[ci][c++] fixed `whitespace/indent_namespace` errors from cpplint (#7056)

* dev

* dev

* dev

* dev

* dev

---------
Co-authored-by: James Lamb <jaylamb20@gmail.com>

.pre-commit-config.yaml

@@ -28,7 +28,7 @@ repos:
       - id: cpplint
         args:
           - --recursive
-          - --filter=-build/include_subdir,-build/header_guard,-whitespace/indent_namespace,-whitespace/line_length
+          - --filter=-build/include_subdir,-build/header_guard,-whitespace/line_length
   - repo: local
     hooks:
       - id: check-omp-pragmas

include/LightGBM/meta.h

@@ -83,7 +83,7 @@ const int kAlignedSize = 32;
 
 // Refer to https://docs.microsoft.com/en-us/cpp/error-messages/compiler-warnings/compiler-warning-level-4-c4127?view=vs-2019
 #ifdef _MSC_VER
-  #pragma warning(disable : 4127)
+#pragma warning(disable : 4127)
 #endif
 
 }  // namespace LightGBM

src/io/dataset.cpp

@@ -28,10 +28,8 @@ namespace LightGBM {
 const int Dataset::kSerializedReferenceVersionLength = 2;
 const char* Dataset::serialized_reference_version = "v1";
 
-const char* Dataset::binary_file_token =
-    "______LightGBM_Binary_File_Token______\n";
-const char* Dataset::binary_serialized_reference_token =
-    "______LightGBM_Binary_Serialized_Token______\n";
+const char* Dataset::binary_file_token = "______LightGBM_Binary_File_Token______\n";
+const char* Dataset::binary_serialized_reference_token = "______LightGBM_Binary_Serialized_Token______\n";
 
 Dataset::Dataset() {
   data_filename_ = "noname";

src/metric/cuda/cuda_binary_metric.cpp

@@ -12,8 +12,8 @@
 
 namespace LightGBM {
 
-CUDABinaryLoglossMetric::CUDABinaryLoglossMetric(const Config& config):
-  CUDABinaryMetricInterface<BinaryLoglossMetric, CUDABinaryLoglossMetric>(config) {}
+CUDABinaryLoglossMetric::CUDABinaryLoglossMetric(
+  const Config& config):CUDABinaryMetricInterface<BinaryLoglossMetric, CUDABinaryLoglossMetric>(config) {}
 
 template <typename HOST_METRIC, typename CUDA_METRIC>
 std::vector<double> CUDABinaryMetricInterface<HOST_METRIC, CUDA_METRIC>::Eval(const double* score, const ObjectiveFunction* objective) const {

src/metric/xentropy_metric.hpp

@@ -30,40 +30,40 @@
 
 namespace LightGBM {
 
-  // label should be in interval [0, 1];
-  // prob should be in interval (0, 1); prob is clipped if needed
-  inline static double XentLoss(label_t label, double prob) {
-    const double log_arg_epsilon = 1.0e-12;
-    double a = label;
-    if (prob > log_arg_epsilon) {
-      a *= std::log(prob);
-    } else {
-      a *= std::log(log_arg_epsilon);
-    }
-    double b = 1.0f - label;
-    if (1.0f - prob > log_arg_epsilon) {
-      b *= std::log(1.0f - prob);
-    } else {
-      b *= std::log(log_arg_epsilon);
-    }
-    return - (a + b);
+// label should be in interval [0, 1];
+// prob should be in interval (0, 1); prob is clipped if needed
+inline static double XentLoss(label_t label, double prob) {
+  const double log_arg_epsilon = 1.0e-12;
+  double a = label;
+  if (prob > log_arg_epsilon) {
+    a *= std::log(prob);
+  } else {
+    a *= std::log(log_arg_epsilon);
   }
-
-  // hhat >(=) 0 assumed; and weight > 0 required; but not checked here
-  inline static double XentLambdaLoss(label_t label, label_t weight, double hhat) {
-    return XentLoss(label, 1.0f - std::exp(-weight * hhat));
-  }
-
-  // Computes the (negative) entropy for label p; p should be in interval [0, 1];
-  // This is used to presum the KL-divergence offset term (to be _added_ to the cross-entropy loss).
-  // NOTE: x*log(x) = 0 for x=0,1; so only add when in (0, 1); avoid log(0)*0
-  inline static double YentLoss(double p) {
-    double hp = 0.0;
-    if (p > 0) hp += p * std::log(p);
-    double q = 1.0f - p;
-    if (q > 0) hp += q * std::log(q);
-    return hp;
+  double b = 1.0f - label;
+  if (1.0f - prob > log_arg_epsilon) {
+    b *= std::log(1.0f - prob);
+  } else {
+    b *= std::log(log_arg_epsilon);
   }
+  return - (a + b);
+}
+
+// hhat >(=) 0 assumed; and weight > 0 required; but not checked here
+inline static double XentLambdaLoss(label_t label, label_t weight, double hhat) {
+  return XentLoss(label, 1.0f - std::exp(-weight * hhat));
+}
+
+// Computes the (negative) entropy for label p; p should be in interval [0, 1];
+// This is used to presum the KL-divergence offset term (to be _added_ to the cross-entropy loss).
+// NOTE: x*log(x) = 0 for x=0,1; so only add when in (0, 1); avoid log(0)*0
+inline static double YentLoss(double p) {
+  double hp = 0.0;
+  if (p > 0) hp += p * std::log(p);
+  double q = 1.0f - p;
+  if (q > 0) hp += q * std::log(q);
+  return hp;
+}
 
