Method RunTest

tests/cpp/test_multi_target.cc:70–105

  }

  void RunTest(Context const* ctx, std::string const& tree_method, bool weight) {
    auto p_fmat = weight ? Xyw_ : Xy_;
    std::unique_ptr<Learner> learner{Learner::Create({p_fmat})};
    learner->SetParams(Args{{"tree_method", tree_method},
                            {"objective", "reg:absoluteerror"},
                            {"device", ctx->DeviceName()}});
    learner->Configure();
    for (auto i = 0; i < 4; ++i) {
      learner->UpdateOneIter(i, p_fmat);
    }
    ASSERT_EQ(learner->Groups(), 3);

    Json config{Object{}};
    learner->SaveConfig(&config);
    auto base_score = GetBaseScore(config);

    std::vector<float> split_scores;
    for (bst_target_t t{0}; t < p_fmat->Info().labels.Shape(1); ++t) {
      auto t_Xy = weight ? single_w_[t] : single_[t];
      std::unique_ptr<Learner> sl{Learner::Create({t_Xy})};
      sl->SetParams(Args{{"tree_method", tree_method},
                         {"objective", "reg:absoluteerror"},
                         {"device", ctx->DeviceName()}});
      sl->Configure();
      sl->UpdateOneIter(0, t_Xy);
      Json s_config{Object{}};
      sl->SaveConfig(&s_config);
      auto s_base_score = GetBaseScore(s_config);
      ASSERT_EQ(s_base_score.size(), 1);
      linalg::Vector<float> out;
      common::Median(sl->Ctx(), t_Xy->Info().labels, t_Xy->Info().weights_, &out);
      ASSERT_FLOAT_EQ(s_base_score[0], out(0));
      split_scores.push_back(s_base_score[0]);
    }
    ASSERT_EQ(split_scores, base_score);
  }

  void RunTest(Context const* ctx, std::string const& tree_method) {
    this->RunTest(ctx, tree_method, false);