Merge pull request #94 from Microsoft/python-package

Python package (#11)

python-package/setup.py

+# pylint: disable=invalid-name, exec-used
+"""Setup lightgbm package."""
+from __future__ import absolute_import
+import sys
+import os
+from setuptools import setup, find_packages
+# import subprocess
+sys.path.insert(0, '.')
+
+CURRENT_DIR = os.path.dirname(__file__)
+
+libpath_py = os.path.join(CURRENT_DIR, 'lightgbm/libpath.py')
+libpath = {'__file__': libpath_py}
+exec(compile(open(libpath_py, "rb").read(), libpath_py, 'exec'), libpath, libpath)
+
+LIB_PATH = libpath['find_lib_path']()
+print("Install lib_lightgbm from: %s" % LIB_PATH)
+
+
+setup(name='lightgbm',
+      version=0.1,
+      description="LightGBM Python Package",
+      install_requires=[
+          'numpy',
+          'scipy',
+      ],
+      maintainer='Guolin Ke',
+      maintainer_email='guolin.ke@microsoft.com',
+      zip_safe=False,
+      packages=find_packages(),
+      include_package_data=True,
+      data_files=[('lightgbm', LIB_PATH)],
+      url='https://github.com/Microsoft/LightGBM')

src/application/application.cpp

@@ -108,7 +108,7 @@ void Application::LoadData() {
   // prediction is needed if using input initial model(continued train)
   PredictFunction predict_fun = nullptr;
   // need to continue training
-  if (boosting_->NumberOfSubModels() > 0) {
+  if (boosting_->NumberOfTotalModel() > 0) {
     Predictor predictor(boosting_.get(), true, false);
     predict_fun = predictor.GetPredictFunction();
   }
@@ -139,12 +139,16 @@ void Application::LoadData() {
     for (auto metric_type : config_.metric_types) {
       auto metric = std::unique_ptr<Metric>(Metric::CreateMetric(metric_type, config_.metric_config));
       if (metric == nullptr) { continue; }
-      metric->Init("training", train_data_->metadata(),
-                              train_data_->num_data());
+      metric->Init(train_data_->metadata(), train_data_->num_data());
       train_metric_.push_back(std::move(metric));
     }
   }
   train_metric_.shrink_to_fit();
+
+
+  if (config_.metric_types.size() > 0) {
+    // only when have metrics then need to construct validation data
+
     // Add validation data, if it exists
     for (size_t i = 0; i < config_.io_config.valid_data_filenames.size(); ++i) {
       // add
@@ -164,8 +168,7 @@ void Application::LoadData() {
       for (auto metric_type : config_.metric_types) {
         auto metric = std::unique_ptr<Metric>(Metric::CreateMetric(metric_type, config_.metric_config));
         if (metric == nullptr) { continue; }
-      metric->Init(config_.io_config.valid_data_filenames[i].c_str(),
-                                     valid_datas_.back()->metadata(),
+        metric->Init(valid_datas_.back()->metadata(),
           valid_datas_.back()->num_data());
         valid_metrics_.back().push_back(std::move(metric));
       }
@@ -173,6 +176,7 @@ void Application::LoadData() {
     }
     valid_datas_.shrink_to_fit();
     valid_metrics_.shrink_to_fit();
+  }
   auto end_time = std::chrono::high_resolution_clock::now();
   // output used time on each iteration
   Log::Info("Finished loading data in %f seconds",
@@ -209,7 +213,7 @@ void Application::InitTrain() {
     Common::ConstPtrInVectorWrapper<Metric>(train_metric_));
   // add validation data into boosting
   for (size_t i = 0; i < valid_datas_.size(); ++i) {
-    boosting_->AddDataset(valid_datas_[i].get(),
+    boosting_->AddValidDataset(valid_datas_[i].get(),
       Common::ConstPtrInVectorWrapper<Metric>(valid_metrics_[i]));
   }
   Log::Info("Finished initializing training");
@@ -227,17 +231,15 @@ void Application::Train() {
     // output used time per iteration
     Log::Info("%f seconds elapsed, finished iteration %d", std::chrono::duration<double,
       std::milli>(end_time - start_time) * 1e-3, iter + 1);
-    boosting_->SaveModelToFile(NO_LIMIT, is_finished, config_.io_config.output_model.c_str());
   }
-  is_finished = true;
   // save model to file
-  boosting_->SaveModelToFile(NO_LIMIT, is_finished, config_.io_config.output_model.c_str());
+  boosting_->SaveModelToFile(-1, config_.io_config.output_model.c_str());
   Log::Info("Finished training");
 }
 
 
 void Application::Predict() {
-  boosting_->SetNumUsedModel(config_.io_config.num_model_predict);
+  boosting_->SetNumIterationForPred(config_.io_config.num_iteration_predict);
   // create predictor
   Predictor predictor(boosting_.get(), config_.io_config.is_predict_raw_score,
     config_.io_config.is_predict_leaf_index);

src/boosting/boosting.cpp

@@ -15,7 +15,7 @@ BoostingType GetBoostingTypeFromModelFile(const char* filename) {
   return BoostingType::kUnknow;
 }
 
