adding verbose option for logger

include/LightGBM/config.h

@@ -93,6 +93,8 @@ public:
   std::string output_result = "LightGBM_predict_result.txt";
   std::string input_model = "";
   std::string input_init_score = "";
+  int verbosity = 0;
+  std::string log_file = "";
   int num_model_predict = -1;
   bool is_pre_partition = false;
   bool is_enable_sparse = true;
@@ -310,7 +312,8 @@ struct ParameterAlias {
       { "two_round", "use_two_round_loading" },
       { "mlist", "machine_list_file" },
       { "is_save_binary", "is_save_binary_file" },
-      { "save_binary", "is_save_binary_file" }
+      { "save_binary", "is_save_binary_file" },
+      { "verbose", "verbosity" }
     });
     std::unordered_map<std::string, std::string> tmp_map;
     for (const auto& pair : *params) {

include/LightGBM/utils/common.h

@@ -157,7 +157,7 @@ inline static const char* Atof(const char* p, double* out) {
         *out = sign * 1e308;
       }
       else {
-        Log::Error("Unknow token %s in data file", tmp_str.c_str());
+        Log::Fatal("Unknow token %s in data file", tmp_str.c_str());
       }
       p += cnt;
     }
@@ -201,7 +201,7 @@ inline static std::string ArrayToString(const T* arr, int n, char delimiter) {
 inline static void StringToIntArray(const std::string& str, char delimiter, size_t n, int* out) {
   std::vector<std::string> strs = Split(str.c_str(), delimiter);
   if (strs.size() != n) {
-    Log::Error("StringToIntArray error, size don't equal.");
+    Log::Fatal("StringToIntArray error, size doesn't matched.");
   }
   for (size_t i = 0; i < strs.size(); ++i) {
     strs[i] = Trim(strs[i]);
@@ -212,7 +212,7 @@ inline static void StringToIntArray(const std::string& str, char delimiter, size
 inline static void StringToDoubleArray(const std::string& str, char delimiter, size_t n, double* out) {
   std::vector<std::string> strs = Split(str.c_str(), delimiter);
   if (strs.size() != n) {
-    Log::Error("StringToDoubleArray error, size don't equal");
+    Log::Fatal("StringToDoubleArray error, size doesn't matched.");
   }
   for (size_t i = 0; i < strs.size(); ++i) {
     strs[i] = Trim(strs[i]);
@@ -223,7 +223,7 @@ inline static void StringToDoubleArray(const std::string& str, char delimiter, s
 inline static void StringToDoubleArray(const std::string& str, char delimiter, size_t n, float* out) {
   std::vector<std::string> strs = Split(str.c_str(), delimiter);
   if (strs.size() != n) {
-    Log::Error("StringToDoubleArray error, size don't equal");
+    Log::Fatal("StringToDoubleArray error, size doesn't matched.");
   }
   double tmp;
   for (size_t i = 0; i < strs.size(); ++i) {

src/application/application.cpp

@@ -69,7 +69,7 @@ void Application::LoadParameters(int argc, char** argv) {
       params[key] = value;
     }
     else {
-      Log::Info("Warning: unknown parameter in command line: %s", argv[i]);
+      Log::Error("Unknown parameter in command line: %s\n", argv[i]);
     }
   }
   // check for alias
@@ -101,11 +101,11 @@ void Application::LoadParameters(int argc, char** argv) {
           }
         }
         else {
-          Log::Info("Warning: unknown parameter in config file: %s", line.c_str());
+          Log::Error("Unknown parameter in config file: %s", line.c_str());
         }
       }
     } else {
-      Log::Info("config file: %s doesn't exist, will ignore",
+      Log::Error("Config file: %s doesn't exist, will ignore\n",
                                 params["config_file"].c_str());
     }
   }
@@ -113,7 +113,7 @@ void Application::LoadParameters(int argc, char** argv) {
   ParameterAlias::KeyAliasTransform(&params);
   // load configs
   config_.Set(params);
-  Log::Info("finished load parameters");
+  Log::Info("Loading parameters .. finished\n");
 }
 
 void Application::LoadData() {
@@ -201,7 +201,7 @@ void Application::LoadData() {
   }
   auto end_time = std::chrono::high_resolution_clock::now();
   // output used time on each iteration
-  Log::Info("Finish loading data, use %f seconds ",
+  Log::Info("Finish loading data, use %f seconds \n",
     std::chrono::duration<double, std::milli>(end_time - start_time) * 1e-3);
 }
 
@@ -209,7 +209,7 @@ void Application::InitTrain() {
   if (config_.is_parallel) {
     // need init network
     Network::Init(config_.network_config);
-    Log::Info("finish network initialization");
+    Log::Info("Finish network initialization\n");
     // sync global random seed for feature patition
     if (config_.boosting_type == BoostingType::kGBDT) {
       GBDTConfig* gbdt_config =
@@ -240,13 +240,13 @@ void Application::InitTrain() {
     boosting_->AddDataset(valid_datas_[i],
       ConstPtrInVectorWarpper<Metric>(valid_metrics_[i]));
   }
-  Log::Info("finish training init");
+  Log::Info("Finish training initilization.\n");
 }
 
 void Application::Train() {
-  Log::Info("start train");
+  Log::Info("Start train\n");
   boosting_->Train();
-  Log::Info("finish train");
+  Log::Info("Finish train\n");
 }
 
