#include <LightGBM/tree.h>

#include <LightGBM/utils/threading.h>
#include <LightGBM/utils/common.h>

#include <LightGBM/dataset.h>

#include <sstream>
#include <unordered_map>
#include <functional>
#include <vector>
#include <string>
#include <memory>
#include <iomanip>

namespace LightGBM {

std::vector<bool(*)(uint32_t, uint32_t)> Tree::inner_decision_funs =
{ Tree::NumericalDecision<uint32_t>, Tree::CategoricalDecision<uint32_t> };
std::vector<bool(*)(double, double)> Tree::decision_funs =
{ Tree::NumericalDecision<double>, Tree::CategoricalDecision<double> };

Tree::Tree(int max_leaves)
  :max_leaves_(max_leaves) {

  num_leaves_ = 0;
  left_child_.resize(max_leaves_ - 1);
  right_child_.resize(max_leaves_ - 1);
  split_feature_inner_.resize(max_leaves_ - 1);
  split_feature_.resize(max_leaves_ - 1);
  threshold_in_bin_.resize(max_leaves_ - 1);
  threshold_.resize(max_leaves_ - 1);
  decision_type_.resize(max_leaves_ - 1, 0);
  split_gain_.resize(max_leaves_ - 1);
  leaf_parent_.resize(max_leaves_);
  leaf_value_.resize(max_leaves_);
  leaf_count_.resize(max_leaves_);
  internal_value_.resize(max_leaves_ - 1);
  internal_count_.resize(max_leaves_ - 1);
  leaf_depth_.resize(max_leaves_);
  // root is in the depth 0
  leaf_depth_[0] = 0;
  num_leaves_ = 1;
  leaf_parent_[0] = -1;
  shrinkage_ = 1.0f;
  has_categorical_ = false;
}

Tree::~Tree() {

}

int Tree::Split(int leaf, int feature, BinType bin_type, uint32_t 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,
                MissingType missing_type, bool default_left) {
  int new_node_idx = num_leaves_ - 1;
  // update parent info
  int parent = leaf_parent_[leaf];
  if (parent >= 0) {
    // if cur node is left child
    if (left_child_[parent] == ~leaf) {
      left_child_[parent] = new_node_idx;
    } else {
      right_child_[parent] = new_node_idx;
    }
  }
  // add new node
  split_feature_inner_[new_node_idx] = feature;
  split_feature_[new_node_idx] = real_feature;

  decision_type_[new_node_idx] = 0;
  if (bin_type == BinType::NumericalBin) {
    SetDecisionType(&decision_type_[new_node_idx], false, kCategoricalMask);
  } else {
    has_categorical_ = true;
    SetDecisionType(&decision_type_[new_node_idx], true, kCategoricalMask);
  }
  SetDecisionType(&decision_type_[new_node_idx], default_left, kDefaultLeftMask);
  if (missing_type == MissingType::None) {
    SetMissingType(&decision_type_[new_node_idx], 0);
  } else if (missing_type == MissingType::Zero) {
    SetMissingType(&decision_type_[new_node_idx], 1);
  } else if (missing_type == MissingType::NaN) {
    SetMissingType(&decision_type_[new_node_idx], 2);
  }

  threshold_in_bin_[new_node_idx] = threshold_bin;
  threshold_[new_node_idx] = Common::AvoidInf(threshold_double);
  split_gain_[new_node_idx] = Common::AvoidInf(gain);
  // add two new leaves
  left_child_[new_node_idx] = ~leaf;
  right_child_[new_node_idx] = ~num_leaves_;
  // update new leaves
  leaf_parent_[leaf] = new_node_idx;
  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] = std::isnan(left_value) ? 0.0f : left_value;
  leaf_count_[leaf] = left_cnt;
  leaf_value_[num_leaves_] = std::isnan(right_value) ? 0.0f : right_value;
  leaf_count_[num_leaves_] = right_cnt;
  // update leaf depth
  leaf_depth_[num_leaves_] = leaf_depth_[leaf] + 1;
  leaf_depth_[leaf]++;

