def test_list_mle_loss_tie(self):
tf.random.set_seed(1)
scores = [[0., ln(2), ln(3)]]
labels = [[0., 0., 1.]]
loss = losses.ListMLELoss()
self.assertAlmostEqual(
loss(labels, scores).numpy(),
-((ln(3. / (3 + 2 + 1)) + ln(2. / (2 + 1)) + ln(1. / 1))),
places=5)
def test_listwise_losses_are_serializable(self):
self.assertIsLossSerializable(
losses.SoftmaxLoss(lambda_weight=self._lambda_weight))
self.assertIsLossSerializable(
losses.ListMLELoss(lambda_weight=self._lambda_weight))
self.assertIsLossSerializable(
losses.ApproxMRRLoss(lambda_weight=self._lambda_weight))
self.assertIsLossSerializable(
losses.ApproxNDCGLoss(lambda_weight=self._lambda_weight))
# TODO: Debug assertIsLossSerializable for Gumbel loss. Right now,
# the loss values got from obj and the deserialized don't match exactly.
self.assertIsSerializable(losses.GumbelApproxNDCGLoss(seed=1))
def test_list_mle_loss_lambda_weight(self):
scores = [[0., ln(3), ln(2)], [0., ln(2), ln(3)]]
labels = [[0., 2., 1.], [1., 0., 2.]]
lw = losses.ListMLELambdaWeight(
rank_discount_fn=lambda rank: tf.pow(2., 3 - rank) - 1.)
loss = losses.ListMLELoss(lambda_weight=lw)
self.assertAlmostEqual(
loss(labels, scores).numpy(),
-((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)
def test_list_mle_loss(self):
scores = [[0., ln(3), ln(2)], [0., ln(2), ln(3)]]
labels = [[0., 2., 1.], [1., 0., 2.]]
weights = [[2.], [1.]]
loss = losses.ListMLELoss()
self.assertAlmostEqual(
loss(labels, scores).numpy(),
-((ln(3. / (3 + 2 + 1)) + ln(2. / (2 + 1)) + ln(1. / 1)) +
(ln(3. / (3 + 2 + 1)) + ln(1. / (1 + 2)) + ln(2. / 2))) / 2,
places=5)
self.assertAlmostEqual(
loss(labels, scores, weights).numpy(),
-(2 * (ln(3. / (3 + 2 + 1)) + ln(2. / (2 + 1)) + ln(1. / 1)) + 1 *
(ln(3. / (3 + 2 + 1)) + ln(1. / (1 + 2)) + ln(2. / 2))) / 2,
places=5)