 
@@ -254,14 +254,14 @@ void Application::Predict() {
   // create predictor
   Predictor predictor(boosting_, config_.io_config.is_sigmoid);
   predictor.Predict(config_.io_config.data_filename.c_str(), config_.io_config.output_result.c_str());
-  Log::Info("finish predict");
+  Log::Info("Finish predict.\n");
 }
 
 void Application::InitPredict() {
   boosting_ =
     Boosting::CreateBoosting(config_.boosting_type, config_.boosting_config);
   LoadModel();
-  Log::Info("finish predict init");
+  Log::Info("Finish predict initilization.\n");
 }
 
 void Application::LoadModel() {

src/application/predictor.hpp

@@ -106,13 +106,13 @@ public:
 #endif
 
     if (result_file == NULL) {
-      Log::Error("predition result file %s doesn't exists", data_filename);
+      Log::Fatal("Predition result file %s doesn't exists", data_filename);
     }
     bool has_label = false;
     Parser* parser = Parser::CreateParser(data_filename, num_features_, &has_label);
 
     if (parser == nullptr) {
-      Log::Error("recongnizing input data format failed, filename %s", data_filename);
+      Log::Fatal("Recongnizing input data format failed, filename %s", data_filename);
     }
 
     // function for parse data
@@ -124,14 +124,14 @@ public:
       (const char* buffer, std::vector<std::pair<int, double>>* feature) {
         parser->ParseOneLine(buffer, feature, &tmp_label);
       };
-      Log::Info("start prediction for data %s, and data has label", data_filename);
+      Log::Info("Start prediction for data %s with labels\n", data_filename);
     } else {
       // parse function without label
       parser_fun = [this, &parser]
       (const char* buffer, std::vector<std::pair<int, double>>* feature) {
         parser->ParseOneLine(buffer, feature);
       };
-      Log::Info("start prediction for data %s, and data doesn't has label", data_filename);
+      Log::Info("Start prediction for data %s without label\n", data_filename);
     }
     std::function<double(const std::vector<std::pair<int, double>>&)> predict_fun;
     if (is_simgoid_) {

src/boosting/gbdt.cpp

@@ -150,7 +150,7 @@ void GBDT::Bagging(int iter) {
       bag_data_cnt_ = cur_left_cnt;
       out_of_bag_data_cnt_ = num_data_ - bag_data_cnt_;
     }
-    Log::Info("re-bagging, using %d data to train", bag_data_cnt_);
+    Log::Info("re-bagging, using %d data to train\n", bag_data_cnt_);
     // set bagging data to tree learner
     tree_learner_->SetBaggingData(bag_data_indices_, bag_data_cnt_);
   }
@@ -176,7 +176,7 @@ void GBDT::Train() {
     Tree * new_tree = TrainOneTree();
     // if cannot learn a new tree, then stop
     if (new_tree->num_leaves() <= 1) {
-      Log::Info("Cannot do any boosting for tree cannot split");
+      Log::Info("Can't training anymore, there isn't any leaf meets split requirements.\n");
       break;
     }
     // shrinkage by learning rate
@@ -194,7 +194,7 @@ void GBDT::Train() {
     fflush(output_model_file);
     auto end_time = std::chrono::high_resolution_clock::now();
     // output used time per iteration
-    Log::Info("%f seconds elapsed, finished %d iteration", std::chrono::duration<double,
+    Log::Info("%f seconds elapsed, finished %d iteration\n", std::chrono::duration<double,
                                      std::milli>(end_time - start_time) * 1e-3, iter + 1);
   }
   // close file
@@ -284,7 +284,7 @@ void GBDT::ModelsFromString(const std::string& model_str, int num_used_model) {
     }
   }
   if (i == lines.size()) {
-    Log::Error("The model doesn't contain max_feature_idx");
+    Log::Fatal("Model file doesn't contain max_feature_idx\n");
     return;
   }
   // get sigmoid parameter
@@ -323,7 +323,7 @@ void GBDT::ModelsFromString(const std::string& model_str, int num_used_model) {
     }
   }
 
-  Log::Info("Loaded %d models\n", models_.size());
+  Log::Info("%d models has been loaded\n\n", models_.size());
 }
 
 double GBDT::PredictRaw(const double* value) const {

src/io/config.cpp

@@ -34,6 +34,15 @@ void OverallConfig::Set(const std::unordered_map<std::string, std::string>& para
   metric_config.Set(params);
   // check for conflicts
   CheckParamConflict();
+
+  if (io_config.verbosity == 1)
+    LightGBM::Log::ResetLogLevel(LightGBM::LogLevel::Info);
+  else if (io_config.verbosity == 0)
+    LightGBM::Log::ResetLogLevel(LightGBM::LogLevel::Error);
+  else if (io_config.verbosity >= 2)
+    LightGBM::Log::ResetLogLevel(LightGBM::LogLevel::Debug);
+  else 
+    LightGBM::Log::ResetLogLevel(LightGBM::LogLevel::Fatal);
 }
 
