Method Predictor

src/application/predictor.hpp:39–116

! * \brief Constructor * \param boosting Input boosting model * \param num_iteration Number of boosting round * \param is_raw_score True if need to predict result with raw score * \param predict_leaf_index True to output leaf index instead of prediction score * \param predict_contrib True to output feature contributions instead of prediction score */

  * \param predict_contrib True to output feature contributions instead of prediction score
  */
  Predictor(Boosting* boosting, int num_iteration,
            bool is_raw_score, bool predict_leaf_index, bool predict_contrib,
            bool early_stop, int early_stop_freq, double early_stop_margin) {
    early_stop_ = CreatePredictionEarlyStopInstance("none", LightGBM::PredictionEarlyStopConfig());
    if (early_stop && !boosting->NeedAccuratePrediction()) {
      PredictionEarlyStopConfig pred_early_stop_config;
      CHECK(early_stop_freq > 0);
      CHECK(early_stop_margin >= 0);
      pred_early_stop_config.margin_threshold = early_stop_margin;
      pred_early_stop_config.round_period = early_stop_freq;
      if (boosting->NumberOfClasses() == 1) {
        early_stop_ = CreatePredictionEarlyStopInstance("binary", pred_early_stop_config);
      } else {
        early_stop_ = CreatePredictionEarlyStopInstance("multiclass", pred_early_stop_config);
      }
    }

    #pragma omp parallel
    #pragma omp master
    {
      num_threads_ = omp_get_num_threads();
    }
    boosting->InitPredict(num_iteration, predict_contrib);
    boosting_ = boosting;
    num_pred_one_row_ = boosting_->NumPredictOneRow(num_iteration, predict_leaf_index, predict_contrib);
    num_feature_ = boosting_->MaxFeatureIdx() + 1;
    predict_buf_ = std::vector<std::vector<double>>(num_threads_, std::vector<double>(num_feature_, 0.0f));
    const int kFeatureThreshold = 100000;
    const size_t KSparseThreshold = static_cast<size_t>(0.01 * num_feature_);
    if (predict_leaf_index) {
      predict_fun_ = [=](const std::vector<std::pair<int, double>>& features, double* output) {
        int tid = omp_get_thread_num();
        if (num_feature_ > kFeatureThreshold && features.size() < KSparseThreshold) {
          auto buf = CopyToPredictMap(features);
          boosting_->PredictLeafIndexByMap(buf, output);
        } else {
          CopyToPredictBuffer(predict_buf_[tid].data(), features);
          // get result for leaf index
          boosting_->PredictLeafIndex(predict_buf_[tid].data(), output);
          ClearPredictBuffer(predict_buf_[tid].data(), predict_buf_[tid].size(), features);
        }
      };
    } else if (predict_contrib) {
        predict_fun_ = [=](const std::vector<std::pair<int, double>>& features, double* output) {
          int tid = omp_get_thread_num();
          CopyToPredictBuffer(predict_buf_[tid].data(), features);
          // get result for leaf index
          boosting_->PredictContrib(predict_buf_[tid].data(), output, &early_stop_);
          ClearPredictBuffer(predict_buf_[tid].data(), predict_buf_[tid].size(), features);
        };
    } else {
      if (is_raw_score) {
        predict_fun_ = [=](const std::vector<std::pair<int, double>>& features, double* output) {
          int tid = omp_get_thread_num();
          if (num_feature_ > kFeatureThreshold && features.size() < KSparseThreshold) {
            auto buf = CopyToPredictMap(features);
            boosting_->PredictRawByMap(buf, output, &early_stop_);
          } else {