move num_used_model out of predict function

include/LightGBM/boosting.h

@@ -55,37 +55,31 @@ public:
   /*!
   * \brief Prediction for one record, not sigmoid transform
   * \param feature_values Feature value on this record
-  * \param num_used_model Number of used model
   * \return Prediction result for this record
   */
-  virtual double PredictRaw(const double* feature_values,
-    int num_used_model) const = 0;
+  virtual double PredictRaw(const double* feature_values) const = 0;
 
   /*!
   * \brief Prediction for one record, sigmoid transformation will be used if needed
   * \param feature_values Feature value on this record
-  * \param num_used_model Number of used model
   * \return Prediction result for this record
   */
-  virtual double Predict(const double* feature_values,
-    int num_used_model) const = 0;
+  virtual double Predict(const double* feature_values) const = 0;
   
   /*!
   * \brief Predtion for one record with leaf index
   * \param feature_values Feature value on this record
-  * \param num_used_model Number of used model
   * \return Predicted leaf index for this record
   */
   virtual std::vector<int> PredictLeafIndex(
-    const double* feature_values,
-    int num_used_model) const = 0;
+    const double* feature_values) const = 0;
   
   /*!
   * \brief Predtion for multiclass classification
   * \param feature_values Feature value on this record
   * \return Prediction result, num_class numbers per line
   */
-  virtual std::vector<double> PredictMulticlass(const double* value, int num_used_model) const = 0;
+  virtual std::vector<double> PredictMulticlass(const double* value) const = 0;
   
   /*!
   * \brief save model to file
@@ -121,6 +115,11 @@ public:
   * \return Number of classes
   */
   virtual int NumberOfClass() const = 0;
+
+  /*!
+  * \brief Set number of used model for prediction
+  */
+  virtual void SetNumUsedModel(int num_used_model) = 0;
   
   /*!
   * \brief Get Type name of this boosting object

src/application/application.cpp

@@ -123,7 +123,7 @@ void Application::LoadData() {
   Predictor* predictor = nullptr;
   // need to continue train
   if (boosting_->NumberOfSubModels() > 0) {
-    predictor = new Predictor(boosting_, config_.io_config.is_sigmoid, config_.predict_leaf_index, -1);
+    predictor = new Predictor(boosting_, config_.io_config.is_sigmoid, config_.predict_leaf_index);
     if (config_.io_config.num_class == 1){
       predict_fun =
         [&predictor](const std::vector<std::pair<int, double>>& features) {
@@ -265,9 +265,10 @@ void Application::Train() {
 
 
 void Application::Predict() {
+  boosting_->SetNumUsedModel(config_.io_config.num_model_predict);
   // create predictor
   Predictor predictor(boosting_, config_.io_config.is_sigmoid, 
-    config_.predict_leaf_index, config_.io_config.num_model_predict);
+    config_.predict_leaf_index);
   predictor.Predict(config_.io_config.data_filename.c_str(), 
     config_.io_config.output_result.c_str(), config_.io_config.has_header);
   Log::Info("Finish predict.");

src/application/predictor.hpp

@@ -28,9 +28,8 @@ public:
   * \param is_sigmoid True if need to predict result with sigmoid transform(if needed, like binary classification)
   * \param predict_leaf_index True if output leaf index instead of prediction score
   */
-  Predictor(const Boosting* boosting, bool is_simgoid, bool is_predict_leaf_index, int num_used_model)
-    : is_simgoid_(is_simgoid), is_predict_leaf_index_(is_predict_leaf_index),
-      num_used_model_(num_used_model) {
+  Predictor(const Boosting* boosting, bool is_simgoid, bool is_predict_leaf_index)
+    : is_simgoid_(is_simgoid), is_predict_leaf_index_(is_predict_leaf_index) {
     boosting_ = boosting;
     num_features_ = boosting_->MaxFeatureIdx() + 1;
     num_class_ = boosting_->NumberOfClass();
@@ -64,7 +63,7 @@ public:
   std::vector<double> PredictRawOneLine(const std::vector<std::pair<int, double>>& features) {
     const int tid = PutFeatureValuesToBuffer(features);
     // get result without sigmoid transformation
-    return std::vector<double>(1, boosting_->PredictRaw(features_[tid], num_used_model_));
+    return std::vector<double>(1, boosting_->PredictRaw(features_[tid]));
   }
   
   /*!
@@ -75,7 +74,7 @@ public:
   std::vector<int> PredictLeafIndexOneLine(const std::vector<std::pair<int, double>>& features) {
     const int tid = PutFeatureValuesToBuffer(features);
     // get result for leaf index
-    return boosting_->PredictLeafIndex(features_[tid], num_used_model_);
+    return boosting_->PredictLeafIndex(features_[tid]);
   }
 
   /*!
@@ -86,7 +85,7 @@ public:
   std::vector<double> PredictOneLine(const std::vector<std::pair<int, double>>& features) {
     const int tid = PutFeatureValuesToBuffer(features);
     // get result with sigmoid transform if needed
-    return std::vector<double>(1, boosting_->Predict(features_[tid], num_used_model_));
+    return std::vector<double>(1, boosting_->Predict(features_[tid]));
   }
   
   /*!
@@ -97,7 +96,7 @@ public:
   std::vector<double> PredictMulticlassOneLine(const std::vector<std::pair<int, double>>& features) {
     const int tid = PutFeatureValuesToBuffer(features);
     // get result with sigmoid transform if needed
-    return boosting_->PredictMulticlass(features_[tid], num_used_model_);
+    return boosting_->PredictMulticlass(features_[tid]);
   }
   
