cpplint whitespaces and new lines (#1986)

include/LightGBM/utils/file_io.h

@@ -7,13 +7,13 @@
 #include <cstdlib>
 #include <cstring>
 
-namespace LightGBM{
+namespace LightGBM {
 
 /*!
  * \brief An interface for writing files from buffers
  */
 struct VirtualFileWriter {
-  virtual ~VirtualFileWriter() {};
+  virtual ~VirtualFileWriter() {}
   /*!
    * \brief Initialize the writer
    * \return True when the file is available for writes
@@ -48,7 +48,7 @@ struct VirtualFileReader {
    * \brief Constructor
    * \param filename Filename of the data
    */
-  virtual ~VirtualFileReader() {};
+  virtual ~VirtualFileReader() {}
   /*!
    * \brief Initialize the reader
    * \return True when the file is available for read

include/LightGBM/utils/log.h

@@ -12,7 +12,7 @@
 namespace LightGBM {
 
 #if defined(_MSC_VER)
-#define THREAD_LOCAL __declspec(thread) 
+#define THREAD_LOCAL __declspec(thread)
 #else
 #define THREAD_LOCAL thread_local
 #endif
@@ -84,7 +84,6 @@ public:
   }
 
 private:
-
   static void Write(LogLevel level, const char* level_str, const char *format, va_list val) {
     if (level <= GetLevel()) {  // omit the message with low level
       // write to STDOUT
@@ -98,7 +97,6 @@ private:
   // a trick to use static variable in header file.
   // May be not good, but avoid to use an additional cpp file
   static LogLevel& GetLevel() { static THREAD_LOCAL LogLevel level = LogLevel::Info; return level; }
-
 };
 
 }  // namespace LightGBM

include/LightGBM/utils/openmp_wrapper.h

@@ -12,11 +12,11 @@
 
 class ThreadExceptionHelper {
 public:
-  ThreadExceptionHelper() { 
-    ex_ptr_ = nullptr; 
+  ThreadExceptionHelper() {
+    ex_ptr_ = nullptr;
   }
 
-  ~ThreadExceptionHelper() { 
+  ~ThreadExceptionHelper() {
     ReThrow();
   }
   void ReThrow() {
@@ -38,7 +38,6 @@ private:
 
 #define OMP_INIT_EX() ThreadExceptionHelper omp_except_helper
 #define OMP_LOOP_EX_BEGIN() try {
-
 #define OMP_LOOP_EX_END() } \
 catch(std::exception& ex) { Log::Warning(ex.what()); omp_except_helper.CaptureException(); } \
 catch(...) { omp_except_helper.CaptureException();  }
@@ -47,7 +46,7 @@ catch(...) { omp_except_helper.CaptureException();  }
 #else
 
 #ifdef _MSC_VER
-  #pragma warning( disable : 4068 ) // disable unknown pragma warning
+  #pragma warning(disable: 4068)  // disable unknown pragma warning
 #endif
 
 #ifdef __cplusplus
@@ -61,7 +60,7 @@ catch(...) { omp_except_helper.CaptureException();  }
   inline int omp_get_num_threads() {return 1;}
   inline int omp_get_thread_num() {return 0;}
 #ifdef __cplusplus
-}; // extern "C"
+};  // extern "C"
 #endif
 
 #define OMP_INIT_EX()

include/LightGBM/utils/pipeline_reader.h

@@ -12,7 +12,7 @@
 #include <vector>
 #include "file_io.h"
 
-namespace LightGBM{
+namespace LightGBM {
 
 /*!
 * \brief A pipeline file reader, use 2 threads, one read block from file, the other process the block
@@ -24,13 +24,13 @@ public:
   * \param filename Filename of data
   * \process_fun Process function
   */
-  static size_t Read(const char* filename, int skip_bytes, const std::function<size_t (const char*, size_t)>& process_fun) {
+  static size_t Read(const char* filename, int skip_bytes, const std::function<size_t(const char*, size_t)>& process_fun) {
     auto reader = VirtualFileReader::Make(filename);
     if (!reader->Init()) {
       return 0;
     }
     size_t cnt = 0;
-    const size_t buffer_size =  16 * 1024 * 1024 ;
+    const size_t buffer_size =  16 * 1024 * 1024;
     // buffer used for the process_fun
     auto buffer_process = std::vector<char>(buffer_size);
     // buffer used for the file reading
@@ -49,8 +49,7 @@ public:
       std::thread read_worker = std::thread(
         [&] {
         last_read_cnt = reader->Read(buffer_read.data(), buffer_size);
-      }
-      );
+      });
       // start process
       cnt += process_fun(buffer_process.data(), read_cnt);
       // wait for read thread
@@ -61,7 +60,6 @@ public:
     }
     return cnt;
   }
-
 };
 