  ++num_leaves_;
  return num_leaves_ - 1;
}

void Tree::AddPredictionToScore(const Dataset* data, data_size_t num_data, double* score) const {
  if (num_leaves_ <= 1) { return; }
  std::vector<uint32_t> default_bins(num_leaves_ - 1);
  std::vector<uint32_t> max_bins(num_leaves_ - 1);
  for (int i = 0; i < num_leaves_ - 1; ++i) {
    const int fidx = split_feature_inner_[i];
    auto bin_mapper = data->FeatureBinMapper(fidx);
    default_bins[i] = bin_mapper->GetDefaultBin();
    max_bins[i] = bin_mapper->num_bin() - 1;
  }
  if (has_categorical_) {
    if (data->num_features() > num_leaves_ - 1) {
      Threading::For<data_size_t>(0, num_data,
        [this, &data, score, &default_bins, &max_bins](int, data_size_t start, data_size_t end) {
        std::vector<std::unique_ptr<BinIterator>> iter(num_leaves_ - 1);
        for (int i = 0; i < num_leaves_ - 1; ++i) {
          const int fidx = split_feature_inner_[i];
          iter[i].reset(data->FeatureIterator(fidx));
          iter[i]->Reset(start);
        }
        for (data_size_t i = start; i < end; ++i) {
          int node = 0;
          while (node >= 0) {
            uint32_t fval = ConvertMissingValue(iter[node]->Get(i), threshold_in_bin_[node], decision_type_[node], default_bins[node], max_bins[node]);
            if (inner_decision_funs[GetDecisionType(decision_type_[node], kCategoricalMask)](
              fval,
              threshold_in_bin_[node])) {
              node = left_child_[node];
            } else {
              node = right_child_[node];
            }
          }
          score[i] += static_cast<double>(leaf_value_[~node]);
        }
      });
    } else {
      Threading::For<data_size_t>(0, num_data,
        [this, &data, score, &default_bins, &max_bins](int, data_size_t start, data_size_t end) {
        std::vector<std::unique_ptr<BinIterator>> iter(data->num_features());
        for (int i = 0; i < data->num_features(); ++i) {
          iter[i].reset(data->FeatureIterator(i));
          iter[i]->Reset(start);
        }
        for (data_size_t i = start; i < end; ++i) {
          int node = 0;
          while (node >= 0) {
            uint32_t fval = ConvertMissingValue(iter[split_feature_inner_[node]]->Get(i), threshold_in_bin_[node], decision_type_[node], default_bins[node], max_bins[node]);
            if (inner_decision_funs[GetDecisionType(decision_type_[node], kCategoricalMask)](
              fval,
              threshold_in_bin_[node])) {
              node = left_child_[node];
            } else {
              node = right_child_[node];
            }
          }
          score[i] += static_cast<double>(leaf_value_[~node]);
        }
      });
    }
  } else {
    if (data->num_features() > num_leaves_ - 1) {
      Threading::For<data_size_t>(0, num_data,
        [this, &data, score, &default_bins, &max_bins](int, data_size_t start, data_size_t end) {
        std::vector<std::unique_ptr<BinIterator>> iter(num_leaves_ - 1);
        for (int i = 0; i < num_leaves_ - 1; ++i) {
          const int fidx = split_feature_inner_[i];
          iter[i].reset(data->FeatureIterator(fidx));
          iter[i]->Reset(start);
        }
        for (data_size_t i = start; i < end; ++i) {
          int node = 0;
          while (node >= 0) {
            uint32_t fval = ConvertMissingValue(iter[node]->Get(i), threshold_in_bin_[node], decision_type_[node], default_bins[node], max_bins[node]);
            if (fval <= threshold_in_bin_[node]) {
              node = left_child_[node];
            } else {
              node = right_child_[node];
            }
          }
          score[i] += static_cast<double>(leaf_value_[~node]);
        }
      });
    } else {
      Threading::For<data_size_t>(0, num_data,
        [this, &data, score, &default_bins, &max_bins](int, data_size_t start, data_size_t end) {
        std::vector<std::unique_ptr<BinIterator>> iter(data->num_features());
        for (int i = 0; i < data->num_features(); ++i) {
          iter[i].reset(data->FeatureIterator(i));
          iter[i]->Reset(start);
        }
        for (data_size_t i = start; i < end; ++i) {
          int node = 0;
          while (node >= 0) {
            uint32_t fval = ConvertMissingValue(iter[split_feature_inner_[node]]->Get(i), threshold_in_bin_[node], decision_type_[node], default_bins[node], max_bins[node]);
            if (fval <= threshold_in_bin_[node]) {
              node = left_child_[node];
            } else {
              node = right_child_[node];
            }
          }
          score[i] += static_cast<double>(leaf_value_[~node]);
        }
      });
    }
  }
}

