347 lines
15 KiB
C++
347 lines
15 KiB
C++
#ifndef XGBOOST_REG_H
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#define XGBOOST_REG_H
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/*!
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* \file xgboost_reg.h
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* \brief class for gradient boosted regression
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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_reg_data.h"
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#include "xgboost_reg_eval.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 regression{
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/*! \brief class for gradient boosted regression */
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class RegBoostLearner{
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public:
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/*! \brief constructor */
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RegBoostLearner( void ){
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silent = 0;
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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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RegBoostLearner( 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 > base_model.param.num_feature ){
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sprintf( str_temp, "%d", num_feature );
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base_model.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_model.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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mparam.SetParam( name, val );
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base_model.SetParam( name, 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_model.InitTrainer();
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if( mparam.loss_type == kLogisticClassify ){
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evaluator_.AddEval( "error" );
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}else{
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evaluator_.AddEval( "rmse" );
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}
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evaluator_.Init();
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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_model.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_model.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_model.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_model.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_model.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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this->GetGradient( preds_, train_->labels, grad_, hess_ );
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std::vector<unsigned> root_index;
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base_model.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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evaluator_.Eval( fo, evname_[i].c_str(), preds, (*evals_[i]).labels );
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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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}
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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.PredTransform
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( mparam.base_score + base_model.Predict( data.data, j, -1 ) );
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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.PredTransform
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( mparam.base_score + base_model.Predict( data.data, j, buffer_offset + j ) );
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}
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}
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/*! \brief get the first order and second order gradient, given the transformed predictions and labels */
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inline void GetGradient( const std::vector<float> &preds,
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const std::vector<float> &labels,
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std::vector<float> &grad,
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std::vector<float> &hess ){
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grad.resize( preds.size() ); hess.resize( preds.size() );
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const unsigned ndata = static_cast<unsigned>( preds.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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grad[j] = mparam.FirstOrderGradient( preds[j], labels[j] );
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hess[j] = mparam.SecondOrderGradient( preds[j], labels[j] );
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}
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}
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private:
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enum LossType{
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kLinearSquare = 0,
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kLogisticNeglik = 1,
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kLogisticClassify = 2
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};
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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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ModelParam( void ){
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base_score = 0.5f;
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loss_type = 0;
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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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}
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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 ){
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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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* \brief transform the linear sum to prediction
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* \param x linear sum of boosting ensemble
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* \return transformed prediction
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*/
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inline float PredTransform( float x ){
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switch( loss_type ){
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case kLinearSquare: return x;
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case kLogisticClassify:
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case kLogisticNeglik: return 1.0f/(1.0f + expf(-x));
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default: utils::Error("unknown loss_type"); return 0.0f;
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}
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}
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/*!
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* \brief calculate first order gradient of loss, given transformed prediction
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* \param predt transformed prediction
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* \param label true label
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* \return first order gradient
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*/
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inline float FirstOrderGradient( float predt, float label ) const{
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switch( loss_type ){
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case kLinearSquare: return predt - label;
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case kLogisticClassify:
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case kLogisticNeglik: return predt - label;
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default: utils::Error("unknown loss_type"); return 0.0f;
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}
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}
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/*!
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* \brief calculate second order gradient of loss, given transformed prediction
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* \param predt transformed prediction
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* \param label true label
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* \return second order gradient
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*/
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inline float SecondOrderGradient( float predt, float label ) const{
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switch( loss_type ){
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case kLinearSquare: return 1.0f;
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case kLogisticClassify:
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case kLogisticNeglik: return predt * ( 1 - predt );
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default: utils::Error("unknown loss_type"); return 0.0f;
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}
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}
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/*!
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* \brief calculating the loss, given the predictions, labels and the loss type
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* \param preds the given predictions
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* \param labels the given labels
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* \return the specified loss
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*/
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inline float Loss(const std::vector<float> &preds, const std::vector<float> &labels) const{
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switch( loss_type ){
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case kLinearSquare: return SquareLoss(preds,labels);
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case kLogisticNeglik:
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case kLogisticClassify: return NegLoglikelihoodLoss(preds,labels);
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default: utils::Error("unknown loss_type"); return 0.0f;
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}
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}
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/*!
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* \brief calculating the square loss, given the predictions and labels
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* \param preds the given predictions
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* \param labels the given labels
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* \return the summation of square loss
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*/
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inline float SquareLoss(const std::vector<float> &preds, const std::vector<float> &labels) const{
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float ans = 0.0;
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for(size_t i = 0; i < preds.size(); i++){
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float dif = preds[i] - labels[i];
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ans += dif * dif;
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}
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return ans;
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}
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/*!
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* \brief calculating the square loss, given the predictions and labels
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* \param preds the given predictions
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* \param labels the given labels
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* \return the summation of square loss
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*/
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inline float NegLoglikelihoodLoss(const std::vector<float> &preds, const std::vector<float> &labels) const{
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float ans = 0.0;
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for(size_t i = 0; i < preds.size(); i++)
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ans -= labels[i] * logf(preds[i]) + ( 1 - labels[i] ) * logf(1 - preds[i]);
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return ans;
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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::GBMBaseModel base_model;
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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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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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