 void OverallConfig::GetBoostingType(const std::unordered_map<std::string, std::string>& params) {
@@ -43,7 +52,7 @@ void OverallConfig::GetBoostingType(const std::unordered_map<std::string, std::s
     if (value == std::string("gbdt") || value == std::string("gbrt")) {
       boosting_type = BoostingType::kGBDT;
     } else {
-      Log::Error("boosting type %s error", value.c_str());
+      Log::Fatal("Boosting type %s error", value.c_str());
     }
   }
 }
@@ -91,7 +100,7 @@ void OverallConfig::GetTaskType(const std::unordered_map<std::string, std::strin
       || value == std::string("test")) {
       task_type = TaskType::kPredict;
     } else {
-      Log::Error("task type error");
+      Log::Fatal("Task type error");
     }
   }
 }
@@ -128,8 +137,9 @@ void IOConfig::Set(const std::unordered_map<std::string, std::string>& params) {
   GetInt(params, "data_random_seed", &data_random_seed);
 
   if (!GetString(params, "data", &data_filename)) {
-    Log::Error("No training/prediction data, application quit");
+    Log::Fatal("No training/prediction data, application quit");
   }
+  GetInt(params, "verbose", &verbosity);
   GetInt(params, "num_model_predict", &num_model_predict);
   GetBool(params, "is_pre_partition", &is_pre_partition);
   GetBool(params, "is_enable_sparse", &is_enable_sparse);
@@ -140,6 +150,7 @@ void IOConfig::Set(const std::unordered_map<std::string, std::string>& params) {
   GetString(params, "input_model", &input_model);
   GetString(params, "output_result", &output_result);
   GetString(params, "input_init_score", &input_init_score);
+  GetString(params, "log_file", &log_file);
   std::string tmp_str = "";
   if (GetString(params, "valid_data", &tmp_str)) {
     valid_data_filenames = Common::Split(tmp_str.c_str(), ',');
@@ -236,7 +247,7 @@ void GBDTConfig::GetTreeLearnerType(const std::unordered_map<std::string, std::s
       tree_learner_type = TreeLearnerType::kDataParallelTreeLearner;
     }
     else {
-      Log::Error("tree learner type error");
+      Log::Fatal("Tree learner type error");
     }
   }
 }

src/io/dataset.cpp

@@ -21,7 +21,7 @@ Dataset::Dataset(const char* data_filename, const char* init_score_filename,
 
   CheckCanLoadFromBin();
   if (is_loading_from_binfile_ && predict_fun != nullptr) {
-    Log::Info("cannot perform initial prediction for binary file, will use text file instead");
+    Log::Info("Cannot performing initialization of prediction by using binary file, using text file instead\n");
     is_loading_from_binfile_ = false;
   }
 
@@ -31,14 +31,14 @@ Dataset::Dataset(const char* data_filename, const char* init_score_filename,
     // create text parser
     parser_ = Parser::CreateParser(data_filename_, 0, nullptr);
     if (parser_ == nullptr) {
-      Log::Error("cannot recognise input data format, filename: %s", data_filename_);
+      Log::Fatal("Cannot recognising input data format, filename: %s", data_filename_);
     }
     // create text reader
     text_reader_ = new TextReader<data_size_t>(data_filename);
   } else {
     // only need to load initilize score, other meta data will load from bin flie
     metadata_.Init(init_score_filename);
-    Log::Info("will load data set from binary file");
+    Log::Info("Loading data set from binary file\n");
     parser_ = nullptr;
     text_reader_ = nullptr;
   }
@@ -82,7 +82,7 @@ void Dataset::LoadDataToMemory(int rank, int num_machines, bool is_pre_partition
         [this, rank, num_machines, &qid, &query_boundaries, &is_query_used, num_queries]
       (data_size_t line_idx) {
         if (qid >= num_queries) {
-          Log::Error("current query is exceed the range of query file, please ensure your query file is correct");
+          Log::Fatal("Current query is exceed the range of query file, please ensure your query file is correct");
         }
         if (line_idx >= query_boundaries[qid + 1]) {
           // if is new query
@@ -139,7 +139,7 @@ void Dataset::SampleDataFromFile(int rank, int num_machines, bool is_pre_partiti
         [this, rank, num_machines, &qid, &query_boundaries, &is_query_used, num_queries]
       (data_size_t line_idx) {
         if (qid >= num_queries) {
-          Log::Error("current query is exceed the range of query file, \
+          Log::Fatal("Query id is exceed the range of query file, \
                              please ensure your query file is correct");
         }
         if (line_idx >= query_boundaries[qid + 1]) {
@@ -209,7 +209,7 @@ void Dataset::ConstructBinMappers(int rank, int num_machines, const std::vector<
                                              num_data_, is_enable_sparse_));
       } else {
         // if feature is trival(only 1 bin), free spaces
-        Log::Info("Warning: feature %d only contains one value, will ignore it", i);
+        Log::Error("Feature %d only contains one value, will be ignored\n", i);
         delete bin_mappers[i];
       }
     }
@@ -486,10 +486,10 @@ void Dataset::SaveBinaryFile() {
     file = fopen(bin_filename.c_str(), "wb");
     #endif
     if (file == NULL) {
-      Log::Error("cannot write binary data to %s ", bin_filename.c_str());
+      Log::Fatal("Cannot write binary data to %s ", bin_filename.c_str());
     }
 
