87 lines
2.9 KiB
C++
87 lines
2.9 KiB
C++
/*!
|
|
* Copyright 2017-2021 by Contributors
|
|
* \brief Data type for fast histogram aggregation.
|
|
*/
|
|
#ifndef XGBOOST_DATA_GRADIENT_INDEX_H_
|
|
#define XGBOOST_DATA_GRADIENT_INDEX_H_
|
|
#include <vector>
|
|
#include "xgboost/base.h"
|
|
#include "xgboost/data.h"
|
|
#include "../common/hist_util.h"
|
|
#include "../common/threading_utils.h"
|
|
|
|
namespace xgboost {
|
|
/*!
|
|
* \brief preprocessed global index matrix, in CSR format
|
|
*
|
|
* Transform floating values to integer index in histogram This is a global histogram
|
|
* index for CPU histogram. On GPU ellpack page is used.
|
|
*/
|
|
class GHistIndexMatrix {
|
|
public:
|
|
/*! \brief row pointer to rows by element position */
|
|
std::vector<size_t> row_ptr;
|
|
/*! \brief The index data */
|
|
common::Index index;
|
|
/*! \brief hit count of each index */
|
|
std::vector<size_t> hit_count;
|
|
/*! \brief The corresponding cuts */
|
|
common::HistogramCuts cut;
|
|
DMatrix* p_fmat;
|
|
size_t max_num_bins;
|
|
|
|
GHistIndexMatrix(DMatrix* x, int32_t max_bin) {
|
|
this->Init(x, max_bin);
|
|
}
|
|
// Create a global histogram matrix, given cut
|
|
void Init(DMatrix* p_fmat, int max_num_bins);
|
|
|
|
// specific method for sparse data as no possibility to reduce allocated memory
|
|
template <typename BinIdxType, typename GetOffset>
|
|
void SetIndexData(common::Span<BinIdxType> index_data_span,
|
|
size_t batch_threads, const SparsePage &batch,
|
|
size_t rbegin, size_t nbins, GetOffset get_offset) {
|
|
const xgboost::Entry *data_ptr = batch.data.HostVector().data();
|
|
const std::vector<bst_row_t> &offset_vec = batch.offset.HostVector();
|
|
const size_t batch_size = batch.Size();
|
|
CHECK_LT(batch_size, offset_vec.size());
|
|
BinIdxType* index_data = index_data_span.data();
|
|
common::ParallelFor(omp_ulong(batch_size), batch_threads, [&](omp_ulong i) {
|
|
const int tid = omp_get_thread_num();
|
|
size_t ibegin = row_ptr[rbegin + i];
|
|
size_t iend = row_ptr[rbegin + i + 1];
|
|
const size_t size = offset_vec[i + 1] - offset_vec[i];
|
|
SparsePage::Inst inst = {data_ptr + offset_vec[i], size};
|
|
CHECK_EQ(ibegin + inst.size(), iend);
|
|
for (bst_uint j = 0; j < inst.size(); ++j) {
|
|
uint32_t idx = cut.SearchBin(inst[j]);
|
|
index_data[ibegin + j] = get_offset(idx, j);
|
|
++hit_count_tloc_[tid * nbins + idx];
|
|
}
|
|
});
|
|
}
|
|
|
|
void ResizeIndex(const size_t n_index,
|
|
const bool isDense);
|
|
|
|
inline void GetFeatureCounts(size_t* counts) const {
|
|
auto nfeature = cut.Ptrs().size() - 1;
|
|
for (unsigned fid = 0; fid < nfeature; ++fid) {
|
|
auto ibegin = cut.Ptrs()[fid];
|
|
auto iend = cut.Ptrs()[fid + 1];
|
|
for (auto i = ibegin; i < iend; ++i) {
|
|
counts[fid] += hit_count[i];
|
|
}
|
|
}
|
|
}
|
|
inline bool IsDense() const {
|
|
return isDense_;
|
|
}
|
|
|
|
private:
|
|
std::vector<size_t> hit_count_tloc_;
|
|
bool isDense_;
|
|
};
|
|
} // namespace xgboost
|
|
#endif // XGBOOST_DATA_GRADIENT_INDEX_H_
|