Method TestBaseScore

tests/cpp/test_learner.cc:636–668

class TestColumnSplit : public ::testing::TestWithParam<std::string> {
  void TestBaseScore(std::string objective, std::vector<float> const& expected_base_score,
                     Json expected_model) {
    auto const world_size = collective::GetWorldSize();
    auto n_threads = collective::GetWorkerLocalThreads(world_size);
    auto const rank = collective::GetRank();

    std::shared_ptr<DMatrix> p_fmat = MakeFmatForObjTest(objective, 10, 10, 3);
    std::shared_ptr<DMatrix> sliced{p_fmat->SliceCol(world_size, rank)};
    std::unique_ptr<Learner> learner{Learner::Create({sliced})};
    learner->SetParams(Args{{"nthread", std::to_string(n_threads)},
                            {"tree_method", "approx"},
                            {"objective", objective}});
    if (objective.find("quantile") != std::string::npos) {
      learner->SetParam("quantile_alpha", "0.5");
    }
    if (objective.find("expectile") != std::string::npos) {
      learner->SetParam("expectile_alpha", "0.5");
    }
    if (objective.find("multi") != std::string::npos) {
      learner->SetParam("num_class", "3");
    }
    learner->UpdateOneIter(0, sliced);
    Json config{Object{}};
    learner->SaveConfig(&config);
    auto base_score = GetBaseScore(config);
    for (size_t idx = 0; idx < base_score.size(); ++idx) {
      ASSERT_NEAR(base_score[idx], expected_base_score[idx], 1e-6);
    }

    Json model{Object{}};
    learner->SaveModel(&model);
    CompareJsonModels(model, expected_model);
  }

 public:
  void Run(std::string objective) {