dense_bin.hpp

#ifndef LIGHTGBM_IO_DENSE_BIN_HPP_
#define LIGHTGBM_IO_DENSE_BIN_HPP_

#include <LightGBM/bin.h>

#include <vector>
#include <cstring>
#include <cstdint>

namespace LightGBM {

template <typename VAL_T>
class DenseBin;

template <typename VAL_T>
class DenseBinIterator: public BinIterator {
public:
  explicit DenseBinIterator(const DenseBin<VAL_T>* bin_data, uint32_t min_bin, uint32_t max_bin, uint32_t default_bin)
    : bin_data_(bin_data), min_bin_(static_cast<VAL_T>(min_bin)),
    max_bin_(static_cast<VAL_T>(max_bin)),
    default_bin_(static_cast<uint8_t>(default_bin)) {
    if (default_bin_ == 0) {
      bias_ = 1;
    } else {
      bias_ = 0;
    }
  }
  inline uint32_t RawGet(data_size_t idx) override;
  inline uint32_t Get(data_size_t idx) override;
  inline void Reset(data_size_t) override { }
private:
  const DenseBin<VAL_T>* bin_data_;
  VAL_T min_bin_;
  VAL_T max_bin_;
  VAL_T default_bin_;
  uint8_t bias_;
};
/*!
* \brief Used to store bins for dense feature
* Use template to reduce memory cost
*/
template <typename VAL_T>
class DenseBin: public Bin {
public:
  friend DenseBinIterator<VAL_T>;
  DenseBin(data_size_t num_data)
    : num_data_(num_data), data_(num_data_, static_cast<VAL_T>(0)) {
  }

  ~DenseBin() {
  }

  void Push(int, data_size_t idx, uint32_t value) override {
    data_[idx] = static_cast<VAL_T>(value);
  }

  void ReSize(data_size_t num_data) override {
    if (num_data_ != num_data) {
      num_data_ = num_data;
      data_.resize(num_data_);
    }
  }

  BinIterator* GetIterator(uint32_t min_bin, uint32_t max_bin, uint32_t default_bin) const override;

  void ConstructHistogram(const data_size_t* data_indices, data_size_t num_data,
                          const score_t* ordered_gradients, const score_t* ordered_hessians,
                          HistogramBinEntry* out) const override {
    // use 4-way unrolling, will be faster
    if (data_indices != nullptr) {  // if use part of data
      const data_size_t rest = num_data & 0x3;
      data_size_t i = 0;
      for (; i < num_data - rest; i += 4) {
        const VAL_T bin0 = data_[data_indices[i]];
        const VAL_T bin1 = data_[data_indices[i + 1]];
        const VAL_T bin2 = data_[data_indices[i + 2]];
        const VAL_T bin3 = data_[data_indices[i + 3]];

        out[bin0].sum_gradients += ordered_gradients[i];
        out[bin1].sum_gradients += ordered_gradients[i + 1];
        out[bin2].sum_gradients += ordered_gradients[i + 2];
        out[bin3].sum_gradients += ordered_gradients[i + 3];

        out[bin0].sum_hessians += ordered_hessians[i];
        out[bin1].sum_hessians += ordered_hessians[i + 1];
        out[bin2].sum_hessians += ordered_hessians[i + 2];
        out[bin3].sum_hessians += ordered_hessians[i + 3];

        ++out[bin0].cnt;
        ++out[bin1].cnt;
        ++out[bin2].cnt;
        ++out[bin3].cnt;
      }
      for (; i < num_data; ++i) {
        const VAL_T bin = data_[data_indices[i]];
        out[bin].sum_gradients += ordered_gradients[i];
        out[bin].sum_hessians += ordered_hessians[i];
        ++out[bin].cnt;
      }
    } else {  // use full data
      const data_size_t rest = num_data & 0x3;
      data_size_t i = 0;
      for (; i < num_data - rest; i += 4) {
        const VAL_T bin0 = data_[i];
        const VAL_T bin1 = data_[i + 1];
        const VAL_T bin2 = data_[i + 2];
        const VAL_T bin3 = data_[i + 3];

        out[bin0].sum_gradients += ordered_gradients[i];
        out[bin1].sum_gradients += ordered_gradients[i + 1];
        out[bin2].sum_gradients += ordered_gradients[i + 2];
        out[bin3].sum_gradients += ordered_gradients[i + 3];

        out[bin0].sum_hessians += ordered_hessians[i];
        out[bin1].sum_hessians += ordered_hessians[i + 1];
        out[bin2].sum_hessians += ordered_hessians[i + 2];
        out[bin3].sum_hessians += ordered_hessians[i + 3];

        ++out[bin0].cnt;
        ++out[bin1].cnt;
        ++out[bin2].cnt;
        ++out[bin3].cnt;
      }
      for (; i < num_data; ++i) {
        const VAL_T bin = data_[i];
        out[bin].sum_gradients += ordered_gradients[i];
        out[bin].sum_hessians += ordered_hessians[i];
        ++out[bin].cnt;
      }
    }
  }

  void ConstructHistogram(const data_size_t* data_indices, data_size_t num_data,
                          const score_t* ordered_gradients,
                          HistogramBinEntry* out) const override {
    // use 4-way unrolling, will be faster
    if (data_indices != nullptr) {  // if use part of data
      const data_size_t rest = num_data & 0x3;
      data_size_t i = 0;
      for (; i < num_data - rest; i += 4) {
        const VAL_T bin0 = data_[data_indices[i]];
        const VAL_T bin1 = data_[data_indices[i + 1]];
        const VAL_T bin2 = data_[data_indices[i + 2]];
        const VAL_T bin3 = data_[data_indices[i + 3]];

        out[bin0].sum_gradients += ordered_gradients[i];
        out[bin1].sum_gradients += ordered_gradients[i + 1];
        out[bin2].sum_gradients += ordered_gradients[i + 2];
        out[bin3].sum_gradients += ordered_gradients[i + 3];