   /*!
@@ -224,8 +223,6 @@ private:
   int num_threads_;
   /*! \brief True if output leaf index instead of prediction score */
   bool is_predict_leaf_index_;
-  /*! \brief Number of used model */
-  int num_used_model_;
 };
 
 }  // namespace LightGBM

src/boosting/gbdt.cpp

@@ -19,7 +19,8 @@ namespace LightGBM {
 GBDT::GBDT()
   : train_score_updater_(nullptr),
   gradients_(nullptr), hessians_(nullptr),
-  out_of_bag_data_indices_(nullptr), bag_data_indices_(nullptr) {
+  out_of_bag_data_indices_(nullptr), bag_data_indices_(nullptr),
+  saved_model_size_(-1), num_used_model_(0) {
 }
 
 GBDT::~GBDT() {
@@ -43,6 +44,7 @@ void GBDT::Init(const BoostingConfig* config, const Dataset* train_data, const O
      const std::vector<const Metric*>& training_metrics) {
   gbdt_config_ = dynamic_cast<const GBDTConfig*>(config);
   iter_ = 0;
+  saved_model_size_ = -1;
   max_feature_idx_ = 0;
   early_stopping_round_ = gbdt_config_->early_stopping_round;
   train_data_ = train_data;
@@ -438,6 +440,7 @@ void GBDT::ModelsFromString(const std::string& model_str) {
     }
   }
   Log::Info("%d models has been loaded\n", models_.size());
+  num_used_model_ = static_cast<int>(models_.size()) / num_class_;
 }
 
 std::string GBDT::FeatureImportance() const {
@@ -467,23 +470,17 @@ std::string GBDT::FeatureImportance() const {
     return str_buf.str();
 }
 
-double GBDT::PredictRaw(const double* value, int num_used_model) const {
-  if (num_used_model < 0) {
-    num_used_model = static_cast<int>(models_.size());
-  }
+double GBDT::PredictRaw(const double* value) const {
   double ret = 0.0f;
-  for (int i = 0; i < num_used_model; ++i) {
+  for (int i = 0; i < num_used_model_; ++i) {
     ret += models_[i]->Predict(value);
   }
   return ret;
 }
 
-double GBDT::Predict(const double* value, int num_used_model) const {
-  if (num_used_model < 0) {
-    num_used_model = static_cast<int>(models_.size());
-  }
+double GBDT::Predict(const double* value) const {
   double ret = 0.0f;
-  for (int i = 0; i < num_used_model; ++i) {
+  for (int i = 0; i < num_used_model_; ++i) {
     ret += models_[i]->Predict(value);
   }
   // if need sigmoid transform
@@ -493,12 +490,9 @@ double GBDT::Predict(const double* value, int num_used_model) const {
   return ret;
 }
 
-std::vector<double> GBDT::PredictMulticlass(const double* value, int num_used_model) const {
-  if (num_used_model < 0) {
-      num_used_model = static_cast<int>(models_.size()) / num_class_;
-  }
+std::vector<double> GBDT::PredictMulticlass(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_used_model_; ++i) {
     for (int j = 0; j < num_class_; ++j){
         ret[j] += models_[i * num_class_ + j] -> Predict(value);
     }
@@ -506,12 +500,9 @@ std::vector<double> GBDT::PredictMulticlass(const double* value, int num_used_mo
   return ret;
 }
 
-std::vector<int> GBDT::PredictLeafIndex(const double* value, int num_used_model) const {
-  if (num_used_model < 0) {
-    num_used_model = static_cast<int>(models_.size());
-  }
+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_used_model_; ++i) {
     ret.push_back(models_[i]->PredictLeafIndex(value));
   }
   return ret;

src/boosting/gbdt.h

@@ -55,33 +55,30 @@ public:
   /*!
   * \brief Predtion for one record without sigmoid transformation
   * \param feature_values Feature value on this record
-  * \param num_used_model Number of used model
   * \return Prediction result for this record
   */
-  double PredictRaw(const double* feature_values, int num_used_model) const override;
+  double PredictRaw(const double* feature_values) const override;
 
   /*!
   * \brief Predtion for one record with sigmoid transformation if enabled
   * \param feature_values Feature value on this record
-  * \param num_used_model Number of used model
   * \return Prediction result for this record
   */
-  double Predict(const double* feature_values, int num_used_model) const override;
+  double Predict(const double* feature_values) const override;
   
   /*!
   * \brief Predtion for multiclass classification
   * \param feature_values Feature value on this record
   * \return Prediction result, num_class numbers per line
   */
-  std::vector<double> PredictMulticlass(const double* value, int num_used_model) const override;
+  std::vector<double> PredictMulticlass(const double* value) const override;
   
   /*!
   * \brief Predtion for one record with leaf index
   * \param feature_values Feature value on this record
-  * \param num_used_model Number of used model
   * \return Predicted leaf index for this record
   */
-  std::vector<int> PredictLeafIndex(const double* value, int num_used_model) const override;
+  std::vector<int> PredictLeafIndex(const double* value) const override;
   
   /*!
   * \brief Serialize models by string
@@ -115,6 +112,16 @@ public:
   * \return Number of classes
   */
   inline int NumberOfClass() const override { return num_class_; }
+
+  /*!
+  * \brief Set number of used model 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_);
+    }
+  }
+
   
   /*!
   * \brief Get Type name of this boosting object
@@ -208,9 +215,11 @@ private:
   /*! \brief Index of label column */
   data_size_t label_idx_;
   /*! \brief Saved number of models */
-  int saved_model_size_ = -1;
+  int saved_model_size_;
   /*! \brief File to write models */
   std::ofstream model_output_file_;
+  /*! \brief number of used model */
+  int num_used_model_;
 };
 
 }  // namespace LightGBM