"""An implementation of TransE
"""
import logging
import numpy as np
import tensorflow as tf
from ..base_model.TranslatingModel import TranslatingModel
from ...score import LpDistance
from ...loss import PairwiseHingeLoss
from ...ns_strategy import UniformStrategy
from ...constraint import normalized_embeddings
logging.getLogger().setLevel(logging.INFO)
[docs]class TransE(TranslatingModel):
"""An implementation of TransE from `[brodes 2013] <http://papers.nips.cc/paper/5071-translating-embeddings-for-modeling-multi-relational-data>`_.
TransE is the most representative translating-based knowledge graph embedding model.
It represents both entities and relations as embedding vectors in the same embedding space,
and models the relation as a **translation** from head to tail:
.. math::
\\textbf{e}_h + \\textbf{r}_r \\approx \\textbf{e}_t
where :math:`\\textbf{e}_i, \\textbf{r}_i \in \mathbb{R}^k` are vector representations of the entities and relations.
The score of :math:`(h,r,t)` is:
.. math::
f(h,r,t) = s(\\textbf{e}_h + \\textbf{r}_r, \\textbf{e}_t)
where :math:`s` is a scoring function (:py:mod:`KGE.score`) that scores the plausibility of matching between :math:`(translation, predicate)`. \n
By default, using :py:mod:`KGE.score.LpDistance`, negative L2-distance:
.. math::
s(\\textbf{e}_h + \\textbf{r}_r, \\textbf{e}_t) =
- \left\| \\textbf{e}_h + \\textbf{r}_r - \\textbf{e}_t \\right\|_2
You can change to L1-distance by giving :code:`score_fn=LpDistance(p=1)` in :py:func:`__init__`,
or change any score function you like by specifying :code:`score_fn` in :py:func:`__init__`.
If :code:`constraint=True` given in :py:func:`__init__`,
renormalized :math:`\left\| \\textbf{e}_i \\right\|_2 = 1` to have unit length every iteration and
:math:`\left\| \\textbf{r}_i \\right\|_2 = 1` after initialized described in
`original TransE paper <http://papers.nips.cc/paper/5071-translating-embeddings-for-modeling-multi-relational-data>`_.
"""
[docs] def __init__(self, embedding_params, negative_ratio, corrupt_side,
score_fn=LpDistance(p=2), loss_fn=PairwiseHingeLoss(margin=1),
ns_strategy=UniformStrategy, constraint=True, n_workers=1):
"""Initialized TransE
Parameters
----------
embedding_params : dict
embedding dimension parameters, should have key :code:`'embedding_size'` for embedding dimension :math:`k`
negative_ratio : int
number of negative sample
corrupt_side : str
corrupt from which side while trainging, can be :code:`'h'`, :code:`'t'`, or :code:`'h+t'`
score_fn : function, optional
scoring function, by default :py:mod:`KGE.score.LpDistance`
loss_fn : class, optional
loss function class :py:mod:`KGE.loss.Loss`, by default :py:mod:`KGE.loss.PairwiseHingeLoss`
ns_strategy : function, optional
negative sampling strategy, by default :py:func:`KGE.ns_strategy.uniform_strategy`
constraint : bool, optional
conduct constraint or not, by default True
n_workers : int, optional
number of workers for negative sampling, by default 1
"""
super(TransE, self).__init__(embedding_params, negative_ratio, corrupt_side,
score_fn, loss_fn, ns_strategy, n_workers)
self.constraint = constraint
def _init_embeddings(self, seed):
"""Initialized the TransE embeddings.
If :code:`model_weight_initial` not given in :py:func:`train`, initialized embeddings randomly,
otherwise, initialized from :code:`model_weight_initial`.
Parameters
----------
seed : int
random seed
"""
if self._model_weights_initial is None:
assert self.embedding_params.get("embedding_size") is not None, "'embedding_size' should be given in embedding_params when using TransE"
limit = 6.0 / np.sqrt(self.embedding_params["embedding_size"])
uniform_initializer = tf.initializers.RandomUniform(minval=-limit, maxval=limit, seed=seed)
ent_emb = tf.Variable(
uniform_initializer([len(self.metadata["ind2ent"]), self.embedding_params["embedding_size"]]),
name="entities_embedding", dtype=np.float32
)
rel_emb = tf.Variable(
uniform_initializer([len(self.metadata["ind2rel"]), self.embedding_params["embedding_size"]]),
name="relations_embedding", dtype=np.float32
)
self.model_weights = {"ent_emb": ent_emb, "rel_emb": rel_emb}
else:
self._check_model_weights(self._model_weights_initial)
self.model_weights = self._model_weights_initial
if self.constraint:
self.model_weights["rel_emb"].assign(normalized_embeddings(X=rel_emb, p=2, value=1, axis=1))
def _check_model_weights(self, model_weights):
"""Check the model_weights have necessary keys and dimensions
Parameters
----------
model_weights : dict
model weights to check.
"""
assert model_weights.get("ent_emb") is not None, "entity embedding should be given in model_weights with key 'ent_emb'"
assert model_weights.get("rel_emb") is not None, "relation embedding should be given in model_weights with key 'rel_emb'"
assert list(model_weights["ent_emb"].shape) == [len(self.metadata["ind2ent"]), self.embedding_params["embedding_size"]], \
"shape of 'ent_emb' should be (len(metadata['ind2ent']), embedding_params['embedding_size'])"
assert list(model_weights["rel_emb"].shape) == [len(self.metadata["ind2rel"]), self.embedding_params["embedding_size"]], \
"shape of 'rel_emb' should be (len(metadata['ind2rel']), embedding_params['embedding_size'])"
[docs] def score_hrt(self, h, r, t):
""" Score the triplets :math:`(h,r,t)`.
If :code:`h` is :code:`None`, score all entities: :math:`(h_i, r, t)`. \n
If :code:`t` is :code:`None`, score all entities: :math:`(h, r, t_i)`. \n
:code:`h` and :code:`t` should not be :code:`None` simultaneously.
Parameters
----------
h : tf.Tensor or np.ndarray or None
index of heads with shape :code:`(n,)`
r : tf.Tensor or np.ndarray
index of relations with shape :code:`(n,)`
t : tf.Tensor or np.ndarray or None
index of tails with shape :code:`(n,)`
Returns
-------
tf.Tensor
triplets scores with shape :code:`(n,)`
"""
h,r,t = super(TransE, self).score_hrt(h,r,t)
h_emb = tf.nn.embedding_lookup(self.model_weights["ent_emb"], h)
r_emb = tf.nn.embedding_lookup(self.model_weights["rel_emb"], r)
t_emb = tf.nn.embedding_lookup(self.model_weights["ent_emb"], t)
return self.score_fn(h_emb + r_emb, t_emb)
def _constraint_loss(self, X):
"""Perform constraint if necessary.
Parameters
----------
X : batch_data
batch data
Returns
-------
tf.Tensor
regularization term with shape (1,)
"""
if self.constraint:
self.model_weights["ent_emb"].assign(normalized_embeddings(X=self.model_weights["ent_emb"], p=2, axis=1, value=1))
return 0