 }  // namespace LightGBM

include/LightGBM/utils/random.h

@@ -93,6 +93,7 @@ public:
     }
     return ret;
   }
+
 private:
   inline int RandInt16() {
     x = (214013 * x + 2531011);

include/LightGBM/utils/text_reader.h

@@ -26,7 +26,7 @@ public:
   * \param is_skip_first_line True if need to skip header
   */
   TextReader(const char* filename, bool is_skip_first_line):
-    filename_(filename), is_skip_first_line_(is_skip_first_line){
+    filename_(filename), is_skip_first_line_(is_skip_first_line) {
     if (is_skip_first_line_) {
       auto reader = VirtualFileReader::Make(filename);
       if (!reader->Init()) {
@@ -210,7 +210,7 @@ public:
         }
         else {
           const size_t idx = static_cast<size_t>(random.NextInt(0, static_cast<int>(out_used_data_indices->size())));
-          if (idx < static_cast<size_t>(sample_cnt) ) {
+          if (idx < static_cast<size_t>(sample_cnt)) {
             out_sampled_data->operator[](idx) = std::string(buffer, size);
           }
         }
@@ -225,7 +225,7 @@ public:
     });
   }
 
-  INDEX_T ReadAllAndProcessParallelWithFilter(const std::function<void(INDEX_T, const std::vector<std::string>&)>& process_fun, const std::function<bool(INDEX_T,INDEX_T)>& filter_fun) {
+  INDEX_T ReadAllAndProcessParallelWithFilter(const std::function<void(INDEX_T, const std::vector<std::string>&)>& process_fun, const std::function<bool(INDEX_T, INDEX_T)>& filter_fun) {
     last_line_ = "";
     INDEX_T total_cnt = 0;
     INDEX_T used_cnt = 0;
@@ -296,7 +296,7 @@ public:
 
   INDEX_T ReadPartAndProcessParallel(const std::vector<INDEX_T>& used_data_indices, const std::function<void(INDEX_T, const std::vector<std::string>&)>& process_fun) {
     return ReadAllAndProcessParallelWithFilter(process_fun,
-      [&used_data_indices](INDEX_T used_cnt ,INDEX_T total_cnt) {
+      [&used_data_indices](INDEX_T used_cnt, INDEX_T total_cnt) {
       if (static_cast<size_t>(used_cnt) < used_data_indices.size() && total_cnt == used_data_indices[used_cnt]) {
         return true;
       }
@@ -314,7 +314,7 @@ private:
   /*! \brief Buffer for last line */
   std::string last_line_;
   /*! \brief first line */
-  std::string first_line_="";
+  std::string first_line_ = "";
   /*! \brief is skip first line */
   bool is_skip_first_line_ = false;
   /*! \brief is skip first line */

include/LightGBM/utils/threading.h

@@ -10,7 +10,6 @@ namespace LightGBM {
 
 class Threading {
 public:
-
   template<typename INDEX_T>
   static inline void For(INDEX_T start, INDEX_T end, const std::function<void(int, INDEX_T, INDEX_T)>& inner_fun) {
     int num_threads = 1;
@@ -22,7 +21,7 @@ public:
     INDEX_T num_inner = (end - start + num_threads - 1) / num_threads;
     if (num_inner <= 0) { num_inner = 1; }
     OMP_INIT_EX();
-    #pragma omp parallel for schedule(static,1)
+    #pragma omp parallel for schedule(static, 1)
     for (int i = 0; i < num_threads; ++i) {
       OMP_LOOP_EX_BEGIN();
       INDEX_T inner_start = start + num_inner * i;

src/application/application.cpp

@@ -136,8 +136,7 @@ void Application::LoadData() {
         dataset_loader.LoadFromFileAlignWithOtherDataset(
           config_.valid[i].c_str(),
           config_.valid_data_initscores[i].c_str(),
-          train_data_.get())
-        );
+          train_data_.get()));
       valid_datas_.push_back(std::move(new_dataset));
       // need save binary file
       if (config_.save_binary) {
@@ -212,7 +211,6 @@ void Application::Train() {
 }
 
