fix a bug when bagging with reset_config (#2149)

* fix a bug when bagging with reset_config

* clean code

include/LightGBM/objective_function.h

@@ -43,18 +43,11 @@ class ObjectiveFunction {
 
   virtual bool IsRenewTreeOutput() const { return false; }
 
-  virtual double RenewTreeOutput(double ori_output, const double*,
+  virtual double RenewTreeOutput(double ori_output, std::function<double(const label_t*, int)> residual_getter,
                                  const data_size_t*,
                                  const data_size_t*,
                                  data_size_t) const { return ori_output; }
 
-  virtual double RenewTreeOutput(double ori_output, double,
-                                  const data_size_t*,
-                                  const data_size_t*,
-                                  data_size_t) const {
-                                  return ori_output;
-  }
-
   virtual double BoostFromScore(int /*class_id*/) const { return 0.0; }
 
   virtual bool ClassNeedTrain(int /*class_id*/) const { return true; }

include/LightGBM/tree_learner.h

@@ -77,10 +77,7 @@ class TreeLearner {
   */
   virtual void AddPredictionToScore(const Tree* tree, double* out_score) const = 0;
 
-  virtual void RenewTreeOutput(Tree* tree, const ObjectiveFunction* obj, const double* prediction,
-                               data_size_t total_num_data, const data_size_t* bag_indices, data_size_t bag_cnt) const = 0;
-
-  virtual void RenewTreeOutput(Tree* tree, const ObjectiveFunction* obj, double prediction,
+  virtual void RenewTreeOutput(Tree* tree, const ObjectiveFunction* obj, std::function<double(const label_t*, int)> residual_getter,
                                data_size_t total_num_data, const data_size_t* bag_indices, data_size_t bag_cnt) const = 0;
 
   TreeLearner() = default;

src/boosting/gbdt.cpp

@@ -364,7 +364,9 @@ bool GBDT::TrainOneIter(const score_t* gradients, const score_t* hessians) {
 
     if (new_tree->num_leaves() > 1) {
       should_continue = true;
-      tree_learner_->RenewTreeOutput(new_tree.get(), objective_function_, train_score_updater_->score() + bias,
+      auto score_ptr = train_score_updater_->score() + bias;
+      auto residual_getter = [score_ptr](const label_t* label, int i) {return static_cast<double>(label[i]) - score_ptr[i]; };
+      tree_learner_->RenewTreeOutput(new_tree.get(), objective_function_, residual_getter,
                                      num_data_, bag_data_indices_.data(), bag_data_cnt_);
       // shrinkage by learning rate
       new_tree->Shrinkage(shrinkage_rate_);
@@ -688,6 +690,11 @@ void GBDT::ResetConfig(const Config* config) {
 void GBDT::ResetBaggingConfig(const Config* config, bool is_change_dataset) {
   // if need bagging, create buffer
   if (config->bagging_fraction < 1.0 && config->bagging_freq > 0) {
+    need_re_bagging_ = false;
+    if (!is_change_dataset &&
+      config_.get() != nullptr && config_->bagging_fraction == config->bagging_fraction && config_->bagging_freq == config->bagging_freq) {
+      return;
+    }
     bag_data_cnt_ =
       static_cast<data_size_t>(config->bagging_fraction * num_data_);
     bag_data_indices_.resize(num_data_);
@@ -719,9 +726,7 @@ void GBDT::ResetBaggingConfig(const Config* config, bool is_change_dataset) {
       Log::Debug("Use subset for bagging");
     }
 
-    if (is_change_dataset) {
     need_re_bagging_ = true;
-    }
 
     if (is_use_subset_ && bag_data_cnt_ < num_data_) {
       if (objective_function_ == nullptr) {

src/boosting/rf.hpp

@@ -130,7 +130,9 @@ class RF : public GBDT {
       }
 
       if (new_tree->num_leaves() > 1) {
-        tree_learner_->RenewTreeOutput(new_tree.get(), objective_function_, init_scores_[cur_tree_id],
+        double pred = init_scores_[cur_tree_id];
+        auto residual_getter = [pred](const label_t* label, int i) {return static_cast<double>(label[i]) - pred; };
+        tree_learner_->RenewTreeOutput(new_tree.get(), objective_function_, residual_getter,
           num_data_, bag_data_indices_.data(), bag_data_cnt_);
         if (std::fabs(init_scores_[cur_tree_id]) > kEpsilon) {
           new_tree->AddBias(init_scores_[cur_tree_id]);

