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feature_group.h
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feature_group.h
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/*!
* Copyright (c) 2017 Microsoft Corporation. All rights reserved.
* Licensed under the MIT License. See LICENSE file in the project root for license information.
*/
#ifndef LIGHTGBM_FEATURE_GROUP_H_
#define LIGHTGBM_FEATURE_GROUP_H_
#include <LightGBM/bin.h>
#include <LightGBM/meta.h>
#include <LightGBM/utils/random.h>
#include <cstdio>
#include <memory>
#include <vector>
namespace LightGBM {
class Dataset;
class DatasetLoader;
/*! \brief Using to store data and providing some operations on one feature group*/
class FeatureGroup {
public:
friend Dataset;
friend DatasetLoader;
/*!
* \brief Constructor
* \param num_feature number of features of this group
* \param bin_mappers Bin mapper for features
* \param num_data Total number of data
* \param is_enable_sparse True if enable sparse feature
*/
FeatureGroup(int num_feature, bool is_multi_val,
std::vector<std::unique_ptr<BinMapper>>* bin_mappers,
data_size_t num_data) : num_feature_(num_feature), is_multi_val_(is_multi_val), is_sparse_(false) {
CHECK_EQ(static_cast<int>(bin_mappers->size()), num_feature);
// use bin at zero to store most_freq_bin
num_total_bin_ = 1;
bin_offsets_.emplace_back(num_total_bin_);
auto& ref_bin_mappers = *bin_mappers;
for (int i = 0; i < num_feature_; ++i) {
bin_mappers_.emplace_back(ref_bin_mappers[i].release());
auto num_bin = bin_mappers_[i]->num_bin();
if (bin_mappers_[i]->GetMostFreqBin() == 0) {
num_bin -= 1;
}
num_total_bin_ += num_bin;
bin_offsets_.emplace_back(num_total_bin_);
}
CreateBinData(num_data, is_multi_val_, true, false);
}
FeatureGroup(const FeatureGroup& other, int num_data) {
num_feature_ = other.num_feature_;
is_multi_val_ = other.is_multi_val_;
is_sparse_ = other.is_sparse_;
num_total_bin_ = other.num_total_bin_;
bin_offsets_ = other.bin_offsets_;
bin_mappers_.reserve(other.bin_mappers_.size());
for (auto& bin_mapper : other.bin_mappers_) {
bin_mappers_.emplace_back(new BinMapper(*bin_mapper));
}
CreateBinData(num_data, is_multi_val_, !is_sparse_, is_sparse_);
}
FeatureGroup(std::vector<std::unique_ptr<BinMapper>>* bin_mappers,
data_size_t num_data) : num_feature_(1), is_multi_val_(false) {
CHECK_EQ(static_cast<int>(bin_mappers->size()), 1);
// use bin at zero to store default_bin
num_total_bin_ = 1;
bin_offsets_.emplace_back(num_total_bin_);
auto& ref_bin_mappers = *bin_mappers;
for (int i = 0; i < num_feature_; ++i) {
bin_mappers_.emplace_back(ref_bin_mappers[i].release());
auto num_bin = bin_mappers_[i]->num_bin();
if (bin_mappers_[i]->GetMostFreqBin() == 0) {
num_bin -= 1;
}
num_total_bin_ += num_bin;
bin_offsets_.emplace_back(num_total_bin_);
}
CreateBinData(num_data, false, false, false);
}
/*!