-void LoadFileToBoosting(Boosting* boosting, const char* filename) {
+void Boosting::LoadFileToBoosting(Boosting* boosting, const char* filename) {
   if (boosting != nullptr) {
     TextReader<size_t> model_reader(filename, true);
     model_reader.ReadAllLines();

src/boosting/dart.hpp

@@ -43,6 +43,7 @@ public:
   * \brief one training iteration
   */
   bool TrainOneIter(const score_t* gradient, const score_t* hessian, bool is_eval) override {
+    is_update_score_cur_iter_ = false;
     GBDT::TrainOneIter(gradient, hessian, false);
     // normalize
     Normalize();
@@ -58,22 +59,15 @@ public:
   * \return training score
   */
   const score_t* GetTrainingScore(data_size_t* out_len) override {
+    if (!is_update_score_cur_iter_) {
+      // only drop one time in one iteration
       DroppingTrees();
+      is_update_score_cur_iter_ = true;
+    }
     *out_len = train_score_updater_->num_data() * num_class_;
     return train_score_updater_->score();
   }
-  /*!
-  * \brief save model to file
-  * \param num_used_model number of model that want to save, -1 means save all
-  * \param is_finish is training finished or not
-  * \param filename filename that want to save to
-  */
-  void SaveModelToFile(int num_used_model, bool is_finish, const char* filename) override {
-    // only save model once when is_finish = true
-    if (is_finish && saved_model_size_ < 0) {
-      GBDT::SaveModelToFile(num_used_model, is_finish, filename);
-    }
-  }
+
   /*!
   * \brief Get Type name of this boosting object
   */
@@ -133,6 +127,8 @@ private:
   double drop_rate_;
   /*! \brief Random generator, used to select dropping trees */
   Random random_for_drop_;
+  /*! \brief Flag that the score is update on current iter or not*/
+  bool is_update_score_cur_iter_;
 };
 
 }  // namespace LightGBM

src/boosting/gbdt.cpp

@@ -16,7 +16,9 @@
 
 namespace LightGBM {
 
-GBDT::GBDT() : saved_model_size_(-1), num_used_model_(0) {
+GBDT::GBDT() 
+  :num_iteration_for_pred_(0), 
+  num_init_iteration_(0) {
 
 }
 
@@ -26,47 +28,66 @@ GBDT::~GBDT() {
 
 void GBDT::Init(const BoostingConfig* config, const Dataset* train_data, const ObjectiveFunction* object_function,
      const std::vector<const Metric*>& training_metrics) {
-  gbdt_config_ = config;
   iter_ = 0;
-  saved_model_size_ = -1;
-  num_used_model_ = 0;
+  num_iteration_for_pred_ = 0;
   max_feature_idx_ = 0;
+  num_class_ = config->num_class;
+  train_data_ = nullptr;
+  ResetTrainingData(config, train_data, object_function, training_metrics);
+}
+
+void GBDT::ResetTrainingData(const BoostingConfig* config, const Dataset* train_data, const ObjectiveFunction* object_function,
+  const std::vector<const Metric*>& training_metrics) {
+  if (train_data_ != nullptr && !train_data_->CheckAlign(*train_data)) {
+    Log::Fatal("cannot reset training data, since new training data has different bin mappers");
+  }
+  gbdt_config_ = config;
   early_stopping_round_ = gbdt_config_->early_stopping_round;
   shrinkage_rate_ = gbdt_config_->learning_rate;
-  train_data_ = train_data;
-  num_class_ = config->num_class;
+  random_ = Random(gbdt_config_->bagging_seed);
   // create tree learner
+  tree_learner_.clear();
   for (int i = 0; i < num_class_; ++i) {
     auto new_tree_learner = std::unique_ptr<TreeLearner>(TreeLearner::CreateTreeLearner(gbdt_config_->tree_learner_type, gbdt_config_->tree_config));
-    new_tree_learner->Init(train_data_);
+    new_tree_learner->Init(train_data);
     // init tree learner
     tree_learner_.push_back(std::move(new_tree_learner));
   }
   tree_learner_.shrink_to_fit();
   object_function_ = object_function;
   // push training metrics
+  training_metrics_.clear();
   for (const auto& metric : training_metrics) {
     training_metrics_.push_back(metric);
   }
   training_metrics_.shrink_to_fit();
-  // create score tracker
-  train_score_updater_.reset(new ScoreUpdater(train_data_, num_class_));
-  num_data_ = train_data_->num_data();
-  // create buffer for gradients and hessians
-  if (object_function_ != nullptr) {
-    gradients_ = std::vector<score_t>(num_data_ * num_class_);
-    hessians_ = std::vector<score_t>(num_data_ * num_class_);
-  }
   sigmoid_ = -1.0f;
   if (object_function_ != nullptr
     && std::string(object_function_->GetName()) == std::string("binary")) {
     // only binary classification need sigmoid transform
     sigmoid_ = gbdt_config_->sigmoid;
   }
+  if (train_data_ != train_data) {
+    // not same training data, need reset score and others
+    // create score tracker
+    train_score_updater_.reset(new ScoreUpdater(train_data, num_class_));
+    // update score
+    for (int i = 0; i < iter_; ++i) {
+      for (int curr_class = 0; curr_class < num_class_; ++curr_class) {
+        auto curr_tree = (i + num_init_iteration_) * num_class_ + curr_class;
+        train_score_updater_->AddScore(models_[curr_tree].get(), curr_class);
+      }
+    }
+    num_data_ = train_data->num_data();
+    // create buffer for gradients and hessians
+    if (object_function_ != nullptr) {
+      gradients_ = std::vector<score_t>(num_data_ * num_class_);
+      hessians_ = std::vector<score_t>(num_data_ * num_class_);
+    }
     // get max feature index
-  max_feature_idx_ = train_data_->num_total_features() - 1;
+    max_feature_idx_ = train_data->num_total_features() - 1;
     // get label index
-  label_idx_ = train_data_->label_idx();
+    label_idx_ = train_data->label_idx();
     // if need bagging, create buffer
     if (gbdt_config_->bagging_fraction < 1.0 && gbdt_config_->bagging_freq > 0) {
       out_of_bag_data_indices_ = std::vector<data_size_t>(num_data_);
@@ -77,18 +98,24 @@ void GBDT::Init(const BoostingConfig* config, const Dataset* train_data, const O
       bag_data_cnt_ = num_data_;
       bag_data_indices_.clear();
     }
-  // initialize random generator
-  random_ = Random(gbdt_config_->bagging_seed);
-
+  }
+  train_data_ = train_data;
 }
 
