fix bug in CEGB when reset training data or config (#3246)

* fix

* Apply suggestions from code review

src/treelearner/cost_effective_gradient_boosting.hpp

 /*!
  * Copyright (c) 2019 Microsoft Corporation. All rights reserved.
- * Licensed under the MIT License. See LICENSE file in the project root for license information.
+ * Licensed under the MIT License. See LICENSE file in the project root for
+ * license information.
  */
 #ifndef LIGHTGBM_TREELEARNER_COST_EFFECTIVE_GRADIENT_BOOSTING_HPP_
 #define LIGHTGBM_TREELEARNER_COST_EFFECTIVE_GRADIENT_BOOSTING_HPP_
@@ -20,11 +21,12 @@ namespace LightGBM {
 
 class CostEfficientGradientBoosting {
  public:
-  explicit CostEfficientGradientBoosting(const SerialTreeLearner* tree_learner):tree_learner_(tree_learner) {
-  }
+  explicit CostEfficientGradientBoosting(const SerialTreeLearner* tree_learner)
+      : init_(false), tree_learner_(tree_learner) {}
   static bool IsEnable(const Config* config) {
-    if (config->cegb_tradeoff >= 1.0f && config->cegb_penalty_split <= 0.0f
-      && config->cegb_penalty_feature_coupled.empty() && config->cegb_penalty_feature_lazy.empty()) {
+    if (config->cegb_tradeoff >= 1.0f && config->cegb_penalty_split <= 0.0f &&
+        config->cegb_penalty_feature_coupled.empty() &&
+        config->cegb_penalty_feature_lazy.empty()) {
       return false;
     } else {
       return true;
@@ -32,44 +34,73 @@ class CostEfficientGradientBoosting {
   }
   void Init() {
     auto train_data = tree_learner_->train_data_;
-    splits_per_leaf_.resize(static_cast<size_t>(tree_learner_->config_->num_leaves) * train_data->num_features());
-    is_feature_used_in_split_.clear();
-    is_feature_used_in_split_.resize(train_data->num_features());
+    if (!init_) {
+      splits_per_leaf_.resize(
+          static_cast<size_t>(tree_learner_->config_->num_leaves) *
+          train_data->num_features());
+      is_feature_used_in_split_.clear();
+      is_feature_used_in_split_.resize(train_data->num_features());
+    }
 
-    if (!tree_learner_->config_->cegb_penalty_feature_coupled.empty()
-        && tree_learner_->config_->cegb_penalty_feature_coupled.size() != static_cast<size_t>(train_data->num_total_features())) {
-      Log::Fatal("cegb_penalty_feature_coupled should be the same size as feature number.");
+    if (!tree_learner_->config_->cegb_penalty_feature_coupled.empty() &&
+        tree_learner_->config_->cegb_penalty_feature_coupled.size() !=
+            static_cast<size_t>(train_data->num_total_features())) {
+      Log::Fatal(
+          "cegb_penalty_feature_coupled should be the same size as feature "
+          "number.");
     }
     if (!tree_learner_->config_->cegb_penalty_feature_lazy.empty()) {
-      if (tree_learner_->config_->cegb_penalty_feature_lazy.size() != static_cast<size_t>(train_data->num_total_features())) {
-        Log::Fatal("cegb_penalty_feature_lazy should be the same size as feature number.");
+      if (tree_learner_->config_->cegb_penalty_feature_lazy.size() !=
+          static_cast<size_t>(train_data->num_total_features())) {
+        Log::Fatal(
+            "cegb_penalty_feature_lazy should be the same size as feature "
+            "number.");
+      }
+      if (!init_) {
+        feature_used_in_data_ = Common::EmptyBitset(train_data->num_features() *
+                                                    tree_learner_->num_data_);
       }
-      feature_used_in_data_ = Common::EmptyBitset(train_data->num_features() * tree_learner_->num_data_);
     }
+    init_ = true;
   }
-  double DetlaGain(int feature_index, int real_fidx, int leaf_index, int num_data_in_leaf, SplitInfo split_info) {
+  double DetlaGain(int feature_index, int real_fidx, int leaf_index,
+                   int num_data_in_leaf, SplitInfo split_info) {
     auto config = tree_learner_->config_;
-    double delta = config->cegb_tradeoff * config->cegb_penalty_split * num_data_in_leaf;
-    if (!config->cegb_penalty_feature_coupled.empty() && !is_feature_used_in_split_[feature_index]) {
-      delta += config->cegb_tradeoff * config->cegb_penalty_feature_coupled[real_fidx];
+    double delta =
+        config->cegb_tradeoff * config->cegb_penalty_split * num_data_in_leaf;
+    if (!config->cegb_penalty_feature_coupled.empty() &&
+        !is_feature_used_in_split_[feature_index]) {
+      delta += config->cegb_tradeoff *
+               config->cegb_penalty_feature_coupled[real_fidx];
     }
     if (!config->cegb_penalty_feature_lazy.empty()) {
-      delta += config->cegb_tradeoff * CalculateOndemandCosts(feature_index, real_fidx, leaf_index);
+      delta += config->cegb_tradeoff *
+               CalculateOndemandCosts(feature_index, real_fidx, leaf_index);
     }
-    splits_per_leaf_[static_cast<size_t>(leaf_index) * tree_learner_->train_data_->num_features() + feature_index] = split_info;
+    splits_per_leaf_[static_cast<size_t>(leaf_index) *
+                         tree_learner_->train_data_->num_features() +
+                     feature_index] = split_info;
     return delta;
   }
-  void UpdateLeafBestSplits(Tree* tree, int best_leaf, const SplitInfo* best_split_info, std::vector<SplitInfo>* best_split_per_leaf) {
+  void UpdateLeafBestSplits(Tree* tree, int best_leaf,
+                            const SplitInfo* best_split_info,
+                            std::vector<SplitInfo>* best_split_per_leaf) {
     auto config = tree_learner_->config_;
     auto train_data = tree_learner_->train_data_;
-    const int inner_feature_index = train_data->InnerFeatureIndex(best_split_info->feature);
+    const int inner_feature_index =
+        train_data->InnerFeatureIndex(best_split_info->feature);
     auto& ref_best_split_per_leaf = *best_split_per_leaf;
-    if (!config->cegb_penalty_feature_coupled.empty() && !is_feature_used_in_split_[inner_feature_index]) {
+    if (!config->cegb_penalty_feature_coupled.empty() &&
+        !is_feature_used_in_split_[inner_feature_index]) {
       is_feature_used_in_split_[inner_feature_index] = true;
       for (int i = 0; i < tree->num_leaves(); ++i) {
         if (i == best_leaf) continue;
-        auto split = &splits_per_leaf_[static_cast<size_t>(i) * train_data->num_features() + inner_feature_index];
-        split->gain += config->cegb_tradeoff * config->cegb_penalty_feature_coupled[best_split_info->feature];
+        auto split = &splits_per_leaf_[static_cast<size_t>(i) *
+                                           train_data->num_features() +
+                                       inner_feature_index];
+        split->gain +=
+            config->cegb_tradeoff *
+            config->cegb_penalty_feature_coupled[best_split_info->feature];
         // Avoid to update the leaf that cannot split
         if (ref_best_split_per_leaf[i].gain > kMinScore &&
             *split > ref_best_split_per_leaf[i]) {
@@ -79,36 +110,45 @@ class CostEfficientGradientBoosting {
     }
     if (!config->cegb_penalty_feature_lazy.empty()) {
       data_size_t cnt_leaf_data = 0;
-      auto tmp_idx = tree_learner_->data_partition_->GetIndexOnLeaf(best_leaf, &cnt_leaf_data);
+      auto tmp_idx = tree_learner_->data_partition_->GetIndexOnLeaf(
+          best_leaf, &cnt_leaf_data);
       for (data_size_t i_input = 0; i_input < cnt_leaf_data; ++i_input) {
         int real_idx = tmp_idx[i_input];
-        Common::InsertBitset(&feature_used_in_data_, train_data->num_data() * inner_feature_index + real_idx);
+        Common::InsertBitset(
+            &feature_used_in_data_,
+            train_data->num_data() * inner_feature_index + real_idx);
       }
     }
   }
 
