Categorical feature support (#108)

Categorical feature support (#108)

src/io/sparse_bin.hpp

@@ -100,7 +100,7 @@ public:
     }
   }
 
-  data_size_t Split(unsigned int threshold, data_size_t* data_indices, data_size_t num_data,
+  virtual data_size_t Split(unsigned int threshold, data_size_t* data_indices, data_size_t num_data,
     data_size_t* lte_indices, data_size_t* gt_indices) const override {
     // not need to split
     if (num_data <= 0) { return 0; }
@@ -261,7 +261,7 @@ public:
 
   }
 
-private:
+protected:
   data_size_t num_data_;
   std::vector<std::pair<data_size_t, VAL_T>> non_zero_pair_;
   std::vector<uint8_t> deltas_;
@@ -299,6 +299,33 @@ BinIterator* SparseBin<VAL_T>::GetIterator(data_size_t start_idx) const {
 }
 
 
+template <typename VAL_T>
+class SparseCategoricalBin: public SparseBin<VAL_T> {
+public:
+  SparseCategoricalBin(data_size_t num_data, int default_bin)
+    : SparseBin<VAL_T>(num_data, default_bin) {
+  }
+
+  virtual data_size_t Split(unsigned int threshold, data_size_t* data_indices, data_size_t num_data,
+    data_size_t* lte_indices, data_size_t* gt_indices) const override {
+    // not need to split
+    if (num_data <= 0) { return 0; }
+    SparseBinIterator<VAL_T> iterator(this, data_indices[0]);
+    data_size_t lte_count = 0;
+    data_size_t gt_count = 0;
+    for (data_size_t i = 0; i < num_data; ++i) {
+      const data_size_t idx = data_indices[i];
+      VAL_T bin = iterator.InnerGet(idx);
+      if (bin != threshold) {
+        gt_indices[gt_count++] = idx;
+      } else {
+        lte_indices[lte_count++] = idx;
+      }
+    }
+    return lte_count;
+  }
+};
+
 
 }  // namespace LightGBM
 #endif   // LightGBM_IO_SPARSE_BIN_HPP_

src/io/tree.cpp

@@ -15,6 +15,12 @@
 
 namespace LightGBM {
 
+std::vector<std::function<bool(unsigned int, unsigned int)>> Tree::inner_decision_funs = 
+          {Tree::NumericalDecision<unsigned int>, Tree::CategoricalDecision<unsigned int> };
+std::vector<std::function<bool(double, double)>> Tree::decision_funs = 
+          { Tree::NumericalDecision<double>, Tree::CategoricalDecision<double> };
+
+
 Tree::Tree(int max_leaves)
   :max_leaves_(max_leaves) {
 
@@ -25,10 +31,13 @@ Tree::Tree(int max_leaves)
   split_feature_real_ = std::vector<int>(max_leaves_ - 1);
   threshold_in_bin_ = std::vector<unsigned int>(max_leaves_ - 1);
   threshold_ = std::vector<double>(max_leaves_ - 1);
+  decision_type_ = std::vector<int8_t>(max_leaves_ - 1);
   split_gain_ = std::vector<double>(max_leaves_ - 1);
   leaf_parent_ = std::vector<int>(max_leaves_);
   leaf_value_ = std::vector<double>(max_leaves_);
+  leaf_count_ = std::vector<data_size_t>(max_leaves_);
   internal_value_ = std::vector<double>(max_leaves_ - 1);
+  internal_count_ = std::vector<data_size_t>(max_leaves_ - 1);
   leaf_depth_ = std::vector<int>(max_leaves_);
   // root is in the depth 1
   leaf_depth_[0] = 1;
@@ -39,8 +48,9 @@ Tree::~Tree() {
 