-    Log::Info("start save binary file for data %s", data_filename_);
+    Log::Info("Saving data to binary file: %s\n", data_filename_);
 
     // get size of header
     size_t size_of_header = sizeof(global_num_data_) + sizeof(is_enable_sparse_)
@@ -556,7 +556,7 @@ void Dataset::LoadDataFromBinFile(int rank, int num_machines, bool is_pre_partit
   #endif
 
   if (file == NULL) {
-    Log::Error("cannot read binary data from %s", bin_filename.c_str());
+    Log::Fatal("Cannot read binary data from %s", bin_filename.c_str());
   }
 
   // buffer to read binary file
@@ -567,7 +567,7 @@ void Dataset::LoadDataFromBinFile(int rank, int num_machines, bool is_pre_partit
   size_t read_cnt = fread(buffer, sizeof(size_t), 1, file);
 
   if (read_cnt != 1) {
-    Log::Error("binary file format error at header size");
+    Log::Fatal("Binary file format error at header size");
   }
 
   size_t size_of_head = *(reinterpret_cast<size_t*>(buffer));
@@ -582,7 +582,7 @@ void Dataset::LoadDataFromBinFile(int rank, int num_machines, bool is_pre_partit
   read_cnt = fread(buffer, 1, size_of_head, file);
 
   if (read_cnt != size_of_head) {
-    Log::Error("binary file format error at header");
+    Log::Fatal("Binary file format error at header");
   }
   // get header 
   const char* mem_ptr = buffer;
@@ -608,7 +608,7 @@ void Dataset::LoadDataFromBinFile(int rank, int num_machines, bool is_pre_partit
   read_cnt = fread(buffer, sizeof(size_t), 1, file);
 
   if (read_cnt != 1) {
-    Log::Error("binary file format error at size of meta data");
+    Log::Fatal("Binary file format error: wrong size of meta data");
   }
 
   size_t size_of_metadata = *(reinterpret_cast<size_t*>(buffer));
@@ -623,7 +623,7 @@ void Dataset::LoadDataFromBinFile(int rank, int num_machines, bool is_pre_partit
   read_cnt = fread(buffer, 1, size_of_metadata, file);
 
   if (read_cnt != size_of_metadata) {
-    Log::Error("binary file format error at meta data");
+    Log::Fatal("Binary file format error: wrong size of meta data");
   }
   // load meta data
   metadata_.LoadFromMemory(buffer);
@@ -647,7 +647,7 @@ void Dataset::LoadDataFromBinFile(int rank, int num_machines, bool is_pre_partit
       bool is_query_used = false;
       for (data_size_t i = 0; i < num_data_; i++) {
         if (qid >= num_queries) {
-          Log::Error("current query is exceed the range of query file, please ensure your query file is correct");
+          Log::Fatal("current query is exceed the range of query file, please ensure your query file is correct");
         }
         if (i >= query_boundaries[qid + 1]) {
           // if is new query
@@ -670,7 +670,7 @@ void Dataset::LoadDataFromBinFile(int rank, int num_machines, bool is_pre_partit
     // read feature size
     read_cnt = fread(buffer, sizeof(size_t), 1, file);
     if (read_cnt != 1) {
-      Log::Error("binary file format error at feature %d's size", i);
+      Log::Fatal("Binary file format error at feature %d's size", i);
     }
     size_t size_of_feature = *(reinterpret_cast<size_t*>(buffer));
     // re-allocmate space if not enough
@@ -683,7 +683,7 @@ void Dataset::LoadDataFromBinFile(int rank, int num_machines, bool is_pre_partit
     read_cnt = fread(buffer, 1, size_of_feature, file);
 
     if (read_cnt != size_of_feature) {
-      Log::Error("binary file format error at feature %d loading , read count %d", i, read_cnt);
+      Log::Fatal("Binary file format error at feature %d loading , read count %d", i, read_cnt);
     }
     features_.push_back(new Feature(buffer, static_cast<data_size_t>(global_num_data_), used_data_indices_));
   }
@@ -693,10 +693,10 @@ void Dataset::LoadDataFromBinFile(int rank, int num_machines, bool is_pre_partit
 
 void Dataset::CheckDataset() {
   if (num_data_ <= 0) {
-    Log::Error("data size of %s is zero", data_filename_);
+    Log::Fatal("Data file %s is empty", data_filename_);
   }
   if (features_.size() <= 0) {
-    Log::Error("not useful feature of data %s", data_filename_);
+    Log::Fatal("Usable feature of data %s is null", data_filename_);
   }
 }
 

src/io/metadata.cpp

@@ -61,7 +61,7 @@ void Metadata::CheckOrPartition(data_size_t num_all_data, const std::vector<data
   if (used_data_indices.size() == 0) {
     // check weights
     if (weights_ != nullptr && num_weights_ != num_data_) {
-      Log::Info("init weight size doesn't equal with data file, will ignore");
+      Log::Error("Initial weight size doesn't equal to data, weights will be ignored\n");
       delete[] weights_;
       num_weights_ = 0;
       weights_ = nullptr;
@@ -69,7 +69,7 @@ void Metadata::CheckOrPartition(data_size_t num_all_data, const std::vector<data
 
