Method TrainOneIter_old

src/boosting/gbdt.cpp:383–464

bool GBDT::TrainOneIter_old(const score_t* gradients, const score_t* hessians) {
  std::vector<double> init_scores(num_tree_per_iteration_, 0.0);
  // boosting first
  if (gradients == nullptr || hessians == nullptr) {
    for (int cur_tree_id = 0; cur_tree_id < num_tree_per_iteration_; ++cur_tree_id) {
      init_scores[cur_tree_id] = BoostFromAverage(cur_tree_id, true);
    }
    Boosting();
    gradients = gradients_.data();
    hessians = hessians_.data();
  }
  // bagging logic
  Bagging(iter_);

  bool should_continue = false;
  for (int cur_tree_id = 0; cur_tree_id < num_tree_per_iteration_; ++cur_tree_id) {
    const size_t offset = static_cast<size_t>(cur_tree_id) * num_data_;
    std::unique_ptr<Tree> new_tree(new Tree(2));
    if (class_need_train_[cur_tree_id] && train_data_->num_features() > 0) {
      auto grad = gradients + offset;
      auto hess = hessians + offset;
      // need to copy gradients for bagging subset.
      if (is_use_subset_ && bag_data_cnt_ < num_data_) {
        for (int i = 0; i < bag_data_cnt_; ++i) {
          gradients_[offset + i] = grad[bag_data_indices_[i]];
          hessians_[offset + i] = hess[bag_data_indices_[i]];
        }
        grad = gradients_.data() + offset;
        hess = hessians_.data() + offset;
      }
      new_tree.reset(tree_learner_->Train(grad, hess, is_constant_hessian_, forced_splits_json_));
    }

    if (new_tree->num_leaves() > 1) {
      should_continue = true;
      auto score_ptr = train_score_updater_->score() + offset;
      auto residual_getter = [score_ptr](const label_t* label, int i) {return static_cast<double>(label[i]) - score_ptr[i]; };
      tree_learner_->RenewTreeOutput(new_tree.get(), objective_function_, residual_getter,
                                     num_data_, bag_data_indices_.data(), bag_data_cnt_);
      // shrinkage by learning rate
      new_tree->Shrinkage(shrinkage_rate_);
      // update score
      UpdateScore(new_tree.get(), cur_tree_id);
      if (std::fabs(init_scores[cur_tree_id]) > kEpsilon) {
        new_tree->AddBias(init_scores[cur_tree_id]);
      }
    } else {
      // only add default score one-time
      if (models_.size() < static_cast<size_t>(num_tree_per_iteration_)) {
        double output = 0.0;
        if (!class_need_train_[cur_tree_id]) {
          if (objective_function_ != nullptr) {
            output = objective_function_->BoostFromScore(cur_tree_id);
          }
        } else {
          output = init_scores[cur_tree_id];
        }
        new_tree->AsConstantTree(output);