remove additional cost for prediction task. (#404)

* refine prediction logic.

* fix test.

* fix out_len in training score of Dart.

* improve predict speed for high dimension data.

* try use unordered_map for sparse prediction.

* avoid using unordered_map.

* clean code.

* fix test.

* move predict buffer to Predictor.

include/LightGBM/boosting.h

@@ -117,14 +117,14 @@ public:
   * \param feature_values Feature value on this record
   * \param output Prediction result for this record
   */
-  virtual void PredictRaw(const double* feature_values, double* output) const = 0;
+  virtual void PredictRaw(const double* features, double* output) const = 0;
 
   /*!
   * \brief Prediction for one record, sigmoid transformation will be used if needed
   * \param feature_values Feature value on this record
   * \param output Prediction result for this record
   */
-  virtual void Predict(const double* feature_values, double* output) const = 0;
+  virtual void Predict(const double* features, double* output) const = 0;
   
   /*!
   * \brief Prediction for one record with leaf index
@@ -132,7 +132,7 @@ public:
   * \param output Prediction result for this record
   */
   virtual void PredictLeafIndex(
-    const double* feature_values, double* output) const = 0;
+    const double* features, double* output) const = 0;
 
   /*!
   * \brief Dump model to json format string
@@ -202,9 +202,8 @@ public:
   /*!
   * \brief Initial work for the prediction
   * \param num_iteration number of used iteration
-  * \return the feature indices mapper
   */
-  virtual std::vector<int> InitPredict(int num_iteration) = 0;
+  virtual void InitPredict(int num_iteration) = 0;
   
   /*!
   * \brief Name of submodel

include/LightGBM/tree.h

@@ -111,13 +111,6 @@ public:
     shrinkage_ *= rate;
   }
 
-  inline void ReMapFeature(const std::vector<int>& feature_mapper) {
-    mapped_feature_ = split_feature_;
-    for (int i = 0; i < num_leaves_ - 1; ++i) {
-      mapped_feature_[i] = feature_mapper[split_feature_[i]];
-    }
-  }
-
   /*! \brief Serialize this object to string*/
   std::string ToString();
 
@@ -201,8 +194,6 @@ private:
   std::vector<int> leaf_depth_;
   double shrinkage_;
   bool has_categorical_;
-  /*! \brief buffer of mapped split_feature_  */
-  std::vector<int> mapped_feature_;
 };
 
 inline double Tree::Predict(const double* feature_values) const {
@@ -228,7 +219,7 @@ inline int Tree::GetLeaf(const double* feature_values) const {
   if (has_categorical_) {
     while (node >= 0) {
       if (decision_funs[decision_type_[node]](
-        feature_values[mapped_feature_[node]],
+        feature_values[split_feature_[node]],
         threshold_[node])) {
         node = left_child_[node];
       } else {
@@ -238,7 +229,7 @@ inline int Tree::GetLeaf(const double* feature_values) const {
   } else {
     while (node >= 0) {
       if (NumericalDecision<double>(
-        feature_values[mapped_feature_[node]],
+        feature_values[split_feature_[node]],
         threshold_[node])) {
         node = left_child_[node];
       } else {

src/application/predictor.hpp

@@ -33,39 +33,36 @@ public:
   Predictor(Boosting* boosting, int num_iteration,
             bool is_raw_score, bool is_predict_leaf_index) {
 
-    feature_mapper_ = boosting->InitPredict(num_iteration);
+    boosting->InitPredict(num_iteration);
     boosting_ = boosting;
     num_pred_one_row_ = boosting_->NumPredictOneRow(num_iteration, is_predict_leaf_index);
+    predict_buf_ = std::vector<double>(boosting_->MaxFeatureIdx() + 1, 0.0f);
 
-    num_total_features_ = static_cast<int>(feature_mapper_.size());
-    num_used_features_ = 1;
-    for (auto fidx : feature_mapper_) {
-      num_used_features_ = std::max(num_used_features_, fidx + 1);
-    }
-
-    features_ = std::vector<double>(num_used_features_);
     if (is_predict_leaf_index) {
       predict_fun_ = [this](const std::vector<std::pair<int, double>>& features, double* output) {
-        PutFeatureValuesToBuffer(features);
+        CopyToPredictBuffer(features);
         // get result for leaf index
-        boosting_->PredictLeafIndex(features_.data(), output);
+        boosting_->PredictLeafIndex(predict_buf_.data(), output);
+        ClearPredictBuffer(features);
       };
 
