move the training into boosting.

include/LightGBM/boosting.h

@@ -56,6 +56,8 @@ public:
   virtual void AddValidDataset(const Dataset* valid_data,
     const std::vector<const Metric*>& valid_metrics) = 0;
 
+  virtual void Train(int snapshot_freq, const std::string& model_output_path) = 0;
+
   /*!
   * \brief Training logic
   * \param gradient nullptr for using default objective, otherwise use self-defined boosting

src/application/application.cpp

@@ -224,24 +224,7 @@ void Application::InitTrain() {
 
 void Application::Train() {
   Log::Info("Started training...");
-  int total_iter = config_.boosting_config.num_iterations;
-  bool is_finished = false;
-  bool need_eval = true;
-  auto start_time = std::chrono::steady_clock::now();
-  for (int iter = 0; iter < total_iter && !is_finished; ++iter) {
-    is_finished = boosting_->TrainOneIter(nullptr, nullptr, need_eval);
-    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 (config_.io_config.snapshot_freq > 0 
-        && (iter+1) % config_.io_config.snapshot_freq == 0) {
-      std::string snapshot_out = config_.io_config.output_model + ".snapshot_iter_" + std::to_string(iter + 1);
-      boosting_->SaveModelToFile(-1, snapshot_out.c_str());
-    }
-  }
-  // save model to file
-  boosting_->SaveModelToFile(-1, config_.io_config.output_model.c_str());
+  boosting_->Train(config_.io_config.snapshot_freq, config_.io_config.output_model);
   // convert model to if-else statement code
   if (config_.convert_model_language == std::string("cpp")) {
     boosting_->SaveModelToIfElse(-1, config_.io_config.convert_model.c_str());

src/boosting/gbdt.cpp

@@ -442,6 +442,25 @@ double ObtainAutomaticInitialScore(const ObjectiveFunction* objf, const float* l
   }
 }
 
+void GBDT::Train(int snapshot_freq, const std::string& model_output_path) {
+  bool is_finished = false;
+  bool need_eval = true;
+  auto start_time = std::chrono::steady_clock::now();
+  for (int iter = 0; iter < gbdt_config_->num_iterations && !is_finished; ++iter) {
+    is_finished = TrainOneIter(nullptr, nullptr, need_eval);
+    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(-1, snapshot_out.c_str());
+    }
+  }
+  SaveModelToFile(-1, model_output_path.c_str());
+}
+
 bool GBDT::TrainOneIter(const score_t* gradient, const score_t* hessian, bool is_eval) {
   // boosting from average label; or customized "average" if implemented for the current objective
   if (models_.empty()
@@ -461,6 +480,7 @@ bool GBDT::TrainOneIter(const score_t* gradient, const score_t* hessian, bool is
     models_.push_back(std::move(new_tree));
     boost_from_average_ = true;
   }
+
   // boosting first
   if (gradient == nullptr || hessian == nullptr) {
     #ifdef TIMETAG
@@ -481,6 +501,7 @@ bool GBDT::TrainOneIter(const score_t* gradient, const score_t* hessian, bool is
   #ifdef TIMETAG
   bagging_time += std::chrono::steady_clock::now() - start_time;
   #endif
+  // need to use subset gradient and hessian
   if (is_use_subset_ && bag_data_cnt_ < num_data_) {
     #ifdef TIMETAG
     start_time = std::chrono::steady_clock::now();

src/boosting/gbdt.h

@@ -74,6 +74,9 @@ public:
   */
   void AddValidDataset(const Dataset* valid_data,
                        const std::vector<const Metric*>& valid_metrics) override;
+
+  void Train(int snapshot_freq, const std::string& model_output_path) override;
+
   /*!
   * \brief Training logic
   * \param gradient nullptr for using default objective, otherwise use self-defined boosting