provide a light weight interface for reset learning rate

include/LightGBM/boosting.h

@@ -51,6 +51,12 @@ public:
   */
   virtual void ResetTrainingData(const BoostingConfig* config, const Dataset* train_data, const ObjectiveFunction* object_function, const std::vector<const Metric*>& training_metrics) = 0;
 
+  /*!
+  * \brief Reset shrinkage_rate data for current boosting
+  * \param shrinkage_rate Configs for boosting
+  */
+  virtual void ResetShrinkageRate(double shrinkage_rate) = 0;
+
   /*!
   * \brief Add a validation data
   * \param valid_data Validation data

python-package/lightgbm/basic.py

@@ -128,9 +128,11 @@ def param_dict_to_str(data):
         return ""
     pairs = []
     for key, val in data.items():
-        if isinstance(val, list):
-            pairs.append(str(key)+'='+','.join(val))
-        elif isinstance(val, (int, float, str, bool)):
+        if is_str(val):
+            pairs.append(str(key)+'='+str(val))
+        elif isinstance(val, (list, tuple)):
+            pairs.append(str(key)+'='+','.join(map(str,val)))
+        elif isinstance(val, (int, float, bool)):
             pairs.append(str(key)+'='+str(val))
         else:
             raise TypeError('unknow type of parameter:%s , got:%s' %(key, type(val).__name__))

src/application/application.cpp

@@ -144,33 +144,39 @@ void Application::LoadData() {
     }
   }
   train_metric_.shrink_to_fit();
-  // Add validation data, if it exists
-  for (size_t i = 0; i < config_.io_config.valid_data_filenames.size(); ++i) {
-    // add
-    auto new_dataset = std::unique_ptr<Dataset>(
-      dataset_loader.LoadFromFileAlignWithOtherDataset(
-        config_.io_config.valid_data_filenames[i].c_str(),
-        train_data_.get())
-      );
-    valid_datas_.push_back(std::move(new_dataset));
-    // need save binary file
-    if (config_.io_config.is_save_binary_file) {
-      valid_datas_.back()->SaveBinaryFile(nullptr);
-    }
 
-    // add metric for validation data
-    valid_metrics_.emplace_back();
-    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(valid_datas_.back()->metadata(), 
-        valid_datas_.back()->num_data());
-      valid_metrics_.back().push_back(std::move(metric));
+
+  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
+      auto new_dataset = std::unique_ptr<Dataset>(
+        dataset_loader.LoadFromFileAlignWithOtherDataset(
+          config_.io_config.valid_data_filenames[i].c_str(),
+          train_data_.get())
+        );
+      valid_datas_.push_back(std::move(new_dataset));
+      // need save binary file
+      if (config_.io_config.is_save_binary_file) {
+        valid_datas_.back()->SaveBinaryFile(nullptr);
+      }
+
+      // add metric for validation data
+      valid_metrics_.emplace_back();
+      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(valid_datas_.back()->metadata(),
+          valid_datas_.back()->num_data());
+        valid_metrics_.back().push_back(std::move(metric));
+      }
+      valid_metrics_.back().shrink_to_fit();
     }
-    valid_metrics_.back().shrink_to_fit();
+    valid_datas_.shrink_to_fit();
+    valid_metrics_.shrink_to_fit();
   }
-  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",

src/boosting/gbdt.h

@@ -68,6 +68,14 @@ public:
   */
   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

src/c_api.cpp

@@ -72,7 +72,12 @@ public:
       Log::Fatal("cannot change boosting_type during training");
     }
     config_.Set(param);
-    ResetTrainingData(train_data_);
+    if (param.size() == 1 && (param.count("learning_rate") || param.count("shrinkage_rate"))) {
+      // only need to set learning rate
+      boosting_->ResetShrinkageRate(config_.boosting_config.learning_rate);
+    } else {
+      ResetTrainingData(train_data_);
+    }
   }
 
   void AddValidData(const Dataset* valid_data) {