def test_approx_ndcg_loss(self):
scores = [[1.4, -2.8, -0.4], [0., 1.8, 10.2], [1., 1.2, -3.2]]
# ranks= [[1, 3, 2], [3, 2, 1], [2, 1, 3]]
labels = [[0., 2., 1.], [1., 0., 3.], [0., 0., 0.]]
weights = [[2.], [1.], [1.]]
example_weights = [[1., 2., 3.], [4., 5., 6.], [7., 8., 9.]]
norm_weights = [
normalize_weights(w, l) for w, l in zip(example_weights, labels)
]
loss = losses.ApproxNDCGLoss()
self.assertAlmostEqual(
loss(labels, scores).numpy(),
-((1 / (3 / ln(2) + 1 / ln(3))) * (3 / ln(4) + 1 / ln(3)) +
(1 / (7 / ln(2) + 1 / ln(3))) * (7 / ln(2) + 1 / ln(4))) / 3.,
places=5)
self.assertAlmostEqual(
loss(labels, scores, weights).numpy(),
-(2 * (1 / (3 / ln(2) + 1 / ln(3))) * (3 / ln(4) + 1 / ln(3)) + 1 *
(1 / (7 / ln(2) + 1 / ln(3))) * (7 / ln(2) + 1 / ln(4))) / 3.,
places=5)
self.assertAlmostEqual(
loss(labels, scores, example_weights).numpy(),
-(norm_weights[0] * (1 / (3 / ln(2) + 1 / ln(3))) *
(3 / ln(4) + 1 / ln(3)) + norm_weights[1] *
(1 / (7 / ln(2) + 1 / ln(3))) * (7 / ln(2) + 1 / ln(4))) / 3.,
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_approx_ndcg_loss_sum_batch(self):
scores = [[1.4, -2.8, -0.4], [0., 1.8, 10.2], [1., 1.2, -3.2]]
# ranks= [[1, 3, 2], [3, 2, 1], [2, 1, 3]]
labels = [[0., 2., 1.], [1., 0., 3.], [0., 0., 0.]]
example_weights = [[1., 2., 3.], [4., 5., 6.], [7., 8., 9.]]
norm_wts = [
normalize_weights(wts, lbls)
for wts, lbls in zip(example_weights, labels)
]
loss = losses.ApproxNDCGLoss(
reduction=tf.losses.Reduction.SUM_OVER_BATCH_SIZE)
self.assertAlmostEqual(
loss(labels, scores).numpy(),
-((1 / (3 / ln(2) + 1 / ln(3))) * (3 / ln(4) + 1 / ln(3)) +
(1 / (7 / ln(2) + 1 / ln(3))) * (7 / ln(2) + 1 / ln(4))) / 3.,
places=5)
self.assertAlmostEqual(
loss(labels, scores, example_weights).numpy(),
-(norm_wts[0] * (1 / (3 / ln(2) + 1 / ln(3))) *
(3 / ln(4) + 1 / ln(3)) + norm_wts[1] *
(1 / (7 / ln(2) + 1 / ln(3))) * (7 / ln(2) + 1 / ln(4))) / 3.,
places=5)