void Tree::AddPredictionToScore(const Dataset* data,
  const data_size_t* used_data_indices,
  data_size_t num_data, double* score) const {
  if (num_leaves_ <= 1) { return; }
  std::vector<uint32_t> default_bins(num_leaves_ - 1);
  std::vector<uint32_t> max_bins(num_leaves_ - 1);
  for (int i = 0; i < num_leaves_ - 1; ++i) {
    const int fidx = split_feature_inner_[i];
    auto bin_mapper = data->FeatureBinMapper(fidx);
    default_bins[i] = bin_mapper->GetDefaultBin();
    max_bins[i] = bin_mapper->num_bin() - 1;
  }
  if (has_categorical_) {
    if (data->num_features() > num_leaves_ - 1) {
      Threading::For<data_size_t>(0, num_data,
        [this, data, used_data_indices, score, &default_bins, &max_bins](int, data_size_t start, data_size_t end) {
        std::vector<std::unique_ptr<BinIterator>> iter(num_leaves_ - 1);
        for (int i = 0; i < num_leaves_ - 1; ++i) {
          const int fidx = split_feature_inner_[i];
          iter[i].reset(data->FeatureIterator(fidx));
          iter[i]->Reset(used_data_indices[start]);
        }
        for (data_size_t i = start; i < end; ++i) {
          int node = 0;
          const data_size_t idx = used_data_indices[i];
          while (node >= 0) {
            uint32_t fval = ConvertMissingValue(iter[node]->Get(idx), threshold_in_bin_[node], decision_type_[node], default_bins[node], max_bins[node]);
            if (inner_decision_funs[GetDecisionType(decision_type_[node], kCategoricalMask)](
              fval,
              threshold_in_bin_[node])) {
              node = left_child_[node];
            } else {
              node = right_child_[node];
            }
          }
          score[idx] += static_cast<double>(leaf_value_[~node]);
        }
      });
    } else {
      Threading::For<data_size_t>(0, num_data,
        [this, data, used_data_indices, score, &default_bins, &max_bins](int, data_size_t start, data_size_t end) {
        std::vector<std::unique_ptr<BinIterator>> iter(data->num_features());
        for (int i = 0; i < data->num_features(); ++i) {
          iter[i].reset(data->FeatureIterator(i));
          iter[i]->Reset(used_data_indices[start]);
        }
        for (data_size_t i = start; i < end; ++i) {
          const data_size_t idx = used_data_indices[i];
          int node = 0;
          while (node >= 0) {
            uint32_t fval = ConvertMissingValue(iter[split_feature_inner_[node]]->Get(idx), threshold_in_bin_[node], decision_type_[node], default_bins[node], max_bins[node]);
            if (inner_decision_funs[GetDecisionType(decision_type_[node], kCategoricalMask)](
              fval,
              threshold_in_bin_[node])) {
              node = left_child_[node];
            } else {
              node = right_child_[node];
            }
          }
          score[idx] += static_cast<double>(leaf_value_[~node]);
        }
      });
    }
  } else {
    if (data->num_features() > num_leaves_ - 1) {
      Threading::For<data_size_t>(0, num_data,
        [this, data, used_data_indices, score, &default_bins, &max_bins](int, data_size_t start, data_size_t end) {
        std::vector<std::unique_ptr<BinIterator>> iter(num_leaves_ - 1);
        for (int i = 0; i < num_leaves_ - 1; ++i) {
          const int fidx = split_feature_inner_[i];
          iter[i].reset(data->FeatureIterator(fidx));
          iter[i]->Reset(used_data_indices[start]);
        }
        for (data_size_t i = start; i < end; ++i) {
          int node = 0;
          const data_size_t idx = used_data_indices[i];
          while (node >= 0) {
            uint32_t fval = ConvertMissingValue(iter[node]->Get(idx), threshold_in_bin_[node], decision_type_[node], default_bins[node], max_bins[node]);
            if (fval <= threshold_in_bin_[node]) {
              node = left_child_[node];
            } else {
              node = right_child_[node];
            }
          }
          score[idx] += static_cast<double>(leaf_value_[~node]);
        }
      });
    } else {
      Threading::For<data_size_t>(0, num_data,
        [this, data, used_data_indices, score, &default_bins, &max_bins](int, data_size_t start, data_size_t end) {
        std::vector<std::unique_ptr<BinIterator>> iter(data->num_features());
        for (int i = 0; i < data->num_features(); ++i) {
          iter[i].reset(data->FeatureIterator(i));
          iter[i]->Reset(used_data_indices[start]);
        }
        for (data_size_t i = start; i < end; ++i) {
          const data_size_t idx = used_data_indices[i];
          int node = 0;
          while (node >= 0) {
            uint32_t fval = ConvertMissingValue(iter[split_feature_inner_[node]]->Get(idx), threshold_in_bin_[node], decision_type_[node], default_bins[node], max_bins[node]);
            if (fval <= threshold_in_bin_[node]) {
              node = left_child_[node];
            } else {
              node = right_child_[node];
            }
          }
          score[idx] += static_cast<double>(leaf_value_[~node]);
        }
      });
    }
  }
}