src/objective/regression_objective.hpp

@@ -232,62 +232,30 @@ class RegressionL1loss: public RegressionL2loss {
 
   bool IsRenewTreeOutput() const override { return true; }
 
-  double RenewTreeOutput(double, const double* pred,
+  double RenewTreeOutput(double, std::function<double(const label_t*, int)> residual_getter,
                          const data_size_t* index_mapper,
                          const data_size_t* bagging_mapper,
                          data_size_t num_data_in_leaf) const override {
     const double alpha = 0.5;
     if (weights_ == nullptr) {
       if (bagging_mapper == nullptr) {
-        #define data_reader(i) (label_[index_mapper[i]] - pred[index_mapper[i]])
+        #define data_reader(i) (residual_getter(label_,index_mapper[i]))
         PercentileFun(double, data_reader, num_data_in_leaf, alpha);
         #undef data_reader
       } else {
-        #define data_reader(i) (label_[bagging_mapper[index_mapper[i]]] - pred[bagging_mapper[index_mapper[i]]])
+        #define data_reader(i) (residual_getter(label_,bagging_mapper[index_mapper[i]]))
         PercentileFun(double, data_reader, num_data_in_leaf, alpha);
         #undef data_reader
       }
     } else {
       if (bagging_mapper == nullptr) {
-        #define data_reader(i) (label_[index_mapper[i]] - pred[index_mapper[i]])
+        #define data_reader(i) (residual_getter(label_,index_mapper[i]))
         #define weight_reader(i) (weights_[index_mapper[i]])
         WeightedPercentileFun(double, data_reader, weight_reader, num_data_in_leaf, alpha);
         #undef data_reader
         #undef weight_reader
       } else {
-        #define data_reader(i) (label_[bagging_mapper[index_mapper[i]]] - pred[bagging_mapper[index_mapper[i]]])
-        #define weight_reader(i) (weights_[bagging_mapper[index_mapper[i]]])
-        WeightedPercentileFun(double, data_reader, weight_reader, num_data_in_leaf, alpha);
-        #undef data_reader
-        #undef weight_reader
-      }
-    }
-  }
-
-  double RenewTreeOutput(double, double pred,
-                         const data_size_t* index_mapper,
-                         const data_size_t* bagging_mapper,
-                         data_size_t num_data_in_leaf) const override {
-    const double alpha = 0.5;
-    if (weights_ == nullptr) {
-      if (bagging_mapper == nullptr) {
-        #define data_reader(i) (label_[index_mapper[i]] - pred)
-        PercentileFun(double, data_reader, num_data_in_leaf, alpha);
-        #undef data_reader
-      } else {
-        #define data_reader(i) (label_[bagging_mapper[index_mapper[i]]] - pred)
-        PercentileFun(double, data_reader, num_data_in_leaf, alpha);
-        #undef data_reader
-      }
-    } else {
-      if (bagging_mapper == nullptr) {
-        #define data_reader(i) (label_[index_mapper[i]] - pred)
-        #define weight_reader(i) (weights_[index_mapper[i]])
-        WeightedPercentileFun(double, data_reader, weight_reader, num_data_in_leaf, alpha);
-        #undef data_reader
-        #undef weight_reader
-      } else {
-        #define data_reader(i) (label_[bagging_mapper[index_mapper[i]]] - pred)
+        #define data_reader(i) (residual_getter(label_,bagging_mapper[index_mapper[i]]))
         #define weight_reader(i) (weights_[bagging_mapper[index_mapper[i]]])
         WeightedPercentileFun(double, data_reader, weight_reader, num_data_in_leaf, alpha);
         #undef data_reader
@@ -552,60 +520,29 @@ class RegressionQuantileloss : public RegressionL2loss {
 