     } else {
       if (is_raw_score) {
         predict_fun_ = [this](const std::vector<std::pair<int, double>>& features, double* output) {
-          PutFeatureValuesToBuffer(features);
-          // get result without sigmoid transformation
-          boosting_->PredictRaw(features_.data(), output);
+          CopyToPredictBuffer(features);
+          boosting_->PredictRaw(predict_buf_.data(), output);
+          ClearPredictBuffer(features);
         };
       } else {
         predict_fun_ = [this](const std::vector<std::pair<int, double>>& features, double* output) {
-          PutFeatureValuesToBuffer(features);
-          boosting_->Predict(features_.data(), output);
+          CopyToPredictBuffer(features);
+          boosting_->Predict(predict_buf_.data(), output);
+          ClearPredictBuffer(features);
         };
       }
     }
   }
+
   /*!
   * \brief Destructor
   */
@@ -93,7 +90,7 @@ public:
     if (result_file == NULL) {
       Log::Fatal("Prediction results file %s doesn't exist", data_filename);
     }
-    auto parser = std::unique_ptr<Parser>(Parser::CreateParser(data_filename, has_header, num_used_features_, boosting_->LabelIdx()));
+    auto parser = std::unique_ptr<Parser>(Parser::CreateParser(data_filename, has_header, boosting_->MaxFeatureIdx() + 1, boosting_->LabelIdx()));
 
     if (parser == nullptr) {
       Log::Fatal("Could not recognize the data format of data file %s", data_filename);
@@ -128,30 +125,33 @@ public:
   }
 
 private:
-  void PutFeatureValuesToBuffer(const std::vector<std::pair<int, double>>& features) {
-    std::memset(features_.data(), 0, sizeof(double)*num_used_features_);
-    // put feature value
+
+  void CopyToPredictBuffer(const std::vector<std::pair<int, double>>& features) {
     int loop_size = static_cast<int>(features.size());
-    #pragma omp parallel for schedule(static, 512) if(loop_size >= 1024) 
+    #pragma omp parallel for schedule(static,128) if (loop_size >= 256)
     for (int i = 0; i < loop_size; ++i) {
-      if (features[i].first >= num_total_features_) continue;
-      auto fidx = feature_mapper_[features[i].first];
-      if (fidx >= 0) {
-        features_[fidx] = features[i].second;
+      predict_buf_[features[i].first] = features[i].second;
+    }
+  }
+
+  void ClearPredictBuffer(const std::vector<std::pair<int, double>>& features) {
+    if (features.size() < static_cast<size_t>(predict_buf_.size() / 2)) {
+      std::memset(predict_buf_.data(), 0, sizeof(double)*(predict_buf_.size()));
+    } else {
+      int loop_size = static_cast<int>(features.size());
+      #pragma omp parallel for schedule(static,128) if (loop_size >= 256)
+      for (int i = 0; i < loop_size; ++i) {
+        predict_buf_[features[i].first] = 0.0f;
       }
     }
   }
+
   /*! \brief Boosting model */
   const Boosting* boosting_;
-  /*! \brief Buffer for feature values */
-  std::vector<double> features_;
-  /*! \brief Number of features */
-  int num_used_features_;
   /*! \brief function for prediction */
   PredictFunction predict_fun_;
   int num_pred_one_row_;
-  std::vector<int> feature_mapper_;
-  int num_total_features_;
+  std::vector<double> predict_buf_;
 };
 
 }  // namespace LightGBM

src/boosting/gbdt.cpp

@@ -870,14 +870,12 @@ std::vector<std::pair<size_t, std::string>> GBDT::FeatureImportance() const {
   return pairs;
 }
 
-
-
-void GBDT::PredictRaw(const double* value, double* output) const {
+void GBDT::PredictRaw(const double* features, double* output) const {
   if (num_threads_ <= num_tree_per_iteration_) {
     #pragma omp parallel for schedule(static)
     for (int k = 0; k < num_tree_per_iteration_; ++k) {
       for (int i = 0; i < num_iteration_for_pred_; ++i) {
-        output[k] += models_[i * num_tree_per_iteration_ + k]->Predict(value);
+        output[k] += models_[i * num_tree_per_iteration_ + k]->Predict(features);
       }
     }
   } else {
@@ -885,19 +883,19 @@ void GBDT::PredictRaw(const double* value, double* output) const {
       double t = 0.0f;
       #pragma omp parallel for schedule(static) reduction(+:t)
       for (int i = 0; i < num_iteration_for_pred_; ++i) {
-        t += models_[i * num_tree_per_iteration_ + k]->Predict(value);
+        t += models_[i * num_tree_per_iteration_ + k]->Predict(features);
       }
       output[k] = t;
     }
   }
 }
 