std::string Tree::ToString() {
  std::stringstream str_buf;
  str_buf << "num_leaves=" << num_leaves_ << std::endl;
  str_buf << "split_feature="
    << Common::ArrayToString<int>(split_feature_, num_leaves_ - 1, ' ') << std::endl;
  str_buf << "split_gain="
    << Common::ArrayToString<double>(split_gain_, num_leaves_ - 1, ' ') << std::endl;
  str_buf << "threshold="
    << Common::ArrayToString<double>(threshold_, num_leaves_ - 1, ' ') << std::endl;
  str_buf << "decision_type="
    << Common::ArrayToString<int>(Common::ArrayCast<int8_t, int>(decision_type_), num_leaves_ - 1, ' ') << std::endl;
  str_buf << "left_child="
    << Common::ArrayToString<int>(left_child_, num_leaves_ - 1, ' ') << std::endl;
  str_buf << "right_child="
    << Common::ArrayToString<int>(right_child_, num_leaves_ - 1, ' ') << std::endl;
  str_buf << "leaf_parent="
    << Common::ArrayToString<int>(leaf_parent_, num_leaves_, ' ') << std::endl;
  str_buf << "leaf_value="
    << Common::ArrayToString<double>(leaf_value_, num_leaves_, ' ') << std::endl;
  str_buf << "leaf_count="
    << Common::ArrayToString<data_size_t>(leaf_count_, num_leaves_, ' ') << std::endl;
  str_buf << "internal_value="
    << Common::ArrayToString<double>(internal_value_, num_leaves_ - 1, ' ') << std::endl;
  str_buf << "internal_count="
    << Common::ArrayToString<data_size_t>(internal_count_, num_leaves_ - 1, ' ') << std::endl;
  str_buf << "shrinkage=" << shrinkage_ << std::endl;
  str_buf << "has_categorical=" << (has_categorical_ ? 1 : 0) << std::endl;
  str_buf << std::endl;
  return str_buf.str();
}

