fixed cpplint issues (#2756)

include/LightGBM/feature_group.h

@@ -333,7 +333,6 @@ class FeatureGroup {
   }
 
  private:
-
   void CreateBinData(int num_data, bool is_multi_val, bool force_dense, bool force_sparse) {
     if (is_multi_val) {
       multi_bin_data_.clear();

src/treelearner/feature_histogram.hpp

@@ -16,8 +16,8 @@
 #include <utility>
 #include <vector>
 
-#include "split_info.hpp"
 #include "monotone_constraints.hpp"
+#include "split_info.hpp"
 
 namespace LightGBM {
 
@@ -59,11 +59,11 @@ class FeatureHistogram {
     meta_ = meta;
     data_ = data;
     if (meta_->bin_type == BinType::NumericalBin) {
-      find_best_threshold_fun_ = std::bind(&FeatureHistogram::FindBestThresholdNumerical, this, std::placeholders::_1
-        , std::placeholders::_2, std::placeholders::_3, std::placeholders::_4, std::placeholders::_5);
+      find_best_threshold_fun_ = std::bind(&FeatureHistogram::FindBestThresholdNumerical, this, std::placeholders::_1,
+        std::placeholders::_2, std::placeholders::_3, std::placeholders::_4, std::placeholders::_5);
     } else {
-      find_best_threshold_fun_ = std::bind(&FeatureHistogram::FindBestThresholdCategorical, this, std::placeholders::_1
-        , std::placeholders::_2, std::placeholders::_3, std::placeholders::_4, std::placeholders::_5);
+      find_best_threshold_fun_ = std::bind(&FeatureHistogram::FindBestThresholdCategorical, this, std::placeholders::_1,
+        std::placeholders::_2, std::placeholders::_3, std::placeholders::_4, std::placeholders::_5);
     }
     rand_ = Random(meta_->config->extra_seed);
   }
@@ -495,9 +495,9 @@ class FeatureHistogram {
   * \param sum_hessians
   * \return leaf output
   */
-  static double
-  CalculateSplittedLeafOutput(double sum_gradients, double sum_hessians,
-                              double l1, double l2, double max_delta_step, const ConstraintEntry& constraints) {
+  static double CalculateSplittedLeafOutput(double sum_gradients, double sum_hessians,
+                                            double l1, double l2, double max_delta_step,
+                                            const ConstraintEntry& constraints) {
     double ret = CalculateSplittedLeafOutput(sum_gradients, sum_hessians, l1, l2, max_delta_step);
     if (ret < constraints.min) {
       ret = constraints.min;
@@ -680,10 +680,10 @@ class FeatureHistogram {
   /*! \brief random number generator for extremely randomized trees */
   Random rand_;
 
-  std::function<void(double, double, data_size_t, const ConstraintEntry&,
-                     SplitInfo*)>
-      find_best_threshold_fun_;
+  std::function<void(double, double, data_size_t, const ConstraintEntry&, SplitInfo*)>
+    find_best_threshold_fun_;
 };
+
 class HistogramPool {
  public:
   /*!

src/treelearner/monotone_constraints.hpp

-#ifndef LIGHTGBM_TREELEARNER_MONOTONE_CONSTRAINTS_H_
-#define LIGHTGBM_TREELEARNER_MONOTONE_CONSTRAINTS_H_
+/*!
+ * Copyright (c) 2020 Microsoft Corporation. All rights reserved.
+ * Licensed under the MIT License. See LICENSE file in the project root for license information.
+ */
+#ifndef LIGHTGBM_TREELEARNER_MONOTONE_CONSTRAINTS_HPP_
+#define LIGHTGBM_TREELEARNER_MONOTONE_CONSTRAINTS_HPP_
 
+#include <limits>
 #include <algorithm>
-#include <vector>
 #include <cstdint>
-#include <limits>
+#include <vector>
 
 namespace LightGBM {
 
@@ -12,7 +16,7 @@ struct ConstraintEntry {
   double min = -std::numeric_limits<double>::max();
   double max = std::numeric_limits<double>::max();
 
-  ConstraintEntry(){};
+  ConstraintEntry() {}
 
   void Reset() {
     min = -std::numeric_limits<double>::max();
@@ -22,20 +26,21 @@ struct ConstraintEntry {
   void UpdateMin(double new_min) { min = std::max(new_min, min); }
 
   void UpdateMax(double new_max) { max = std::min(new_max, max); }
-
 };
 
 template <typename ConstraintEntry>
 class LeafConstraints {
  public:
-  LeafConstraints(int num_leaves) : num_leaves_(num_leaves) {
+  explicit LeafConstraints(int num_leaves) : num_leaves_(num_leaves) {
     entries_.resize(num_leaves_);
   }
+
   void Reset() {
     for (auto& entry : entries_) {
       entry.Reset();
     }
   }
+
   void UpdateConstraints(bool is_numerical_split, int leaf, int new_leaf,
                          int8_t monotone_type, double right_output,
                          double left_output) {
@@ -59,5 +64,5 @@ class LeafConstraints {
   std::vector<ConstraintEntry> entries_;
 };
 
-} // namespace LightGBM
-#endif // LightGBM_TREELEARNER_MONOTONE_CONSTRAINTS_H_
+}  // namespace LightGBM
+#endif  // LIGHTGBM_TREELEARNER_MONOTONE_CONSTRAINTS_HPP_

src/treelearner/serial_tree_learner.h

@@ -21,8 +21,8 @@
 #include "data_partition.hpp"
 #include "feature_histogram.hpp"
 #include "leaf_splits.hpp"
-#include "split_info.hpp"
 #include "monotone_constraints.hpp"
+#include "split_info.hpp"
 
 #ifdef USE_GPU
 // Use 4KBytes aligned allocator for ordered gradients and ordered hessians when GPU is enabled.
@@ -158,7 +158,7 @@ class SerialTreeLearner: public TreeLearner {
   std::vector<SplitInfo> best_split_per_leaf_;
   /*! \brief store best split per feature for all leaves */
   std::vector<SplitInfo> splits_per_leaf_;
-  // Stores minimum and maximum constraints for each leaf
+  /*! \brief stores minimum and maximum constraints for each leaf */
   std::unique_ptr<LeafConstraints<ConstraintEntry>> constraints_;
 
   /*! \brief stores best thresholds for all feature for smaller leaf */

src/treelearner/voting_parallel_tree_learner.cpp

@@ -399,7 +399,6 @@ void VotingParallelTreeLearner<TREELEARNER_T>::FindBestSplitsFromHistograms(cons
     }
 
     if (larger_is_feature_aggregated_[feature_index]) {
-
       // restore from buffer
       larger_leaf_histogram_array_global_[feature_index].FromMemory(output_buffer_.data() + larger_buffer_read_start_pos_[feature_index]);
 
@@ -409,7 +408,7 @@ void VotingParallelTreeLearner<TREELEARNER_T>::FindBestSplitsFromHistograms(cons
 
       this->ComputeBestSplitForFeature(
           larger_leaf_histogram_array_global_.get(), feature_index,
-          real_feature_index, 
+          real_feature_index,
           larger_node_used_features[feature_index],
           GetGlobalDataCountInLeaf(larger_leaf_splits_global_->leaf_index()),
           larger_leaf_splits_global_.get(),