  private:
-  double CalculateOndemandCosts(int feature_index, int real_fidx, int leaf_index) const {
+  double CalculateOndemandCosts(int feature_index, int real_fidx,
+                                int leaf_index) const {
     if (tree_learner_->config_->cegb_penalty_feature_lazy.empty()) {
       return 0.0f;
     }
     auto train_data = tree_learner_->train_data_;
-    double penalty = tree_learner_->config_->cegb_penalty_feature_lazy[real_fidx];
+    double penalty =
+        tree_learner_->config_->cegb_penalty_feature_lazy[real_fidx];
 
     double total = 0.0f;
     data_size_t cnt_leaf_data = 0;
-    auto tmp_idx = tree_learner_->data_partition_->GetIndexOnLeaf(leaf_index, &cnt_leaf_data);
+    auto tmp_idx = tree_learner_->data_partition_->GetIndexOnLeaf(
+        leaf_index, &cnt_leaf_data);
 
     for (data_size_t i_input = 0; i_input < cnt_leaf_data; ++i_input) {
       int real_idx = tmp_idx[i_input];
-      if (Common::FindInBitset(feature_used_in_data_.data(), train_data->num_data() * train_data->num_features(), train_data->num_data() * feature_index + real_idx)) {
+      if (Common::FindInBitset(
+              feature_used_in_data_.data(),
+              train_data->num_data() * train_data->num_features(),
+              train_data->num_data() * feature_index + real_idx)) {
         continue;
       }
       total += penalty;
     }
     return total;
   }
-
+  bool init_;
   const SerialTreeLearner* tree_learner_;
   std::vector<SplitInfo> splits_per_leaf_;
   std::vector<bool> is_feature_used_in_split_;

src/treelearner/serial_tree_learner.cpp

@@ -141,7 +141,9 @@ void SerialTreeLearner::ResetConfig(const Config* config) {
   col_sampler_.SetConfig(config_);
   histogram_pool_.ResetConfig(train_data_, config_);
   if (CostEfficientGradientBoosting::IsEnable(config_)) {
-    cegb_.reset(new CostEfficientGradientBoosting(this));
+    if (cegb_ == nullptr) {
+      cegb_.reset(new CostEfficientGradientBoosting(this));
+    }
     cegb_->Init();
   }
   constraints_.reset(LeafConstraintsBase::Create(config_, config_->num_leaves));