 }
 
-int Tree::Split(int leaf, int feature, unsigned int threshold_bin, int real_feature,
-  double threshold, double left_value, double right_value, double gain) {
+int Tree::Split(int leaf, int feature, BinType bin_type, unsigned int threshold_bin, int real_feature,
+    double threshold_double, double left_value,
+    double right_value, data_size_t left_cnt, data_size_t right_cnt, double gain) {
   int new_node_idx = num_leaves_ - 1;
   // update parent info
   int parent = leaf_parent_[leaf];
@@ -56,7 +66,12 @@ int Tree::Split(int leaf, int feature, unsigned int threshold_bin, int real_feat
   split_feature_[new_node_idx] = feature;
   split_feature_real_[new_node_idx] = real_feature;
   threshold_in_bin_[new_node_idx] = threshold_bin;
-  threshold_[new_node_idx] = threshold;
+  threshold_[new_node_idx] = threshold_double;
+  if (bin_type == BinType::NumericalBin) {
+    decision_type_[new_node_idx] = 0;
+  } else {
+    decision_type_[new_node_idx] = 1;
+  }
   split_gain_[new_node_idx] = gain;
   // add two new leaves
   left_child_[new_node_idx] = ~leaf;
@@ -66,8 +81,11 @@ int Tree::Split(int leaf, int feature, unsigned int threshold_bin, int real_feat
   leaf_parent_[num_leaves_] = new_node_idx;
   // save current leaf value to internal node before change
   internal_value_[new_node_idx] = leaf_value_[leaf];
+  internal_count_[new_node_idx] = left_cnt + right_cnt;
   leaf_value_[leaf] = left_value;
+  leaf_count_[leaf] = left_cnt;
   leaf_value_[num_leaves_] = right_value;
+  leaf_count_[num_leaves_] = right_cnt;
   // update leaf depth
   leaf_depth_[num_leaves_] = leaf_depth_[leaf] + 1;
   leaf_depth_[leaf]++;
@@ -106,21 +124,27 @@ std::string Tree::ToString() {
   std::stringstream ss;
   ss << "num_leaves=" << num_leaves_ << std::endl;
   ss << "split_feature="
-    << Common::ArrayToString<int>(split_feature_real_.data(), num_leaves_ - 1, ' ') << std::endl;
+    << Common::ArrayToString<int>(split_feature_real_, ' ') << std::endl;
   ss << "split_gain="
-    << Common::ArrayToString<double>(split_gain_.data(), num_leaves_ - 1, ' ') << std::endl;
+    << Common::ArrayToString<double>(split_gain_, ' ') << std::endl;
   ss << "threshold="
-    << Common::ArrayToString<double>(threshold_.data(), num_leaves_ - 1, ' ') << std::endl;
+    << Common::ArrayToString<double>(threshold_, ' ') << std::endl;
+  ss << "decision_type="
+    << Common::ArrayToString<int>(Common::ArrayCast<int8_t, int>(decision_type_), ' ') << std::endl;
   ss << "left_child="
-    << Common::ArrayToString<int>(left_child_.data(), num_leaves_ - 1, ' ') << std::endl;
+    << Common::ArrayToString<int>(left_child_, ' ') << std::endl;
   ss << "right_child="
-    << Common::ArrayToString<int>(right_child_.data(), num_leaves_ - 1, ' ') << std::endl;
+    << Common::ArrayToString<int>(right_child_, ' ') << std::endl;
   ss << "leaf_parent="
-    << Common::ArrayToString<int>(leaf_parent_.data(), num_leaves_, ' ') << std::endl;
+    << Common::ArrayToString<int>(leaf_parent_, ' ') << std::endl;
   ss << "leaf_value="
-    << Common::ArrayToString<double>(leaf_value_.data(), num_leaves_, ' ') << std::endl;
+    << Common::ArrayToString<double>(leaf_value_, ' ') << std::endl;
+  ss << "leaf_count="
+    << Common::ArrayToString<data_size_t>(leaf_count_, ' ') << std::endl;
   ss << "internal_value="
-    << Common::ArrayToString<double>(internal_value_.data(), num_leaves_ - 1, ' ') << std::endl;
+    << Common::ArrayToString<double>(internal_value_, ' ') << std::endl;
+  ss << "internal_count="
+    << Common::ArrayToString<data_size_t>(internal_count_, ' ') << std::endl;
   ss << std::endl;
   return ss.str();
 }
@@ -142,20 +166,23 @@ std::string Tree::NodeToJSON(int index) {
     // non-leaf
     ss << "{" << std::endl;
     ss << "\"split_index\":" << index << "," << std::endl;
-    ss << "\"split_feature\":" << split_feature_real_.data()[index] << "," << std::endl;
-    ss << "\"split_gain\":" << split_gain_.data()[index] << "," << std::endl;
-    ss << "\"threshold\":" << threshold_.data()[index] << "," << std::endl;
-    ss << "\"internal_value\":" << internal_value_.data()[index] << "," << std::endl;
-    ss << "\"left_child\":" << NodeToJSON(left_child_.data()[index]) << "," << std::endl;
-    ss << "\"right_child\":" << NodeToJSON(right_child_.data()[index]) << std::endl;
+    ss << "\"split_feature\":" << split_feature_real_[index] << "," << std::endl;
+    ss << "\"split_gain\":" << split_gain_[index] << "," << std::endl;
+    ss << "\"threshold\":" << threshold_[index] << "," << std::endl;
+    ss << "\"decision_type\":\"" << Tree::GetDecisionTypeName(decision_type_[index]) << "\"," << std::endl;
+    ss << "\"internal_value\":" << internal_value_[index] << "," << std::endl;
+    ss << "\"internal_count\":" << internal_count_[index] << "," << std::endl;
+    ss << "\"left_child\":" << NodeToJSON(left_child_[index]) << "," << std::endl;
+    ss << "\"right_child\":" << NodeToJSON(right_child_[index]) << std::endl;
     ss << "}";
   } else {
     // leaf
     index = ~index;
     ss << "{" << std::endl;
     ss << "\"leaf_index\":" << index << "," << std::endl;
-    ss << "\"leaf_parent\":" << leaf_parent_.data()[index] << "," << std::endl;
-    ss << "\"leaf_value\":" << leaf_value_.data()[index] << std::endl;
+    ss << "\"leaf_parent\":" << leaf_parent_[index] << "," << std::endl;
+    ss << "\"leaf_value\":" << leaf_value_[index] << "," << std::endl;
+    ss << "\"leaf_count\":" << leaf_count_[index] << std::endl;
     ss << "}";
   }
 
