304 lines
13 KiB
C++
304 lines
13 KiB
C++
#ifndef XGBOOST_REGRANK_H
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#define XGBOOST_REGRANK_H
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/*!
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* \file xgboost_regrank.h
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* \brief class for gradient boosted regression and ranking
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* \author Kailong Chen: chenkl198812@gmail.com, Tianqi Chen: tianqi.tchen@gmail.com
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*/
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#include <cmath>
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#include <cstdlib>
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#include <cstring>
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#include "xgboost_regrank_data.h"
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#include "xgboost_regrank_eval.h"
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#include "xgboost_regrank_obj.h"
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#include "../utils/xgboost_omp.h"
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#include "../booster/xgboost_gbmbase.h"
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#include "../utils/xgboost_utils.h"
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#include "../utils/xgboost_stream.h"
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namespace xgboost{
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namespace regrank{
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/*! \brief class for gradient boosted regression and ranking */
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class RegRankBoostLearner{
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public:
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/*! \brief constructor */
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RegRankBoostLearner(void){
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silent = 0;
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obj_ = NULL;
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name_obj_ = "reg";
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}
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/*!
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* \brief a regression booter associated with training and evaluating data
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* \param train pointer to the training data
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* \param evals array of evaluating data
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* \param evname name of evaluation data, used print statistics
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*/
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RegRankBoostLearner(const DMatrix *train,
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const std::vector<DMatrix *> &evals,
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const std::vector<std::string> &evname){
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silent = 0;
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this->SetData(train, evals, evname);
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}
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/*!
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* \brief associate regression booster with training and evaluating data
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* \param train pointer to the training data
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* \param evals array of evaluating data
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* \param evname name of evaluation data, used print statistics
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*/
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inline void SetData(const DMatrix *train,
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const std::vector<DMatrix *> &evals,
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const std::vector<std::string> &evname){
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this->train_ = train;
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this->evals_ = evals;
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this->evname_ = evname;
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// estimate feature bound
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int num_feature = (int)(train->data.NumCol());
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// assign buffer index
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unsigned buffer_size = static_cast<unsigned>(train->Size());
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for (size_t i = 0; i < evals.size(); ++i){
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buffer_size += static_cast<unsigned>(evals[i]->Size());
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num_feature = std::max(num_feature, (int)(evals[i]->data.NumCol()));
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}
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char str_temp[25];
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if (num_feature > mparam.num_feature){
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mparam.num_feature = num_feature;
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sprintf(str_temp, "%d", num_feature);
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base_gbm.SetParam("bst:num_feature", str_temp);
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}
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sprintf(str_temp, "%u", buffer_size);
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base_gbm.SetParam("num_pbuffer", str_temp);
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if (!silent){
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printf("buffer_size=%u\n", buffer_size);
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}
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// set eval_preds tmp sapce
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this->eval_preds_.resize(evals.size(), std::vector<float>());
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}
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/*!
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* \brief set parameters from outside
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* \param name name of the parameter
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* \param val value of the parameter
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*/
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inline void SetParam(const char *name, const char *val){
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if (!strcmp(name, "silent")) silent = atoi(val);
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if (!strcmp(name, "eval_metric")) evaluator_.AddEval(val);
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if (!strcmp(name, "objective") ) name_obj_ = val;
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mparam.SetParam(name, val);
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base_gbm.SetParam(name, val);
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cfg_.push_back( std::make_pair( std::string(name), std::string(val) ) );
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}
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/*!
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* \brief initialize solver before training, called before training
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* this function is reserved for solver to allocate necessary space and do other preparation
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*/
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inline void InitTrainer(void){
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base_gbm.InitTrainer();
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obj_ = CreateObjFunction( name_obj_.c_str() );
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for( size_t i = 0; i < cfg_.size(); ++ i ){
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obj_->SetParam( cfg_[i].first.c_str(), cfg_[i].second.c_str() );
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}
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evaluator_.AddEval( obj_->DefaultEvalMetric() );
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}
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/*!
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* \brief initialize the current data storage for model, if the model is used first time, call this function
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*/
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inline void InitModel(void){
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base_gbm.InitModel();
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mparam.AdjustBase();
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}
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/*!
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* \brief load model from stream
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* \param fi input stream
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*/
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inline void LoadModel(utils::IStream &fi){
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base_gbm.LoadModel(fi);
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utils::Assert(fi.Read(&mparam, sizeof(ModelParam)) != 0);
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}
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/*!
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* \brief DumpModel
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* \param fo text file
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* \param fmap feature map that may help give interpretations of feature
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* \param with_stats whether print statistics as well
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*/
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inline void DumpModel(FILE *fo, const utils::FeatMap& fmap, bool with_stats){
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base_gbm.DumpModel(fo, fmap, with_stats);
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}
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/*!
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* \brief Dump path of all trees
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* \param fo text file
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* \param data input data
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*/
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inline void DumpPath(FILE *fo, const DMatrix &data){
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base_gbm.DumpPath(fo, data.data);
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}
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/*!
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* \brief save model to stream
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* \param fo output stream
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*/
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inline void SaveModel(utils::IStream &fo) const{
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base_gbm.SaveModel(fo);
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fo.Write(&mparam, sizeof(ModelParam));
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}
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/*!
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* \brief update the model for one iteration
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* \param iteration iteration number
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*/
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inline void UpdateOneIter(int iter){
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this->PredictBuffer(preds_, *train_, 0);
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obj_->GetGradient(preds_, train_->info, base_gbm.NumBoosters(), grad_, hess_);
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std::vector<unsigned> root_index;
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base_gbm.DoBoost(grad_, hess_, train_->data, root_index);
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}
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/*!