     // check query boundries
     if (query_boundaries_ != nullptr && query_boundaries_[num_queries_] != num_data_) {
-      Log::Info("init query size doesn't equal with data file, will ignore");
+      Log::Error("Initial query size doesn't equal to data, queies will be ignored\n");
       delete[] query_boundaries_;
       num_queries_ = 0;
       query_boundaries_ = nullptr;
@@ -78,21 +78,21 @@ void Metadata::CheckOrPartition(data_size_t num_all_data, const std::vector<data
     // contain initial score file
     if (init_score_ != nullptr && num_init_score_ != num_data_) {
       delete[] init_score_;
-      Log::Info("init score size doesn't equal with data file, will ignore");
+      Log::Error("Initial score size doesn't equal to data, score file will be ignored\n");
       num_init_score_ = 0;
     }
   } else {
     data_size_t num_used_data = static_cast<data_size_t>(used_data_indices.size());
     // check weights
     if (weights_ != nullptr && num_weights_ != num_all_data) {
-      Log::Info("init weight size doesn't equal with data file, will ignore");
+      Log::Error("Initial weights size doesn't equal to data, weights will be ignored\n");
       delete[] weights_;
       num_weights_ = 0;
       weights_ = nullptr;
     }
     // check query boundries
     if (query_boundaries_ != nullptr && query_boundaries_[num_queries_] != num_all_data) {
-      Log::Info("init query size doesn't equal with data file, will ignore");
+      Log::Error("Initial query size doesn't equal to data , queries will be ignored\n");
       delete[] query_boundaries_;
       num_queries_ = 0;
       query_boundaries_ = nullptr;
@@ -100,7 +100,7 @@ void Metadata::CheckOrPartition(data_size_t num_all_data, const std::vector<data
 
     // contain initial score file
     if (init_score_ != nullptr && num_init_score_ != num_all_data) {
-      Log::Info("init score size doesn't equal with data file, will ignore");
+      Log::Error("Initial score size doesn't equal to data , initial scores will be ignored\n");
       delete[] init_score_;
       num_init_score_ = 0;
     }
@@ -131,10 +131,10 @@ void Metadata::CheckOrPartition(data_size_t num_all_data, const std::vector<data
             used_query.push_back(qid);
             data_idx += len;
           } else {
-            Log::Error("data partition error, not according to query");
+            Log::Fatal("Data partition error, data didn't match queies\n");
           }
         } else {
-          Log::Error("data partition error, not according to query");
+          Log::Fatal("Data partition error, data didn't match queies\n");
         }
       }
       data_size_t * old_query_boundaries = query_boundaries_;
@@ -182,7 +182,7 @@ void Metadata::LoadWeights() {
   if (reader.Lines().size() <= 0) {
     return;
   }
-  Log::Info("Start to load weights");
+  Log::Info("Start loading weights\n");
   num_weights_ = static_cast<data_size_t>(reader.Lines().size());
   weights_ = new float[num_weights_];
   for (data_size_t i = 0; i < num_weights_; ++i) {
@@ -198,7 +198,7 @@ void Metadata::LoadInitialScore() {
   TextReader<size_t> reader(init_score_filename_);
   reader.ReadAllLines();
 
-  Log::Info("Start to load initial score");
+  Log::Info("Start loading initial scores\n");
   num_init_score_ = static_cast<data_size_t>(reader.Lines().size());
   init_score_ = new score_t[num_init_score_];
   double tmp = 0.0f;
@@ -218,7 +218,7 @@ void Metadata::LoadQueryBoundaries() {
   if (reader.Lines().size() <= 0) {
     return;
   }
-  Log::Info("Start to load query boundries");
+  Log::Info("Start loading query boundries\n");
   query_boundaries_ = new data_size_t[reader.Lines().size() + 1];
   num_queries_ = static_cast<data_size_t>(reader.Lines().size());
   query_boundaries_[0] = 0;
@@ -233,7 +233,7 @@ void Metadata::LoadQueryWeights() {
   if (weights_ == nullptr || query_boundaries_ == nullptr) {
     return;
   }
-  Log::Info("Start to load query weights");
+  Log::Info("Start loading query weights\n");
   query_weights_ = new float[num_queries_];
   for (data_size_t i = 0; i < num_queries_; ++i) {
     query_weights_[i] = 0.0f;

src/io/parser.cpp

@@ -55,18 +55,18 @@ Parser* Parser::CreateParser(const char* filename, int num_features, bool* has_l
   std::ifstream tmp_file;
   tmp_file.open(filename);
   if (!tmp_file.is_open()) {
-    Log::Error("Data file: %s doesn't exist", filename);
+    Log::Fatal("Data file: %s doesn't exist", filename);
   }
   std::string line1, line2;
   if (!tmp_file.eof()) {
     std::getline(tmp_file, line1);
   } else {
-    Log::Error("Data file: %s at least should have one line", filename);
+    Log::Fatal("Data file: %s at least should have one line", filename);
   }
   if (!tmp_file.eof()) {
     std::getline(tmp_file, line2);
   } else {
-    Log::Info("Data file: %s only have one line", filename);
+    Log::Error("Data file: %s only have one line", filename);
   }
   tmp_file.close();
   int comma_cnt = 0, comma_cnt2 = 0;