 void Application::Predict() {
-
   if (config_.task == TaskType::KRefitTree) {
     // create predictor
     Predictor predictor(boosting_.get(), -1, false, true, false, false, 1, 1);

src/application/predictor.hpp

@@ -35,7 +35,6 @@ public:
   Predictor(Boosting* boosting, int num_iteration,
             bool is_raw_score, bool predict_leaf_index, bool predict_contrib,
             bool early_stop, int early_stop_freq, double early_stop_margin) {
-
     early_stop_ = CreatePredictionEarlyStopInstance("none", LightGBM::PredictionEarlyStopConfig());
     if (early_stop && !boosting->NeedAccuratePrediction()) {
       PredictionEarlyStopConfig pred_early_stop_config;
@@ -76,16 +75,16 @@ public:
         }
       };
     } else if (predict_contrib) {
-	    predict_fun_ = [=](const std::vector<std::pair<int, double>>& features, double* output) {
-	      int tid = omp_get_thread_num();
-			CopyToPredictBuffer(predict_buf_[tid].data(), features);
+        predict_fun_ = [=](const std::vector<std::pair<int, double>>& features, double* output) {
+          int tid = omp_get_thread_num();
+          CopyToPredictBuffer(predict_buf_[tid].data(), features);
           // get result for leaf index
           boosting_->PredictContrib(predict_buf_[tid].data(), output, &early_stop_);
           ClearPredictBuffer(predict_buf_[tid].data(), predict_buf_[tid].size(), features);
         };
     } else {
       if (is_raw_score) {
-		predict_fun_ = [=](const std::vector<std::pair<int, double>>& features, double* output) {
+        predict_fun_ = [=](const std::vector<std::pair<int, double>>& features, double* output) {
           int tid = omp_get_thread_num();
           if (num_feature_ > kFeatureThreshold && features.size() < KSparseThreshold) {
             auto buf = CopyToPredictMap(features);
@@ -97,7 +96,7 @@ public:
           }
         };
       } else {
-		predict_fun_ = [=](const std::vector<std::pair<int, double>>& features, double* output) {
+        predict_fun_ = [=](const std::vector<std::pair<int, double>>& features, double* output) {
           int tid = omp_get_thread_num();
           if (num_feature_ > kFeatureThreshold && features.size() < KSparseThreshold) {
             auto buf = CopyToPredictMap(features);
@@ -173,7 +172,7 @@ public:
             (*feature)[i].first = feature_names_map_[(*feature)[i].first];
             ++i;
           } else {
-            //move the non-used features to the end of the feature vector
+            // move the non-used features to the end of the feature vector
             std::swap((*feature)[i], (*feature)[--j]);
           }
         }
@@ -181,8 +180,8 @@ public:
       }
     };
 
-	std::function<void(data_size_t, const std::vector<std::string>&)> process_fun = [&]
-	(data_size_t, const std::vector<std::string>& lines) {
+    std::function<void(data_size_t, const std::vector<std::string>&)> process_fun = [&]
+    (data_size_t, const std::vector<std::string>& lines) {
       std::vector<std::pair<int, double>> oneline_features;
       std::vector<std::string> result_to_write(lines.size());
       OMP_INIT_EX();
@@ -209,7 +208,6 @@ public:
   }
 
 private:
-
   void CopyToPredictBuffer(double* pred_buf, const std::vector<std::pair<int, double>>& features) {
     int loop_size = static_cast<int>(features.size());
     for (int i = 0; i < loop_size; ++i) {

src/boosting/dart.hpp

@@ -78,7 +78,7 @@ public:
     *out_len = static_cast<int64_t>(train_score_updater_->num_data()) * num_class_;
     return train_score_updater_->score();
   }
-  
+
   bool EvalAndCheckEarlyStopping() override {
     GBDT::OutputMetric(iter_);
     return false;

src/boosting/gbdt.cpp

@@ -30,7 +30,6 @@ num_iteration_for_pred_(0),
 shrinkage_rate_(0.1f),
 num_init_iteration_(0),
 need_re_bagging_(false) {
-
   #pragma omp parallel
   #pragma omp master
   {
@@ -41,7 +40,6 @@ need_re_bagging_(false) {
 }
 