@@ -179,37 +206,29 @@ Tree::Tree(const std::string& str) {
     || key_vals.count("split_gain") <= 0 || key_vals.count("threshold") <= 0
     || key_vals.count("left_child") <= 0 || key_vals.count("right_child") <= 0
     || key_vals.count("leaf_parent") <= 0 || key_vals.count("leaf_value") <= 0
-    || key_vals.count("internal_value") <= 0) {
+    || key_vals.count("internal_value") <= 0 || key_vals.count("internal_count") <= 0
+    || key_vals.count("leaf_count") <= 0 || key_vals.count("decision_type") <= 0
+    ) {
     Log::Fatal("Tree model string format error");
   }
 
   Common::Atoi(key_vals["num_leaves"].c_str(), &num_leaves_);
 
-  left_child_ = std::vector<int>(num_leaves_ - 1);
-  right_child_ = std::vector<int>(num_leaves_ - 1);
-  split_feature_real_ = std::vector<int>(num_leaves_ - 1);
-  threshold_ = std::vector<double>(num_leaves_ - 1);
-  split_gain_ = std::vector<double>(num_leaves_ - 1);
-  leaf_parent_ = std::vector<int>(num_leaves_);
-  leaf_value_ = std::vector<double>(num_leaves_);
-  internal_value_ = std::vector<double>(num_leaves_ - 1);
-
-  Common::StringToIntArray(key_vals["split_feature"], ' ',
-                           num_leaves_ - 1, split_feature_real_.data());
-  Common::StringToDoubleArray(key_vals["split_gain"], ' ',
-                              num_leaves_ - 1, split_gain_.data());
-  Common::StringToDoubleArray(key_vals["threshold"], ' ',
-                              num_leaves_ - 1, threshold_.data());
-  Common::StringToIntArray(key_vals["left_child"], ' ',
-                           num_leaves_ - 1, left_child_.data());
-  Common::StringToIntArray(key_vals["right_child"], ' ',
-                           num_leaves_ - 1, right_child_.data());
-  Common::StringToIntArray(key_vals["leaf_parent"], ' ',
-                           num_leaves_ , leaf_parent_.data());
-  Common::StringToDoubleArray(key_vals["leaf_value"], ' ',
-                              num_leaves_ , leaf_value_.data());
-  Common::StringToDoubleArray(key_vals["internal_value"], ' ',
-                              num_leaves_ - 1 , internal_value_.data());
+  left_child_ = Common::StringToArray<int>(key_vals["left_child"], ' ', num_leaves_ - 1);
+  right_child_ = Common::StringToArray<int>(key_vals["right_child"], ' ', num_leaves_ - 1);
+  split_feature_real_ = Common::StringToArray<int>(key_vals["split_feature"], ' ', num_leaves_ - 1);
+  threshold_ = Common::StringToArray<double>(key_vals["threshold"], ' ', num_leaves_ - 1);
+  split_gain_ = Common::StringToArray<double>(key_vals["split_gain"], ' ', num_leaves_ - 1);
+  internal_count_ = Common::StringToArray<data_size_t>(key_vals["internal_count"], ' ', num_leaves_ - 1);
+  internal_value_ = Common::StringToArray<double>(key_vals["internal_value"], ' ', num_leaves_ - 1);
+  decision_type_ = Common::StringToArray<int8_t>(key_vals["decision_type"], ' ', num_leaves_ - 1);
+
+  leaf_count_ = Common::StringToArray<data_size_t>(key_vals["leaf_count"], ' ', num_leaves_);
+  leaf_parent_ = Common::StringToArray<int>(key_vals["leaf_parent"], ' ', num_leaves_);
+  leaf_value_ = Common::StringToArray<double>(key_vals["leaf_value"], ' ', num_leaves_);
+
+
+
 }
 
