def create_p_list_mle_lambda_weight(list_size):
"""Creates _LambdaWeight based on Position-Aware ListMLE paper.
Produces a weight based on the formulation presented in the
"Position-Aware ListMLE" paper (Lan et al.) and available using
create_p_list_mle_lambda_weight() factory function above.
Args:
list_size: Size of the input list.
Returns:
A _LambdaWeight for Position-Aware ListMLE.
"""
return losses_impl.ListMLELambdaWeight(
rank_discount_fn=lambda rank: tf.pow(2., list_size - rank) - 1.)
def test_list_mle_loss_lambda_weight(self):
with tf.Graph().as_default():
scores = [[0., ln(3), ln(2)], [0., ln(2), ln(3)]]
labels = [[0., 2., 1.], [1., 0., 2.]]
lw = losses_impl.ListMLELambdaWeight(
rank_discount_fn=lambda rank: tf.pow(2., 3 - rank) - 1.)
reduction = tf.compat.v1.losses.Reduction.SUM_BY_NONZERO_WEIGHTS
with self.cached_session():
loss_fn = losses_impl.ListMLELoss(name=None, lambda_weight=lw)
self.assertAlmostEqual(
loss_fn.compute(labels, scores, None, reduction).eval(),
-((3 * ln(3. / (3 + 2 + 1)) + 1 * ln(2. / (2 + 1)) +
0 * ln(1. / 1)) +
(3 * ln(3. /
(3 + 2 + 1)) + 1 * ln(1. /
(1 + 2)) + 0 * ln(2. / 2)))
/ 2,
places=5)