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* \brief evaluate the model for specific iteration
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* \param iter iteration number
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* \param fo file to output log
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*/
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inline void EvalOneIter(int iter, FILE *fo = stderr){
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fprintf(fo, "[%d]", iter);
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int buffer_offset = static_cast<int>(train_->Size());
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for (size_t i = 0; i < evals_.size(); ++i){
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std::vector<float> &preds = this->eval_preds_[i];
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this->PredictBuffer(preds, *evals_[i], buffer_offset);
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obj_->PredTransform(preds);
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evaluator_.Eval(fo, evname_[i].c_str(), preds, evals_[i]->info);
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buffer_offset += static_cast<int>(evals_[i]->Size());
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}
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fprintf(fo, "\n");
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fflush(fo);
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}
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/*! \brief get prediction, without buffering */
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inline void Predict(std::vector<float> &preds, const DMatrix &data){
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preds.resize(data.Size());
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const unsigned ndata = static_cast<unsigned>(data.Size());
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#pragma omp parallel for schedule( static )
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for (unsigned j = 0; j < ndata; ++j){
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preds[j] = mparam.base_score + base_gbm.Predict(data.data, j, -1);
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}
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obj_->PredTransform( preds );
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}
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public:
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/*!
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* \brief interactive update
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* \param action action type
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*/
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inline void UpdateInteract(std::string action){
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this->InteractPredict(preds_, *train_, 0);
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int buffer_offset = static_cast<int>(train_->Size());
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for (size_t i = 0; i < evals_.size(); ++i){
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std::vector<float> &preds = this->eval_preds_[i];
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this->InteractPredict(preds, *evals_[i], buffer_offset);
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buffer_offset += static_cast<int>(evals_[i]->Size());
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}
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if (action == "remove"){
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base_gbm.DelteBooster(); return;
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}
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obj_->GetGradient(preds_, train_->info, base_gbm.NumBoosters(), grad_, hess_);
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std::vector<unsigned> root_index;
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base_gbm.DoBoost(grad_, hess_, train_->data, root_index);
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this->InteractRePredict(*train_, 0);
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buffer_offset = static_cast<int>(train_->Size());
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for (size_t i = 0; i < evals_.size(); ++i){
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this->InteractRePredict(*evals_[i], buffer_offset);
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buffer_offset += static_cast<int>(evals_[i]->Size());
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}
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}
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private:
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/*! \brief get the transformed predictions, given data */
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inline void InteractPredict(std::vector<float> &preds, const DMatrix &data, unsigned buffer_offset){
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preds.resize(data.Size());
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const unsigned ndata = static_cast<unsigned>(data.Size());
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#pragma omp parallel for schedule( static )
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for (unsigned j = 0; j < ndata; ++j){
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preds[j] = mparam.base_score + base_gbm.InteractPredict(data.data, j, buffer_offset + j);
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}
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obj_->PredTransform( preds );
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}
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/*! \brief repredict trial */
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inline void InteractRePredict(const DMatrix &data, unsigned buffer_offset){
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const unsigned ndata = static_cast<unsigned>(data.Size());
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#pragma omp parallel for schedule( static )
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for (unsigned j = 0; j < ndata; ++j){
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base_gbm.InteractRePredict(data.data, j, buffer_offset + j);
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}
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}
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private:
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/*! \brief get the transformed predictions, given data */
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inline void PredictBuffer(std::vector<float> &preds, const DMatrix &data, unsigned buffer_offset){
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preds.resize(data.Size());
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const unsigned ndata = static_cast<unsigned>(data.Size());
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#pragma omp parallel for schedule( static )
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for (unsigned j = 0; j < ndata; ++j){
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preds[j] = mparam.base_score + base_gbm.Predict(data.data, j, buffer_offset + j);
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}
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}
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private:
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/*! \brief training parameter for regression */
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struct ModelParam{
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/* \brief global bias */
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float base_score;
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/* \brief type of loss function */
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int loss_type;
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/* \brief number of features */
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int num_feature;
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/*! \brief reserved field */
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int reserved[16];
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/*! \brief constructor */
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ModelParam(void){
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base_score = 0.5f;
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loss_type = 0;
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num_feature = 0;
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memset(reserved, 0, sizeof(reserved));
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}
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/*!
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* \brief set parameters from outside
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* \param name name of the parameter
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* \param val value of the parameter
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*/
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inline void SetParam(const char *name, const char *val){
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if (!strcmp("base_score", name)) base_score = (float)atof(val);
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if (!strcmp("loss_type", name)) loss_type = atoi(val);
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if (!strcmp("bst:num_feature", name)) num_feature = atoi(val);
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}
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/*!
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* \brief adjust base_score
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*/
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inline void AdjustBase(void){
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if (loss_type == 1 || loss_type == 2){
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utils::Assert(base_score > 0.0f && base_score < 1.0f, "sigmoid range constrain");
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base_score = -logf(1.0f / base_score - 1.0f);
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}
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}
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};
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private:
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int silent;
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EvalSet evaluator_;
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booster::GBMBase base_gbm;
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ModelParam mparam;
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const DMatrix *train_;
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std::vector<DMatrix *> evals_;
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std::vector<std::string> evname_;
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std::vector<unsigned> buffer_index_;
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// objective fnction
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IObjFunction *obj_;
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// name of objective function
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std::string name_obj_;
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std::vector< std::pair<std::string, std::string> > cfg_;
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private:
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std::vector<float> grad_, hess_, preds_;
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std::vector< std::vector<float> > eval_preds_;
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};
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}
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};
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#endif
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