* \brief Constructor from memory
* \param memory Pointer of memory
* \param num_all_data Number of global data
* \param local_used_indices Local used indices, empty means using all data
*/
FeatureGroup(const void* memory, data_size_t num_all_data,
const std::vector<data_size_t>& local_used_indices) {
const char* memory_ptr = reinterpret_cast<const char*>(memory);
// get is_sparse
is_multi_val_ = *(reinterpret_cast<const bool*>(memory_ptr));
memory_ptr += sizeof(is_multi_val_);
is_sparse_ = *(reinterpret_cast<const bool*>(memory_ptr));
memory_ptr += sizeof(is_sparse_);
num_feature_ = *(reinterpret_cast<const int*>(memory_ptr));
memory_ptr += sizeof(num_feature_);
// get bin mapper
bin_mappers_.clear();
bin_offsets_.clear();
// start from 1, due to need to store zero bin in this slot
num_total_bin_ = 1;
bin_offsets_.emplace_back(num_total_bin_);
for (int i = 0; i < num_feature_; ++i) {
bin_mappers_.emplace_back(new BinMapper(memory_ptr));
auto num_bin = bin_mappers_[i]->num_bin();
if (bin_mappers_[i]->GetMostFreqBin() == 0) {
num_bin -= 1;
}
num_total_bin_ += num_bin;
bin_offsets_.emplace_back(num_total_bin_);
memory_ptr += bin_mappers_[i]->SizesInByte();
}
data_size_t num_data = num_all_data;
if (!local_used_indices.empty()) {
num_data = static_cast<data_size_t>(local_used_indices.size());
}
if (is_multi_val_) {
for (int i = 0; i < num_feature_; ++i) {
int addi = bin_mappers_[i]->GetMostFreqBin() == 0 ? 0 : 1;
if (bin_mappers_[i]->sparse_rate() >= kSparseThreshold) {
multi_bin_data_.emplace_back(Bin::CreateSparseBin(num_data, bin_mappers_[i]->num_bin() + addi));
} else {
multi_bin_data_.emplace_back(Bin::CreateDenseBin(num_data, bin_mappers_[i]->num_bin() + addi));
}
multi_bin_data_.back()->LoadFromMemory(memory_ptr, local_used_indices);
memory_ptr += multi_bin_data_.back()->SizesInByte();
}
} else {
if (is_sparse_) {
bin_data_.reset(Bin::CreateSparseBin(num_data, num_total_bin_));
} else {
bin_data_.reset(Bin::CreateDenseBin(num_data, num_total_bin_));
}
// get bin data
bin_data_->LoadFromMemory(memory_ptr, local_used_indices);
}
}
/*! \brief Destructor */
~FeatureGroup() {
}
/*!
* \brief Push one record, will auto convert to bin and push to bin data
* \param tid Thread id
* \param idx Index of record
* \param value feature value of record
*/
inline void PushData(int tid, int sub_feature_idx, data_size_t line_idx, double value) {
uint32_t bin = bin_mappers_[sub_feature_idx]->ValueToBin(value);
if (bin == bin_mappers_[sub_feature_idx]->GetMostFreqBin()) { return; }
if (bin_mappers_[sub_feature_idx]->GetMostFreqBin() == 0) {
bin -= 1;
}
if (is_multi_val_) {
multi_bin_data_[sub_feature_idx]->Push(tid, line_idx, bin + 1);
} else {
bin += bin_offsets_[sub_feature_idx];
bin_data_->Push(tid, line_idx, bin);
}
}
void ReSize(int num_data) {
if (!is_multi_val_) {
bin_data_->ReSize(num_data);
} else {
for (int i = 0; i < num_feature_; ++i) {
multi_bin_data_[i]->ReSize(num_data);
}
}
}
inline void CopySubrow(const FeatureGroup* full_feature, const data_size_t* used_indices, data_size_t num_used_indices) {
if (!is_multi_val_) {
bin_data_->CopySubrow(full_feature->bin_data_.get(), used_indices, num_used_indices);
} else {
for (int i = 0; i < num_feature_; ++i) {
multi_bin_data_[i]->CopySubrow(full_feature->multi_bin_data_[i].get(), used_indices, num_used_indices);
}
}
}
inline BinIterator* SubFeatureIterator(int sub_feature) {
uint32_t most_freq_bin = bin_mappers_[sub_feature]->GetMostFreqBin();
if (!is_multi_val_) {
uint32_t min_bin = bin_offsets_[sub_feature];
uint32_t max_bin = bin_offsets_[sub_feature + 1] - 1;
return bin_data_->GetIterator(min_bin, max_bin, most_freq_bin);
} else {
int addi = bin_mappers_[sub_feature]->GetMostFreqBin() == 0 ? 0 : 1;
uint32_t min_bin = 1;
uint32_t max_bin = bin_mappers_[sub_feature]->num_bin() - 1 + addi;
return multi_bin_data_[sub_feature]->GetIterator(min_bin, max_bin, most_freq_bin);
}
}
inline void FinishLoad() {
if (is_multi_val_) {
OMP_INIT_EX();
#pragma omp parallel for schedule(guided)
for (int i = 0; i < num_feature_; ++i) {
OMP_LOOP_EX_BEGIN();
multi_bin_data_[i]->FinishLoad();
OMP_LOOP_EX_END();
}
OMP_THROW_EX();
} else {
bin_data_->FinishLoad();
}
}
/*!
