354 lines
13 KiB
C++
354 lines
13 KiB
C++
// implementations in ctypes
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#define _CRT_SECURE_NO_WARNINGS
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#define _CRT_SECURE_NO_DEPRECATE
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#include <cstdio>
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#include <vector>
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#include <string>
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#include <cstring>
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#include <cmath>
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#include <algorithm>
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// include all std functions
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using namespace std;
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#ifdef _MSC_VER
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#define isnan(x) (_isnan(x) != 0)
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#endif
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#include "./xgboost_wrapper.h"
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#include "../src/data.h"
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#include "../src/learner/learner-inl.hpp"
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#include "../src/io/io.h"
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#include "../src/utils/utils.h"
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#include "../src/utils/group_data.h"
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#include "../src/io/simple_dmatrix-inl.hpp"
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using namespace xgboost;
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using namespace xgboost::io;
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namespace xgboost {
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namespace wrapper {
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// booster wrapper class
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class Booster: public learner::BoostLearner {
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public:
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explicit Booster(const std::vector<DataMatrix*>& mats) {
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this->silent = 1;
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this->init_model = false;
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this->SetCacheData(mats);
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}
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inline const float *Pred(const DataMatrix &dmat, int option_mask, unsigned ntree_limit, bst_ulong *len) {
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this->CheckInitModel();
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this->Predict(dmat, (option_mask&1) != 0, &this->preds_, ntree_limit, (option_mask&2) != 0);
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*len = static_cast<bst_ulong>(this->preds_.size());
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return BeginPtr(this->preds_);
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}
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inline void BoostOneIter(const DataMatrix &train,
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float *grad, float *hess, bst_ulong len) {
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this->gpair_.resize(len);
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const bst_omp_uint ndata = static_cast<bst_omp_uint>(len);
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#pragma omp parallel for schedule(static)
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for (bst_omp_uint j = 0; j < ndata; ++j) {
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gpair_[j] = bst_gpair(grad[j], hess[j]);
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}
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gbm_->DoBoost(train.fmat(), this->FindBufferOffset(train), train.info.info, &gpair_);
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}
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inline void CheckInitModel(void) {
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if (!init_model) {
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this->InitModel(); init_model = true;
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}
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}
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inline void LoadModel(const char *fname) {
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learner::BoostLearner::LoadModel(fname);
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this->init_model = true;
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}
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inline void LoadModelFromBuffer(const void *buf, size_t size) {
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utils::MemoryFixSizeBuffer fs((void*)buf, size);
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learner::BoostLearner::LoadModel(fs);
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this->init_model = true;
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}
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inline const char *GetModelRaw(bst_ulong *out_len) {
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model_str.resize(0);
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utils::MemoryBufferStream fs(&model_str);
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learner::BoostLearner::SaveModel(fs);
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*out_len = static_cast<bst_ulong>(model_str.length());
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if (*out_len == 0) {
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return NULL;
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} else {
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return &model_str[0];
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}
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}
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inline const char** GetModelDump(const utils::FeatMap& fmap, bool with_stats, bst_ulong *len) {
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model_dump = this->DumpModel(fmap, with_stats);
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model_dump_cptr.resize(model_dump.size());
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for (size_t i = 0; i < model_dump.size(); ++i) {
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model_dump_cptr[i] = model_dump[i].c_str();
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}
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*len = static_cast<bst_ulong>(model_dump.size());
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return BeginPtr(model_dump_cptr);
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}
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// temporal fields
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// temporal data to save evaluation dump
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std::string eval_str;
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// temporal data to save model dump
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std::string model_str;
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// temporal space to save model dump
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std::vector<std::string> model_dump;
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std::vector<const char*> model_dump_cptr;
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private:
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bool init_model;
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};
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#if !defined(XGBOOST_STRICT_CXX98_)
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inline bool CheckNAN(float v) {
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return isnan(v);
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}
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#else
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// redirect to defs in R
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bool CheckNAN(float v);
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#endif
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} // namespace wrapper
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} // namespace xgboost
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using namespace xgboost::wrapper;
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extern "C"{
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void* XGDMatrixCreateFromFile(const char *fname, int silent) {
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return LoadDataMatrix(fname, silent != 0, false, false);
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}
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void* XGDMatrixCreateFromCSR(const bst_ulong *indptr,
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const unsigned *indices,
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const float *data,
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bst_ulong nindptr,
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bst_ulong nelem) {
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DMatrixSimple *p_mat = new DMatrixSimple();
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DMatrixSimple &mat = *p_mat;
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mat.row_ptr_.resize(nindptr);
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for (bst_ulong i = 0; i < nindptr; ++i) {
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mat.row_ptr_[i] = static_cast<size_t>(indptr[i]);