 }  // namespace LightGBM

src/treelearner/data_parallel_tree_learner.cpp

@@ -37,7 +37,7 @@ void DataParallelTreeLearner::Init(const Dataset* train_data) {
 
   buffer_write_start_pos_.resize(num_features_);
   buffer_read_start_pos_.resize(num_features_);
-  global_data_count_in_leaf_.resize(num_leaves_);
+  global_data_count_in_leaf_.resize(tree_config_.num_leaves);
 }
 
 

src/treelearner/feature_histogram.hpp

@@ -28,17 +28,17 @@ public:
   * \param feature the feature data for this histogram
   * \param min_num_data_one_leaf minimal number of data in one leaf
   */
-  void Init(const Feature* feature, int feature_idx, data_size_t min_num_data_one_leaf,
-    double min_sum_hessian_one_leaf, double lambda_l1, double lambda_l2, double min_gain_to_split) {
+  void Init(const Feature* feature, int feature_idx, const TreeConfig* tree_config) {
     feature_idx_ = feature_idx;
-    min_num_data_one_leaf_ = min_num_data_one_leaf;
-    min_sum_hessian_one_leaf_ = min_sum_hessian_one_leaf;
-    lambda_l1_ = lambda_l1;
-    lambda_l2_ = lambda_l2;
-    min_gain_to_split_ = min_gain_to_split;
+    tree_config_ = tree_config;
     bin_data_ = feature->bin_data();
     num_bins_ = feature->num_bin();
     data_.resize(num_bins_);
+    if (feature->bin_type() == BinType::NumericalBin) {
+      find_best_threshold_fun_ = std::bind(&FeatureHistogram::FindBestThresholdForNumerical, this, std::placeholders::_1);
+    } else {
+      find_best_threshold_fun_ = std::bind(&FeatureHistogram::FindBestThresholdForCategorical, this, std::placeholders::_1);
+    }
   }
 
 
@@ -110,6 +110,10 @@ public:
   * \param output The best split result
   */
   void FindBestThreshold(SplitInfo* output) {
+    find_best_threshold_fun_(output);
+  }
+
+  void FindBestThresholdForNumerical(SplitInfo* output) {
     double best_sum_left_gradient = NAN;
     double best_sum_left_hessian = NAN;
     double best_gain = kMinScore;
@@ -119,7 +123,7 @@ public:
     double sum_right_hessian = kEpsilon;
     data_size_t right_count = 0;
     double gain_shift = GetLeafSplitGain(sum_gradients_, sum_hessians_);
-    double min_gain_shift = gain_shift + min_gain_to_split_;
+    double min_gain_shift = gain_shift + tree_config_->min_gain_to_split;
     is_splittable_ = false;
     // from right to left, and we don't need data in bin0
     for (unsigned int t = num_bins_ - 1; t > 0; --t) {
@@ -127,18 +131,20 @@ public:
       sum_right_hessian += data_[t].sum_hessians;
       right_count += data_[t].cnt;
       // if data not enough, or sum hessian too small
-      if (right_count < min_num_data_one_leaf_ || sum_right_hessian < min_sum_hessian_one_leaf_) continue;
+      if (right_count < tree_config_->min_data_in_leaf 
+          || sum_right_hessian < tree_config_->min_sum_hessian_in_leaf) continue;
       data_size_t left_count = num_data_ - right_count;
       // if data not enough
-      if (left_count < min_num_data_one_leaf_) break;
+      if (left_count < tree_config_->min_data_in_leaf) break;
 
       double sum_left_hessian = sum_hessians_ - sum_right_hessian;
       // if sum hessian too small
-      if (sum_left_hessian < min_sum_hessian_one_leaf_) break;
+      if (sum_left_hessian < tree_config_->min_sum_hessian_in_leaf) break;
 
       double sum_left_gradient = sum_gradients_ - sum_right_gradient;
       // current split gain
-      double current_gain = GetLeafSplitGain(sum_left_gradient, sum_left_hessian) + GetLeafSplitGain(sum_right_gradient, sum_right_hessian);
+      double current_gain = GetLeafSplitGain(sum_left_gradient, sum_left_hessian)
+        + GetLeafSplitGain(sum_right_gradient, sum_right_hessian);
       // gain with split is worse than without split
       if (current_gain < min_gain_shift) continue;
 
