code refactoring: cost effective gradient boosting (#2407)

* refactoring

* fix style

* fix style

* Update cost_effective_gradient_boosting.hpp

* Update serial_tree_learner.cpp

* Update serial_tree_learner.h

* fix style

* update vc project

* Update cost_effective_gradient_boosting.hpp

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.
+ */
+#ifndef LIGHTGBM_TREELEARNER_COST_EFFECTIVE_GRADIENT_BOOSTING_HPP_
+#define LIGHTGBM_TREELEARNER_COST_EFFECTIVE_GRADIENT_BOOSTING_HPP_
+
+#include <LightGBM/config.h>
+#include <LightGBM/dataset.h>
+#include <LightGBM/utils/common.h>
+#include <LightGBM/utils/log.h>
+
+#include <vector>
+
+#include "data_partition.hpp"
+#include "serial_tree_learner.h"
+#include "split_info.hpp"
+
+namespace LightGBM {
+
+class CostEfficientGradientBoosting {
+ public:
+  explicit CostEfficientGradientBoosting(const SerialTreeLearner* tree_learner):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()) {
+      return false;
+    } else {
+      return true;
+    }
+  }
+  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 (!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.");
+      }
+      feature_used_in_data_ = Common::EmptyBitset(train_data->num_features() * tree_learner_->num_data_);
+    }
+  }
+  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];
+    }
+    if (!config->cegb_penalty_feature_lazy.empty()) {
+      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;
+    return delta;
+  }
+  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);
+    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];
+        if (*split > best_split_per_leaf->at(i))
+          best_split_per_leaf->at(i) = *split;
+      }
+    }
+    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);
+      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);
+      }
+    }
+  }
+
+ private:
+  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 total = 0.0f;
+    data_size_t cnt_leaf_data = 0;
+    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)) {
+        continue;
+      }
+      total += penalty;
+    }
+    return total;
+  }
+
+  const SerialTreeLearner* tree_learner_;
+  std::vector<SplitInfo> splits_per_leaf_;
+  std::vector<bool> is_feature_used_in_split_;
+  std::vector<uint32_t> feature_used_in_data_;
+};
+
+}  // namespace LightGBM
+
+#endif  // LIGHTGBM_TREELEARNER_COST_EFFECTIVE_GRADIENT_BOOSTING_HPP_

src/treelearner/serial_tree_learner.cpp

@@ -14,6 +14,8 @@
 #include <unordered_map>
 #include <utility>
 
+#include "cost_effective_gradient_boosting.hpp"
+
 namespace LightGBM {
 
 #ifdef TIMETAG
@@ -69,8 +71,6 @@ void SerialTreeLearner::Init(const Dataset* train_data, bool is_constant_hessian
   histogram_pool_.DynamicChangeSize(train_data_, config_, max_cache_size, config_->num_leaves);
   // push split information for all leaves
   best_split_per_leaf_.resize(config_->num_leaves);
-  splits_per_leaf_.resize(config_->num_leaves*train_data_->num_features());
-
   // get ordered bin
   train_data_->CreateOrderedBins(&ordered_bins_);
 
@@ -104,15 +104,9 @@ void SerialTreeLearner::Init(const Dataset* train_data, bool is_constant_hessian
     }
   }
   Log::Info("Number of data: %d, number of used features: %d", num_data_, num_features_);
-  is_feature_used_in_split_.clear();
-  is_feature_used_in_split_.resize(train_data->num_features());
-
-  if (!config_->cegb_penalty_feature_coupled.empty()) {
-    CHECK(config_->cegb_penalty_feature_coupled.size() == static_cast<size_t>(train_data_->num_total_features()));
-  }
-  if (!config_->cegb_penalty_feature_lazy.empty()) {
-    CHECK(config_->cegb_penalty_feature_lazy.size() == static_cast<size_t>(train_data_->num_total_features()));
-    feature_used_in_data = Common::EmptyBitset(train_data->num_features() * num_data_);
+  if (CostEfficientGradientBoosting::IsEnable(config_)) {
+    cegb_.reset(new CostEfficientGradientBoosting(this));
+    cegb_->Init();
   }
 }
 