 GBDT::~GBDT() {
-
 }
 
 void GBDT::Init(const Config* config, const Dataset* train_data, const ObjectiveFunction* objective_function,
@@ -57,7 +55,7 @@ void GBDT::Init(const Config* config, const Dataset* train_data, const Objective
   shrinkage_rate_ = config_->learning_rate;
 
   std::string forced_splits_path = config->forcedsplits_filename;
-  //load forced_splits file
+  // load forced_splits file
   if (forced_splits_path != "") {
       std::ifstream forced_splits_file(forced_splits_path.c_str());
       std::stringstream buffer;
@@ -188,7 +186,7 @@ void GBDT::Bagging(int iter) {
     data_size_t inner_size = (num_data_ + num_threads_ - 1) / num_threads_;
     if (inner_size < min_inner_size) { inner_size = min_inner_size; }
     OMP_INIT_EX();
-    #pragma omp parallel for schedule(static,1)
+    #pragma omp parallel for schedule(static, 1)
     for (int i = 0; i < num_threads_; ++i) {
       OMP_LOOP_EX_BEGIN();
       left_cnts_buf_[i] = 0;
@@ -451,7 +449,6 @@ bool GBDT::EvalAndCheckEarlyStopping() {
 }
 
 void GBDT::UpdateScore(const Tree* tree, const int cur_tree_id) {
-
   // update training score
   if (!is_use_subset_) {
     train_score_updater_->AddScore(tree_learner_.get(), tree, cur_tree_id);
@@ -624,7 +621,6 @@ void GBDT::GetPredictAt(int data_idx, double* out_result, int64_t* out_len) {
 
 void GBDT::ResetTrainingData(const Dataset* train_data, const ObjectiveFunction* objective_function,
                              const std::vector<const Metric*>& training_metrics) {
-
   if (train_data != train_data_ && !train_data_->CheckAlign(*train_data)) {
     Log::Fatal("Cannot reset training data, since new training data has different bin mappers");
   }

src/boosting/gbdt.h

@@ -25,7 +25,6 @@ namespace LightGBM {
 */
 class GBDT : public GBDTBase {
 public:
-
   /*!
   * \brief Constructor
   */
@@ -212,7 +211,7 @@ public:
         num_preb_in_one_row *= max_iteration;
       }
     } else if (is_pred_contrib) {
-      num_preb_in_one_row = num_tree_per_iteration_ * (max_feature_idx_ + 2); // +1 for 0-based indexing, +1 for baseline
+      num_preb_in_one_row = num_tree_per_iteration_ * (max_feature_idx_ + 2);  // +1 for 0-based indexing, +1 for baseline
     }
     return num_preb_in_one_row;
   }
@@ -356,7 +355,6 @@ public:
   virtual const char* SubModelName() const override { return "tree"; }
 
 protected:
-
   /*!
   * \brief Print eval result and check early stopping
   */
@@ -488,7 +486,6 @@ protected:
   std::string loaded_parameter_;
 
   Json forced_splits_json_;
-
 };
 
 }  // namespace LightGBM

src/boosting/gbdt_model_text.cpp

@@ -194,7 +194,7 @@ std::string GBDT::ModelToIfElse(int num_iteration) const {
   str_buf << "\t" << "}" << '\n';
   str_buf << "}" << '\n';
 
-  //PredictLeafIndexByMap
+  // PredictLeafIndexByMap
   str_buf << "double (*PredictTreeLeafByMapPtr[])(const std::unordered_map<int, double>&) = { ";
   for (int i = 0; i < num_used_model; ++i) {
     if (i > 0) {
@@ -511,7 +511,6 @@ bool GBDT::LoadModelFromString(const char* buffer, size_t len) {
 }
 
 std::vector<double> GBDT::FeatureImportance(int num_iteration, int importance_type) const {
-
   int num_used_model = static_cast<int>(models_.size());
   if (num_iteration > 0) {
     num_iteration += 0;

src/boosting/goss.hpp

@@ -29,7 +29,6 @@ public:
   * \brief Constructor
   */
   GOSS() : GBDT() {
-
   }
 
