Method TrainOneIter

src/boosting/rf.hpp:103–166

  }

  bool TrainOneIter(const score_t* gradients, const score_t* hessians) override {
    // bagging logic
    Bagging(iter_);
    CHECK(gradients == nullptr);
    CHECK(hessians == nullptr);

    gradients = gradients_.data();
    hessians = hessians_.data();
    for (int cur_tree_id = 0; cur_tree_id < num_tree_per_iteration_; ++cur_tree_id) {
      std::unique_ptr<Tree> new_tree(new Tree(2));
      size_t offset = static_cast<size_t>(cur_tree_id)* num_data_;
      if (class_need_train_[cur_tree_id]) {
        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) {
            tmp_grad_[i] = grad[bag_data_indices_[i]];
            tmp_hess_[i] = hess[bag_data_indices_[i]];
          }
          grad = tmp_grad_.data();
          hess = tmp_hess_.data();
        }

        new_tree.reset(tree_learner_->Train(grad, hess, is_constant_hessian_,
          forced_splits_json_));
      }

      if (new_tree->num_leaves() > 1) {
        double pred = init_scores_[cur_tree_id];
        auto residual_getter = [pred](const label_t* label, int i) {return static_cast<double>(label[i]) - pred; };
        tree_learner_->RenewTreeOutput(new_tree.get(), objective_function_, residual_getter,
          num_data_, bag_data_indices_.data(), bag_data_cnt_);
        if (std::fabs(init_scores_[cur_tree_id]) > kEpsilon) {
          new_tree->AddBias(init_scores_[cur_tree_id]);
        }
        // update score
        MultiplyScore(cur_tree_id, (iter_ + num_init_iteration_));
        UpdateScore(new_tree.get(), cur_tree_id);
        MultiplyScore(cur_tree_id, 1.0 / (iter_ + num_init_iteration_ + 1));
      } 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);
          MultiplyScore(cur_tree_id, (iter_ + num_init_iteration_));
          UpdateScore(new_tree.get(), cur_tree_id);
          MultiplyScore(cur_tree_id, 1.0 / (iter_ + num_init_iteration_ + 1));
        }
      }