Method GetPredictAt

src/boosting/gbdt.cpp:723–761

}

void GBDT::GetPredictAt(int data_idx, double* out_result, int64_t* out_len) {
  CHECK(data_idx >= 0 && data_idx <= static_cast<int>(valid_score_updater_.size()));

  const double* raw_scores = nullptr;
  data_size_t num_data = 0;
  if (data_idx == 0) {
    raw_scores = GetTrainingScore(out_len);
    num_data = train_score_updater_->num_data();

  } else {
    auto used_idx = data_idx - 1;
    raw_scores = valid_score_updater_[used_idx]->score();
    num_data = valid_score_updater_[used_idx]->num_data();
    *out_len = static_cast<int64_t>(num_data) * num_class_ * num_labels_;
  }
  if (objective_function_ != nullptr) {
#pragma omp parallel for schedule(static)
    for (data_size_t i = 0; i < num_data; ++i) {
      std::vector<double> tree_pred(num_tree_per_iteration_);
      for (int j = 0; j < num_tree_per_iteration_; ++j) {
        tree_pred[j] = raw_scores[j * num_data + i];
      }
      std::vector<double> tmp_result(num_class_);
      objective_function_->ConvertOutput(tree_pred.data(), tmp_result.data());
      for (int j = 0; j < num_class_; ++j) {
        out_result[j * num_data + i] = static_cast<double>(tmp_result[j]);
      }
    }
  } else {
#pragma omp parallel for schedule(static)
    for (data_size_t i = 0; i < num_data; ++i) {
      for (int j = 0; j < num_tree_per_iteration_; ++j) {
        for (int k = 0; k < num_labels_; ++k) {
          out_result[k * num_tree_per_iteration_ * num_data + j * num_data + i] = static_cast<double>(raw_scores[k * num_tree_per_iteration_ * num_data + j * num_data + i]);
        }
      }
    }
  }
}

void GBDT::ResetTrainingData(const Dataset* train_data, const ObjectiveFunction* objective_function,
                             const std::vector<const Metric*>& training_metrics) {