail entity embedding.
mode: Choose head-predict or tail-predict.
Returns:
score: The score of triples.
"""
re_head, re_tail = torch.chunk(relation_emb, 2, dim=2)
head = F.normalize(head_emb, 2, -1)
tail = F.normalize(tail_emb, 2, -1)
score = head * re_head - tail * re_tail
return self.margin.item() - torch.norm(score, p=1, dim=2)
[docs] def forward(self, triples, negs=None, mode='single'):
"""The functions used in the training phase
Args:
triples: The triples ids, as (h, r, t), shape:[batch_size, 3].
negs: Negative samples, defaults to None.
mode: Choose head-predict or tail-predict, Defaults to 'single'.
Returns:
score: The score of triples.
"""
head_emb, relation_emb, tail_emb = self.tri2emb(triples, negs, mode=mode)
score = self.score_func(head_emb, relation_emb, tail_emb, mode)
return score
[docs] def get_score(self, batch, mode):
"""The functions used in the testing phase
Args:
batch: A batch of data.
mode: Choose head-predict or tail-predict.
Returns:
score: The score of triples.
"""
triples = batch['positive_sample']
head_emb, relation_emb, tail_emb = self.tri2emb(triples, mode=mode)
score = self.score_func(head_emb, relation_emb, tail_emb, mode)
return score