   bool IsRenewTreeOutput() const override { return true; }
 
-  double RenewTreeOutput(double, const double* pred,
-                         const data_size_t* index_mapper,
-                         const data_size_t* bagging_mapper,
-                         data_size_t num_data_in_leaf) const override {
-    if (weights_ == nullptr) {
-      if (bagging_mapper == nullptr) {
-        #define data_reader(i) (label_[index_mapper[i]] - pred[index_mapper[i]])
-        PercentileFun(double, data_reader, num_data_in_leaf, alpha_);
-        #undef data_reader
-      } else {
-        #define data_reader(i) (label_[bagging_mapper[index_mapper[i]]] - pred[bagging_mapper[index_mapper[i]]])
-        PercentileFun(double, data_reader, num_data_in_leaf, alpha_);
-        #undef data_reader
-      }
-    } else {
-      if (bagging_mapper == nullptr) {
-        #define data_reader(i) (label_[index_mapper[i]] - pred[index_mapper[i]])
-        #define weight_reader(i) (weights_[index_mapper[i]])
-        WeightedPercentileFun(double, data_reader, weight_reader, num_data_in_leaf, alpha_);
-        #undef data_reader
-        #undef weight_reader
-      } else {
-        #define data_reader(i) (label_[bagging_mapper[index_mapper[i]]] - pred[bagging_mapper[index_mapper[i]]])
-        #define weight_reader(i) (weights_[bagging_mapper[index_mapper[i]]])
-        WeightedPercentileFun(double, data_reader, weight_reader, num_data_in_leaf, alpha_);
-        #undef data_reader
-        #undef weight_reader
-      }
-    }
-  }
-
-  double RenewTreeOutput(double, double pred,
+  double RenewTreeOutput(double, std::function<double(const label_t*, int)> residual_getter,
                          const data_size_t* index_mapper,
                          const data_size_t* bagging_mapper,
                          data_size_t num_data_in_leaf) const override {
     if (weights_ == nullptr) {
       if (bagging_mapper == nullptr) {
-        #define data_reader(i) (label_[index_mapper[i]] - pred)
+        #define data_reader(i) (residual_getter(label_,index_mapper[i]))
         PercentileFun(double, data_reader, num_data_in_leaf, alpha_);
         #undef data_reader
       } else {
-        #define data_reader(i) (label_[bagging_mapper[index_mapper[i]]] - pred)
+        #define data_reader(i) (residual_getter(label_,bagging_mapper[index_mapper[i]]))
         PercentileFun(double, data_reader, num_data_in_leaf, alpha_);
         #undef data_reader
       }
     } else {
       if (bagging_mapper == nullptr) {
-        #define data_reader(i) (label_[index_mapper[i]] - pred)
+        #define data_reader(i) (residual_getter(label_,index_mapper[i]))
         #define weight_reader(i) (weights_[index_mapper[i]])
         WeightedPercentileFun(double, data_reader, weight_reader, num_data_in_leaf, alpha_);
         #undef data_reader
         #undef weight_reader
       } else {
-        #define data_reader(i) (label_[bagging_mapper[index_mapper[i]]] - pred)
+        #define data_reader(i) (residual_getter(label_,bagging_mapper[index_mapper[i]]))
         #define weight_reader(i) (weights_[bagging_mapper[index_mapper[i]]])
         WeightedPercentileFun(double, data_reader, weight_reader, num_data_in_leaf, alpha_);
         #undef data_reader
@@ -684,39 +621,19 @@ class RegressionMAPELOSS : public RegressionL1loss {
 
   bool IsRenewTreeOutput() const override { return true; }
 
-  double RenewTreeOutput(double, const double* pred,
-                         const data_size_t* index_mapper,
-                         const data_size_t* bagging_mapper,
-                         data_size_t num_data_in_leaf) const override {
-    const double alpha = 0.5;
-    if (bagging_mapper == nullptr) {
-      #define data_reader(i) (label_[index_mapper[i]] - pred[index_mapper[i]])
-      #define weight_reader(i) (label_weight_[index_mapper[i]])
-      WeightedPercentileFun(double, data_reader, weight_reader, num_data_in_leaf, alpha);
-      #undef data_reader
-      #undef weight_reader
-    } else {
-      #define data_reader(i) (label_[bagging_mapper[index_mapper[i]]] - pred[bagging_mapper[index_mapper[i]]])
-      #define weight_reader(i) (label_weight_[bagging_mapper[index_mapper[i]]])
-      WeightedPercentileFun(double, data_reader, weight_reader, num_data_in_leaf, alpha);
-      #undef data_reader
-      #undef weight_reader
-    }
-  }
-
-  double RenewTreeOutput(double, double pred,
+  double RenewTreeOutput(double, std::function<double(const label_t*, int)> residual_getter,
                          const data_size_t* index_mapper,
                          const data_size_t* bagging_mapper,
                          data_size_t num_data_in_leaf) const override {
     const double alpha = 0.5;
     if (bagging_mapper == nullptr) {
-      #define data_reader(i) (label_[index_mapper[i]] - pred)
+      #define data_reader(i) (residual_getter(label_,index_mapper[i]))
       #define weight_reader(i) (label_weight_[index_mapper[i]])
       WeightedPercentileFun(double, data_reader, weight_reader, num_data_in_leaf, alpha);
       #undef data_reader
       #undef weight_reader
     } else {
-      #define data_reader(i) (label_[bagging_mapper[index_mapper[i]]] - pred)
+      #define data_reader(i) (residual_getter(label_,bagging_mapper[index_mapper[i]]))
       #define weight_reader(i) (label_weight_[bagging_mapper[index_mapper[i]]])
       WeightedPercentileFun(double, data_reader, weight_reader, num_data_in_leaf, alpha);
       #undef data_reader

src/treelearner/serial_tree_learner.cpp

@@ -851,7 +851,7 @@ void SerialTreeLearner::Split(Tree* tree, int best_leaf, int* left_leaf, int* ri
 }
 