-void GBDT::AddDataset(const Dataset* valid_data,
+void GBDT::AddValidDataset(const Dataset* valid_data,
   const std::vector<const Metric*>& valid_metrics) {
-  if (iter_ > 0) {
-    Log::Fatal("Cannot add validation data after training started");
+  if (!train_data_->CheckAlign(*valid_data)) {
+    Log::Fatal("cannot add validation data, since it has different bin mappers with training data");
   }
   // for a validation dataset, we need its score and metric
   auto new_score_updater = std::unique_ptr<ScoreUpdater>(new ScoreUpdater(valid_data, num_class_));
+  // update score
+  for (int i = 0; i < iter_; ++i) {
+    for (int curr_class = 0; curr_class < num_class_; ++curr_class) {
+      auto curr_tree = (i + num_init_iteration_) * num_class_ + curr_class;
+      new_score_updater->AddScore(models_[curr_tree].get(), curr_class);
+    }
+  }
   valid_score_updater_.push_back(std::move(new_score_updater));
   valid_metrics_.emplace_back();
   if (early_stopping_round_ > 0) {
@@ -204,6 +231,25 @@ bool GBDT::TrainOneIter(const score_t* gradient, const score_t* hessian, bool is
 
 }
 
+void GBDT::RollbackOneIter() {
+  if (iter_ == 0) { return; }
+  int cur_iter = iter_ + num_init_iteration_ - 1;
+  // reset score
+  for (int curr_class = 0; curr_class < num_class_; ++curr_class) {
+    auto curr_tree = cur_iter * num_class_ + curr_class;
+    models_[curr_tree]->Shrinkage(-1.0);
+    train_score_updater_->AddScore(models_[curr_tree].get(), curr_class);
+    for (auto& score_updater : valid_score_updater_) {
+      score_updater->AddScore(models_[curr_tree].get(), curr_class);
+    }
+  }
+  // remove model
+  for (int curr_class = 0; curr_class < num_class_; ++curr_class) {
+    models_.pop_back();
+  }
+  --iter_;
+}
+
 bool GBDT::EvalAndCheckEarlyStopping() {
   bool is_met_early_stopping = false;
   // print message for metric
@@ -236,7 +282,7 @@ bool GBDT::OutputMetric(int iter) {
       auto name = sub_metric->GetName();
       auto scores = sub_metric->Eval(train_score_updater_->score());
       for (size_t k = 0; k < name.size(); ++k) {
-        Log::Info("Iteration: %d, %s : %f", iter, name[k].c_str(), scores[k]);
+        Log::Info("Iteration:%d, training %s : %f", iter, name[k].c_str(), scores[k]);
       }
     }
   }
@@ -248,7 +294,7 @@ bool GBDT::OutputMetric(int iter) {
         if ((iter % gbdt_config_->output_freq) == 0) {
           auto name = valid_metrics_[i][j]->GetName();
           for (size_t k = 0; k < name.size(); ++k) {
-            Log::Info("Iteration: %d, %s : %f", iter, name[k].c_str(), test_scores[k]);
+            Log::Info("Iteration:%d, valid_%d %s : %f", iter, i + 1, name[k].c_str(), test_scores[k]);
           }
         }
         if (!ret && early_stopping_round_ > 0) {
@@ -296,24 +342,23 @@ const score_t* GBDT::GetTrainingScore(data_size_t* out_len) {
   return train_score_updater_->score();
 }
 
-void GBDT::GetPredictAt(int data_idx, score_t* out_result, data_size_t* out_len) const {
+void GBDT::GetPredictAt(int data_idx, score_t* out_result, data_size_t* out_len) {
   CHECK(data_idx >= 0 && data_idx <= static_cast<int>(valid_metrics_.size()));
   std::vector<double> ret;
 
   const score_t* raw_scores = nullptr;
   data_size_t num_data = 0;
   if (data_idx == 0) {
-    raw_scores = train_score_updater_->score();
+    raw_scores = GetTrainingScore(out_len);
     num_data = train_score_updater_->num_data();
   } else {
     auto used_idx = data_idx - 1;
     raw_scores = valid_score_updater_[used_idx]->score();
     num_data = valid_score_updater_[used_idx]->num_data();
-  }
     *out_len = num_data * num_class_;
-
+  }
   if (num_class_ > 1) {
-#pragma omp parallel for schedule(guided)
+#pragma omp parallel for schedule(static)
     for (data_size_t i = 0; i < num_data; ++i) {
       std::vector<double> tmp_result;
       for (int j = 0; j < num_class_; ++j) {
@@ -325,12 +370,12 @@ void GBDT::GetPredictAt(int data_idx, score_t* out_result, data_size_t* out_len)
       }
     }
   } else if(sigmoid_ > 0.0f){
-#pragma omp parallel for schedule(guided)
+#pragma omp parallel for schedule(static)
     for (data_size_t i = 0; i < num_data; ++i) {
       out_result[i] = static_cast<score_t>(1.0f / (1.0f + std::exp(-2.0f * sigmoid_ * raw_scores[i])));
     }
   } else {
-#pragma omp parallel for schedule(guided)
+#pragma omp parallel for schedule(static)
     for (data_size_t i = 0; i < num_data; ++i) {
       out_result[i] = raw_scores[i];
     }
@@ -348,55 +393,41 @@ void GBDT::Boosting() {
     GetGradients(GetTrainingScore(&num_score), gradients_.data(), hessians_.data());
 }
 
