import math
import torch
import torch.nn.functional as F
from torch import Tensor
from torch.nn import Embedding
from torch_geometric.nn.kge import KGEModel
[docs]class RotatE(KGEModel):
r"""The RotatE model from the `"RotatE: Knowledge Graph Embedding by
Relational Rotation in Complex Space" <https://arxiv.org/abs/
1902.10197>`_ paper.
:class:`RotatE` models relations as a rotation in complex space
from head to tail such that
.. math::
\mathbf{e}_t = \mathbf{e}_h \circ \mathbf{e}_r,
resulting in the scoring function
.. math::
d(h, r, t) = - {\| \mathbf{e}_h \circ \mathbf{e}_r - \mathbf{e}_t \|}_p
.. note::
For an example of using the :class:`RotatE` model, see
`examples/kge_fb15k_237.py
<https://github.com/pyg-team/pytorch_geometric/blob/master/examples/
kge_fb15k_237.py>`_.
Args:
num_nodes (int): The number of nodes/entities in the graph.
num_relations (int): The number of relations in the graph.
hidden_channels (int): The hidden embedding size.
margin (float, optional): The margin of the ranking loss.
sparse (bool, optional): If set to :obj:`True`, gradients w.r.t. to
the embedding matrices will be sparse. (default: :obj:`False`)
"""
def __init__(
self,
num_nodes: int,
num_relations: int,
hidden_channels: int,
margin: float = 1.0,
sparse: bool = False,
):
super().__init__(num_nodes, num_relations, hidden_channels, sparse)
self.margin = margin
self.node_emb_im = Embedding(num_nodes, hidden_channels, sparse=sparse)
self.reset_parameters()
[docs] def reset_parameters(self):
torch.nn.init.xavier_uniform_(self.node_emb.weight)
torch.nn.init.xavier_uniform_(self.node_emb_im.weight)
torch.nn.init.uniform_(self.rel_emb.weight, 0, 2 * math.pi)
[docs] def forward(
self,
head_index: Tensor,
rel_type: Tensor,
tail_index: Tensor,
) -> Tensor:
head_re = self.node_emb(head_index)
head_im = self.node_emb_im(head_index)
tail_re = self.node_emb(tail_index)
tail_im = self.node_emb_im(tail_index)
rel_theta = self.rel_emb(rel_type)
rel_re, rel_im = torch.cos(rel_theta), torch.sin(rel_theta)
re_score = (rel_re * head_re - rel_im * head_im) - tail_re
im_score = (rel_re * head_im + rel_im * head_re) - tail_im
complex_score = torch.stack([re_score, im_score], dim=2)
score = torch.linalg.vector_norm(complex_score, dim=(1, 2))
return self.margin - score
[docs] def loss(
self,
head_index: Tensor,
rel_type: Tensor,
tail_index: Tensor,
) -> Tensor:
pos_score = self(head_index, rel_type, tail_index)
neg_score = self(*self.random_sample(head_index, rel_type, tail_index))
scores = torch.cat([pos_score, neg_score], dim=0)
pos_target = torch.ones_like(pos_score)
neg_target = torch.zeros_like(neg_score)
target = torch.cat([pos_target, neg_target], dim=0)
return F.binary_cross_entropy_with_logits(scores, target)