 //
 // CrossEntropyMetric : "xentropy" : (optional) weights are used linearly

src/treelearner/data_parallel_tree_learner.cpp

@@ -12,8 +12,7 @@
 namespace LightGBM {
 
 template <typename TREELEARNER_T>
-DataParallelTreeLearner<TREELEARNER_T>::DataParallelTreeLearner(const Config* config)
-  :TREELEARNER_T(config) {
+DataParallelTreeLearner<TREELEARNER_T>::DataParallelTreeLearner(const Config* config):TREELEARNER_T(config) {
 }
 
 template <typename TREELEARNER_T>

src/treelearner/feature_histogram.cpp

@@ -384,8 +384,11 @@ void FeatureHistogram::FindBestThresholdCategoricalInner(double sum_gradient,
   }
 }
 
-template <bool USE_RAND, bool USE_MC, bool USE_L1, bool USE_MAX_OUTPUT, bool USE_SMOOTHING, typename PACKED_HIST_BIN_T, typename PACKED_HIST_ACC_T,
-          typename HIST_BIN_T, typename HIST_ACC_T, int HIST_BITS_BIN, int HIST_BITS_ACC>
+template <
+bool USE_RAND, bool USE_MC, bool USE_L1, bool USE_MAX_OUTPUT, bool USE_SMOOTHING,
+typename PACKED_HIST_BIN_T, typename PACKED_HIST_ACC_T, typename HIST_BIN_T, typename HIST_ACC_T,
+int HIST_BITS_BIN, int HIST_BITS_ACC
+>
 void FeatureHistogram::FindBestThresholdCategoricalIntInner(int64_t int_sum_gradient_and_hessian,
                                           const double grad_scale, const double hess_scale,
                                           data_size_t num_data,

src/treelearner/feature_parallel_tree_learner.cpp

@@ -11,8 +11,7 @@ namespace LightGBM {
 
 
 template <typename TREELEARNER_T>
-FeatureParallelTreeLearner<TREELEARNER_T>::FeatureParallelTreeLearner(const Config* config)
-  :TREELEARNER_T(config) {
+FeatureParallelTreeLearner<TREELEARNER_T>::FeatureParallelTreeLearner(const Config* config):TREELEARNER_T(config) {
 }
 
 template <typename TREELEARNER_T>

src/treelearner/voting_parallel_tree_learner.cpp

@@ -15,8 +15,7 @@
 namespace LightGBM {
 
 template <typename TREELEARNER_T>
-VotingParallelTreeLearner<TREELEARNER_T>::VotingParallelTreeLearner(const Config* config)
-  :TREELEARNER_T(config) {
+VotingParallelTreeLearner<TREELEARNER_T>::VotingParallelTreeLearner(const Config* config):TREELEARNER_T(config) {
   top_k_ = this->config_->top_k;
 }