@@ -154,6 +160,7 @@ public:
         best_gain = current_gain;
       }
     }
+    if (is_splittable_) {
       // update split information
       output->feature = feature_idx_;
       output->threshold = best_threshold;
@@ -167,6 +174,75 @@ public:
       output->right_sum_gradient = sum_gradients_ - best_sum_left_gradient;
       output->right_sum_hessian = sum_hessians_ - best_sum_left_hessian;
       output->gain = best_gain - gain_shift;
+    } else {
+      output->feature = feature_idx_;
+      output->gain = kMinScore;
+    }
+  }
+
+  /*!
+  * \brief Find best threshold for this histogram
+  * \param output The best split result
+  */
+  void FindBestThresholdForCategorical(SplitInfo* output) {
+    double best_gain = kMinScore;
+    unsigned int best_threshold = static_cast<unsigned int>(num_bins_);
+
+    double gain_shift = GetLeafSplitGain(sum_gradients_, sum_hessians_);
+    double min_gain_shift = gain_shift + tree_config_->min_gain_to_split;
+    is_splittable_ = false;
+
+    for (int t = num_bins_ - 1; t >= 0; --t) {
+      double sum_current_gradient = data_[t].sum_gradients;
+      double sum_current_hessian = data_[t].sum_hessians;
+      data_size_t current_count = data_[t].cnt;
+      // if data not enough, or sum hessian too small
+      if (current_count < tree_config_->min_data_in_leaf
+          || sum_current_hessian < tree_config_->min_sum_hessian_in_leaf) continue;
+      data_size_t other_count = num_data_ - current_count;
+      // if data not enough
+      if (other_count < tree_config_->min_data_in_leaf) continue;
+
+      double sum_other_hessian = sum_hessians_ - sum_current_hessian;
+      // if sum hessian too small
+      if (sum_other_hessian < tree_config_->min_sum_hessian_in_leaf) continue;
+
+      double sum_other_gradient = sum_gradients_ - sum_current_gradient;
+      // current split gain
+      double current_gain = GetLeafSplitGain(sum_other_gradient, sum_other_hessian) 
+        + GetLeafSplitGain(sum_current_gradient, sum_current_hessian);
+      // gain with split is worse than without split
+      if (current_gain < min_gain_shift) continue;
+
+      // mark to is splittable
+      is_splittable_ = true;
+      // better split point
+      if (current_gain > best_gain) {
+        best_threshold = static_cast<unsigned int>(t);
+        best_gain = current_gain;
+      }
+    }
+    // update split information
+    if (is_splittable_) {
+      output->feature = feature_idx_;
+      output->threshold = best_threshold;
+      output->left_output = CalculateSplittedLeafOutput(data_[best_threshold].sum_gradients,
+        data_[best_threshold].sum_hessians);
+      output->left_count = data_[best_threshold].cnt;
+      output->left_sum_gradient = data_[best_threshold].sum_gradients;
+      output->left_sum_hessian = data_[best_threshold].sum_hessians;
+
+      output->right_output = CalculateSplittedLeafOutput(sum_gradients_ - data_[best_threshold].sum_gradients,
+        sum_hessians_ - data_[best_threshold].sum_hessians);
+      output->right_count = num_data_ - data_[best_threshold].cnt;
+      output->right_sum_gradient = sum_gradients_ - data_[best_threshold].sum_gradients;
+      output->right_sum_hessian = sum_hessians_ - data_[best_threshold].sum_hessians;
+
+      output->gain = best_gain - gain_shift;
+    } else {
+      output->feature = feature_idx_;
+      output->gain = kMinScore;
+    }
   }
 
   /*!
@@ -190,20 +266,6 @@ public:
     std::memcpy(data_.data(), memory_data, num_bins_ * sizeof(HistogramBinEntry));
   }
 
-  /*!
-  * \brief Set min number data in one leaf
-  */
-  void SetMinNumDataOneLeaf(data_size_t new_val) {
-    min_num_data_one_leaf_ = new_val;
-  }
-
-  /*!
-  * \brief Set min sum hessian in one leaf
-  */
-  void SetMinSumHessianOneLeaf(double new_val) {
-    min_sum_hessian_one_leaf_ = new_val;
-  }
-
   /*!
   * \brief True if this histogram can be splitted
   */
@@ -223,9 +285,10 @@ private:
   */
   double GetLeafSplitGain(double sum_gradients, double sum_hessians) const {
     double abs_sum_gradients = std::fabs(sum_gradients);
-    if (abs_sum_gradients > lambda_l1_) {
-      double reg_abs_sum_gradients = abs_sum_gradients - lambda_l1_;
-      return (reg_abs_sum_gradients * reg_abs_sum_gradients) / (sum_hessians + lambda_l2_);
+    if (abs_sum_gradients > tree_config_->lambda_l1) {
+      double reg_abs_sum_gradients = abs_sum_gradients - tree_config_->lambda_l1;
+      return (reg_abs_sum_gradients * reg_abs_sum_gradients) 
+             / (sum_hessians + tree_config_->lambda_l2);
     }
     return 0.0f;
   }
@@ -238,23 +301,16 @@ private:
   */
   double CalculateSplittedLeafOutput(double sum_gradients, double sum_hessians) const {
     double abs_sum_gradients = std::fabs(sum_gradients);
-    if (abs_sum_gradients > lambda_l1_) {
-      return -std::copysign(abs_sum_gradients - lambda_l1_, sum_gradients) / (sum_hessians + lambda_l2_);
+    if (abs_sum_gradients > tree_config_->lambda_l1) {
+      return -std::copysign(abs_sum_gradients - tree_config_->lambda_l1, sum_gradients) 
+                            / (sum_hessians + tree_config_->lambda_l2);
     }
     return 0.0f;
   }
 
