def test_gumbel_approx_ndcg_loss(self):
scores = [[1.4, -2.8, -0.4], [0., 1.8, 10.2], [1., 1.2, -3.2]]
labels = [[0., 2., 1.], [1., 0., 3.], [0., 0., 0.]]
# sampled_scores = [[-.291, -1.643, -2.826],
# [-.0866, -2.924, -3.530],
# [-12.42, -9.492, -7.939e-5],
# [-8.859, -6.830, -1.223e-3],
# [-.8930, -.5266, -45.80183],
# [-.6650, -.7220, -45.94149]]
# ranks = [[1, 2, 3],
# [1, 2, 3],
# [3, 2, 1],
# [3, 2, 1],
# [2, 1, 3],
# [1, 2, 3]]
# expanded_labels = [[0., 2., 1.],
# [0., 2., 1.],
# [1., 0., 3.],
# [1., 0., 3.],
# [0., 0., 0.],
# [0., 0., 0.]]
# expanded_weights = [[2.], [2.],
# [1.], [1.],
# [1.], [1.]]
loss = losses.GumbelApproxNDCGLoss(sample_size=2, seed=1)
self.assertAlmostEqual(
loss(labels, scores).numpy(),
-(2 * (1 / (3 / ln(2) + 1 / ln(3))) * (3 / ln(3) + 1 / ln(4)) + 2 *
(1 / (7 / ln(2) + 1 / ln(3))) * (7 / ln(2) + 1 / ln(4))) / 6,
places=3)
def test_gumbel_approx_ndcg_weighted_loss(self):
scores = [[1.4, -2.8, -0.4], [0., 1.8, 10.2], [1., 1.2, -3.2]]
labels = [[0., 2., 1.], [1., 0., 3.], [0., 0., 0.]]
weights = [[2.], [1.], [1.]]
loss = losses.GumbelApproxNDCGLoss(sample_size=2, seed=1)
self.assertAlmostEqual(
loss(labels, scores, weights).numpy(),
-(2 * 2 * (1 / (3 / ln(2) + 1 / ln(3))) *
(3 / ln(3) + 1 / ln(4)) + 1 * 2 * (1 / (7 / ln(2) + 1 / ln(3))) *
(7 / ln(2) + 1 / ln(4))) / 6,
places=3)
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))