std::string Tree::ToJSON() {
  std::stringstream str_buf;
  str_buf << std::setprecision(std::numeric_limits<double>::digits10 + 2);
  str_buf << "\"num_leaves\":" << num_leaves_ << "," << std::endl;
  str_buf << "\"shrinkage\":" << shrinkage_ << "," << std::endl;
  str_buf << "\"has_categorical\":" << (has_categorical_ ? 1 : 0) << "," << std::endl;
  if (num_leaves_ == 1) {
    str_buf << "\"tree_structure\":" << NodeToJSON(-1) << std::endl;
  } else {
    str_buf << "\"tree_structure\":" << NodeToJSON(0) << std::endl;
  }

  return str_buf.str();
}

std::string Tree::NodeToJSON(int index) {
  std::stringstream str_buf;
  str_buf << std::setprecision(std::numeric_limits<double>::digits10 + 2);
  if (index >= 0) {
    // non-leaf
    str_buf << "{" << std::endl;
    str_buf << "\"split_index\":" << index << "," << std::endl;
    str_buf << "\"split_feature\":" << split_feature_[index] << "," << std::endl;
    str_buf << "\"split_gain\":" << split_gain_[index] << "," << std::endl;
    str_buf << "\"threshold\":" << Common::AvoidInf(threshold_[index]) << "," << std::endl;
    str_buf << "\"decision_type\":\"" << Tree::GetDecisionTypeName(decision_type_[index]) << "\"," << std::endl;
    str_buf << "\"internal_value\":" << internal_value_[index] << "," << std::endl;
    str_buf << "\"internal_count\":" << internal_count_[index] << "," << std::endl;
    str_buf << "\"left_child\":" << NodeToJSON(left_child_[index]) << "," << std::endl;
    str_buf << "\"right_child\":" << NodeToJSON(right_child_[index]) << std::endl;
    str_buf << "}";
  } else {
    // leaf
    index = ~index;
    str_buf << "{" << std::endl;
    str_buf << "\"leaf_index\":" << index << "," << std::endl;
    str_buf << "\"leaf_parent\":" << leaf_parent_[index] << "," << std::endl;
    str_buf << "\"leaf_value\":" << leaf_value_[index] << "," << std::endl;
    str_buf << "\"leaf_count\":" << leaf_count_[index] << std::endl;
    str_buf << "}";
  }

  return str_buf.str();
}

std::string Tree::ToIfElse(int index, bool is_predict_leaf_index) {
  std::stringstream str_buf;
  str_buf << "double PredictTree" << index;
  if (is_predict_leaf_index) {
    str_buf << "Leaf";
  }
  str_buf << "(const double* arr) { ";
  if (num_leaves_ == 1) {
    str_buf << "return 0";
  } else {
    str_buf << NodeToIfElse(0, is_predict_leaf_index);
  }
  str_buf << " }" << std::endl;
  return str_buf.str();
}

std::string Tree::NodeToIfElse(int index, bool is_predict_leaf_index) {
  std::stringstream str_buf;
  str_buf << std::setprecision(std::numeric_limits<double>::digits10 + 2);
  if (index >= 0) {
    // non-leaf
    str_buf << "if (Tree::ConvertMissingValue(arr[" << split_feature_[index] << "], " << threshold_[index] << ", " << static_cast<int>(decision_type_[index]) << ") ";
    if (GetDecisionType(decision_type_[index], kCategoricalMask) == 0) {
      str_buf << "<";
    } else {
      str_buf << "=";
    }
    str_buf << "= " << threshold_[index] << " ) { ";
    // left subtree
    str_buf << NodeToIfElse(left_child_[index], is_predict_leaf_index);
    str_buf << " } else { ";
    // right subtree
    str_buf << NodeToIfElse(right_child_[index], is_predict_leaf_index);
    str_buf << " }";
  } else {
    // leaf
    str_buf << "return ";
    if (is_predict_leaf_index) {
      str_buf << ~index;
    } else {
      str_buf << leaf_value_[~index];
    }
    str_buf << ";";
  }

