def test_dcg_lambda_weight_is_serializable(self):
self.assertIsSerializable(losses.DCGLambdaWeight())
self.assertIsSerializable(
losses.DCGLambdaWeight(gain_fn=utils.identity,
rank_discount_fn=utils.log2_inverse))
self.assertIsSerializable(losses.NDCGLambdaWeight())
self.assertIsSerializable(
losses.NDCGLambdaWeight(gain_fn=utils.identity,
rank_discount_fn=utils.log2_inverse))
def test_softmax_loss(self):
scores = [[1., 3., 2.], [1., 2., 3.], [1., 2., 3.]]
labels = [[0., 0., 1.], [0., 0., 2.], [0., 0., 0.]]
weights = [[2.], [1.], [1.]]
loss = losses.get(loss=losses.RankingLossKey.SOFTMAX_LOSS)
self.assertAlmostEqual(
loss(labels, scores).numpy(),
-(ln(_softmax(scores[0])[2]) + ln(_softmax(scores[1])[2]) * 2.) /
3.,
places=5)
self.assertAlmostEqual(loss(labels, scores, weights).numpy(),
-(ln(_softmax(scores[0])[2]) * 2. +
ln(_softmax(scores[1])[2]) * 2. * 1.) / 3.,
places=5)
# Test LambdaWeight.
rank_discount_fn = lambda r: 1. / tf.math.log1p(r)
lambda_weight = losses.DCGLambdaWeight(
rank_discount_fn=rank_discount_fn)
loss = losses.get(loss=losses.RankingLossKey.SOFTMAX_LOSS,
lambda_weight=lambda_weight)
self.assertAlmostEqual(
loss(labels, scores).numpy(),
-(ln(_softmax(scores[0])[2]) / ln(1. + 2.) +
ln(_softmax(scores[1])[2]) * 2. / ln(1. + 1.)) / 3.,
places=5)
def _check_pairwise_loss(self, loss_form):
"""Helper function to test `loss_fn`."""
scores = [[1., 3., 2.], [1., 2., 3.]]
labels = [[0., 0., 1.], [0., 0., 2.]]
listwise_weights = [[2.], [1.]]
listwise_weights_expanded = [[2.] * 3, [1.] * 3]
itemwise_weights = [[2., 3., 4.], [1., 1., 1.]]
default_weights = [1.] * 3
list_size = 3.
loss_form_dict = {
'hinge':
losses.get(loss=losses.RankingLossKey.PAIRWISE_HINGE_LOSS,
name='hinge'),
'logistic':
losses.get(loss=losses.RankingLossKey.PAIRWISE_LOGISTIC_LOSS,
name='logistic'),
'soft_zero_one':
losses.get(loss=losses.RankingLossKey.PAIRWISE_SOFT_ZERO_ONE_LOSS,
name='soft_zero_one'),
}
loss_fn = loss_form_dict[loss_form]
# Individual lists.
self.assertAlmostEqual(loss_fn([labels[0]], [scores[0]]).numpy(),
_batch_aggregation([
_pairwise_loss(labels[0], scores[0],
default_weights, loss_form)
]),
places=5)
self.assertAlmostEqual(loss_fn([labels[1]], [scores[1]]).numpy(),
_batch_aggregation([
_pairwise_loss(labels[1], scores[1],
default_weights, loss_form)
]),
places=5)
# Itemwise weights.
self.assertAlmostEqual(loss_fn([labels[0]], [scores[0]],
sample_weight=[itemwise_weights[0]
]).numpy(),
_batch_aggregation([
_pairwise_loss(labels[0], scores[0],
itemwise_weights[0],
loss_form)
]),
places=5)
self.assertAlmostEqual(loss_fn([labels[1]], [scores[1]],
sample_weight=[itemwise_weights[1]
]).numpy(),
_batch_aggregation([
_pairwise_loss(labels[1], scores[1],
itemwise_weights[1],
loss_form)
]),
places=5)
# Multiple lists.
self.assertAlmostEqual(loss_fn(labels,
scores,
sample_weight=listwise_weights).numpy(),
_batch_aggregation([
_pairwise_loss(labels[0], scores[0],
listwise_weights_expanded[0],
loss_form),
_pairwise_loss(labels[1], scores[1],
listwise_weights_expanded[1],
loss_form)
]),
places=5)
# Test LambdaWeight.
rank_discount_fn = lambda r: 1. / tf.math.log1p(r)
lambda_weight = losses.DCGLambdaWeight(
rank_discount_fn=rank_discount_fn, smooth_fraction=1.)
loss_form_dict = {
'hinge':
losses.get(loss=losses.RankingLossKey.PAIRWISE_HINGE_LOSS,
name='hinge',
lambda_weight=lambda_weight),
'logistic':
losses.get(loss=losses.RankingLossKey.PAIRWISE_LOGISTIC_LOSS,
name='logistic',
lambda_weight=lambda_weight),
'soft_zero_one':
losses.get(loss=losses.RankingLossKey.PAIRWISE_SOFT_ZERO_ONE_LOSS,
name='soft_zero_one',
lambda_weight=lambda_weight),
}
loss_fn = loss_form_dict[loss_form]
self.assertAlmostEqual(loss_fn(labels,
scores,
sample_weight=listwise_weights).numpy(),
_batch_aggregation([
_pairwise_loss(labels[0],
scores[0],
listwise_weights_expanded[0],
loss_form,
rank_discount_form='LOG'),
_pairwise_loss(labels[1],
scores[1],
listwise_weights_expanded[1],
loss_form,
rank_discount_form='LOG')
]) * list_size,
places=5)