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}
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mat.row_data_.resize(nelem);
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for (bst_ulong i = 0; i < nelem; ++i) {
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mat.row_data_[i] = RowBatch::Entry(indices[i], data[i]);
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mat.info.info.num_col = std::max(mat.info.info.num_col,
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static_cast<size_t>(indices[i]+1));
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}
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mat.info.info.num_row = nindptr - 1;
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return p_mat;
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}
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XGB_DLL void* XGDMatrixCreateFromCSC(const bst_ulong *col_ptr,
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const unsigned *indices,
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const float *data,
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bst_ulong nindptr,
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bst_ulong nelem) {
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int nthread;
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#pragma omp parallel
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{
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nthread = omp_get_num_threads();
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}
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DMatrixSimple *p_mat = new DMatrixSimple();
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DMatrixSimple &mat = *p_mat;
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utils::ParallelGroupBuilder<RowBatch::Entry> builder(&mat.row_ptr_, &mat.row_data_);
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builder.InitBudget(0, nthread);
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bst_ulong ncol = nindptr - 1;
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#pragma omp parallel for schedule(static)
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for (bst_ulong i = 0; i < ncol; ++i) {
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int tid = omp_get_thread_num();
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for (unsigned j = col_ptr[i]; j < col_ptr[i+1]; ++j) {
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builder.AddBudget(indices[j], tid);
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}
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}
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builder.InitStorage();
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#pragma omp parallel for schedule(static)
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for (bst_ulong i = 0; i < ncol; ++i) {
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int tid = omp_get_thread_num();
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for (unsigned j = col_ptr[i]; j < col_ptr[i+1]; ++j) {
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builder.Push(indices[j],
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RowBatch::Entry(static_cast<bst_uint>(i), data[j]),
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tid);
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}
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}
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mat.info.info.num_row = mat.row_ptr_.size() - 1;
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mat.info.info.num_col = static_cast<size_t>(ncol);
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return p_mat;
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}
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void* XGDMatrixCreateFromMat(const float *data,
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bst_ulong nrow,
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bst_ulong ncol,
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float missing) {
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bool nan_missing = CheckNAN(missing);
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DMatrixSimple *p_mat = new DMatrixSimple();
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DMatrixSimple &mat = *p_mat;
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mat.info.info.num_row = nrow;
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mat.info.info.num_col = ncol;
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for (bst_ulong i = 0; i < nrow; ++i, data += ncol) {
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bst_ulong nelem = 0;
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for (bst_ulong j = 0; j < ncol; ++j) {
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if (CheckNAN(data[j])) {
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utils::Check(nan_missing,
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"There are NAN in the matrix, however, you did not set missing=NAN");
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} else {
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if (nan_missing || data[j] != missing) {
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mat.row_data_.push_back(RowBatch::Entry(j, data[j]));
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++nelem;
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}
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}
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}
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mat.row_ptr_.push_back(mat.row_ptr_.back() + nelem);
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}
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return p_mat;
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}
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void* XGDMatrixSliceDMatrix(void *handle,
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const int *idxset,
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bst_ulong len) {
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DMatrixSimple tmp;
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DataMatrix &dsrc = *static_cast<DataMatrix*>(handle);
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if (dsrc.magic != DMatrixSimple::kMagic) {
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tmp.CopyFrom(dsrc);
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}
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DataMatrix &src = (dsrc.magic == DMatrixSimple::kMagic ?
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*static_cast<DMatrixSimple*>(handle): tmp);
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DMatrixSimple *p_ret = new DMatrixSimple();
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DMatrixSimple &ret = *p_ret;
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utils::Check(src.info.group_ptr.size() == 0,
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"slice does not support group structure");
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ret.Clear();
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ret.info.info.num_row = len;
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ret.info.info.num_col = src.info.num_col();
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utils::IIterator<RowBatch> *iter = src.fmat()->RowIterator();
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iter->BeforeFirst();
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utils::Assert(iter->Next(), "slice");
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const RowBatch &batch = iter->Value();
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for (bst_ulong i = 0; i < len; ++i) {
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const int ridx = idxset[i];
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RowBatch::Inst inst = batch[ridx];
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utils::Check(static_cast<bst_ulong>(ridx) < batch.size, "slice index exceed number of rows");
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ret.row_data_.resize(ret.row_data_.size() + inst.length);
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memcpy(&ret.row_data_[ret.row_ptr_.back()], inst.data,
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sizeof(RowBatch::Entry) * inst.length);
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ret.row_ptr_.push_back(ret.row_ptr_.back() + inst.length);
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if (src.info.labels.size() != 0) {
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ret.info.labels.push_back(src.info.labels[ridx]);
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}
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if (src.info.weights.size() != 0) {
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ret.info.weights.push_back(src.info.weights[ridx]);
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}
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if (src.info.info.root_index.size() != 0) {
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ret.info.info.root_index.push_back(src.info.info.root_index[ridx]);
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}
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if (src.info.info.fold_index.size() != 0) {
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ret.info.info.fold_index.push_back(src.info.info.fold_index[ridx]);
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}
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}
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return p_ret;
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}
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void XGDMatrixFree(void *handle) {