   int feature_idx_;
-  /*! \brief minimal number of data in one leaf */
-  data_size_t min_num_data_one_leaf_;
-  /*! \brief minimal sum hessian of data in one leaf */
-  double min_sum_hessian_one_leaf_;
-  /*! \brief lambda of the L1 weights regularization */
-  double lambda_l1_;
-  /*! \brief lambda of the L2 weights regularization */
-  double lambda_l2_;
-  /*! \brief minimal gain (loss reduction) to split */
-  double min_gain_to_split_;
+  /*! \brief pointer of tree config */
+  const TreeConfig* tree_config_;
   /*! \brief the bin data of current feature */
   const Bin* bin_data_;
   /*! \brief number of bin of histogram */
@@ -269,6 +325,8 @@ private:
   double sum_hessians_;
   /*! \brief False if this histogram cannot split */
   bool is_splittable_ = true;
+  /*! \brief function that used to find best threshold */
+  std::function<void(SplitInfo*)> find_best_threshold_fun_;
 };
 
 

src/treelearner/serial_tree_learner.cpp

@@ -7,18 +7,9 @@
 
 namespace LightGBM {
 
-SerialTreeLearner::SerialTreeLearner(const TreeConfig& tree_config) {
-  // initialize with nullptr
-  num_leaves_ = tree_config.num_leaves;
-  min_num_data_one_leaf_ = static_cast<data_size_t>(tree_config.min_data_in_leaf);
-  min_sum_hessian_one_leaf_ = static_cast<double>(tree_config.min_sum_hessian_in_leaf);
-  lambda_l1_ = tree_config.lambda_l1;
-  lambda_l2_ = tree_config.lambda_l2;
-  min_gain_to_split_ = tree_config.min_gain_to_split;
-  feature_fraction_ = tree_config.feature_fraction;
+SerialTreeLearner::SerialTreeLearner(const TreeConfig& tree_config)
+  :tree_config_(tree_config){
   random_ = Random(tree_config.feature_fraction_seed);
-  histogram_pool_size_ = tree_config.histogram_pool_size;
-  max_depth_ = tree_config.max_depth;
 }
 
 SerialTreeLearner::~SerialTreeLearner() {
@@ -31,36 +22,32 @@ void SerialTreeLearner::Init(const Dataset* train_data) {
   num_features_ = train_data_->num_features();
   int max_cache_size = 0;
   // Get the max size of pool
-  if (histogram_pool_size_ < 0) {
-    max_cache_size = num_leaves_;
+  if (tree_config_.histogram_pool_size < 0) {
+    max_cache_size = tree_config_.num_leaves;
   } else {
     size_t total_histogram_size = 0;
     for (int i = 0; i < train_data_->num_features(); ++i) {
       total_histogram_size += sizeof(HistogramBinEntry) * train_data_->FeatureAt(i)->num_bin();
     }
-    max_cache_size = static_cast<int>(histogram_pool_size_ * 1024 * 1024 / total_histogram_size);
+    max_cache_size = static_cast<int>(tree_config_.histogram_pool_size * 1024 * 1024 / total_histogram_size);
   }
   // at least need 2 leaves
   max_cache_size = std::max(2, max_cache_size);
-  max_cache_size = std::min(max_cache_size, num_leaves_);
-  histogram_pool_.ResetSize(max_cache_size, num_leaves_);
+  max_cache_size = std::min(max_cache_size, tree_config_.num_leaves);
+  histogram_pool_.ResetSize(max_cache_size, tree_config_.num_leaves);
 
   auto histogram_create_function = [this]() {
     auto tmp_histogram_array = std::unique_ptr<FeatureHistogram[]>(new FeatureHistogram[train_data_->num_features()]);
     for (int j = 0; j < train_data_->num_features(); ++j) {
       tmp_histogram_array[j].Init(train_data_->FeatureAt(j),
-        j, min_num_data_one_leaf_,
-        min_sum_hessian_one_leaf_,
-        lambda_l1_,
-        lambda_l2_,
-        min_gain_to_split_);
+        j, &tree_config_);
     }
     return tmp_histogram_array.release();
   };
   histogram_pool_.Fill(histogram_create_function);
 