-void GBDT::Predict(const double* value, double* output) const {
+void GBDT::Predict(const double* features, double* output) const {
   if (num_threads_ <= num_tree_per_iteration_) {
     #pragma omp parallel for schedule(static)
     for (int k = 0; k < num_tree_per_iteration_; ++k) {
       for (int i = 0; i < num_iteration_for_pred_; ++i) {
-        output[k] += models_[i * num_tree_per_iteration_ + k]->Predict(value);
+        output[k] += models_[i * num_tree_per_iteration_ + k]->Predict(features);
       }
     }
   } else {
@@ -905,7 +903,7 @@ void GBDT::Predict(const double* value, double* output) const {
       double t = 0.0f;
       #pragma omp parallel for schedule(static) reduction(+:t)
       for (int i = 0; i < num_iteration_for_pred_; ++i) {
-        t += models_[i * num_tree_per_iteration_ + k]->Predict(value);
+        t += models_[i * num_tree_per_iteration_ + k]->Predict(features);
       }
       output[k] = t;
     }
@@ -915,11 +913,11 @@ void GBDT::Predict(const double* value, double* output) const {
   }
 }
 
-void GBDT::PredictLeafIndex(const double* value, double* output) const {
+void GBDT::PredictLeafIndex(const double* features, double* output) const {
   int total_tree = num_iteration_for_pred_ * num_tree_per_iteration_;
   #pragma omp parallel for schedule(static)
   for (int i = 0; i < total_tree; ++i) {
-    output[i] = models_[i]->PredictLeafIndex(value);
+    output[i] = models_[i]->PredictLeafIndex(features);
   }
 }
 

src/boosting/gbdt.h

@@ -9,6 +9,7 @@
 #include <string>
 #include <fstream>
 #include <memory>
+#include <mutex>
 
 namespace LightGBM {
 /*!
@@ -135,11 +136,11 @@ public:
     return num_preb_in_one_row;
   }
 
-  void PredictRaw(const double* feature_values, double* output) const override;
+  void PredictRaw(const double* features, double* output) const override;
 
-  void Predict(const double* feature_values, double* output) const override;
+  void Predict(const double* features, double* output) const override;
 
-  void PredictLeafIndex(const double* value, double* output) const override;
+  void PredictLeafIndex(const double* features, double* output) const override;
 
   /*!
   * \brief Dump model to json format string
@@ -203,39 +204,11 @@ public:
   */
   inline int NumberOfClasses() const override { return num_class_; }
 
-  inline std::vector<int> InitPredict(int num_iteration) override {
+  inline void InitPredict(int num_iteration) override {
     num_iteration_for_pred_ = static_cast<int>(models_.size()) / num_tree_per_iteration_;
     if (num_iteration > 0) {
       num_iteration_for_pred_ = std::min(num_iteration + (boost_from_average_ ? 1 : 0), num_iteration_for_pred_);
     }
-    int used_fidx = 0;
-    // Construct used feature mapper
-    std::vector<int> feature_mapper(max_feature_idx_ + 1, -1);
-    int total_tree = num_iteration_for_pred_ * num_tree_per_iteration_;
-
-    #pragma omp parallel for schedule(static, 64) if (total_tree >= 128)
-    for (int i = 0; i < total_tree; ++i) {
-      int num_leaves = models_[i]->num_leaves();
-      for (int j = 0; j < num_leaves - 1; ++j) {
-        int fidx = models_[i]->split_feature(j);
-        if (feature_mapper[fidx] == -1) {
-          #pragma omp critical
-          {
-            if (feature_mapper[fidx] == -1) {
-              feature_mapper[fidx] = used_fidx;
-              ++used_fidx;
-            }
-          }
-        }
-      }
-    }
-
-    #pragma omp parallel for schedule(static, 64) if (total_tree >= 128)
-    for (int i = 0; i < total_tree; ++i) {
-      models_[i]->ReMapFeature(feature_mapper);
-    }
-
-    return feature_mapper;
   }
 
   inline double GetLeafValue(int tree_idx, int leaf_idx) const {
@@ -297,6 +270,7 @@ protected:
   * \brief Calculate feature importances
   */
   std::vector<std::pair<size_t, std::string>> FeatureImportance() const;
+
   /*! \brief current iteration */
   int iter_;
   /*! \brief Pointer to training data */
@@ -373,6 +347,7 @@ protected:
   std::vector<double> class_default_output_;
   bool is_constant_hessian_;
   std::unique_ptr<ObjectiveFunction> loaded_objective_;
+
 };
 
 }  // namespace LightGBM