-void GBDT::SaveModelToFile(int num_used_model, bool is_finish, const char* filename) {
-  // first time to this function, open file
-  if (saved_model_size_ < 0) {
-    model_output_file_.open(filename);
+void GBDT::SaveModelToFile(int num_iteration, const char* filename) const {
+  /*! \brief File to write models */
+  std::ofstream output_file;
+  output_file.open(filename);
   // output model type
-    model_output_file_ << Name() << std::endl;
+  output_file << Name() << std::endl;
   // output number of class
-    model_output_file_ << "num_class=" << num_class_ << std::endl;
+  output_file << "num_class=" << num_class_ << std::endl;
   // output label index
-    model_output_file_ << "label_index=" << label_idx_ << std::endl;
+  output_file << "label_index=" << label_idx_ << std::endl;
   // output max_feature_idx
-    model_output_file_ << "max_feature_idx=" << max_feature_idx_ << std::endl;
+  output_file << "max_feature_idx=" << max_feature_idx_ << std::endl;
   // output objective name
   if (object_function_ != nullptr) {
-      model_output_file_ << "objective=" << object_function_->GetName() << std::endl;
+    output_file << "objective=" << object_function_->GetName() << std::endl;
   }
   // output sigmoid parameter
-    model_output_file_ << "sigmoid=" << sigmoid_ << std::endl;
-    model_output_file_ << std::endl;
-    saved_model_size_ = 0;
-  }
-  // already saved
-  if (!model_output_file_.is_open()) {
-    return;
-  }
-  if (num_used_model == NO_LIMIT) {
+  output_file << "sigmoid=" << sigmoid_ << std::endl;
+  output_file << std::endl;
+
+  int num_used_model = 0;
+  if (num_iteration <= 0) {
     num_used_model = static_cast<int>(models_.size());
   } else {
-    num_used_model = num_used_model * num_class_;
+    num_used_model = num_iteration * num_class_;
   }
-  int rest = num_used_model - early_stopping_round_ * num_class_;
+  num_used_model = std::min(num_used_model, static_cast<int>(models_.size()));
   // output tree models
-  for (int i = saved_model_size_; i < rest; ++i) {
-    model_output_file_ << "Tree=" << i << std::endl;
-    model_output_file_ << models_[i]->ToString() << std::endl;
+  for (int i = 0; i < num_used_model; ++i) {
+    output_file << "Tree=" << i << std::endl;
+    output_file << models_[i]->ToString() << std::endl;
   }
 
-  saved_model_size_ = std::max(saved_model_size_, rest);
-
-  model_output_file_.flush();
-  // training finished, can close file
-  if (is_finish) {
-    for (int i = saved_model_size_; i < num_used_model; ++i) {
-      model_output_file_ << "Tree=" << i << std::endl;
-      model_output_file_ << models_[i]->ToString() << std::endl;
-    }
-    model_output_file_ << std::endl << FeatureImportance() << std::endl;
-    model_output_file_.close();
-  }
+  output_file << std::endl << FeatureImportance() << std::endl;
+  output_file.close();
 }
 
 void GBDT::LoadModelFromString(const std::string& model_str) {
@@ -452,7 +483,8 @@ void GBDT::LoadModelFromString(const std::string& model_str) {
     }
   }
   Log::Info("Finished loading %d models", models_.size());
-  num_used_model_ = static_cast<int>(models_.size()) / num_class_;
+  num_iteration_for_pred_ = static_cast<int>(models_.size()) / num_class_;
+  num_init_iteration_ = num_iteration_for_pred_;
 }
 
 std::string GBDT::FeatureImportance() const {
@@ -486,7 +518,7 @@ std::string GBDT::FeatureImportance() const {
 
 std::vector<double> GBDT::PredictRaw(const double* value) const {
   std::vector<double> ret(num_class_, 0.0f);
-  for (int i = 0; i < num_used_model_; ++i) {
+  for (int i = 0; i < num_iteration_for_pred_; ++i) {
     for (int j = 0; j < num_class_; ++j) {
       ret[j] += models_[i * num_class_ + j]->Predict(value);
     }
@@ -496,7 +528,7 @@ std::vector<double> GBDT::PredictRaw(const double* value) const {
 
 std::vector<double> GBDT::Predict(const double* value) const {
   std::vector<double> ret(num_class_, 0.0f);
-  for (int i = 0; i < num_used_model_; ++i) {
+  for (int i = 0; i < num_iteration_for_pred_; ++i) {
     for (int j = 0; j < num_class_; ++j) {
       ret[j] += models_[i * num_class_ + j]->Predict(value);
     }
@@ -512,7 +544,7 @@ std::vector<double> GBDT::Predict(const double* value) const {
 
 std::vector<int> GBDT::PredictLeafIndex(const double* value) const {
   std::vector<int> ret;
-  for (int i = 0; i < num_used_model_; ++i) {
+  for (int i = 0; i < num_iteration_for_pred_; ++i) {
     for (int j = 0; j < num_class_; ++j) {
       ret.push_back(models_[i * num_class_ + j]->PredictLeafIndex(value));
     }