src/io/parser.hpp

@@ -27,7 +27,7 @@ public:
       if (*str == ',') {
         ++str;
       } else if (*str != '\0') {
-        Log::Error("input format error, should be CSV");
+        Log::Fatal("input format error, should be CSV");
       }
     }
   }
@@ -38,7 +38,7 @@ public:
     if (*str == ',') {
       ++str;
     } else if (*str != '\0') {
-      Log::Error("input format error, should be CSV");
+      Log::Fatal("input format error, should be CSV");
     }
     return ParseOneLine(str, out_features);
   }
@@ -58,7 +58,7 @@ public:
       if (*str == '\t') {
         ++str;
       } else if (*str != '\0') {
-        Log::Error("input format error, should be TSV");
+        Log::Fatal("input format error, should be TSV");
       }
     }
   }
@@ -69,7 +69,7 @@ public:
     if (*str == '\t') {
       ++str;
     } else if (*str != '\0') {
-      Log::Error("input format error, should be TSV");
+      Log::Fatal("input format error, should be TSV");
     }
     return ParseOneLine(str, out_features);
   }
@@ -88,7 +88,7 @@ public:
         str = Common::Atof(str, &val);
         out_features->emplace_back(idx, val);
       } else {
-        Log::Error("input format error, should be LibSVM");
+        Log::Fatal("input format error, should be LibSVM");
       }
       str = Common::SkipSpaceAndTab(str);
     }

src/io/tree.cpp

@@ -140,7 +140,7 @@ Tree::Tree(const std::string& str) {
     || key_vals.count("split_gain") <= 0 || key_vals.count("threshold") <= 0
     || key_vals.count("left_child") <= 0 || key_vals.count("right_child") <= 0
     || key_vals.count("leaf_parent") <= 0 || key_vals.count("leaf_value") <= 0) {
-    Log::Error("tree model string format error");
+    Log::Fatal("tree model string format error");
   }
 
   Common::Atoi(key_vals["num_leaves"].c_str(), &num_leaves_);

src/metric/binary_metric.hpp

@@ -23,7 +23,7 @@ public:
     the_bigger_the_better = false;
     sigmoid_ = static_cast<score_t>(config.sigmoid);
     if (sigmoid_ <= 0.0f) {
-      Log::Error("sigmoid param %f should greater than zero", sigmoid_);
+      Log::Fatal("Sigmoid param %f should greater than zero", sigmoid_);
     }
   }
 
@@ -72,7 +72,7 @@ public:
       }
       loss = sum_loss / sum_weights_;
       if (output_freq_ > 0 && iter % output_freq_ == 0){
-        Log::Info("Iteration:%d, %s's %s: %f", iter, name, PointWiseLossCalculator::Name(), loss);
+        Log::Info("Iteration:%d, %s's %s: %f\n", iter, name, PointWiseLossCalculator::Name(), loss);
       }
     }
   }
@@ -229,7 +229,7 @@ public:
       }
       loss = auc;
       if (output_freq_ > 0 && iter % output_freq_ == 0){
-        Log::Info("iteration:%d, %s's %s: %f", iter, name, "auc", loss);
+        Log::Info("Iteration:%d, %s's %s: %f\n", iter, name, "auc", loss);
       }
     }
   }

src/metric/dcg_calculator.cpp

@@ -57,7 +57,7 @@ void DCGCalculator::CalMaxDCG(const std::vector<data_size_t>& ks,
   std::vector<data_size_t> label_cnt(label_gain_.size(), 0);
   // counts for all labels
   for (data_size_t i = 0; i < num_data; ++i) {
-    if (static_cast<size_t>(label[i]) >= label_cnt.size()) { Log::Error("label excel %d\n", label[i]); }
+    if (static_cast<size_t>(label[i]) >= label_cnt.size()) { Log::Fatal("label excel %d\n", label[i]); }
     ++label_cnt[static_cast<int>(label[i])];
   }
   double cur_result = 0.0;

src/metric/rank_metric.hpp

@@ -43,7 +43,7 @@ public:
     // get query boundaries
     query_boundaries_ = metadata.query_boundaries();
     if (query_boundaries_ == nullptr) {
-      Log::Error("For NDCG metric, should have query information");
+      Log::Fatal("For NDCG metric, there should be query information");
     }
     num_queries_ = metadata.num_queries();
     // get query weights
@@ -136,7 +136,7 @@ public:
       }
       loss = result[0];
       if (output_freq_ > 0 && iter % output_freq_ == 0){
-        Log::Info("Iteration:%d, Test:%s, %s ", iter, name, result_ss.str().c_str());
+        Log::Info("Iteration:%d, Test:%s, %s \n", iter, name, result_ss.str().c_str());
       }
     }
   }

