reduce the cost of bagging.

src/treelearner/serial_tree_learner.cpp

@@ -98,19 +98,11 @@ void SerialTreeLearner::Init(const Dataset* train_data, bool is_constant_hessian
 void SerialTreeLearner::ResetTrainingData(const Dataset* train_data) {
   train_data_ = train_data;
   num_data_ = train_data_->num_data();
-  num_features_ = train_data_->num_features();
+  CHECK(num_features_ == train_data_->num_features());
 
   // get ordered bin
   train_data_->CreateOrderedBins(&ordered_bins_);
 
-  has_ordered_bin_ = false;
-  // check existing for ordered bin
-  for (int i = 0; i < static_cast<int>(ordered_bins_.size()); ++i) {
-    if (ordered_bins_[i] != nullptr) {
-      has_ordered_bin_ = true;
-      break;
-    }
-  }
   // initialize splits for leaf
   smaller_leaf_splits_->ResetNumData(num_data_);
   larger_leaf_splits_->ResetNumData(num_data_);
@@ -118,8 +110,6 @@ void SerialTreeLearner::ResetTrainingData(const Dataset* train_data) {
   // initialize data partition
   data_partition_->ResetNumData(num_data_);
 
-  is_feature_used_.resize(num_features_);
-
   // initialize ordered gradients and hessians
   ordered_gradients_.resize(num_data_);
   ordered_hessians_.resize(num_data_);
@@ -127,12 +117,6 @@ void SerialTreeLearner::ResetTrainingData(const Dataset* train_data) {
   if (has_ordered_bin_) {
     is_data_in_leaf_.resize(num_data_);
     std::fill(is_data_in_leaf_.begin(), is_data_in_leaf_.end(), static_cast<char>(0));
-    ordered_bin_indices_.clear();
-    for (int i = 0; i < static_cast<int>(ordered_bins_.size()); i++) {
-      if (ordered_bins_[i] != nullptr) {
-        ordered_bin_indices_.push_back(i);
-      }
-    }
   }
 }