   // push split information for all leaves
-  best_split_per_leaf_.resize(num_leaves_);
+  best_split_per_leaf_.resize(tree_config_.num_leaves);
   // initialize ordered_bins_ with nullptr
   ordered_bins_.resize(num_features_);
 
@@ -82,7 +69,7 @@ void SerialTreeLearner::Init(const Dataset* train_data) {
   larger_leaf_splits_.reset(new LeafSplits(train_data_->num_features(), train_data_->num_data()));
 
   // initialize data partition
-  data_partition_.reset(new DataPartition(num_data_, num_leaves_));
+  data_partition_.reset(new DataPartition(num_data_, tree_config_.num_leaves));
 
   is_feature_used_.resize(num_features_);
 
@@ -102,14 +89,14 @@ Tree* SerialTreeLearner::Train(const score_t* gradients, const score_t *hessians
   hessians_ = hessians;
   // some initial works before training
   BeforeTrain();
-  auto tree = std::unique_ptr<Tree>(new Tree(num_leaves_));
+  auto tree = std::unique_ptr<Tree>(new Tree(tree_config_.num_leaves));
   // save pointer to last trained tree
   last_trained_tree_ = tree.get();
   // root leaf
   int left_leaf = 0;
   // only root leaf can be splitted on first time
   int right_leaf = -1;
-  for (int split = 0; split < num_leaves_ - 1; split++) {
+  for (int split = 0; split < tree_config_.num_leaves - 1; split++) {
     // some initial works before finding best split
     if (BeforeFindBestSplit(left_leaf, right_leaf)) {
       // find best threshold for every feature
@@ -141,7 +128,7 @@ void SerialTreeLearner::BeforeTrain() {
     is_feature_used_[i] = false;
   }
   // Get used feature at current tree
-  int used_feature_cnt = static_cast<int>(num_features_*feature_fraction_);
+  int used_feature_cnt = static_cast<int>(num_features_*tree_config_.feature_fraction);
   auto used_feature_indices = random_.Sample(num_features_, used_feature_cnt);
   for (auto idx : used_feature_indices) {
     is_feature_used_[idx] = true;
@@ -151,7 +138,7 @@ void SerialTreeLearner::BeforeTrain() {
   data_partition_->Init();
 
   // reset the splits for leaves
-  for (int i = 0; i < num_leaves_; ++i) {
+  for (int i = 0; i < tree_config_.num_leaves; ++i) {
     best_split_per_leaf_[i].Reset();
   }
 
@@ -190,7 +177,7 @@ void SerialTreeLearner::BeforeTrain() {
       #pragma omp parallel for schedule(guided)
       for (int i = 0; i < num_features_; ++i) {
         if (ordered_bins_[i] != nullptr) {
-          ordered_bins_[i]->Init(nullptr, num_leaves_);
+          ordered_bins_[i]->Init(nullptr, tree_config_.num_leaves);
         }
       }
     } else {
@@ -209,7 +196,7 @@ void SerialTreeLearner::BeforeTrain() {
       #pragma omp parallel for schedule(guided)
       for (int i = 0; i < num_features_; ++i) {
         if (ordered_bins_[i] != nullptr) {
-          ordered_bins_[i]->Init(is_data_in_leaf_.data(), num_leaves_);
+          ordered_bins_[i]->Init(is_data_in_leaf_.data(), tree_config_.num_leaves);
         }
       }
     }
@@ -218,9 +205,9 @@ void SerialTreeLearner::BeforeTrain() {
 