src/network/linkers_socket.cpp

@@ -44,7 +44,7 @@ Linkers::Linkers(NetworkConfig config) {
     }
   }
   if (rank_ == -1) {
-    Log::Error("machine list file doesn't contain local machine, app quit");
+    Log::Fatal("Machine list file doesn't contain local machine");
   }
   // construct listener
   listener_ = new TcpSocket();
@@ -73,14 +73,14 @@ Linkers::~Linkers() {
     }
   }
   TcpSocket::Finalize();
-  Log::Info("network used %f seconds", network_time_ * 1e-3);
+  Log::Info("Network using %f seconds\n", network_time_ * 1e-3);
 }
 
 void Linkers::ParseMachineList(const char * filename) {
   TextReader<size_t> machine_list_reader(filename);
   machine_list_reader.ReadAllLines();
   if (machine_list_reader.Lines().size() <= 0) {
-    Log::Error("machine list file:%s doesn't exist", filename);
+    Log::Fatal("Machine list file:%s doesn't exist", filename);
   }
 
   for (auto& line : machine_list_reader.Lines()) {
@@ -95,7 +95,7 @@ void Linkers::ParseMachineList(const char * filename) {
       continue;
     }
     if (client_ips_.size() >= static_cast<size_t>(num_machines_)) {
-      Log::Info("The #machine in machine list is larger than parameter num_machines, will ignore rest");
+      Log::Error("The #machine in machine_list is larger than parameter num_machines, the redundant will ignored\n");
       break;
     }
     str_after_split[0] = Common::Trim(str_after_split[0]);
@@ -104,17 +104,17 @@ void Linkers::ParseMachineList(const char * filename) {
     client_ports_.push_back(atoi(str_after_split[1].c_str()));
   }
   if (client_ips_.size() != static_cast<size_t>(num_machines_)) {
-    Log::Info("The world size is bigger the #machine in machine list, change world size to %d .", client_ips_.size());
+    Log::Error("The world size is bigger the #machine in machine list, change world size to %d .\n", client_ips_.size());
     num_machines_ = static_cast<int>(client_ips_.size());
   }
 }
 
 void Linkers::TryBind(int port) {
-  Log::Info("try to bind port %d.", port);
+  Log::Info("try to bind port %d.\n", port);
   if (listener_->Bind(port)) {
-    Log::Info("bind port %d success.", port);
+    Log::Info("Binding port %d success.\n", port);
   } else {
-    Log::Error("bind port %d failed.", port);
+    Log::Fatal("Binding port %d failed.\n", port);
   }
 }
 
@@ -125,7 +125,7 @@ void Linkers::SetLinker(int rank, const TcpSocket& socket) {
 }
 
 void Linkers::ListenThread(int incoming_cnt) {
-  Log::Info("Listening...");
+  Log::Info("Listening...\n");
   char buffer[100];
   int connected_cnt = 0;
   while (connected_cnt < incoming_cnt) {
@@ -192,7 +192,7 @@ void Linkers::Construct() {
         if (cur_socket.Connect(client_ips_[out_rank].c_str(), client_ports_[out_rank])) {
           break;
         } else {
-          Log::Info("connect to rank %d failed, wait for %d milliseconds", out_rank, connect_fail_delay_time);
+          Log::Error("Connect to rank %d failed, wait for %d milliseconds\n", out_rank, connect_fail_delay_time);
           std::this_thread::sleep_for(std::chrono::milliseconds(connect_fail_delay_time));
         }
       }
@@ -217,7 +217,7 @@ bool Linkers::CheckLinker(int rank) {
 void Linkers::PrintLinkers() {
   for (int i = 0; i < num_machines_; ++i) {
     if (CheckLinker(i)) {
-      Log::Info("Connected to rank %d.", i);
+      Log::Info("Connected to rank %d.\n", i);
     }
   }
 }

