Method Set

python-package/compile/src/io/config.cpp:158–211

}

void Config::Set(const std::unordered_map<std::string, std::string>& params) {
  // generate seeds by seed.
  if (GetInt(params, "seed", &seed)) {
    Random rand(seed);
    int int_max = std::numeric_limits<int16_t>::max();
    data_random_seed = static_cast<int>(rand.NextShort(0, int_max));
    bagging_seed = static_cast<int>(rand.NextShort(0, int_max));
    drop_seed = static_cast<int>(rand.NextShort(0, int_max));
    feature_fraction_seed = static_cast<int>(rand.NextShort(0, int_max));
  }

  GetTaskType(params, &task);
  GetBoostingType(params, &boosting);
  GetMetricType(params, &metric);
  GetObjectiveType(params, &objective);
  GetDeviceType(params, &device_type);
  GetTreeLearnerType(params, &tree_learner);

  GetMembersFromString(params);

  // sort eval_at
  std::sort(eval_at.begin(), eval_at.end());

  if (valid_data_initscores.size() == 0 && valid.size() > 0) {
    valid_data_initscores = std::vector<std::string>(valid.size(), "");
  }
  CHECK(valid.size() == valid_data_initscores.size());

  if (valid_data_initscores.empty()) {
    std::vector<std::string> new_valid;
    for (size_t i = 0; i < valid.size(); ++i) {
      if (valid[i] != data) {
        // Only push the non-training data
        new_valid.push_back(valid[i]);
      } else {
        is_provide_training_metric = true;
      }
    }
    valid = new_valid;
  }

  // check for conflicts
  CheckParamConflict();

  if (verbosity == 1) {
    LightGBM::Log::ResetLogLevel(LightGBM::LogLevel::Info);
  } else if (verbosity == 0) {
    LightGBM::Log::ResetLogLevel(LightGBM::LogLevel::Warning);
  } else if (verbosity >= 2) {
    LightGBM::Log::ResetLogLevel(LightGBM::LogLevel::Debug);
  } else {
    LightGBM::Log::ResetLogLevel(LightGBM::LogLevel::Fatal);
  }
}

bool CheckMultiClassObjective(const std::string& objective) {
  return (objective == std::string("multiclass") || objective == std::string("multiclassova"));