        ++out[bin0].cnt;
        ++out[bin1].cnt;
        ++out[bin2].cnt;
        ++out[bin3].cnt;
      }
      for (; i < num_data; ++i) {
        const VAL_T bin = data_[data_indices[i]];
        out[bin].sum_gradients += ordered_gradients[i];
        ++out[bin].cnt;
      }
    } else {  // use full data
      const data_size_t rest = num_data & 0x3;
      data_size_t i = 0;
      for (; i < num_data - rest; i += 4) {
        const VAL_T bin0 = data_[i];
        const VAL_T bin1 = data_[i + 1];
        const VAL_T bin2 = data_[i + 2];
        const VAL_T bin3 = data_[i + 3];

        out[bin0].sum_gradients += ordered_gradients[i];
        out[bin1].sum_gradients += ordered_gradients[i + 1];
        out[bin2].sum_gradients += ordered_gradients[i + 2];
        out[bin3].sum_gradients += ordered_gradients[i + 3];

        ++out[bin0].cnt;
        ++out[bin1].cnt;
        ++out[bin2].cnt;
        ++out[bin3].cnt;
      }
      for (; i < num_data; ++i) {
        const VAL_T bin = data_[i];
        out[bin].sum_gradients += ordered_gradients[i];
        ++out[bin].cnt;
      }
    }
  }

  virtual data_size_t Split(
    uint32_t min_bin, uint32_t max_bin, uint32_t default_bin,
    uint32_t threshold, data_size_t* data_indices, data_size_t num_data,
    data_size_t* lte_indices, data_size_t* gt_indices, BinType bin_type) const override {
    if (num_data <= 0) { return 0; }
    VAL_T th = static_cast<VAL_T>(threshold + min_bin);
    VAL_T minb = static_cast<VAL_T>(min_bin);
    VAL_T maxb = static_cast<VAL_T>(max_bin);
    if (default_bin == 0) {
      th -= 1;
    }
    data_size_t lte_count = 0;
    data_size_t gt_count = 0;
    data_size_t* default_indices = gt_indices;
    data_size_t* default_count = &gt_count;
    if (bin_type == BinType::NumericalBin) {
      if (default_bin <= threshold) {
        default_indices = lte_indices;
        default_count = &lte_count;
      }
      for (data_size_t i = 0; i < num_data; ++i) {
        const data_size_t idx = data_indices[i];
        VAL_T bin = data_[idx];
        if (bin > maxb || bin < minb) {
          default_indices[(*default_count)++] = idx;
        } else if (bin > th) {
          gt_indices[gt_count++] = idx;
        } else {
          lte_indices[lte_count++] = idx;
        }
      }
    } else {
      if (default_bin == threshold) {
        default_indices = lte_indices;
        default_count = &lte_count;
      }
      for (data_size_t i = 0; i < num_data; ++i) {
        const data_size_t idx = data_indices[i];
        VAL_T bin = data_[idx];
        if (bin > maxb || bin < minb) {
          default_indices[(*default_count)++] = idx;
        } else if (bin != th) {
          gt_indices[gt_count++] = idx;
        } else {
          lte_indices[lte_count++] = idx;
        }
      }
    }
    return lte_count;
  }
  data_size_t num_data() const override { return num_data_; }

  /*! \brief not ordered bin for dense feature */
  OrderedBin* CreateOrderedBin() const override { return nullptr; }

  void FinishLoad() override {}

  void LoadFromMemory(const void* memory, const std::vector<data_size_t>& local_used_indices) override {
    const VAL_T* mem_data = reinterpret_cast<const VAL_T*>(memory);
    if (!local_used_indices.empty()) {
      for (int i = 0; i < num_data_; ++i) {
        data_[i] = mem_data[local_used_indices[i]];
      }
    } else {
      for (int i = 0; i < num_data_; ++i) {
        data_[i] = mem_data[i];
      }
    }
  }

  void CopySubset(const Bin* full_bin, const data_size_t* used_indices, data_size_t num_used_indices) override {
    auto other_bin = reinterpret_cast<const DenseBin<VAL_T>*>(full_bin);
    for (int i = 0; i < num_used_indices; ++i) {
      data_[i] = other_bin->data_[used_indices[i]];
    }
  }

  void SaveBinaryToFile(FILE* file) const override {
    fwrite(data_.data(), sizeof(VAL_T), num_data_, file);
  }

  size_t SizesInByte() const override {
    return sizeof(VAL_T) * num_data_;
  }

protected:
  data_size_t num_data_;
  std::vector<VAL_T> data_;
};

template <typename VAL_T>
uint32_t DenseBinIterator<VAL_T>::Get(data_size_t idx) {
  auto ret = bin_data_->data_[idx];
  if (ret >= min_bin_ && ret <= max_bin_) {
    return ret - min_bin_ + bias_;
  } else {
    return default_bin_;
  }
}

template <typename VAL_T>
inline uint32_t DenseBinIterator<VAL_T>::RawGet(data_size_t idx) {
  return bin_data_->data_[idx];
}

template <typename VAL_T>
BinIterator* DenseBin<VAL_T>::GetIterator(uint32_t min_bin, uint32_t max_bin, uint32_t default_bin) const {
  return new DenseBinIterator<VAL_T>(this, min_bin, max_bin, default_bin);
}

}  // namespace LightGBM
#endif   // LightGBM_IO_DENSE_BIN_HPP_