 bool SerialTreeLearner::BeforeFindBestSplit(int left_leaf, int right_leaf) {
   // check depth of current leaf
-  if (max_depth_ > 0) {
+  if (tree_config_.max_depth > 0) {
     // only need to check left leaf, since right leaf is in same level of left leaf
-    if (last_trained_tree_->leaf_depth(left_leaf) >= max_depth_) {
+    if (last_trained_tree_->leaf_depth(left_leaf) >= tree_config_.max_depth) {
       best_split_per_leaf_[left_leaf].gain = kMinScore;
       if (right_leaf >= 0) {
         best_split_per_leaf_[right_leaf].gain = kMinScore;
@@ -231,8 +218,8 @@ bool SerialTreeLearner::BeforeFindBestSplit(int left_leaf, int right_leaf) {
   data_size_t num_data_in_left_child = GetGlobalDataCountInLeaf(left_leaf);
   data_size_t num_data_in_right_child = GetGlobalDataCountInLeaf(right_leaf);
   // no enough data to continue
-  if (num_data_in_right_child < static_cast<data_size_t>(min_num_data_one_leaf_ * 2)
-    && num_data_in_left_child < static_cast<data_size_t>(min_num_data_one_leaf_ * 2)) {
+  if (num_data_in_right_child < static_cast<data_size_t>(tree_config_.min_data_in_leaf * 2)
+    && num_data_in_left_child < static_cast<data_size_t>(tree_config_.min_data_in_leaf * 2)) {
     best_split_per_leaf_[left_leaf].gain = kMinScore;
     if (right_leaf >= 0) {
       best_split_per_leaf_[right_leaf].gain = kMinScore;
@@ -393,11 +380,15 @@ void SerialTreeLearner::Split(Tree* tree, int best_Leaf, int* left_leaf, int* ri
   // left = parent
   *left_leaf = best_Leaf;
   // split tree, will return right leaf
-  *right_leaf = tree->Split(best_Leaf, best_split_info.feature, best_split_info.threshold,
+  *right_leaf = tree->Split(best_Leaf, best_split_info.feature, 
+    train_data_->FeatureAt(best_split_info.feature)->bin_type(),
+    best_split_info.threshold,
     train_data_->FeatureAt(best_split_info.feature)->feature_index(),
     train_data_->FeatureAt(best_split_info.feature)->BinToValue(best_split_info.threshold),
     static_cast<double>(best_split_info.left_output),
     static_cast<double>(best_split_info.right_output),
+    static_cast<data_size_t>(best_split_info.left_count),
+    static_cast<data_size_t>(best_split_info.right_count),
     static_cast<double>(best_split_info.gain));
 
   // split data partition

src/treelearner/serial_tree_learner.h

@@ -109,20 +109,6 @@ protected:
   const score_t* gradients_;
   /*! \brief hessians of current iteration */
   const score_t* hessians_;
-  /*! \brief number of total leaves */
-  int num_leaves_;
-  /*! \brief minimal data on one leaf */
-  data_size_t min_num_data_one_leaf_;
-  /*! \brief minimal sum hessian on one leaf */
-  double min_sum_hessian_one_leaf_;
-  /*! \brief lambda of the L1 weights regularization */
-  double lambda_l1_;
-  /*! \brief lambda of the L2 weights regularization */
-  double lambda_l2_;
-  /*! \brief minimal gain (loss reduction) to split */
-  double min_gain_to_split_;
-  /*! \brief sub-feature fraction rate */
-  double feature_fraction_;
   /*! \brief training data partition on leaves */
   std::unique_ptr<DataPartition> data_partition_;
   /*! \brief used for generate used features */
@@ -158,19 +144,16 @@ protected:
   const score_t* ptr_to_ordered_gradients_larger_leaf_;
   /*! \brief Pointer to ordered_hessians_, use this to avoid copy at BeforeTrain*/
   const score_t* ptr_to_ordered_hessians_larger_leaf_;
-
   /*! \brief Store ordered bin */
   std::vector<std::unique_ptr<OrderedBin>> ordered_bins_;
   /*! \brief True if has ordered bin */
   bool has_ordered_bin_ = false;
   /*! \brief  is_data_in_leaf_[i] != 0 means i-th data is marked */
   std::vector<char> is_data_in_leaf_;
-  /*! \brief  max cache size(unit:GB) for historical histogram. < 0 means not limit */
-  double histogram_pool_size_;
   /*! \brief used to cache historical histogram to speed up*/
   HistogramPool histogram_pool_;
-  /*! \brief  max depth of tree model */
-  int max_depth_;
+  /*! \brief config of tree learner*/
+  const TreeConfig& tree_config_;
 };
 
 

tests/python_package_test/test_basic.py

@@ -7,6 +7,11 @@ X, Y = datasets.make_classification(n_samples=100000, n_features=100)
 x_train, x_test, y_train, y_test = model_selection.train_test_split(X, Y, test_size=0.1)
 
 train_data = lgb.Dataset(x_train, max_bin=255, label=y_train)
+
+num_features = train_data.num_feature()
+names = ["name_%d" %(i) for i in range(num_features)]
+train_data.set_feature_name(names)
+
 valid_data = train_data.create_valid(x_test, label=y_test)
 
 config={"objective":"binary","metric":"auc", "min_data":1, "num_leaves":15}