src/boosting/gbdt.h

@@ -35,12 +35,53 @@ public:
   void Init(const BoostingConfig* gbdt_config, const Dataset* train_data, const ObjectiveFunction* object_function,
                              const std::vector<const Metric*>& training_metrics)
                                                                        override;
+
+  /*!
+  * \brief Merge model from other boosting object
+           Will insert to the front of current boosting object
+  * \param other
+  */
+  void MergeFrom(const Boosting* other) override {
+    auto other_gbdt = reinterpret_cast<const GBDT*>(other);
+    // tmp move to other vector
+    auto original_models = std::move(models_);
+    models_ = std::vector<std::unique_ptr<Tree>>();
+    // push model from other first
+    for (const auto& tree : other_gbdt->models_) {
+      auto new_tree = std::unique_ptr<Tree>(new Tree(*(tree.get())));
+      models_.push_back(std::move(new_tree));
+    }
+    num_init_iteration_ = static_cast<int>(models_.size()) / num_class_;
+    // push model in current object
+    for (const auto& tree : original_models) {
+      auto new_tree = std::unique_ptr<Tree>(new Tree(*(tree.get())));
+      models_.push_back(std::move(new_tree));
+    }
+    num_iteration_for_pred_ = static_cast<int>(models_.size()) / num_class_;
+  }
+
+  /*!
+  * \brief Reset training data for current boosting
+  * \param train_data Training data
+  * \param object_function Training objective function
+  * \param training_metrics Training metric
+  */
+  void ResetTrainingData(const BoostingConfig* config, const Dataset* train_data, const ObjectiveFunction* object_function, const std::vector<const Metric*>& training_metrics) override;
+
+  /*!
+  * \brief Reset shrinkage_rate data for current boosting
+  * \param shrinkage_rate Configs for boosting
+  */
+  void ResetShrinkageRate(double shrinkage_rate) override {
+    shrinkage_rate_ = shrinkage_rate;
+  }
+
   /*!
   * \brief Adding a validation dataset
   * \param valid_data Validation dataset
   * \param valid_metrics Metrics for validation dataset
   */
-  void AddDataset(const Dataset* valid_data,
+  void AddValidDataset(const Dataset* valid_data,
        const std::vector<const Metric*>& valid_metrics) override;
   /*!
   * \brief Training logic
@@ -51,6 +92,13 @@ public:
   */
   virtual bool TrainOneIter(const score_t* gradient, const score_t* hessian, bool is_eval) override;
 
+  /*!
+  * \brief Rollback one iteration
+  */
+  void RollbackOneIter() override;
+
+  int GetCurrentIteration() const override { return iter_ + num_init_iteration_; }
+
   bool EvalAndCheckEarlyStopping() override;
 
   /*!
@@ -73,7 +121,7 @@ public:
   * \param result used to store prediction result, should allocate memory before call this function
   * \param out_len lenght of returned score
   */
-  void GetPredictAt(int data_idx, score_t* out_result, data_size_t* out_len) const override;
+  void GetPredictAt(int data_idx, score_t* out_result, data_size_t* out_len) override;
 
   /*!
   * \brief Prediction for one record without sigmoid transformation
@@ -98,11 +146,11 @@ public:
 
   /*!
   * \brief save model to file
-  * \param num_used_model number of model that want to save, -1 means save all
-  * \param is_finish is training finished or not
+  * \param num_iterations Iterations that want to save, -1 means save all
   * \param filename filename that want to save to
   */
-  virtual void SaveModelToFile(int num_used_model, bool is_finish, const char* filename) override;
+  virtual void SaveModelToFile(int num_iterations, const char* filename) const override ;
+
   /*!
   * \brief Restore from a serialized string
   */
@@ -119,11 +167,12 @@ public:
   */
   inline int LabelIdx() const override { return label_idx_; }
 
+
   /*!
   * \brief Get number of weak sub-models
   * \return Number of weak sub-models
   */
-  inline int NumberOfSubModels() const override { return static_cast<int>(models_.size()); }
+  inline int NumberOfTotalModel() const override { return static_cast<int>(models_.size()); }
 
   /*!
   * \brief Get number of classes
@@ -132,14 +181,16 @@ public:
   inline int NumberOfClasses() const override { return num_class_; }
 
   /*!
-  * \brief Set number of used model for prediction
+  * \brief Set number of iterations for prediction
   */
-  inline void SetNumUsedModel(int num_used_model) {
-    if (num_used_model >= 0) {
-      num_used_model_ = static_cast<int>(num_used_model / num_class_);
+  inline void SetNumIterationForPred(int num_iteration) override {
+    if (num_iteration > 0) {
+      num_iteration_for_pred_ = num_iteration;
     } else {
-      num_used_model_ = static_cast<int>(models_.size()) / num_class_;
+      num_iteration_for_pred_ = static_cast<int>(models_.size()) / num_class_;
     }
+    num_iteration_for_pred_ = std::min(num_iteration_for_pred_, 
+      static_cast<int>(models_.size()) / num_class_);
   }
 
   /*!
@@ -233,14 +284,12 @@ protected:
   double sigmoid_;
   /*! \brief Index of label column */
   data_size_t label_idx_;
-  /*! \brief Saved number of models */
-  int saved_model_size_;
-  /*! \brief File to write models */
-  std::ofstream model_output_file_;
   /*! \brief number of used model */
-  int num_used_model_;
+  int num_iteration_for_pred_;
   /*! \brief Shrinkage rate for one iteration */
   double shrinkage_rate_;
+  /*! \brief Number of loaded initial models */
+  int num_init_iteration_;
 };
 