* \brief Returns a BinIterator that can access the entire feature group's raw data.
* The RawGet() function of the iterator should be called for best efficiency.
* \return A pointer to the BinIterator object
*/
inline BinIterator* FeatureGroupIterator() {
if (is_multi_val_) {
return nullptr;
}
uint32_t min_bin = bin_offsets_[0];
uint32_t max_bin = bin_offsets_.back() - 1;
uint32_t most_freq_bin = 0;
return bin_data_->GetIterator(min_bin, max_bin, most_freq_bin);
}
inline size_t FeatureGroupSizesInByte() {
return bin_data_->SizesInByte();
}
inline void* FeatureGroupData() {
if (is_multi_val_) {
return nullptr;
}
return bin_data_->get_data();
}
inline data_size_t Split(int sub_feature, const uint32_t* threshold,
int num_threshold, bool default_left,
const data_size_t* data_indices, data_size_t cnt,
data_size_t* lte_indices,
data_size_t* gt_indices) const {
uint32_t default_bin = bin_mappers_[sub_feature]->GetDefaultBin();
uint32_t most_freq_bin = bin_mappers_[sub_feature]->GetMostFreqBin();
if (!is_multi_val_) {
uint32_t min_bin = bin_offsets_[sub_feature];
uint32_t max_bin = bin_offsets_[sub_feature + 1] - 1;
if (bin_mappers_[sub_feature]->bin_type() == BinType::NumericalBin) {
auto missing_type = bin_mappers_[sub_feature]->missing_type();
if (num_feature_ == 1) {
return bin_data_->Split(max_bin, default_bin, most_freq_bin,
missing_type, default_left, *threshold,
data_indices, cnt, lte_indices, gt_indices);
} else {
return bin_data_->Split(min_bin, max_bin, default_bin, most_freq_bin,
missing_type, default_left, *threshold,
data_indices, cnt, lte_indices, gt_indices);
}
} else {
if (num_feature_ == 1) {
return bin_data_->SplitCategorical(max_bin, most_freq_bin, threshold,
num_threshold, data_indices, cnt,
lte_indices, gt_indices);
} else {
return bin_data_->SplitCategorical(
min_bin, max_bin, most_freq_bin, threshold, num_threshold,
data_indices, cnt, lte_indices, gt_indices);
}
}
} else {
int addi = bin_mappers_[sub_feature]->GetMostFreqBin() == 0 ? 0 : 1;
uint32_t max_bin = bin_mappers_[sub_feature]->num_bin() - 1 + addi;
if (bin_mappers_[sub_feature]->bin_type() == BinType::NumericalBin) {
auto missing_type = bin_mappers_[sub_feature]->missing_type();
return multi_bin_data_[sub_feature]->Split(
max_bin, default_bin, most_freq_bin, missing_type, default_left,
*threshold, data_indices, cnt, lte_indices, gt_indices);
} else {
return multi_bin_data_[sub_feature]->SplitCategorical(
max_bin, most_freq_bin, threshold, num_threshold, data_indices, cnt,
lte_indices, gt_indices);
}
}
}
/*!
* \brief From bin to feature value
* \param bin
* \return FeatureGroup value of this bin
*/
inline double BinToValue(int sub_feature_idx, uint32_t bin) const {
return bin_mappers_[sub_feature_idx]->BinToValue(bin);
}
/*!