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delete static_cast<DataMatrix*>(handle);
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}
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void XGDMatrixSaveBinary(void *handle, const char *fname, int silent) {
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SaveDataMatrix(*static_cast<DataMatrix*>(handle), fname, silent != 0);
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}
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void XGDMatrixSetFloatInfo(void *handle, const char *field, const float *info, bst_ulong len) {
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std::vector<float> &vec =
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static_cast<DataMatrix*>(handle)->info.GetFloatInfo(field);
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vec.resize(len);
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memcpy(BeginPtr(vec), info, sizeof(float) * len);
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}
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void XGDMatrixSetUIntInfo(void *handle, const char *field, const unsigned *info, bst_ulong len) {
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std::vector<unsigned> &vec =
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static_cast<DataMatrix*>(handle)->info.GetUIntInfo(field);
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vec.resize(len);
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memcpy(BeginPtr(vec), info, sizeof(unsigned) * len);
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}
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void XGDMatrixSetGroup(void *handle, const unsigned *group, bst_ulong len) {
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DataMatrix *pmat = static_cast<DataMatrix*>(handle);
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pmat->info.group_ptr.resize(len + 1);
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pmat->info.group_ptr[0] = 0;
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for (uint64_t i = 0; i < len; ++i) {
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pmat->info.group_ptr[i+1] = pmat->info.group_ptr[i]+group[i];
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}
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}
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const float* XGDMatrixGetFloatInfo(const void *handle, const char *field, bst_ulong* len) {
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const std::vector<float> &vec =
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static_cast<const DataMatrix*>(handle)->info.GetFloatInfo(field);
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*len = static_cast<bst_ulong>(vec.size());
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return BeginPtr(vec);
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}
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const unsigned* XGDMatrixGetUIntInfo(const void *handle, const char *field, bst_ulong* len) {
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const std::vector<unsigned> &vec =
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static_cast<const DataMatrix*>(handle)->info.GetUIntInfo(field);
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*len = static_cast<bst_ulong>(vec.size());
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return BeginPtr(vec);
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}
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bst_ulong XGDMatrixNumRow(const void *handle) {
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return static_cast<bst_ulong>(static_cast<const DataMatrix*>(handle)->info.num_row());
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}
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// xgboost implementation
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void *XGBoosterCreate(void *dmats[], bst_ulong len) {
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std::vector<DataMatrix*> mats;
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for (bst_ulong i = 0; i < len; ++i) {
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DataMatrix *dtr = static_cast<DataMatrix*>(dmats[i]);
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mats.push_back(dtr);
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}
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return new Booster(mats);
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}
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void XGBoosterFree(void *handle) {
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delete static_cast<Booster*>(handle);
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}
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void XGBoosterSetParam(void *handle, const char *name, const char *value) {
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static_cast<Booster*>(handle)->SetParam(name, value);
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}
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void XGBoosterUpdateOneIter(void *handle, int iter, void *dtrain) {
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Booster *bst = static_cast<Booster*>(handle);
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DataMatrix *dtr = static_cast<DataMatrix*>(dtrain);
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bst->CheckInitModel();
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bst->CheckInit(dtr);
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bst->UpdateOneIter(iter, *dtr);
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}
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void XGBoosterBoostOneIter(void *handle, void *dtrain,
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float *grad, float *hess, bst_ulong len) {
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Booster *bst = static_cast<Booster*>(handle);
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DataMatrix *dtr = static_cast<DataMatrix*>(dtrain);
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bst->CheckInitModel();
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bst->CheckInit(dtr);
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bst->BoostOneIter(*dtr, grad, hess, len);
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}
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const char* XGBoosterEvalOneIter(void *handle, int iter, void *dmats[],
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const char *evnames[], bst_ulong len) {
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Booster *bst = static_cast<Booster*>(handle);
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std::vector<std::string> names;
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std::vector<const DataMatrix*> mats;
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for (bst_ulong i = 0; i < len; ++i) {
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mats.push_back(static_cast<DataMatrix*>(dmats[i]));
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names.push_back(std::string(evnames[i]));
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}
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bst->CheckInitModel();
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bst->eval_str = bst->EvalOneIter(iter, mats, names);
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return bst->eval_str.c_str();
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}
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const float *XGBoosterPredict(void *handle, void *dmat, int option_mask, unsigned ntree_limit, bst_ulong *len) {
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return static_cast<Booster*>(handle)->Pred(*static_cast<DataMatrix*>(dmat), option_mask, ntree_limit, len);
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}
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void XGBoosterLoadModel(void *handle, const char *fname) {
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static_cast<Booster*>(handle)->LoadModel(fname);
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}
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void XGBoosterSaveModel(const void *handle, const char *fname) {
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static_cast<const Booster*>(handle)->SaveModel(fname);
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}
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void XGBoosterLoadModelFromBuffer(void *handle, const void *buf, bst_ulong len) {
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static_cast<Booster*>(handle)->LoadModelFromBuffer(buf, len);
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}
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const char *XGBoosterGetModelRaw(void *handle, bst_ulong *out_len) {
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return static_cast<Booster*>(handle)->GetModelRaw(out_len);
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}
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const char** XGBoosterDumpModel(void *handle, const char *fmap, int with_stats, bst_ulong *len){
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utils::FeatMap featmap;
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if (strlen(fmap) != 0) {
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featmap.LoadText(fmap);
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}
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return static_cast<Booster*>(handle)->GetModelDump(featmap, with_stats != 0, len);
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}
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}
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