import math
import torch
from torch.nn import Parameter, GRU, Linear
import torch.nn.functional as F
from torch_scatter import scatter_mean
[docs]class RENet(torch.nn.Module):
r"""The Recurrent Event Network model from the `"Recurrent Event Network
for Reasoning over Temporal Knowledge Graphs"
<https://arxiv.org/abs/1904.05530>`_ paper
.. math::
f_{\mathbf{\Theta}}(\mathbf{e}_s, \mathbf{e}_r,
\mathbf{h}^{(t-1)}(s, r))
based on a RNN encoder
.. math::
\mathbf{h}^{(t)}(s, r) = \textrm{RNN}(\mathbf{e}_s, \mathbf{e}_r,
g(\mathcal{O}^{(t)}_r(s)), \mathbf{h}^{(t-1)}(s, r))
where :math:`\mathbf{e}_s` and :math:`\mathbf{e}_r` denote entity and
relation embeddings, and :math:`\mathcal{O}^{(t)}_r(s)` represents the set
of objects interacted with subject :math:`s` under relation :math:`r` at
timestamp :math:`t`.
This model implements :math:`g` as the **Mean Aggregator** and
:math:`f_{\mathbf{\Theta}}` as a linear projection.
Args:
num_nodes (int): The number of nodes in the knowledge graph.
num_rels (int): The number of relations in the knowledge graph.
hidden_channels (int): Hidden size of node and relation embeddings.
seq_len (int): The sequence length of past events.
num_layers (int, optional): The number of recurrent layers.
(default: :obj:`1`)
dropout (float): If non-zero, introduces a dropout layer before the
final prediction. (default: :obj:`0.`)
bias (bool, optional): If set to :obj:`False`, all layers will not
learn an additive bias. (default: :obj:`True`)
"""
def __init__(self, num_nodes, num_rels, hidden_channels, seq_len,
num_layers=1, dropout=0., bias=True):
super(RENet, self).__init__()
self.num_nodes = num_nodes
self.hidden_channels = hidden_channels
self.num_rels = num_rels
self.seq_len = seq_len
self.dropout = dropout
self.ent = Parameter(torch.Tensor(num_nodes, hidden_channels))
self.rel = Parameter(torch.Tensor(num_rels, hidden_channels))
self.sub_gru = GRU(3 * hidden_channels, hidden_channels, num_layers,
batch_first=True, bias=bias)
self.obj_gru = GRU(3 * hidden_channels, hidden_channels, num_layers,
batch_first=True, bias=bias)
self.sub_lin = Linear(3 * hidden_channels, num_nodes, bias=bias)
self.obj_lin = Linear(3 * hidden_channels, num_nodes, bias=bias)
self.reset_parameters()
[docs] def reset_parameters(self):
torch.nn.init.xavier_uniform_(self.ent, gain=math.sqrt(2.0))
torch.nn.init.xavier_uniform_(self.rel, gain=math.sqrt(2.0))
self.sub_gru.reset_parameters()
self.obj_gru.reset_parameters()
self.sub_lin.reset_parameters()
self.obj_lin.reset_parameters()
[docs] def forward(self, data):
"""Given a :obj:`data` batch, computes the forward pass.
Args:
data (torch_geometric.data.Data): The input data, holding subject
:obj:`sub`, relation :obj:`rel` and object :obj:`obj`
information with shape :obj:`[batch_size]`.
In addition, :obj:`data` needs to hold history information for
subjects, given by a vector of node indices :obj:`h_sub` and
their relative timestamps :obj:`h_sub_t` and batch assignments
:obj:`h_sub_batch`.
The same information must be given for objects (:obj:`h_obj`,
:obj:`h_obj_t`, :obj:`h_obj_batch`).
"""
assert 'h_sub_batch' in data and 'h_obj_batch' in data
batch_size, seq_len = data.sub.size(0), self.seq_len
h_sub_t = data.h_sub_t + data.h_sub_batch * seq_len
h_obj_t = data.h_obj_t + data.h_obj_batch * seq_len
h_sub = scatter_mean(self.ent[data.h_sub], h_sub_t, dim=0,
dim_size=batch_size * seq_len).view(
batch_size, seq_len, -1)
h_obj = scatter_mean(self.ent[data.h_obj], h_obj_t, dim=0,
dim_size=batch_size * seq_len).view(
batch_size, seq_len, -1)
sub = self.ent[data.sub].unsqueeze(1).repeat(1, seq_len, 1)
rel = self.rel[data.rel].unsqueeze(1).repeat(1, seq_len, 1)
obj = self.ent[data.obj].unsqueeze(1).repeat(1, seq_len, 1)
_, h_sub = self.sub_gru(torch.cat([sub, h_sub, rel], dim=-1))
_, h_obj = self.obj_gru(torch.cat([obj, h_obj, rel], dim=-1))
h_sub, h_obj = h_sub.squeeze(0), h_obj.squeeze(0)
h_sub = torch.cat([self.ent[data.sub], h_sub, self.rel[data.rel]],
dim=-1)
h_obj = torch.cat([self.ent[data.obj], h_obj, self.rel[data.rel]],
dim=-1)
h_sub = F.dropout(h_sub, p=self.dropout, training=self.training)
h_obj = F.dropout(h_obj, p=self.dropout, training=self.training)
log_prob_obj = F.log_softmax(self.sub_lin(h_sub), dim=1)
log_prob_sub = F.log_softmax(self.obj_lin(h_obj), dim=1)
return log_prob_obj, log_prob_sub
[docs] def test(self, logits, y):
"""Given ground-truth :obj:`y`, computes Mean Reciprocal Rank (MRR)
and Hits at 1/3/10."""
_, perm = logits.sort(dim=1, descending=True)
mask = (y.view(-1, 1) == perm)
mrr = (1 / (mask.nonzero()[:, -1] + 1).to(torch.float)).mean().item()
hits1 = mask[:, :1].sum().item() / y.size(0)
hits3 = mask[:, :3].sum().item() / y.size(0)
hits10 = mask[:, :10].sum().item() / y.size(0)
return torch.tensor([mrr, hits1, hits3, hits10])