"""TransR."""
# pylint: disable= no-member, arguments-differ, invalid-name, W0235
import torch
import torch.nn as nn
[docs]class TransR(nn.Module):
r"""Similarity measure from
`Learning entity and relation embeddings for knowledge graph completion
<https://ojs.aaai.org/index.php/AAAI/article/view/9491>`__
Mathematically, it is defined as follows:
.. math::
- {\| M_r h + r - M_r t \|}_p
where :math:`M_r` is a relation-specific projection matrix, :math:`h` is the
head embedding, :math:`r` is the relation embedding, and :math:`t` is the tail embedding.
Parameters
----------
num_rels : int
Number of relation types.
rfeats : int
Relation embedding size.
nfeats : int
Entity embedding size.
p : int, optional
The p to use for Lp norm, which can be 1 or 2.
Attributes
----------
rel_emb : torch.nn.Embedding
The learnable relation type embedding.
rel_project : torch.nn.Embedding
The learnable relation-type-specific projection.
Examples
--------
>>> import dgl
>>> import torch as th
>>> from dgl.nn import TransR
>>> # input features
>>> num_nodes = 10
>>> num_edges = 30
>>> num_rels = 3
>>> feats = 4
>>> scorer = TransR(num_rels=num_rels, rfeats=2, nfeats=feats)
>>> g = dgl.rand_graph(num_nodes=num_nodes, num_edges=num_edges)
>>> src, dst = g.edges()
>>> h = th.randn(num_nodes, feats)
>>> h_head = h[src]
>>> h_tail = h[dst]
>>> # Randomly initialize edge relation types for demonstration
>>> rels = th.randint(low=0, high=num_rels, size=(num_edges,))
>>> scorer(h_head, h_tail, rels).shape
torch.Size([30])
"""
def __init__(self, num_rels, rfeats, nfeats, p=1):
super(TransR, self).__init__()
self.rel_emb = nn.Embedding(num_rels, rfeats)
self.rel_project = nn.Embedding(num_rels, nfeats * rfeats)
self.rfeats = rfeats
self.nfeats = nfeats
self.p = p
[docs] def reset_parameters(self):
r"""
Description
-----------
Reinitialize learnable parameters.
"""
self.rel_emb.reset_parameters()
self.rel_project.reset_parameters()
[docs] def forward(self, h_head, h_tail, rels):
r"""
Score triples.
Parameters
----------
h_head : torch.Tensor
Head entity features. The tensor is of shape :math:`(E, D)`, where
:math:`E` is the number of triples, and :math:`D` is the feature size.
h_tail : torch.Tensor
Tail entity features. The tensor is of shape :math:`(E, D)`, where
:math:`E` is the number of triples, and :math:`D` is the feature size.
rels : torch.Tensor
Relation types. It is a LongTensor of shape :math:`(E)`, where
:math:`E` is the number of triples.
Returns
-------
torch.Tensor
The triple scores. The tensor is of shape :math:`(E)`.
"""
h_rel = self.rel_emb(rels)
proj_rel = self.rel_project(rels).reshape(-1, self.nfeats, self.rfeats)
h_head = (h_head.unsqueeze(1) @ proj_rel).squeeze(1)
h_tail = (h_tail.unsqueeze(1) @ proj_rel).squeeze(1)
return - torch.norm(h_head + h_rel - h_tail, p=self.p, dim=-1)