Function TestAbsoluteError

tests/cpp/objective/test_regression_obj.cc:307–344

}

void TestAbsoluteError(const Context* ctx) {
  std::unique_ptr<ObjFunction> obj{ObjFunction::Create("reg:absoluteerror", ctx)};
  obj->Configure({});
  CheckConfigReload(obj, "reg:absoluteerror");

  MetaInfo info;
  std::vector<float> labels{0.f, 3.f, 2.f, 5.f, 4.f, 7.f};
  info.labels.Reshape(6, 1);
  info.labels.Data()->HostVector() = labels;
  info.num_row_ = labels.size();

  HostDeviceVector<float> predt{1.f, 2.f, 3.f, 4.f, 5.f, 6.f};
  info.weights_.HostVector() = {1.f, 1.f, 1.f, 1.f, 1.f, 1.f};

  CheckObjFunction(obj, predt.HostVector(), labels, info.weights_.HostVector(),
                   {1.f, -1.f, 1.f, -1.f, 1.f, -1.f}, info.weights_.HostVector());

  RegTree tree;
  tree.ExpandNode(0, /*split_index=*/1, 2, true, 0.0f, 2.f, 3.f, 4.f, 2.f, 1.f, 1.f);
  bst_node_t left_nidx = tree.LeftChild(RegTree::kRoot);
  bst_node_t right_nidx = tree.RightChild(RegTree::kRoot);

  HostDeviceVector<bst_node_t> position;
  MakePositionsForTest(info.num_row_, left_nidx, right_nidx, &position);

  auto& h_predt = predt.HostVector();
  for (size_t i = 0; i < h_predt.size(); ++i) {
    h_predt[i] = labels[i] + i;
  }

  tree::TrainParam param;
  param.Init(Args{});
  auto lr = param.learning_rate;

  obj->UpdateTreeLeaf(position, info, lr, predt, 0, &tree);
  ASSERT_EQ(tree[1].LeafValue(), -1.0f * lr);
  ASSERT_EQ(tree[2].LeafValue(), -4.0f * lr);
}

void TestAbsoluteErrorLeaf(const Context* ctx) {
  bst_target_t constexpr kTargets = 3, kRows = 16;