clean code.

include/LightGBM/config.h

@@ -94,7 +94,6 @@ public:
   std::string input_model = "";
   std::string input_init_score = "";
   int verbosity = 1;
-  std::string log_file = "";
   int num_model_predict = -1;
   bool is_pre_partition = false;
   bool is_enable_sparse = true;

include/LightGBM/utils/lru_pool.h

@@ -5,6 +5,7 @@
 #include <LightGBM/utils/log.h>
 
 #include <cstring>
+#include <functional>
 
 namespace LightGBM {
 
@@ -66,12 +67,13 @@ public:
   }
 
   /*!
-  * \brief Set data for the pool for specific index
-  * \param idx which index want to set to
-  * \param data
+  * \brief Fill the pool
+  * \param obj_create_fun that used to generate object
   */
-  void Set(int idx, const T& data) {
-    pool_[idx] = data;
+  void Fill(std::function<T()> obj_create_fun) {
+    for (int i = 0; i < cache_size_; ++i) {
+      pool_[i] = obj_create_fun();
+    }
   }
 
   /*!

src/io/config.cpp

@@ -159,7 +159,6 @@ void IOConfig::Set(const std::unordered_map<std::string, std::string>& params) {
   GetString(params, "input_model", &input_model);
   GetString(params, "output_result", &output_result);
   GetString(params, "input_init_score", &input_init_score);
-  GetString(params, "log_file", &log_file);
   std::string tmp_str = "";
   if (GetString(params, "valid_data", &tmp_str)) {
     valid_data_filenames = Common::Split(tmp_str.c_str(), ',');

src/treelearner/serial_tree_learner.cpp

@@ -63,16 +63,17 @@ void SerialTreeLearner::Init(const Dataset* train_data) {
   max_cache_size = Common::Min(max_cache_size, num_leaves_);
   histogram_pool_.ResetSize(max_cache_size, num_leaves_);
 
-  for (int i = 0; i < max_cache_size; ++i) {
+  auto histogram_create_function = [this]() {
     FeatureHistogram* tmp_histogram_array = new FeatureHistogram[train_data_->num_features()];
     for (int j = 0; j < train_data_->num_features(); ++j) {
       tmp_histogram_array[j].Init(train_data_->FeatureAt(j),
-                                             j, min_num_data_one_leaf_,
-                                             min_sum_hessian_one_leaf_);
+        j, min_num_data_one_leaf_,
+        min_sum_hessian_one_leaf_);
     }
-    // set data at i-th position
-    histogram_pool_.Set(i, tmp_histogram_array);
-  }
+    return tmp_histogram_array;
+  };
+  histogram_pool_.Fill(histogram_create_function);
+
   // push split information for all leaves
   for (int i = 0; i < num_leaves_; ++i) {
     best_split_per_leaf_.push_back(SplitInfo());