@@ -139,6 +133,9 @@ void SerialTreeLearner::ResetTrainingData(const Dataset* train_data) {
     is_data_in_leaf_.resize(num_data_);
     std::fill(is_data_in_leaf_.begin(), is_data_in_leaf_.end(), static_cast<char>(0));
   }
+  if (cegb_ != nullptr) {
+    cegb_->Init();
+  }
 }
 
 void SerialTreeLearner::ResetConfig(const Config* config) {
@@ -166,8 +163,11 @@ void SerialTreeLearner::ResetConfig(const Config* config) {
   } else {
     config_ = config;
   }
-
   histogram_pool_.ResetConfig(config_);
+  if (CostEfficientGradientBoosting::IsEnable(config_)) {
+    cegb_.reset(new CostEfficientGradientBoosting(this));
+    cegb_->Init();
+  }
 }
 
 Tree* SerialTreeLearner::Train(const score_t* gradients, const score_t *hessians, bool is_constant_hessian, const Json& forced_split_json) {
@@ -521,28 +521,6 @@ void SerialTreeLearner::ConstructHistograms(const std::vector<int8_t>& is_featur
   #endif
 }
 
-double SerialTreeLearner::CalculateOndemandCosts(int feature_index, int leaf_index) {
-  if (config_->cegb_penalty_feature_lazy.empty())
-    return 0.0f;
-
-  double penalty = config_->cegb_penalty_feature_lazy[feature_index];
-
-  const int inner_fidx = train_data_->InnerFeatureIndex(feature_index);
-
-  double total = 0.0f;
-  data_size_t cnt_leaf_data = 0;
-  auto tmp_idx = 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() * inner_fidx + real_idx))
-      continue;
-    total += penalty;
-  }
-
-  return total;
-}
-
 void SerialTreeLearner::FindBestSplitsFromHistograms(const std::vector<int8_t>& is_feature_used, bool use_subtract) {
   #ifdef TIMETAG
   auto start_time = std::chrono::steady_clock::now();
@@ -576,14 +554,9 @@ void SerialTreeLearner::FindBestSplitsFromHistograms(const std::vector<int8_t>&
       smaller_leaf_splits_->max_constraint(),
       &smaller_split);
     smaller_split.feature = real_fidx;
-    smaller_split.gain -= config_->cegb_tradeoff * config_->cegb_penalty_split * smaller_leaf_splits_->num_data_in_leaf();
-    if (!config_->cegb_penalty_feature_coupled.empty() && !is_feature_used_in_split_[feature_index]) {
-      smaller_split.gain -= config_->cegb_tradeoff * config_->cegb_penalty_feature_coupled[real_fidx];
+    if (cegb_ != nullptr) {
+      smaller_split.gain -= cegb_->DetlaGain(feature_index, real_fidx, smaller_leaf_splits_->LeafIndex(), smaller_leaf_splits_->num_data_in_leaf(), smaller_split);
     }
-    if (!config_->cegb_penalty_feature_lazy.empty()) {
-      smaller_split.gain -= config_->cegb_tradeoff * CalculateOndemandCosts(real_fidx, smaller_leaf_splits_->LeafIndex());
-    }
-    splits_per_leaf_[smaller_leaf_splits_->LeafIndex()*train_data_->num_features() + feature_index] = smaller_split;
     if (smaller_split > smaller_best[tid] && smaller_node_used_features[feature_index]) {
       smaller_best[tid] = smaller_split;
     }
@@ -607,14 +580,9 @@ void SerialTreeLearner::FindBestSplitsFromHistograms(const std::vector<int8_t>&
       larger_leaf_splits_->max_constraint(),
       &larger_split);
     larger_split.feature = real_fidx;
-    larger_split.gain -= config_->cegb_tradeoff * config_->cegb_penalty_split * larger_leaf_splits_->num_data_in_leaf();
-    if (!config_->cegb_penalty_feature_coupled.empty() && !is_feature_used_in_split_[feature_index]) {
-      larger_split.gain -= config_->cegb_tradeoff * config_->cegb_penalty_feature_coupled[real_fidx];
-    }
-    if (!config_->cegb_penalty_feature_lazy.empty()) {
-      larger_split.gain -= config_->cegb_tradeoff*CalculateOndemandCosts(real_fidx, larger_leaf_splits_->LeafIndex());
+    if (cegb_ != nullptr) {
+      larger_split.gain -= cegb_->DetlaGain(feature_index, real_fidx, larger_leaf_splits_->LeafIndex(), larger_leaf_splits_->num_data_in_leaf(), larger_split);
     }
-    splits_per_leaf_[larger_leaf_splits_->LeafIndex()*train_data_->num_features() + feature_index] = larger_split;
     if (larger_split > larger_best[tid] && larger_node_used_features[feature_index]) {
       larger_best[tid] = larger_split;
     }
@@ -803,26 +771,9 @@ int32_t SerialTreeLearner::ForceSplits(Tree* tree, const Json& forced_split_json
 void SerialTreeLearner::Split(Tree* tree, int best_leaf, int* left_leaf, int* right_leaf) {
   const SplitInfo& best_split_info = best_split_per_leaf_[best_leaf];
   const int inner_feature_index = train_data_->InnerFeatureIndex(best_split_info.feature);
-  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_[i*train_data_->num_features() + inner_feature_index];
-      split->gain += config_->cegb_tradeoff*config_->cegb_penalty_feature_coupled[best_split_info.feature];
-      if (*split > best_split_per_leaf_[i])
-        best_split_per_leaf_[i] = *split;
-    }
-  }
-
-  if (!config_->cegb_penalty_feature_lazy.empty()) {
-    data_size_t cnt_leaf_data = 0;
-    auto tmp_idx = 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);
-    }
+  if (cegb_ != nullptr) {
+    cegb_->UpdateLeafBestSplits(tree, best_leaf, &best_split_info, &best_split_per_leaf_);
   }
-
   // left = parent
   *left_leaf = best_leaf;
   bool is_numerical_split = train_data_->FeatureBinMapper(inner_feature_index)->bin_type() == BinType::NumericalBin;

