wehub-resource-sync
100 lines
2.7 KiB
Python
100 lines
2.7 KiB
Python
"""TransE."""
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# pylint: disable= no-member, arguments-differ, invalid-name, W0235
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import torch
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import torch.nn as nn
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class TransE(nn.Module):
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r"""Similarity measure from `Translating Embeddings for Modeling Multi-relational Data
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<https://papers.nips.cc/paper/2013/hash/1cecc7a77928ca8133fa24680a88d2f9-Abstract.html>`__
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Mathematically, it is defined as follows:
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.. math::
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- {\| h + r - t \|}_p
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where :math:`h` is the head embedding, :math:`r` is the relation embedding, and
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:math:`t` is the tail embedding.
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Parameters
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----------
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num_rels : int
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Number of relation types.
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feats : int
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Embedding size.
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p : int, optional
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The p to use for Lp norm, which can be 1 or 2.
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Attributes
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----------
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rel_emb : torch.nn.Embedding
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The learnable relation type embedding.
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Examples
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--------
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>>> import dgl
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>>> import torch as th
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>>> from dgl.nn import TransE
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>>> # input features
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>>> num_nodes = 10
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>>> num_edges = 30
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>>> num_rels = 3
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>>> feats = 4
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>>> scorer = TransE(num_rels=num_rels, feats=feats)
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>>> g = dgl.rand_graph(num_nodes=num_nodes, num_edges=num_edges)
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>>> src, dst = g.edges()
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>>> h = th.randn(num_nodes, feats)
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>>> h_head = h[src]
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>>> h_tail = h[dst]
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>>> # Randomly initialize edge relation types for demonstration
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>>> rels = th.randint(low=0, high=num_rels, size=(num_edges,))
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>>> scorer(h_head, h_tail, rels).shape
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torch.Size([30])
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"""
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def __init__(self, num_rels, feats, p=1):
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super(TransE, self).__init__()
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self.rel_emb = nn.Embedding(num_rels, feats)
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self.p = p
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def reset_parameters(self):
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r"""
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Description
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-----------
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Reinitialize learnable parameters.
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"""
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self.rel_emb.reset_parameters()
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def forward(self, h_head, h_tail, rels):
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r"""
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Description
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-----------
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Score triples.
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Parameters
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----------
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h_head : torch.Tensor
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Head entity features. The tensor is of shape :math:`(E, D)`, where
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:math:`E` is the number of triples, and :math:`D` is the feature size.
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h_tail : torch.Tensor
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Tail entity features. The tensor is of shape :math:`(E, D)`, where
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:math:`E` is the number of triples, and :math:`D` is the feature size.
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rels : torch.Tensor
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Relation types. It is a LongTensor of shape :math:`(E)`, where
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:math:`E` is the number of triples.
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Returns
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-------
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torch.Tensor
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The triple scores. The tensor is of shape :math:`(E)`.
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"""
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h_rel = self.rel_emb(rels)
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return -torch.norm(h_head + h_rel - h_tail, p=self.p, dim=-1)
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