fixed cpplint issues (#2771)

include/LightGBM/dataset.h

@@ -297,7 +297,7 @@ struct TrainingTempState {
 #pragma omp parallel
 #pragma omp master
     { num_threads = omp_get_num_threads(); }
-   num_bin_aligned =
+    num_bin_aligned =
         (bin->num_bin() + kAlignedSize - 1) / kAlignedSize * kAlignedSize;
     size_t new_size = static_cast<size_t>(num_bin_aligned) * 2 * num_threads;
     if (new_size > hist_buf.size()) {
@@ -447,8 +447,8 @@ class Dataset {
   MultiValBin* GetMultiBinFromAllFeatures() const;
 
   TrainingTempState* TestMultiThreadingMethod(
-      score_t* gradients, score_t* hessians,
-      const std::vector<int8_t>& is_feature_used, bool is_constant_hessian,
+    score_t* gradients, score_t* hessians,
+    const std::vector<int8_t>& is_feature_used, bool is_constant_hessian,
     bool force_colwise, bool force_rowwise, bool* is_hist_col_wise) const;
 
   LIGHTGBM_EXPORT void FinishLoad();

src/io/dataset.cpp

@@ -639,9 +639,8 @@ TrainingTempState* Dataset::TestMultiThreadingMethod(
       auto overhead_cost = row_wise_init_time + row_wise_time + col_wise_time;
       Log::Warning(
           "Auto-choosing col-wise multi-threading, the overhead of testing was "
-          "%f "
-          "seconds.\nYou can set `force_col_wise=true` to remove the "
-          "overhead.",
+          "%f seconds.\n"
+          "You can set `force_col_wise=true` to remove the overhead.",
           overhead_cost * 1e-3);
       return colwise_state.release();
     } else {
@@ -649,10 +648,9 @@ TrainingTempState* Dataset::TestMultiThreadingMethod(
       auto overhead_cost = col_wise_init_time + row_wise_time + col_wise_time;
       Log::Warning(
           "Auto-choosing row-wise multi-threading, the overhead of testing was "
-          "%f "
-          "seconds.\nYou can set `force_row_wise=true` to remove the "
-          "overhead.\nAnd if memory is not enough, you can set "
-          "`force_col_wise=true`.",
+          "%f seconds.\n"
+          "You can set `force_row_wise=true` to remove the overhead.\n"
+          "And if memory is not enough, you can set `force_col_wise=true`.",
           overhead_cost * 1e-3);
       if (rowwise_state->multi_val_bin->IsSparse()) {
         Log::Debug("Using Sparse Multi-Val Bin");
@@ -1008,7 +1006,6 @@ void Dataset::DumpTextFile(const char* text_filename) {
   fclose(file);
 }
 
-
 void Dataset::InitTrain(const std::vector<int8_t>& is_feature_used,
                         bool is_colwise, TrainingTempState* temp_state) const {
   Common::FunctionTimer fun_time("Dataset::InitTrain", global_timer);
@@ -1024,7 +1021,7 @@ void Dataset::InitTrain(const std::vector<int8_t>& is_feature_used,
   std::vector<int> used_feature_index;
   for (int i = 0; i < num_groups_; ++i) {
     int f_start = group_feature_start_[i];
-    if (feature_groups_[i]->is_multi_val_ ) {
+    if (feature_groups_[i]->is_multi_val_) {
       for (int j = 0; j < feature_groups_[i]->num_feature_; ++j) {
         const auto dense_rate =
             1.0 - feature_groups_[i]->bin_mappers_[j]->sparse_rate();
@@ -1086,11 +1083,11 @@ void Dataset::InitTrain(const std::vector<int8_t>& is_feature_used,
 
           lower_bound.push_back(num_total_bin - cur_num_bin);
           upper_bound.push_back(num_total_bin);
-          
+
           temp_state->hist_move_src.push_back(
               (new_num_total_bin - cur_num_bin) * 2);
-          temp_state->hist_move_dest.push_back((num_total_bin - cur_num_bin) *
-                                               2);
+          temp_state->hist_move_dest.push_back(
+              (num_total_bin - cur_num_bin) * 2);
           temp_state->hist_move_size.push_back(cur_num_bin * 2);
           delta.push_back(num_total_bin - new_num_total_bin);
         }
@@ -1111,8 +1108,8 @@ void Dataset::InitTrain(const std::vector<int8_t>& is_feature_used,
         lower_bound.push_back(num_total_bin - cur_num_bin);
         upper_bound.push_back(num_total_bin);
 
-        temp_state->hist_move_src.push_back((new_num_total_bin - cur_num_bin) *
-                                            2);
+        temp_state->hist_move_src.push_back(
+            (new_num_total_bin - cur_num_bin) * 2);
         temp_state->hist_move_dest.push_back((num_total_bin - cur_num_bin) * 2);
         temp_state->hist_move_size.push_back(cur_num_bin * 2);
         delta.push_back(num_total_bin - new_num_total_bin);

src/io/multi_val_dense_bin.hpp

@@ -5,9 +5,10 @@
 #ifndef LIGHTGBM_IO_MULTI_VAL_DENSE_BIN_HPP_
 #define LIGHTGBM_IO_MULTI_VAL_DENSE_BIN_HPP_
 
-#include <LightGBM/utils/openmp_wrapper.h>
 #include <LightGBM/bin.h>
+#include <LightGBM/utils/openmp_wrapper.h>
 
+#include <algorithm>
 #include <cstdint>
 #include <cstring>
 #include <vector>

src/io/multi_val_sparse_bin.hpp

 /*!
  * Copyright (c) 2020 Microsoft Corporation. All rights reserved.
- * Licensed under the MIT License. See LICENSE file in the project root for
- * license information.
+ * Licensed under the MIT License. See LICENSE file in the project root for license information.
  */
 #ifndef LIGHTGBM_IO_MULTI_VAL_SPARSE_BIN_HPP_
 #define LIGHTGBM_IO_MULTI_VAL_SPARSE_BIN_HPP_
@@ -9,6 +8,7 @@
 #include <LightGBM/bin.h>
 #include <LightGBM/utils/openmp_wrapper.h>
 
+#include <algorithm>
 #include <cstdint>
 #include <cstring>
 #include <vector>

src/treelearner/serial_tree_learner.cpp

@@ -81,12 +81,12 @@ void SerialTreeLearner::GetMultiValBin(const Dataset* dataset, bool is_first_tim
   if (is_first_time) {
     auto used_feature = GetUsedFeatures(true);
     temp_state_.reset(dataset->TestMultiThreadingMethod(
-        ordered_gradients_.data(), ordered_hessians_.data(), used_feature,
+      ordered_gradients_.data(), ordered_hessians_.data(), used_feature,
       is_constant_hessian_, config_->force_col_wise, config_->force_row_wise, &is_hist_colwise_));
   } else {
     // cannot change is_hist_col_wise during training
     temp_state_.reset(dataset->TestMultiThreadingMethod(
-        ordered_gradients_.data(), ordered_hessians_.data(), is_feature_used_,
+      ordered_gradients_.data(), ordered_hessians_.data(), is_feature_used_,
       is_constant_hessian_, is_hist_colwise_, !is_hist_colwise_, &is_hist_colwise_));
   }
 }