 }  // namespace LightGBM

src/io/config.cpp

@@ -5,14 +5,14 @@
 
 #include <vector>
 #include <string>
-#include <unordered_map>
+#include <unordered_set>
 #include <algorithm>
 
 namespace LightGBM {
 
-void OverallConfig::LoadFromString(const char* str) {
+std::unordered_map<std::string, std::string> ConfigBase::Str2Map(const char* parameters) {
   std::unordered_map<std::string, std::string> params;
-  auto args = Common::Split(str, " \t\n\r");
+  auto args = Common::Split(parameters, " \t\n\r");
   for (auto arg : args) {
     std::vector<std::string> tmp_strs = Common::Split(arg.c_str(), '=');
     if (tmp_strs.size() == 2) {
@@ -27,7 +27,7 @@ void OverallConfig::LoadFromString(const char* str) {
     }
   }
   ParameterAlias::KeyAliasTransform(&params);
-  Set(params);
+  return params;
 }
 
 void OverallConfig::Set(const std::unordered_map<std::string, std::string>& params) {
@@ -95,16 +95,15 @@ void OverallConfig::GetMetricType(const std::unordered_map<std::string, std::str
     // split
     std::vector<std::string> metrics = Common::Split(value.c_str(), ',');
     // remove dumplicate
-    std::unordered_map<std::string, int> metric_maps;
+    std::unordered_set<std::string> metric_sets;
     for (auto& metric : metrics) {
       std::transform(metric.begin(), metric.end(), metric.begin(), Common::tolower);
-      if (metric_maps.count(metric) <= 0) {
-        metric_maps[metric] = 1;
+      if (metric_sets.count(metric) <= 0) {
+        metric_sets.insert(metric);
       }
     }
-    for (auto& pair : metric_maps) {
-      std::string sub_metric_str = pair.first;
-      metric_types.push_back(sub_metric_str);
+    for (auto& metric : metric_sets) {
+      metric_types.push_back(metric);
     }
     metric_types.shrink_to_fit();
   }
@@ -183,7 +182,7 @@ void IOConfig::Set(const std::unordered_map<std::string, std::string>& params) {
   GetInt(params, "data_random_seed", &data_random_seed);
   GetString(params, "data", &data_filename);
   GetInt(params, "verbose", &verbosity);
-  GetInt(params, "num_model_predict", &num_model_predict);
+  GetInt(params, "num_iteration_predict", &num_iteration_predict);
   GetInt(params, "bin_construct_sample_cnt", &bin_construct_sample_cnt);
   GetBool(params, "is_pre_partition", &is_pre_partition);
   GetBool(params, "is_enable_sparse", &is_enable_sparse);
@@ -214,6 +213,7 @@ void ObjectiveConfig::Set(const std::unordered_map<std::string, std::string>& pa
   CHECK(max_position > 0);
   GetInt(params, "num_class", &num_class);
   CHECK(num_class >= 1);
+  GetDouble(params, "scale_pos_weight", &scale_pos_weight);
   std::string tmp_str = "";
   if (GetString(params, "label_gain", &tmp_str)) {
     label_gain = Common::StringToDoubleArray(tmp_str, ',');

src/io/dataset.cpp

@@ -14,17 +14,16 @@
 
 namespace LightGBM {
 
+const char* Dataset::binary_file_token = "______LightGBM_Binary_File_Token______\n";
 
 Dataset::Dataset() {
   num_class_ = 1;
   num_data_ = 0;
-  is_loading_from_binfile_ = false;
 }
 
 Dataset::Dataset(data_size_t num_data, int num_class) {
   num_class_ = num_class;
   num_data_ = num_data;
-  is_loading_from_binfile_ = false;
   metadata_.Init(num_data_, num_class_, -1, -1);
 }
 
@@ -56,6 +55,21 @@ void Dataset::CopyFeatureMapperFrom(const Dataset* dataset, bool is_enable_spars
   num_features_ = static_cast<int>(features_.size());
   num_total_features_ = dataset->num_total_features_;
   feature_names_ = dataset->feature_names_;
+  label_idx_ = dataset->label_idx_;
+}
+
+Dataset* Dataset::Subset(const data_size_t* used_indices, data_size_t num_used_indices, bool is_enable_sparse) const {
+  auto ret = std::unique_ptr<Dataset>(new Dataset(num_used_indices, num_class_));
+  ret->CopyFeatureMapperFrom(this, is_enable_sparse);
+#pragma omp parallel for schedule(guided)
+  for (int fidx = 0; fidx < num_features_; ++fidx) {
+    auto iterator = features_[fidx]->bin_data()->GetIterator(0);
+    for (data_size_t i = 0; i < num_used_indices; ++i) {
+      ret->features_[fidx]->PushBin(0, i, iterator->Get(used_indices[i]));
+    }
+  }
+  ret->metadata_.Init(metadata_, used_indices, num_used_indices);
+  return ret.release();
 }
 
 bool Dataset::SetFloatField(const char* field_name, const float* field_data, data_size_t num_element) {
@@ -78,6 +92,8 @@ bool Dataset::SetIntField(const char* field_name, const int* field_data, data_si
   name = Common::Trim(name);
   if (name == std::string("query") || name == std::string("group")) {
     metadata_.SetQueryBoundaries(field_data, num_element);
+  } else if (name == std::string("query_id") || name == std::string("group_id")) {
+    metadata_.SetQueryId(field_data, num_element);
   } else {
     return false;
   }
@@ -107,7 +123,7 @@ bool Dataset::GetIntField(const char* field_name, int64_t* out_len, const int**
   name = Common::Trim(name);
   if (name == std::string("query") || name == std::string("group")) {
     *out_ptr = metadata_.query_boundaries();
-    *out_len = num_data_;
+    *out_len = metadata_.num_queries();
   } else {
     return false;
   }
@@ -115,15 +131,27 @@ bool Dataset::GetIntField(const char* field_name, int64_t* out_len, const int**
 }
 
 void Dataset::SaveBinaryFile(const char* bin_filename) {
+  bool is_file_existed = false;
+  FILE* file;
+#ifdef _MSC_VER
+  fopen_s(&file, bin_filename, "rb");
+#else
+  file = fopen(bin_filename, "rb");
+#endif
 
