message: TST: reorder test cases
diff: @@ -313,38 +313,41 @@ class FeatureColumnTest(test.TestCase):
  expected_shape = (id_tensor_shape[:output_rank - 1] + [vocab_size])
  self.assertEquals(expected_shape, list(one_hot_value.shape))
  - def testMissingValueInOneHotColumnForSparseColumnWithKeys(self):
  + def testOneHotColumnForWeightedSparseColumn(self):
  ids = fc.sparse_column_with_keys("ids", ["marlo", "omar", "stringer"])
  - one_hot = fc.one_hot_column(ids)
  + weighted_ids = fc.weighted_sparse_column(ids, "weights")
  + one_hot = fc.one_hot_column(weighted_ids)
  + self.assertEqual(one_hot.sparse_id_column.name, "ids_weighted_by_weights")
  + self.assertEqual(one_hot.length, 3)
  +
  + def testMissingValueInOneHotColumnForWeightedSparseColumn(self):
  + # Github issue 12583
  + ids = fc.sparse_column_with_keys("ids", ["marlo", "omar", "stringer"])
  + weighted_ids = fc.weighted_sparse_column(ids, "weights")
  + one_hot = fc.one_hot_column(weighted_ids)
  features = {
  - 'ids': constant_op.constant(['marlo', 'skywalker', 'omar'],
  - shape=(1, 3)),
  + 'ids': constant_op.constant([['marlo', 'unknown', 'omar']]),
  + 'weights': constant_op.constant([[2., 4., 6.]])
  }
  one_hot_tensor = feature_column_ops.input_from_feature_columns(
  features, [one_hot])
  with self.test_session() as sess:
  sess.run(variables.global_variables_initializer())
  sess.run(lookup_ops.tables_initializer())
  - self.assertAllEqual([[1., 1., 0.]], one_hot_tensor.eval())
  + self.assertAllEqual([[2., 6., 0.]], one_hot_tensor.eval())
  - def testMissingValueInOneHotColumnForWeightedSparseColumn(self):
  + def testMissingValueInOneHotColumnForSparseColumnWithKeys(self):
  ids = fc.sparse_column_with_keys("ids", ["marlo", "omar", "stringer"])
  - weighted_ids = fc.weighted_sparse_column(ids, "weights")
  - one_hot = fc.one_hot_column(weighted_ids)
  - self.assertEqual(one_hot.sparse_id_column.name, "ids_weighted_by_weights")
  - self.assertEqual(one_hot.length, 3)
  -
  + one_hot = fc.one_hot_column(ids)
  features = {
  - 'ids': constant_op.constant(['marlo', 'skywalker', 'omar'],
  - shape=(1, 3)),
  - 'weights': constant_op.constant([2., 4., 6.], shape=(1, 3))
  + 'ids': constant_op.constant([['marlo', 'unknown', 'omar']])
  }
  one_hot_tensor = feature_column_ops.input_from_feature_columns(
  features, [one_hot])
  with self.test_session() as sess:
  sess.run(variables.global_variables_initializer())
  sess.run(lookup_ops.tables_initializer())
  - self.assertAllEqual([[2., 6., 0.]], one_hot_tensor.eval())
  + self.assertAllEqual([[1., 1., 0.]], one_hot_tensor.eval())
  def testOneHotColumnDeepCopy(self):
  a = fc.sparse_column_with_keys("a", ["a", "b", "c", "d"])
  