   ~GOSS() {
@@ -86,7 +85,7 @@ public:
   }
 
   data_size_t BaggingHelper(Random& cur_rand, data_size_t start, data_size_t cnt, data_size_t* buffer, data_size_t* buffer_right) {
-    if (cnt <= 0) { 
+    if (cnt <= 0) {
       return 0;
     }
     std::vector<score_t> tmp_gradients(cnt, 0.0f);

src/boosting/prediction_early_stop.cpp

@@ -15,7 +15,7 @@ PredictionEarlyStopInstance CreateNone(const PredictionEarlyStopConfig&) {
     [](const double*, int) {
     return false;
   },
-    std::numeric_limits<int>::max() // make sure the lambda is almost never called
+    std::numeric_limits<int>::max()  // make sure the lambda is almost never called
   };
 }
 
@@ -69,7 +69,7 @@ PredictionEarlyStopInstance CreateBinary(const PredictionEarlyStopConfig& config
   };
 }
 
-}
+}  // namespace
 
 namespace LightGBM {
 
@@ -86,4 +86,4 @@ PredictionEarlyStopInstance CreatePredictionEarlyStopInstance(const std::string&
   }
 }
 
-}
+}  // namespace LightGBM

src/boosting/rf.hpp

@@ -17,7 +17,6 @@ namespace LightGBM {
 */
 class RF : public GBDT {
 public:
-
   RF() : GBDT() {
     average_output_ = true;
   }
@@ -106,7 +105,6 @@ public:
       std::unique_ptr<Tree> new_tree(new Tree(2));
       size_t bias = static_cast<size_t>(cur_tree_id)* num_data_;
       if (class_need_train_[cur_tree_id]) {
-
         auto grad = gradients + bias;
         auto hess = hessians + bias;
 
@@ -202,12 +200,10 @@ public:
   };
 
 private:
-
   std::vector<score_t> tmp_grad_;
   std::vector<score_t> tmp_hess_;
   std::vector<double> init_scores_;
-
 };
 
 }  // namespace LightGBM
-#endif   // LIGHTGBM_BOOSTING_RF_H_
+#endif  // LIGHTGBM_BOOSTING_RF_H_
\ No newline at end of file

src/boosting/score_updater.hpp

@@ -46,7 +46,6 @@ public:
   }
   /*! \brief Destructor */
   ~ScoreUpdater() {
-
   }
 
   inline bool has_init_score() const { return has_init_score_; }
@@ -109,6 +108,7 @@ public:
   ScoreUpdater& operator=(const ScoreUpdater&) = delete;
   /*! \brief Disable copy */
   ScoreUpdater(const ScoreUpdater&) = delete;
+
 private:
   /*! \brief Number of total data */
   data_size_t num_data_;

src/c_api.cpp

@@ -37,7 +37,6 @@ inline int LGBM_APIHandleException(const std::string& ex) {
 }
 
 #define API_BEGIN() try {
-
 #define API_END() } \
 catch(std::exception& ex) { return LGBM_APIHandleException(ex); } \
 catch(std::string& ex) { return LGBM_APIHandleException(ex); } \
@@ -77,7 +76,6 @@ public:
     }
     boosting_->Init(&config_, train_data_, objective_fun_.get(),
                     Common::ConstPtrInVectorWrapper<Metric>(train_metric_));
-
   }
 
   void MergeFrom(const Booster* other) {
@@ -86,7 +84,6 @@ public:
   }
 
   ~Booster() {
-
   }
 
   void CreateObjectiveAndMetrics() {
@@ -158,7 +155,6 @@ public:
     }
 
     boosting_->ResetConfig(&config_);
-
   }
 
   void AddValidData(const Dataset* valid_data) {
@@ -275,7 +271,7 @@ public:
     return boosting_->SaveModelToString(start_iteration, num_iteration);
   }
 
-  std::string DumpModel(int start_iteration,int num_iteration) {
+  std::string DumpModel(int start_iteration, int num_iteration) {
     return boosting_->DumpModel(start_iteration, num_iteration);
   }
 
@@ -328,7 +324,6 @@ public:
   const Boosting* GetBoosting() const { return boosting_.get(); }
 
 private:
-
   const Dataset* train_data_;
   std::unique_ptr<Boosting> boosting_;
   /*! \brief All configs */
@@ -343,7 +338,7 @@ private:
   std::mutex mutex_;
 };
 
-}
+}  // namespace LightGBM
 
 using namespace LightGBM;
 