 
-void SerialTreeLearner::RenewTreeOutput(Tree* tree, const ObjectiveFunction* obj, const double* prediction,
+void SerialTreeLearner::RenewTreeOutput(Tree* tree, const ObjectiveFunction* obj, std::function<double(const label_t*, int)> residual_getter,
                                         data_size_t total_num_data, const data_size_t* bag_indices, data_size_t bag_cnt) const {
   if (obj != nullptr && obj->IsRenewTreeOutput()) {
     CHECK(tree->num_leaves() <= data_partition_->num_leaves());
@@ -869,47 +869,7 @@ void SerialTreeLearner::RenewTreeOutput(Tree* tree, const ObjectiveFunction* obj
       auto index_mapper = data_partition_->GetIndexOnLeaf(i, &cnt_leaf_data);
       if (cnt_leaf_data > 0) {
         // bag_mapper[index_mapper[i]]
-        const double new_output = obj->RenewTreeOutput(output, prediction, index_mapper, bag_mapper, cnt_leaf_data);
-        tree->SetLeafOutput(i, new_output);
-      } else {
-        CHECK(num_machines > 1);
-        tree->SetLeafOutput(i, 0.0);
-        n_nozeroworker_perleaf[i] = 0;
-      }
-    }
-    if (num_machines > 1) {
-      std::vector<double> outputs(tree->num_leaves());
-      for (int i = 0; i < tree->num_leaves(); ++i) {
-        outputs[i] = static_cast<double>(tree->LeafOutput(i));
-      }
-      Network::GlobalSum(outputs);
-      Network::GlobalSum(n_nozeroworker_perleaf);
-      for (int i = 0; i < tree->num_leaves(); ++i) {
-        tree->SetLeafOutput(i, outputs[i] / n_nozeroworker_perleaf[i]);
-      }
-    }
-  }
-}
-
-void SerialTreeLearner::RenewTreeOutput(Tree* tree, const ObjectiveFunction* obj, double prediction,
-  data_size_t total_num_data, const data_size_t* bag_indices, data_size_t bag_cnt) const {
-  if (obj != nullptr && obj->IsRenewTreeOutput()) {
-    CHECK(tree->num_leaves() <= data_partition_->num_leaves());
-    const data_size_t* bag_mapper = nullptr;
-    if (total_num_data != num_data_) {
-      CHECK(bag_cnt == num_data_);
-      bag_mapper = bag_indices;
-    }
-    std::vector<int> n_nozeroworker_perleaf(tree->num_leaves(), 1);
-    int num_machines = Network::num_machines();
-    #pragma omp parallel for schedule(static)
-    for (int i = 0; i < tree->num_leaves(); ++i) {
-      const double output = static_cast<double>(tree->LeafOutput(i));
-      data_size_t cnt_leaf_data = 0;
-      auto index_mapper = data_partition_->GetIndexOnLeaf(i, &cnt_leaf_data);
-      if (cnt_leaf_data > 0) {
-        // bag_mapper[index_mapper[i]]
-        const double new_output = obj->RenewTreeOutput(output, prediction, index_mapper, bag_mapper, cnt_leaf_data);
+        const double new_output = obj->RenewTreeOutput(output, residual_getter, index_mapper, bag_mapper, cnt_leaf_data);
         tree->SetLeafOutput(i, new_output);
       } else {
         CHECK(num_machines > 1);

src/treelearner/serial_tree_learner.h

@@ -74,10 +74,7 @@ class SerialTreeLearner: public TreeLearner {
     }
   }
 
-  void RenewTreeOutput(Tree* tree, const ObjectiveFunction* obj, const double* prediction,
-                       data_size_t total_num_data, const data_size_t* bag_indices, data_size_t bag_cnt) const override;
-
-  void RenewTreeOutput(Tree* tree, const ObjectiveFunction* obj, double prediction,
+  void RenewTreeOutput(Tree* tree, const ObjectiveFunction* obj, std::function<double(const label_t*, int)> residual_getter,
                        data_size_t total_num_data, const data_size_t* bag_indices, data_size_t bag_cnt) const override;
 
  protected: