Method Train

python-package/compile/src/boosting/gbdt.cpp:288–306

}

void GBDT::Train(int snapshot_freq, const std::string& model_output_path) {
  bool is_finished = false;
  auto start_time = std::chrono::steady_clock::now();
  for (int iter = 0; iter < config_->num_iterations && !is_finished; ++iter) {
    is_finished = TrainOneIter(nullptr, nullptr);
    if (!is_finished) {
      is_finished = EvalAndCheckEarlyStopping();
    }
    auto end_time = std::chrono::steady_clock::now();
    // output used time per iteration
    Log::Info("%f seconds elapsed, finished iteration %d", std::chrono::duration<double,
              std::milli>(end_time - start_time) * 1e-3, iter + 1);
    if (snapshot_freq > 0
        && (iter + 1) % snapshot_freq == 0) {
      std::string snapshot_out = model_output_path + ".snapshot_iter_" + std::to_string(iter + 1);
      SaveModelToFile(0, -1, snapshot_out.c_str());
    }
  }
}

void GBDT::RefitTree(const std::vector<std::vector<int>>& tree_leaf_prediction) {
  CHECK(tree_leaf_prediction.size() > 0);