Class Booster

python-package/compile/src/c_api.cpp:98–432

};

class Booster {
 public:
  explicit Booster(const char* filename) {
    boosting_.reset(Boosting::CreateBoosting("gbdt", filename));
  }

  Booster(const Dataset* train_data,
          const char* parameters) {
    auto param = Config::Str2Map(parameters);
    config_.Set(param);
    if (config_.num_threads > 0) {
      omp_set_num_threads(config_.num_threads);
    }
    // create boosting
    if (config_.input_model.size() > 0) {
      Log::Warning("Continued train from model is not supported for c_api,\n"
                   "please use continued train with input score");
    }

    boosting_.reset(Boosting::CreateBoosting(config_.boosting, nullptr));
    train_data_ = train_data;
    CreateObjectiveAndMetrics();
    // initialize the boosting
    if (config_.tree_learner == std::string("feature")) {
      Log::Fatal("Do not support feature parallel in c api");
    }
    if (Network::num_machines() == 1 && config_.tree_learner != std::string("serial") && config_.tree_learner != std::string("serial2")) {
      Log::Warning("Only find one worker, will switch to serial tree learner");
      config_.tree_learner = "serial";
    }
    boosting_->Init(&config_, train_data_, objective_fun_.get(),
                    Common::ConstPtrInVectorWrapper<Metric>(train_metric_));
  }

  void MergeFrom(const Booster* other) {
    std::lock_guard<std::mutex> lock(mutex_);
    boosting_->MergeFrom(other->boosting_.get());
  }

  ~Booster() {
  }

  void CreateObjectiveAndMetrics() {
    // create objective function
    objective_fun_.reset(ObjectiveFunction::CreateObjectiveFunction(config_.objective,
                                                                    config_));
    if (objective_fun_ == nullptr) {
      Log::Warning("Using self-defined objective function");
    }
    // initialize the objective function
    if (objective_fun_ != nullptr) {
      objective_fun_->Init(train_data_->metadata(), train_data_->num_data());
    }

    // create training metric
    train_metric_.clear();
    for (auto metric_type : config_.metric) {
      auto metric = std::unique_ptr<Metric>(