Method Predict

src/c_api.cpp:278–314

  void Predict(int num_iteration, int predict_type, int nrow, int ncol,
               std::function<std::vector<std::pair<int, double>>(int row_idx)> get_row_fun,
               const Config& config,
               double* out_result, int64_t* out_len) {
    if (ncol != boosting_->MaxFeatureIdx() + 1) {
      Log::Fatal("The number of features in data (%d) is not the same as it was in training data (%d).", ncol, boosting_->MaxFeatureIdx() + 1);
    }
    std::lock_guard<std::mutex> lock(mutex_);
    bool is_predict_leaf = false;
    bool is_raw_score = false;
    bool predict_contrib = false;
    if (predict_type == C_API_PREDICT_LEAF_INDEX) {
      is_predict_leaf = true;
    } else if (predict_type == C_API_PREDICT_RAW_SCORE) {
      is_raw_score = true;
    } else if (predict_type == C_API_PREDICT_CONTRIB) {
      predict_contrib = true;
    } else {
      is_raw_score = false;
    }

    Predictor predictor(boosting_.get(), num_iteration, is_raw_score, is_predict_leaf, predict_contrib,
                        config.pred_early_stop, config.pred_early_stop_freq, config.pred_early_stop_margin);
    int64_t num_pred_in_one_row = boosting_->NumPredictOneRow(num_iteration, is_predict_leaf, predict_contrib);
    auto pred_fun = predictor.GetPredictFunction();
    OMP_INIT_EX();
    #pragma omp parallel for schedule(static)
    for (int i = 0; i < nrow; ++i) {
      OMP_LOOP_EX_BEGIN();
      auto one_row = get_row_fun(i);
      auto pred_wrt_ptr = out_result + static_cast<size_t>(num_pred_in_one_row) * i;
      pred_fun(one_row, pred_wrt_ptr);
      OMP_LOOP_EX_END();
    }
    OMP_THROW_EX();
    *out_len = num_pred_in_one_row * nrow;
  }

  void Predict(int num_iteration, int predict_type, const char* data_filename,
               int data_has_header, const Config& config,