@@ -394,7 +389,7 @@ int LGBM_DatasetCreateFromFile(const char* filename,
   if (config.num_threads > 0) {
     omp_set_num_threads(config.num_threads);
   }
-  DatasetLoader loader(config,nullptr, 1, filename);
+  DatasetLoader loader(config, nullptr, 1, filename);
   if (reference == nullptr) {
     if (Network::num_machines() == 1) {
       *out = loader.LoadFromFile(filename, "");
@@ -545,7 +540,7 @@ int LGBM_DatasetCreateFromMats(int32_t nmat,
   for (int j = 0; j < nmat; ++j) {
     get_row_fun.push_back(RowFunctionFromDenseMatric(data[j], nrow[j], ncol, data_type, is_row_major));
   }
-  
+
   if (reference == nullptr) {
     // sample data first
     Random rand(config.data_random_seed);
@@ -563,7 +558,7 @@ int LGBM_DatasetCreateFromMats(int32_t nmat,
         offset += nrow[j];
         ++j;
       }
-      
+
       auto row = get_row_fun[j](static_cast<int>(idx - offset));
       for (size_t k = 0; k < row.size(); ++k) {
         if (std::fabs(row[k]) > kZeroThreshold || std::isnan(row[k])) {
@@ -1249,7 +1244,7 @@ int LGBM_BoosterSaveModel(BoosterHandle handle,
 int LGBM_BoosterSaveModelToString(BoosterHandle handle,
                                   int start_iteration,
                                   int num_iteration,
-                                  int64_t buffer_len, 
+                                  int64_t buffer_len,
                                   int64_t* out_len,
                                   char* out_str) {
   API_BEGIN();

src/io/bin.cpp

@@ -44,7 +44,6 @@ namespace LightGBM {
   }
 
   BinMapper::~BinMapper() {
-
   }
 
   bool NeedFilter(const std::vector<int>& cnt_in_bin, int total_cnt, int filter_cnt, BinType bin_type) {

src/io/config.cpp

@@ -19,7 +19,7 @@ void Config::KV2Map(std::unordered_map<std::string, std::string>& params, const
     std::string value = Common::RemoveQuotationSymbol(Common::Trim(tmp_strs[1]));
     if (key.size() > 0) {
       auto value_search = params.find(key);
-      if (value_search == params.end()) { // not set
+      if (value_search == params.end()) {  // not set
         params.emplace(key, value);
       } else {
         Log::Warning("%s is set=%s, %s=%s will be ignored. Current value: %s=%s",
@@ -151,7 +151,6 @@ void GetTreeLearnerType(const std::unordered_map<std::string, std::string>& para
 }
 
 void Config::Set(const std::unordered_map<std::string, std::string>& params) {
-
   // generate seeds by seed.
   if (GetInt(params, "seed", &seed)) {
     Random rand(seed);
@@ -202,10 +201,10 @@ bool CheckMultiClassObjective(const std::string& objective) {
 void Config::CheckParamConflict() {
   // check if objective, metric, and num_class match
   int num_class_check = num_class;
-  bool objective_custom = objective == std::string("none") || objective == std::string("null") 
+  bool objective_custom = objective == std::string("none") || objective == std::string("null")
                                        || objective == std::string("custom") || objective == std::string("na");
   bool objective_type_multiclass = CheckMultiClassObjective(objective) || (objective_custom && num_class_check > 1);
-  
+
   if (objective_type_multiclass) {
     if (num_class_check <= 1) {
       Log::Fatal("Number of classes should be specified and greater than 1 for multiclass training");
@@ -216,7 +215,7 @@ void Config::CheckParamConflict() {
     }
   }
   for (std::string metric_type : metric) {
-    bool metric_custom_or_none = metric_type == std::string("none") || metric_type == std::string("null") 
+    bool metric_custom_or_none = metric_type == std::string("none") || metric_type == std::string("null")
                                  || metric_type == std::string("custom") || metric_type == std::string("na");
     bool metric_type_multiclass = (CheckMultiClassObjective(metric_type)
                                    || metric_type == std::string("multi_logloss")
@@ -259,7 +258,7 @@ void Config::CheckParamConflict() {
   // Check max_depth and num_leaves
   if (max_depth > 0) {
     int full_num_leaves = static_cast<int>(std::pow(2, max_depth));
-    if (full_num_leaves > num_leaves 
+    if (full_num_leaves > num_leaves
         && num_leaves == kDefaultNumLeaves) {
       Log::Warning("Accuracy may be bad since you didn't set num_leaves and 2^max_depth > num_leaves");
     }