fix some formats

include/LightGBM/tree.h

@@ -478,8 +478,8 @@ inline void Tree::PredictContrib(const double* feature_values, int num_features,
   output[num_features] += ExpectedValue();
   // Run the recursion with preallocated space for the unique path data
   if (num_leaves_ > 1) {
-    const int max_path_len = MaxDepth()+1;
-    PathElement *unique_path_data = new PathElement[(max_path_len*(max_path_len+1))/2];
+    const int max_path_len = MaxDepth() + 1;
+    PathElement *unique_path_data = new PathElement[(max_path_len*(max_path_len + 1)) / 2];
     TreeSHAP(feature_values, output, 0, 0, unique_path_data, 1, 1, -1);
     delete[] unique_path_data;
   }
@@ -490,8 +490,8 @@ inline void Tree::RecomputeLeafDepths(int node, int depth) {
   if (node < 0) {
     leaf_depth_[~node] = depth;
   } else {
-    RecomputeLeafDepths(left_child_[node], depth+1);
-    RecomputeLeafDepths(right_child_[node], depth+1);
+    RecomputeLeafDepths(left_child_[node], depth + 1);
+    RecomputeLeafDepths(right_child_[node], depth + 1);
   }
 }
 

src/application/predictor.hpp

@@ -65,7 +65,7 @@ public:
     if (is_predict_leaf_index) {
       predict_fun_ = [this](const std::vector<std::pair<int, double>>& features, double* output) {
         int tid = omp_get_thread_num();
-        if(num_feature_ > MAX_FEATURE && num_feature_/static_cast<int>(features.size()) > SPARSITY) {
+        if (num_feature_ > MAX_FEATURE && num_feature_ / static_cast<int>(features.size()) > SPARSITY) {
           CopyToPredictMap(tid, features);
           boosting_->PredictLeafIndexByMap(predict_buf_map_[tid], output);
           ClearPredictMap(tid);
@@ -88,7 +88,7 @@ public:
       if (is_raw_score) {
         predict_fun_ = [this](const std::vector<std::pair<int, double>>& features, double* output) {
           int tid = omp_get_thread_num();
-          if(num_feature_ > MAX_FEATURE && num_feature_/static_cast<int>(features.size()) > SPARSITY) {
+          if (num_feature_ > MAX_FEATURE && num_feature_ / static_cast<int>(features.size()) > SPARSITY) {
             CopyToPredictMap(tid, features);
             boosting_->PredictRawByMap(predict_buf_map_[tid], output, &early_stop_);
             ClearPredictMap(tid);
@@ -101,7 +101,7 @@ public:
       } else {
         predict_fun_ = [this](const std::vector<std::pair<int, double>>& features, double* output) {
           int tid = omp_get_thread_num();
-          if(num_feature_ > MAX_FEATURE && num_feature_/static_cast<int>(features.size()) > SPARSITY) {
+          if (num_feature_ > MAX_FEATURE && num_feature_ / static_cast<int>(features.size()) > SPARSITY) {
             CopyToPredictMap(tid, features);
             boosting_->PredictByMap(predict_buf_map_[tid], output, &early_stop_);
             ClearPredictMap(tid);
@@ -151,20 +151,20 @@ public:
     TextReader<data_size_t> predict_data_reader(data_filename, has_header);
     std::unordered_map<int, int> feature_names_map_;
     bool need_adjust = false;
-    if(has_header) {
+    if (has_header) {
       std::string first_line = predict_data_reader.first_line();
       std::vector<std::string> header = Common::Split(first_line.c_str(), "\t,");
       header.erase(header.begin() + boosting_->LabelIdx());
-      for(int i = 0; i < static_cast<int>(header.size()); ++i) {
-        for(int j = 0; j < static_cast<int>(boosting_->FeatureNames().size()); ++j) {
-          if(header[i] == boosting_->FeatureNames()[j]) {
+      for (int i = 0; i < static_cast<int>(header.size()); ++i) {
+        for (int j = 0; j < static_cast<int>(boosting_->FeatureNames().size()); ++j) {
+          if (header[i] == boosting_->FeatureNames()[j]) {
             feature_names_map_[i] = j;
             break;
           }
         }
       }
-      for(auto s:feature_names_map_) {
-        if(s.first != s.second) {
+      for (auto s : feature_names_map_) {
+        if (s.first != s.second) {
           need_adjust = true;
           break;
         }
@@ -176,14 +176,13 @@ public:
     parser_fun = [this, &parser, &tmp_label, &need_adjust, &feature_names_map_]
     (const char* buffer, std::vector<std::pair<int, double>>* feature) {
       parser->ParseOneLine(buffer, feature, &tmp_label);
-      if(need_adjust) {
+      if (need_adjust) {
         int i = 0, j = static_cast<int>(feature->size());
-        while(i < j) {
-          if(feature_names_map_.find((*feature)[i].first) != feature_names_map_.end()) {
+        while (i < j) {
+          if (feature_names_map_.find((*feature)[i].first) != feature_names_map_.end()) {
             (*feature)[i].first = feature_names_map_[(*feature)[i].first];
             ++i;
-          }
-          else {
+          } else {
             //move the non-used features to the end of the feature vector
             std::swap((*feature)[i], (*feature)[--j]);
           }

src/boosting/gbdt.cpp

@@ -30,16 +30,16 @@ std::chrono::duration<double, std::milli> tree_time;
 #endif // TIMETAG
 
 GBDT::GBDT() : iter_(0),
-    train_data_(nullptr),
-    objective_function_(nullptr),
-    early_stopping_round_(0),
-    max_feature_idx_(0),
-    num_tree_per_iteration_(1),
-    num_class_(1),
-    num_iteration_for_pred_(0),
-    shrinkage_rate_(0.1f),
-    num_init_iteration_(0),
-    need_re_bagging_(false) {
+train_data_(nullptr),
+objective_function_(nullptr),
+early_stopping_round_(0),
+max_feature_idx_(0),
+num_tree_per_iteration_(1),
+num_class_(1),
+num_iteration_for_pred_(0),
+shrinkage_rate_(0.1f),
+num_init_iteration_(0),
+need_re_bagging_(false) {
 
   #pragma omp parallel
   #pragma omp master

src/boosting/gbdt.h

@@ -20,7 +20,7 @@ namespace LightGBM {
 /*!
 * \brief GBDT algorithm implementation. including Training, prediction, bagging.
 */
-class GBDT: public GBDTBase {
+class GBDT : public GBDTBase {
 public:
 
   /*!

src/io/tree.cpp

@@ -382,15 +382,13 @@ std::string Tree::ToIfElse(int index, bool is_predict_leaf_index) const {
   str_buf << "double PredictTree" << index;
   if (is_predict_leaf_index) {
     str_buf << "LeafByMap";
-  }
-  else {
+  } else {
     str_buf << "ByMap";
   }
   str_buf << "(const std::unordered_map<int, double>& arr) { ";
   if (num_leaves_ <= 1) {
     str_buf << "return " << leaf_value_[0] << ";";
-  }
-  else {
+  } else {
     str_buf << "const std::vector<uint32_t> cat_threshold = {";
     for (size_t i = 0; i < cat_threshold_.size(); ++i) {
       if (i != 0) {
@@ -696,7 +694,7 @@ double Tree::ExpectedValue() const {
   const double total_count = internal_count_[0];
   double exp_value = 0.0;
   for (int i = 0; i < num_leaves(); ++i) {
-    exp_value += (leaf_count_[i]/total_count)*LeafOutput(i);
+    exp_value += (leaf_count_[i] / total_count)*LeafOutput(i);
   }
   return exp_value;
 }