  return str_buf.str();
}

Tree::Tree(const std::string& str) {
  std::vector<std::string> lines = Common::SplitLines(str.c_str());
  std::unordered_map<std::string, std::string> key_vals;
  for (const std::string& line : lines) {
    std::vector<std::string> tmp_strs = Common::Split(line.c_str(), '=');
    if (tmp_strs.size() == 2) {
      std::string key = Common::Trim(tmp_strs[0]);
      std::string val = Common::Trim(tmp_strs[1]);
      if (key.size() > 0 && val.size() > 0) {
        key_vals[key] = val;
      }
    }
  }
  if (key_vals.count("num_leaves") <= 0) {
    Log::Fatal("Tree model should contain num_leaves field.");
  }

  Common::Atoi(key_vals["num_leaves"].c_str(), &num_leaves_);

  if (num_leaves_ <= 1) { return; }

  if (key_vals.count("left_child")) {
    left_child_ = Common::StringToArray<int>(key_vals["left_child"], ' ', num_leaves_ - 1);
  } else {
    Log::Fatal("Tree model string format error, should contain left_child field");
  }

  if (key_vals.count("right_child")) {
    right_child_ = Common::StringToArray<int>(key_vals["right_child"], ' ', num_leaves_ - 1);
  } else {
    Log::Fatal("Tree model string format error, should contain right_child field");
  }

  if (key_vals.count("split_feature")) {
    split_feature_ = Common::StringToArray<int>(key_vals["split_feature"], ' ', num_leaves_ - 1);
  } else {
    Log::Fatal("Tree model string format error, should contain split_feature field");
  }

  if (key_vals.count("threshold")) {
    threshold_ = Common::StringToArray<double>(key_vals["threshold"], ' ', num_leaves_ - 1);
  } else {
    Log::Fatal("Tree model string format error, should contain threshold field");
  }

  if (key_vals.count("leaf_value")) {
    leaf_value_ = Common::StringToArray<double>(key_vals["leaf_value"], ' ', num_leaves_);
  } else {
    Log::Fatal("Tree model string format error, should contain leaf_value field");
  }

  if (key_vals.count("split_gain")) {
    split_gain_ = Common::StringToArray<double>(key_vals["split_gain"], ' ', num_leaves_ - 1);
  } else {
    split_gain_.resize(num_leaves_ - 1);
  }

  if (key_vals.count("internal_count")) {
    internal_count_ = Common::StringToArray<data_size_t>(key_vals["internal_count"], ' ', num_leaves_ - 1);
  } else {
    internal_count_.resize(num_leaves_ - 1);
  }

  if (key_vals.count("internal_value")) {
    internal_value_ = Common::StringToArray<double>(key_vals["internal_value"], ' ', num_leaves_ - 1);
  } else {
    internal_value_.resize(num_leaves_ - 1);
  }

  if (key_vals.count("leaf_count")) {
    leaf_count_ = Common::StringToArray<data_size_t>(key_vals["leaf_count"], ' ', num_leaves_);
  } else {
    leaf_count_.resize(num_leaves_);
  }

  if (key_vals.count("leaf_parent")) {
    leaf_parent_ = Common::StringToArray<int>(key_vals["leaf_parent"], ' ', num_leaves_);
  } else {
    leaf_parent_.resize(num_leaves_);
  }

  if (key_vals.count("decision_type")) {
    decision_type_ = Common::StringToArray<int8_t>(key_vals["decision_type"], ' ', num_leaves_ - 1);
  } else {
    decision_type_ = std::vector<int8_t>(num_leaves_ - 1, 0);
  }

  if (key_vals.count("shrinkage")) {
    Common::Atof(key_vals["shrinkage"].c_str(), &shrinkage_);
  } else {
    shrinkage_ = 1.0f;
  }

  if (key_vals.count("has_categorical")) {
    int t = 0;
    Common::Atoi(key_vals["has_categorical"].c_str(), &t);
    has_categorical_ = t > 0;
  } else {
    has_categorical_ = false;
  }

}

}  // namespace LightGBM
