Method ResetConfig

src/treelearner/serial_tree_learner.cpp:141–171

}

void SerialTreeLearner::ResetConfig(const Config* config) {
  if (config_->num_leaves != config->num_leaves) {
    config_ = config;
    int max_cache_size = 0;
    // Get the max size of pool
    if (config->histogram_pool_size <= 0) {
      max_cache_size = config_->num_leaves;
    } else {
      size_t total_histogram_size = 0;
      for (int i = 0; i < train_data_->num_features(); ++i) {
        total_histogram_size += sizeof(HistogramBinEntry) * train_data_->FeatureNumBin(i);
      }
      max_cache_size = static_cast<int>(config_->histogram_pool_size * 1024 * 1024 / total_histogram_size);
    }
    // at least need 2 leaves
    max_cache_size = std::max(2, max_cache_size);
    max_cache_size = std::min(max_cache_size, config_->num_leaves);
    histogram_pool_.DynamicChangeSize(train_data_, config_, max_cache_size, config_->num_leaves);

    // push split information for all leaves
    best_split_per_leaf_.resize(config_->num_leaves);
    data_partition_->ResetLeaves(config_->num_leaves);
  } else {
    config_ = config;
  }
  histogram_pool_.ResetConfig(config_);
  if (CostEfficientGradientBoosting::IsEnable(config_)) {
    cegb_.reset(new CostEfficientGradientBoosting(this));
    cegb_->Init();
  }
}

Tree* SerialTreeLearner::Train(const score_t* gradients, const score_t *hessians, bool is_constant_hessian, const Json& forced_split_json) {
  gradients_ = gradients;