reduce memory cost for multi classification

src/boosting/gbdt.cpp

@@ -35,7 +35,7 @@ void GBDT::Init(const BoostingConfig* config, const Dataset* train_data, const O
   random_ = Random(config->bagging_seed);
   train_data_ = nullptr;
   gbdt_config_ = nullptr;
-  tree_learner_.clear();
+  tree_learner_ = nullptr;
   ResetTrainingData(config, train_data, object_function, training_metrics);
 }
 
@@ -58,17 +58,11 @@ void GBDT::ResetTrainingData(const BoostingConfig* config, const Dataset* train_
   }
 
   if (train_data_ != train_data && train_data != nullptr) {
-    if (tree_learner_.empty()) {
-      for (int i = 0; i < num_class_; ++i) {
-        auto new_tree_learner = std::unique_ptr<TreeLearner>(TreeLearner::CreateTreeLearner(new_config->tree_learner_type, &new_config->tree_config));
-        tree_learner_.push_back(std::move(new_tree_learner));
-      }
-      tree_learner_.shrink_to_fit();
+    if (tree_learner_ == nullptr) {
+      tree_learner_ = std::unique_ptr<TreeLearner>(TreeLearner::CreateTreeLearner(new_config->tree_learner_type, &new_config->tree_config));
     }
     // init tree learner
-    for (int i = 0; i < num_class_; ++i) {
-      tree_learner_[i]->Init(train_data);
-    }
+    tree_learner_->Init(train_data);
 
     // push training metrics
     training_metrics_.clear();
@@ -114,9 +108,7 @@ void GBDT::ResetTrainingData(const BoostingConfig* config, const Dataset* train_
   train_data_ = train_data;
   if (train_data_ != nullptr) {
     // reset config for tree learner
-    for (int i = 0; i < num_class_; ++i) {
-      tree_learner_[i]->ResetConfig(&new_config->tree_config);
-    }
+    tree_learner_->ResetConfig(&new_config->tree_config);
   }
   gbdt_config_.reset(new_config.release());
 }
@@ -154,7 +146,7 @@ void GBDT::AddValidDataset(const Dataset* valid_data,
 }
 
 
-void GBDT::Bagging(int iter, const int curr_class) {
+void GBDT::Bagging(int iter) {
   // if need bagging
   if (!out_of_bag_data_indices_.empty() && iter % gbdt_config_->bagging_freq == 0) {
     // if doesn't have query data
@@ -203,7 +195,7 @@ void GBDT::Bagging(int iter, const int curr_class) {
     }
     Log::Debug("Re-bagging, using %d data to train", bag_data_cnt_);
     // set bagging data to tree learner
-    tree_learner_[curr_class]->SetBaggingData(bag_data_indices_.data(), bag_data_cnt_);
+    tree_learner_->SetBaggingData(bag_data_indices_.data(), bag_data_cnt_);
   }
 }
 
@@ -221,13 +213,12 @@ bool GBDT::TrainOneIter(const score_t* gradient, const score_t* hessian, bool is
     gradient = gradients_.data();
     hessian = hessians_.data();
   }
-
-  for (int curr_class = 0; curr_class < num_class_; ++curr_class) {
   // bagging logic
-    Bagging(iter_, curr_class);
+  Bagging(iter_);
+  for (int curr_class = 0; curr_class < num_class_; ++curr_class) {
 
     // train a new tree
-    std::unique_ptr<Tree> new_tree(tree_learner_[curr_class]->Train(gradient + curr_class * num_data_, hessian + curr_class * num_data_));
+    std::unique_ptr<Tree> new_tree(tree_learner_->Train(gradient + curr_class * num_data_, hessian + curr_class * num_data_));
     // if cannot learn a new tree, then stop
     if (new_tree->num_leaves() <= 1) {
       Log::Info("Stopped training because there are no more leafs that meet the split requirements.");
@@ -290,7 +281,7 @@ bool GBDT::EvalAndCheckEarlyStopping() {
 
 void GBDT::UpdateScore(const Tree* tree, const int curr_class) {
   // update training score
-  train_score_updater_->AddScore(tree_learner_[curr_class].get(), curr_class);
+  train_score_updater_->AddScore(tree_learner_.get(), curr_class);
   // update validation score
   for (auto& score_updater : valid_score_updater_) {
     score_updater->AddScore(tree, curr_class);
@@ -301,7 +292,7 @@ std::string GBDT::OutputMetric(int iter) {
   bool need_output = (iter % gbdt_config_->output_freq) == 0;
   std::string ret = "";
   std::stringstream msg_buf;
-  std::vector<std::pair<int, int>> meet_early_stopping_pairs;
+  std::vector<std::pair<size_t, size_t>> meet_early_stopping_pairs;
   // print training metric
   if (need_output) {
     for (auto& sub_metric : training_metrics_) {

src/boosting/gbdt.h

@@ -214,9 +214,8 @@ protected:
   /*!
   * \brief Implement bagging logic
   * \param iter Current interation
-  * \param curr_class Current class for multiclass training
   */
-  void Bagging(int iter, const int curr_class);
+  void Bagging(int iter);
   /*!
   * \brief updating score for out-of-bag data.
   *        Data should be update since we may re-bagging data on training
@@ -252,7 +251,7 @@ protected:
   /*! \brief Config of gbdt */
   std::unique_ptr<BoostingConfig> gbdt_config_;
   /*! \brief Tree learner, will use this class to learn trees */
-  std::vector<std::unique_ptr<TreeLearner>> tree_learner_;
+  std::unique_ptr<TreeLearner> tree_learner_;
   /*! \brief Objective function */
   const ObjectiveFunction* object_function_;
   /*! \brief Store and update training data's score */