# -*- coding: utf-8 -*-
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# Copyright 2020 Data61, CSIRO
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# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
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# http://www.apache.org/licenses/LICENSE-2.0
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__all__ = [
"graph_log_likelihood",
"SelfAdversarialNegativeSampling",
]
import tensorflow as tf
from .core.experimental import experimental
[docs]@experimental(reason="lack of unit tests", issues=[804])
def graph_log_likelihood(batch_adj, wys_output):
"""
Computes the graph log likelihood loss function as in https://arxiv.org/abs/1710.09599.
This is different to most Keras loss functions in that it doesn't directly compare predicted values to expected
values. It uses `wys_output` which contains the dot products of embeddings and expected random walks,
and part of the adjacency matrix `batch_adj` to calculate how well the node embeddings capture the graph
structure in some sense.
.. seealso: The :class:`.WatchYourStep` model, for which this loss function is designed.
Args:
batch_adj: tensor with shape ``batch_rows x 1 x num_nodes`` containing rows of the adjacency matrix
wys_output: tensor with shape ``batch_rows x 2 x num_nodes`` containing the embedding outer product
scores with shape ``batch_rows x 1 x num_nodes`` and attentive expected random walk
with shape ``batch_rows x 1, num_nodes`` concatenated.
Returns:
the graph log likelihood loss for the batch
"""
expected_walks = tf.gather(wys_output, [0], axis=1)
scores = tf.gather(wys_output, [1], axis=1)
adj_mask = tf.cast((batch_adj == 0), "float32")
log_sigmoid = tf.math.log_sigmoid(scores)
log1m_sigmoid = log_sigmoid - scores # log(1 - σ(scores)), simplified
matrix = -expected_walks * log_sigmoid - adj_mask * log1m_sigmoid
loss = tf.math.reduce_sum(tf.abs(matrix))
return tf.expand_dims(loss, 0)
[docs]class SelfAdversarialNegativeSampling(tf.keras.losses.Loss):
"""
Computes the self-adversarial binary cross entropy for negative sampling, from [1].
[1] Z. Sun, Z.-H. Deng, J.-Y. Nie, and J. Tang, “RotatE: Knowledge Graph Embedding by Relational Rotation in Complex Space,” `arXiv:1902.10197 <http://arxiv.org/abs/1902.10197>`_
Args:
temperature (float, optional): a scaling factor for the weighting of negative samples
"""
def __init__(
self, temperature=1.0, name="self_adversarial_negative_sampling",
):
self._temperature = temperature
super().__init__(name=name)
[docs] def call(self, labels, logit_scores):
"""
Args:
labels: tensor of integer labels for each row, either 1 for a true sample, or any value <= 0 for negative samples. Negative samples with identical labels are combined for the softmax normalisation.
logit_scores: tensor of scores for each row in logits
"""
scores = tf.math.sigmoid(logit_scores)
if labels.dtype != tf.int32:
labels = tf.cast(labels, tf.int64)
flipped_labels = -labels
exp_scores = tf.math.exp(self._temperature * scores)
sums = tf.math.unsorted_segment_sum(
exp_scores, flipped_labels, tf.reduce_max(flipped_labels) + 1
)
denoms = tf.gather(sums, tf.maximum(flipped_labels, 0))
# adversarial sampling shouldn't influence the gradient/update
negative_weights = tf.stop_gradient(exp_scores / denoms)
loss_elems = tf.where(
labels > 0,
-tf.math.log_sigmoid(logit_scores),
-tf.math.log_sigmoid(-logit_scores) * negative_weights,
)
return tf.reduce_mean(loss_elems, axis=-1)
