def test_list_mle_loss(self):
with tf.Graph().as_default():
scores = [[0., ln(3), ln(2)], [0., ln(2), ln(3)],
[0., ln(2), ln(3)]]
labels = [[0., 2., 1.], [1., 0., 2.], [0., 0., 0.]]
weights = [[2.], [1.], [1.]]
with self.cached_session():
self.assertAlmostEqual(
ranking_losses._list_mle_loss(labels, scores,
seed=1).eval(),
-((ln(3. / (3 + 2 + 1)) + ln(2. / (2 + 1)) + ln(1. / 1)) +
(ln(3. / (3 + 2 + 1)) + ln(1. / (1 + 2)) + ln(2. / 2)) +
(ln(3. / (3 + 2 + 1)) + ln(2. /
(2 + 1)) + ln(1. / 1))) / 3,
places=5)
self.assertAlmostEqual(
ranking_losses._list_mle_loss(labels,
scores,
weights,
seed=1).eval(),
-(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)) + 1 *
(ln(3. / (3 + 2 + 1)) + ln(2. /
(2 + 1)) + ln(1. / 1))) / 3,
places=5)
def test_list_mle_loss_tie(self):
with tf.Graph().as_default():
tf.compat.v1.set_random_seed(3)
scores = [[0., ln(2), ln(3)]]
labels = [[0., 0., 0.]]
with self.cached_session():
self.assertAlmostEqual(
ranking_losses._list_mle_loss(labels, scores).eval(),
-((ln(3. / (3 + 2 + 1)) + ln(2. / (2 + 1)) + ln(1. / 1))),
places=5)
def test_list_mle_loss_lambda_weight(self):
scores = [[0., ln(3), ln(2)], [0., ln(2), ln(3)], [0., ln(2), ln(3)]]
labels = [[0., 2., 1.], [1., 0., 2.], [0., 0., 0.]]
lw = ranking_losses.create_p_list_mle_lambda_weight(3)
with self.cached_session():
self.assertAlmostEqual(
ranking_losses._list_mle_loss(
labels, scores, lambda_weight=lw, seed=1).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)) +
(3 * ln(3. / (3 + 2 + 1)) + 1 * ln(2. /
(2 + 1)) + 0 * ln(1. / 1))) / 3,
places=5)