* \brief Save binary data to file
* \param file File want to write
*/
void SaveBinaryToFile(const VirtualFileWriter* writer) const {
writer->Write(&is_multi_val_, sizeof(is_multi_val_));
writer->Write(&is_sparse_, sizeof(is_sparse_));
writer->Write(&num_feature_, sizeof(num_feature_));
for (int i = 0; i < num_feature_; ++i) {
bin_mappers_[i]->SaveBinaryToFile(writer);
}
if (is_multi_val_) {
for (int i = 0; i < num_feature_; ++i) {
multi_bin_data_[i]->SaveBinaryToFile(writer);
}
} else {
bin_data_->SaveBinaryToFile(writer);
}
}
/*!
* \brief Get sizes in byte of this object
*/
size_t SizesInByte() const {
size_t ret = sizeof(is_multi_val_) + sizeof(is_sparse_) + sizeof(num_feature_);
for (int i = 0; i < num_feature_; ++i) {
ret += bin_mappers_[i]->SizesInByte();
}
if (!is_multi_val_) {
ret += bin_data_->SizesInByte();
} else {
for (int i = 0; i < num_feature_; ++i) {
ret += multi_bin_data_[i]->SizesInByte();
}
}
return ret;
}
/*! \brief Disable copy */
FeatureGroup& operator=(const FeatureGroup&) = delete;
/*! \brief Deep copy */
FeatureGroup(const FeatureGroup& other) {
num_feature_ = other.num_feature_;
is_multi_val_ = other.is_multi_val_;
is_sparse_ = other.is_sparse_;
num_total_bin_ = other.num_total_bin_;
bin_offsets_ = other.bin_offsets_;
bin_mappers_.reserve(other.bin_mappers_.size());
for (auto& bin_mapper : other.bin_mappers_) {
bin_mappers_.emplace_back(new BinMapper(*bin_mapper));
}
if (!is_multi_val_) {
bin_data_.reset(other.bin_data_->Clone());
} else {
multi_bin_data_.clear();
for (int i = 0; i < num_feature_; ++i) {
multi_bin_data_.emplace_back(other.multi_bin_data_[i]->Clone());
}
}
}
private:
void CreateBinData(int num_data, bool is_multi_val, bool force_dense, bool force_sparse) {
if (is_multi_val) {
multi_bin_data_.clear();
for (int i = 0; i < num_feature_; ++i) {
int addi = bin_mappers_[i]->GetMostFreqBin() == 0 ? 0 : 1;
if (bin_mappers_[i]->sparse_rate() >= kSparseThreshold) {
multi_bin_data_.emplace_back(Bin::CreateSparseBin(
num_data, bin_mappers_[i]->num_bin() + addi));
} else {
multi_bin_data_.emplace_back(
Bin::CreateDenseBin(num_data, bin_mappers_[i]->num_bin() + addi));
}
}
is_multi_val_ = true;
} else {
if (force_sparse || (!force_dense && num_feature_ == 1 &&
bin_mappers_[0]->sparse_rate() >= kSparseThreshold)) {
is_sparse_ = true;
bin_data_.reset(Bin::CreateSparseBin(num_data, num_total_bin_));
} else {
is_sparse_ = false;
bin_data_.reset(Bin::CreateDenseBin(num_data, num_total_bin_));
}
is_multi_val_ = false;
}
}
/*! \brief Number of features */
int num_feature_;
/*! \brief Bin mapper for sub features */
std::vector<std::unique_ptr<BinMapper>> bin_mappers_;
/*! \brief Bin offsets for sub features */
std::vector<uint32_t> bin_offsets_;
/*! \brief Bin data of this feature */
std::unique_ptr<Bin> bin_data_;
std::vector<std::unique_ptr<Bin>> multi_bin_data_;
/*! \brief True if this feature is sparse */
bool is_multi_val_;
bool is_sparse_;
int num_total_bin_;
};
} // namespace LightGBM
#endif // LIGHTGBM_FEATURE_GROUP_H_