Method FitByExistingTree

src/treelearner/serial_tree_learner.cpp:239–264

}

Tree* SerialTreeLearner::FitByExistingTree(const Tree* old_tree, const score_t* gradients, const score_t *hessians) const {
  auto tree = std::unique_ptr<Tree>(new Tree(*old_tree));
  CHECK(data_partition_->num_leaves() >= tree->num_leaves());
  OMP_INIT_EX();
  #pragma omp parallel for schedule(static)
  for (int i = 0; i < tree->num_leaves(); ++i) {
    OMP_LOOP_EX_BEGIN();
    data_size_t cnt_leaf_data = 0;
    auto tmp_idx = data_partition_->GetIndexOnLeaf(i, &cnt_leaf_data);
    double sum_grad = 0.0f;
    double sum_hess = kEpsilon;
    for (data_size_t j = 0; j < cnt_leaf_data; ++j) {
      auto idx = tmp_idx[j];
      sum_grad += gradients[idx];
      sum_hess += hessians[idx];
    }
    double output = FeatureHistogram::CalculateSplittedLeafOutput(sum_grad, sum_hess,
                                                                  config_->lambda_l1, config_->lambda_l2, config_->max_delta_step);
    auto old_leaf_output = tree->LeafOutput(i);
    auto new_leaf_output = output * tree->shrinkage();
    tree->SetLeafOutput(i, config_->refit_decay_rate * old_leaf_output + (1.0 - config_->refit_decay_rate) * new_leaf_output);
    OMP_LOOP_EX_END();
  }
  OMP_THROW_EX();
  return tree.release();
}

Tree* SerialTreeLearner::FitByExistingTree(const Tree* old_tree, const std::vector<int>& leaf_pred, const score_t* gradients, const score_t *hessians) {
  data_partition_->ResetByLeafPred(leaf_pred, old_tree->num_leaves());