-  if (!is_loading_from_binfile_) {
+  if (file != NULL) {
+    is_file_existed = true;
+    Log::Warning("File %s existed, cannot save binary to it", bin_filename);
+    fclose(file);
+  }
+
+  if (!is_file_existed) {
     std::string bin_filename_str(data_filename_);
     // if not pass a filename, just append ".bin" of original file
     if (bin_filename == nullptr || bin_filename[0] == '\0') {
       bin_filename_str.append(".bin");
       bin_filename = bin_filename_str.c_str();
     }
-    FILE* file;
 #ifdef _MSC_VER
     fopen_s(&file, bin_filename, "wb");
 #else
@@ -133,7 +161,8 @@ void Dataset::SaveBinaryFile(const char* bin_filename) {
       Log::Fatal("Cannot write binary data to %s ", bin_filename);
     }
     Log::Info("Saving data to binary file %s", bin_filename);
-
+    size_t size_of_token = std::strlen(binary_file_token);
+    fwrite(binary_file_token, sizeof(char), size_of_token, file);
     // get size of header
     size_t size_of_header = sizeof(num_data_) + sizeof(num_class_) + sizeof(num_features_) + sizeof(num_total_features_) 
       + sizeof(size_t) + sizeof(int) * used_feature_map_.size();

src/io/dataset_loader.cpp

@@ -142,18 +142,18 @@ Dataset* DatasetLoader::LoadFromFile(const char* filename, int rank, int num_mac
                   Please use an additional query file or pre-partition the data");
     }
   }
+  auto dataset = std::unique_ptr<Dataset>(new Dataset());
+  data_size_t num_global_data = 0;
+  std::vector<data_size_t> used_data_indices;
+  auto bin_filename = CheckCanLoadFromBin(filename);
+  if (bin_filename.size() == 0) {
     auto parser = std::unique_ptr<Parser>(Parser::CreateParser(filename, io_config_.has_header, 0, label_idx_));
     if (parser == nullptr) {
       Log::Fatal("Could not recognize data format of %s", filename);
     }
-  data_size_t num_global_data = 0;
-  std::vector<data_size_t> used_data_indices;
-  auto dataset = std::unique_ptr<Dataset>(new Dataset());
     dataset->data_filename_ = filename;
     dataset->num_class_ = io_config_.num_class;
     dataset->metadata_.Init(filename, dataset->num_class_);
-  bool is_loading_from_binfile = CheckCanLoadFromBin(filename);
-  if (!is_loading_from_binfile) {
     if (!io_config_.use_two_round_loading) {
       // read data to memory
       auto text_data = LoadTextDataToMemory(filename, dataset->metadata_, rank, num_machines,&num_global_data, &used_data_indices);
@@ -185,8 +185,6 @@ Dataset* DatasetLoader::LoadFromFile(const char* filename, int rank, int num_mac
     }
   } else {
     // load data from binary file
-    std::string bin_filename(filename);
-    bin_filename.append(".bin");
     dataset.reset(LoadFromBinFile(bin_filename.c_str(), rank, num_machines));
   }
   // check meta data
@@ -199,18 +197,18 @@ Dataset* DatasetLoader::LoadFromFile(const char* filename, int rank, int num_mac
 
 
 Dataset* DatasetLoader::LoadFromFileAlignWithOtherDataset(const char* filename, const Dataset* train_data) {
+  data_size_t num_global_data = 0;
+  std::vector<data_size_t> used_data_indices;
+  auto dataset = std::unique_ptr<Dataset>(new Dataset());
+  auto bin_filename = CheckCanLoadFromBin(filename);
+  if (bin_filename.size() == 0) {
     auto parser = std::unique_ptr<Parser>(Parser::CreateParser(filename, io_config_.has_header, 0, label_idx_));
     if (parser == nullptr) {
       Log::Fatal("Could not recognize data format of %s", filename);
     }
-  data_size_t num_global_data = 0;
-  std::vector<data_size_t> used_data_indices;
-  auto dataset = std::unique_ptr<Dataset>(new Dataset());
     dataset->data_filename_ = filename;
     dataset->num_class_ = io_config_.num_class;
     dataset->metadata_.Init(filename, dataset->num_class_);
-  bool is_loading_from_binfile = CheckCanLoadFromBin(filename);
-  if (!is_loading_from_binfile) {
     if (!io_config_.use_two_round_loading) {
       // read data in memory
       auto text_data = LoadTextDataToMemory(filename, dataset->metadata_, 0, 1, &num_global_data, &used_data_indices);
@@ -234,8 +232,6 @@ Dataset* DatasetLoader::LoadFromFileAlignWithOtherDataset(const char* filename,
     }
   } else {
     // load data from binary file
-    std::string bin_filename(filename);
-    bin_filename.append(".bin");
     dataset.reset(LoadFromBinFile(bin_filename.c_str(), 0, 1));
   }
   // not need to check validation data
@@ -261,8 +257,18 @@ Dataset* DatasetLoader::LoadFromBinFile(const char* bin_filename, int rank, int
   size_t buffer_size = 16 * 1024 * 1024;
   auto buffer = std::vector<char>(buffer_size);
   
