39fcf3cf46d394a6242e4094a11c544394c2a79f,tensorflow_ranking/python/losses_test.py,LossesTest,test_approx_ndcg_loss,#LossesTest#,894

Before Change

      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_wts = [
          sum([wt * l
               for wt, l in zip(wts, lbls)]) / sum(lbls) if sum(lbls) else 0
          for wts, lbls in zip(example_weights, labels)
      ]

      with self.cached_session():
        self.assertAlmostEqual(
            ranking_losses._approx_ndcg_loss(labels, scores).eval(),

After Change

      weights = [[2.], [1.], [1.]]
      example_weights = [[1., 2., 3.], [4., 5., 6.], [7., 8., 9.]]
      norm_weights = []
      for weight, label in zip(example_weights, labels):
        sum_label = sum(max(0, l) for l in label)
        norm_weights.append(
            sum(w * max(0, l) for w, l in zip(weight, label)) / sum_label
            if sum_label else 0)

      with self.cached_session():
        self.assertAlmostEqual(
            ranking_losses._approx_ndcg_loss(labels, scores).eval(),
            -((1 / (3 / ln(2) + 1 / ln(3))) * (3 / ln(4) + 1 / ln(3)) +

Instances