src/treelearner/serial_tree_learner.h

@@ -32,12 +32,14 @@
 using namespace json11;
 
 namespace LightGBM {
-
+/*! \brief forward declaration */
+class CostEfficientGradientBoosting;
 /*!
 * \brief Used for learning a tree by single machine
 */
 class SerialTreeLearner: public TreeLearner {
  public:
+  friend CostEfficientGradientBoosting;
   explicit SerialTreeLearner(const Config* config);
 
   ~SerialTreeLearner();
@@ -116,8 +118,6 @@ class SerialTreeLearner: public TreeLearner {
   */
   inline virtual data_size_t GetGlobalDataCountInLeaf(int leaf_idx) const;
 
-  double CalculateOndemandCosts(int feature_index, int leaf_index);
-
   /*! \brief number of data */
   data_size_t num_data_;
   /*! \brief number of features */
@@ -179,9 +179,7 @@ class SerialTreeLearner: public TreeLearner {
   int num_threads_;
   std::vector<int> ordered_bin_indices_;
   bool is_constant_hessian_;
-
-  std::vector<bool> is_feature_used_in_split_;
-  std::vector<uint32_t> feature_used_in_data;
+  std::unique_ptr<CostEfficientGradientBoosting> cegb_;
 };
 
 inline data_size_t SerialTreeLearner::GetGlobalDataCountInLeaf(int leaf_idx) const {

windows/LightGBM.vcxproj

@@ -240,6 +240,7 @@
     <ClInclude Include="..\src\objective\regression_objective.hpp" />
     <ClInclude Include="..\src\objective\multiclass_objective.hpp" />
     <ClInclude Include="..\src\objective\xentropy_objective.hpp" />
+    <ClInclude Include="..\src\treelearner\cost_effective_gradient_boosting.hpp" />
     <ClInclude Include="..\src\treelearner\data_partition.hpp" />
     <ClInclude Include="..\src\treelearner\feature_histogram.hpp" />
     <ClInclude Include="..\src\treelearner\leaf_splits.hpp" />
@@ -283,4 +284,4 @@
   <Import Project="$(VCTargetsPath)\Microsoft.Cpp.targets" />
   <ImportGroup Label="ExtensionTargets">
   </ImportGroup>
-</Project>
+</Project>
\ No newline at end of file

windows/LightGBM.vcxproj.filters

@@ -207,6 +207,9 @@
     <ClInclude Include="..\include\LightGBM\json11.hpp">
       <Filter>include\LightGBM\utils</Filter>
     </ClInclude>
+    <ClInclude Include="..\src\treelearner\cost_effective_gradient_boosting.hpp">
+      <Filter>src\treelearner</Filter>
+    </ClInclude>
   </ItemGroup>
   <ItemGroup>
     <ClCompile Include="..\src\application\application.cpp">
@@ -303,4 +306,4 @@
       <Filter>src\io</Filter>
     </ClCompile>
   </ItemGroup>
-</Project>
+</Project>
\ No newline at end of file