+  // check token
+  size_t size_of_token = std::strlen(Dataset::binary_file_token);
+  size_t read_cnt = fread(buffer.data(), sizeof(char), size_of_token, file);
+  if (read_cnt != size_of_token) {
+    Log::Fatal("Binary file error: token has the wrong size");
+  }
+  if (std::string(buffer.data()) != std::string(Dataset::binary_file_token)) {
+    Log::Fatal("input file is not LightGBM binary file");
+  }
+
   // read size of header
-  size_t read_cnt = fread(buffer.data(), sizeof(size_t), 1, file);
+  read_cnt = fread(buffer.data(), sizeof(size_t), 1, file);
 
   if (read_cnt != 1) {
     Log::Fatal("Binary file error: header has the wrong size");
@@ -401,7 +407,6 @@ Dataset* DatasetLoader::LoadFromBinFile(const char* bin_filename, int rank, int
   }
   dataset->features_.shrink_to_fit();
   fclose(file);
-  dataset->is_loading_from_binfile_ = true;
   return dataset.release();
 }
 
@@ -849,7 +854,7 @@ void DatasetLoader::ExtractFeaturesFromFile(const char* filename, const Parser*
 }
 
 /*! \brief Check can load from binary file */
-bool DatasetLoader::CheckCanLoadFromBin(const char* filename) {
+std::string DatasetLoader::CheckCanLoadFromBin(const char* filename) {
   std::string bin_filename(filename);
   bin_filename.append(".bin");
 
@@ -860,12 +865,32 @@ bool DatasetLoader::CheckCanLoadFromBin(const char* filename) {
 #else
   file = fopen(bin_filename.c_str(), "rb");
 #endif
+
   if (file == NULL) {
-    return false;
-  } else {
+    bin_filename = std::string(filename);
+#ifdef _MSC_VER
+    fopen_s(&file, bin_filename.c_str(), "rb");
+#else
+    file = fopen(bin_filename.c_str(), "rb");
+#endif
+    if (file == NULL) {
+      Log::Fatal("cannot open data file %s", bin_filename.c_str());
+    }
+  } 
+
+  size_t buffer_size = 256;
+  auto buffer = std::vector<char>(buffer_size);
+  // read size of token
+  size_t size_of_token = std::strlen(Dataset::binary_file_token);
+  size_t read_cnt = fread(buffer.data(), sizeof(char), size_of_token, file);
   fclose(file);
-    return true;
+  if (read_cnt == size_of_token 
+    && std::string(buffer.data()) == std::string(Dataset::binary_file_token)) {
+    return bin_filename;
+  } else {
+    return std::string();
   }
+
 }
 
 }
\ No newline at end of file

src/metric/binary_metric.hpp

@@ -29,11 +29,8 @@ public:
 
   }
 
-  void Init(const char* test_name, const Metadata& metadata, data_size_t num_data) override {
-
-    std::stringstream str_buf;
-    str_buf << test_name << "'s : " << PointWiseLossCalculator::Name();
-    name_.emplace_back(str_buf.str());
+  void Init(const Metadata& metadata, data_size_t num_data) override {
+    name_.emplace_back(PointWiseLossCalculator::Name());
 
     num_data_ = num_data;
     // get label
@@ -119,7 +116,7 @@ public:
   }
 
   inline static const char* Name() {
-    return "log loss";
+    return "logloss";
   }
 };
 /*!
@@ -138,7 +135,7 @@ public:
   }
 
   inline static const char* Name() {
-    return "error rate";
+    return "error";
   }
 };
 
@@ -162,10 +159,8 @@ public:
     return 1.0f;
   }
 
-  void Init(const char* test_name, const Metadata& metadata, data_size_t num_data) override {
-    std::stringstream str_buf;
-    str_buf << test_name << "'s : AUC";
-    name_.emplace_back(str_buf.str());
+  void Init(const Metadata& metadata, data_size_t num_data) override {
+    name_.emplace_back("auc");
 
     num_data_ = num_data;
     // get label

src/metric/multiclass_metric.hpp

@@ -23,10 +23,9 @@ public:
 
   }
 
-  void Init(const char* test_name, const Metadata& metadata, data_size_t num_data) override {
-    std::stringstream str_buf;
-    str_buf << test_name << " : " << PointWiseLossCalculator::Name();
-    name_.emplace_back(str_buf.str());
+  void Init(const Metadata& metadata, data_size_t num_data) override {
+
+    name_.emplace_back(PointWiseLossCalculator::Name());
     num_data_ = num_data;
     // get label
     label_ = metadata.label();
@@ -110,7 +109,7 @@ public:
   }
 
   inline static const char* Name() {
-    return "multi error";
+    return "multi_error";
   }
 };
 
@@ -130,7 +129,7 @@ public:
   }
   
   inline static const char* Name() {
-    return "multi logloss";
+    return "multi_logloss";
   }
 };
 

src/metric/rank_metric.hpp

@@ -33,12 +33,9 @@ public:
 
   ~NDCGMetric() {
   }
-  void Init(const char* test_name, const Metadata& metadata, data_size_t num_data) override {
+  void Init(const Metadata& metadata, data_size_t num_data) override {
     for (auto k : eval_at_) {
-      std::stringstream str_buf;
-      str_buf << test_name << "'s : ";
-      str_buf << "NDCG@" + std::to_string(k) + " ";
-      name_.emplace_back(str_buf.str());
+      name_.emplace_back(std::string("ndcg@") + std::to_string(k));
     }
     num_data_ = num_data;
     // get label