src/network/socket_wrapper.hpp

@@ -60,7 +60,7 @@ public:
   TcpSocket() {
     sockfd_ = socket(AF_INET, SOCK_STREAM, IPPROTO_TCP);
     if (sockfd_ == INVALID_SOCKET) {
-      Log::Error("socket construct error");
+      Log::Fatal("Socket construct error\n");
       return;
     }
     ConfigSocket();
@@ -69,7 +69,7 @@ public:
   explicit TcpSocket(SOCKET socket) {
     sockfd_ = socket;
     if (sockfd_ == INVALID_SOCKET) {
-      Log::Error("passed socket error");
+      Log::Fatal("Passed socket error\n");
       return;
     }
     ConfigSocket();
@@ -97,11 +97,11 @@ public:
 #if defined(_WIN32)
     WSADATA wsa_data;
     if (WSAStartup(MAKEWORD(2, 2), &wsa_data) == -1) {
-      Log::Error("socket error: start up error");
+      Log::Fatal("Socket error: WSAStart up error\n");
     }
     if (LOBYTE(wsa_data.wVersion) != 2 || HIBYTE(wsa_data.wVersion) != 2) {
       WSACleanup();
-      Log::Error("socket error: Winsock.dll version error");
+      Log::Fatal("Socket error: Winsock.dll version error\n");
     }
 #else
 #endif
@@ -128,7 +128,7 @@ public:
     char buffer[512];
     // get hostName
     if (gethostname(buffer, sizeof(buffer)) == SOCKET_ERROR) {
-      Log::Error("Error code: %d, when getting local host name.", WSAGetLastError());
+      Log::Fatal("Error code: %d, when getting local host name.\n", WSAGetLastError());
     }
     // push local ip
     PIP_ADAPTER_INFO pAdapterInfo;
@@ -137,7 +137,7 @@ public:
     ULONG ulOutBufLen = sizeof(IP_ADAPTER_INFO);
     pAdapterInfo = (IP_ADAPTER_INFO *)MALLOC(sizeof(IP_ADAPTER_INFO));
     if (pAdapterInfo == NULL) {
-      Log::Error("Error allocating memory needed to call GetAdaptersinfo\n");
+      Log::Fatal("GetAdaptersinfo error: allocating memory \n");
     }
     // Make an initial call to GetAdaptersInfo to get
     // the necessary size into the ulOutBufLen variable
@@ -145,7 +145,7 @@ public:
       FREE(pAdapterInfo);
       pAdapterInfo = (IP_ADAPTER_INFO *)MALLOC(ulOutBufLen);
       if (pAdapterInfo == NULL) {
-        Log::Error("Error allocating memory needed to call GetAdaptersinfo\n");
+        Log::Fatal("GetAdaptersinfo error: allocating memory \n");
       }
     }
     if ((dwRetVal = GetAdaptersInfo(pAdapterInfo, &ulOutBufLen)) == NO_ERROR) {
@@ -155,7 +155,7 @@ public:
         pAdapter = pAdapter->Next;
       }
     } else {
-      printf("GetAdaptersInfo failed with error: %d\n", dwRetVal);
+      Log::Error("GetAdaptersinfo error: code %d \n", dwRetVal);
     }
     if (pAdapterInfo)
       FREE(pAdapterInfo);
@@ -218,7 +218,7 @@ public:
   inline TcpSocket Accept() {
     SOCKET newfd = accept(sockfd_, NULL, NULL);
     if (newfd == INVALID_SOCKET) {
-      Log::Error("socket accept error,error code: %d", GetLastError());
+      Log::Fatal("Socket accept error, code: %d", GetLastError());
     }
     return TcpSocket(newfd);
   }
@@ -226,7 +226,7 @@ public:
   inline int Send(const char *buf_, int len, int flag = 0) {
     int cur_cnt = send(sockfd_, buf_, len, flag);
     if (cur_cnt == SOCKET_ERROR) {
-      Log::Error("socket send error, error code: %d", GetLastError());
+      Log::Fatal("Socket send error, code: %d", GetLastError());
     }
     return cur_cnt;
   }
@@ -234,7 +234,7 @@ public:
   inline int Recv(char *buf_, int len, int flags = 0) {
     int cur_cnt = recv(sockfd_, buf_ , len , flags);
     if (cur_cnt == SOCKET_ERROR) {
-      Log::Error("socket recv error, error code: %d", GetLastError());
+      Log::Fatal("Socket recv error, code: %d", GetLastError());
     }
     return cur_cnt;
   }

src/objective/binary_objective.hpp

@@ -16,7 +16,7 @@ public:
     is_unbalance_ = config.is_unbalance;
     sigmoid_ = static_cast<score_t>(config.sigmoid);
     if (sigmoid_ <= 0.0) {
-      Log::Error("sigmoid param %f should greater than zero", sigmoid_);
+      Log::Fatal("Sigmoid parameter %f :should greater than zero\n", sigmoid_);
     }
   }
   ~BinaryLogloss() {}
@@ -34,10 +34,10 @@ public:
         ++cnt_negative;
       }
     }
-    Log::Info("number of postive:%d number of negative:%d", cnt_positive, cnt_negative);
+    Log::Info("Number of postive:%d,  number of negative:%d\n", cnt_positive, cnt_negative);
     // cannot continue if all sample are same class
     if (cnt_positive == 0 || cnt_negative == 0) {
-      Log::Error("input training data only contain one class");
+      Log::Fatal("Input training data only contains one class\n");
     }
     // use -1 for negative class, and 1 for positive class
     label_val_[0] = -1;

src/objective/rank_objective.hpp

@@ -31,7 +31,7 @@ public:
     optimize_pos_at_ = config.max_position;
     sigmoid_table_ = nullptr;
     if (sigmoid_ <= 0.0) {
-      Log::Error("sigmoid param %f should greater than zero", sigmoid_);
+      Log::Fatal("sigmoid param %f should greater than zero", sigmoid_);
     }
   }
   ~LambdarankNDCG() {
@@ -47,7 +47,7 @@ public:
     // get boundries
     query_boundaries_ = metadata.query_boundaries();
     if (query_boundaries_ == nullptr) {
-      Log::Error("For NDCG metric, should have query information");
+      Log::Fatal("For NDCG metric, should have query information");
     }
     num_queries_ = metadata.num_queries();
     // cache inverse max DCG, avoid compution many times

src/treelearner/serial_tree_learner.cpp

@@ -95,7 +95,7 @@ void SerialTreeLearner::Init(const Dataset* train_data) {
   if (has_ordered_bin_) {
     is_data_in_leaf_ = new char[num_data_];
   }
-  Log::Info("#data:%d #feature:%d\n", num_data_, num_features_);
+  Log::Info("Number of data:%d, Number of features:%d\n", num_data_, num_features_);
 }