Method RefitTree

src/boosting/gbdt.cpp:308–331

}

void GBDT::RefitTree(const std::vector<std::vector<int>>& tree_leaf_prediction) {
  CHECK(tree_leaf_prediction.size() > 0);
  CHECK(static_cast<size_t>(num_data_) == tree_leaf_prediction.size());
  CHECK(static_cast<size_t>(models_.size()) == tree_leaf_prediction[0].size());
  int num_iterations = static_cast<int>(models_.size() / num_tree_per_iteration_);
  std::vector<int> leaf_pred(num_data_);
  for (int iter = 0; iter < num_iterations; ++iter) {
    Boosting();
    for (int tree_id = 0; tree_id < num_tree_per_iteration_; ++tree_id) {
      int model_index = iter * num_tree_per_iteration_ + tree_id;
#pragma omp parallel for schedule(static)
      for (int i = 0; i < num_data_; ++i) {
        leaf_pred[i] = tree_leaf_prediction[i][model_index];
        CHECK(leaf_pred[i] < models_[model_index]->num_leaves());
      }
      size_t offset = static_cast<size_t>(tree_id) * num_data_;
      auto grad = gradients_.data() + offset;
      auto hess = hessians_.data() + offset;
      auto new_tree = tree_learner_->FitByExistingTree(models_[model_index].get(), leaf_pred, grad, hess);
      train_score_updater_->AddScore(tree_learner_.get(), new_tree, tree_id);
      models_[model_index].reset(new_tree);
    }
  }
}

/* If the custom "average" is implemented it will be used inplace of the label average (if enabled)
*