cleanup reg
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booster/gbrt.h
102
booster/gbrt.h
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#ifndef _GBRT_H_
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#define _GBRT_H_
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#include "../utils/xgboost_config.h"
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#include "../utils/xgboost_stream.h"
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#include "xgboost_regression_data_reader.h"
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#include "xgboost_gbmbase.h"
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#include <math.h>
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using namespace xgboost::utils;
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using namespace xgboost::booster;
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class gbrt{
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public:
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gbrt(const char* config_path){
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ConfigIterator config_itr(config_path);
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while(config_itr.Next()){
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SetParam(config_itr.name,config_itr.val);
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base_model.SetParam(config_itr.name,config_itr.val);
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}
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}
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void SetParam( const char *name, const char *val ){
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param.SetParam(name, val);
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}
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void train(){
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xgboost_regression_data_reader data_reader(param.train_file_path);
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base_model.InitModel();
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base_model.InitTrainer();
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std::vector<float> grad,hess;
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std::vector<unsigned> root_index;
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int instance_num = data_reader.InsNum();
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float label = 0,pred_transform = 0;
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grad.resize(instance_num); hess.resize(instance_num);
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for(int i = 0; i < 100; i++){
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grad.clear();hess.clear();
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for(int j = 0; j < instance_num; j++){
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label = data_reader.GetLabel(j);
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pred_transform = Logistic(Predict(data_reader.GetLine(j)));
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grad.push_back(FirstOrderGradient(pred_transform,label));
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hess.push_back(SecondOrderGradient(pred_transform));
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}
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base_model.DoBoost(grad,hess,data_reader.GetImage(),root_index );
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}
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}
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inline void SaveModel(IStream &fo ){
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base_model.SaveModel(fo);
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}
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inline void LoadModel(IStream &fi ){
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base_model.LoadModel(fi);
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}
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float Predict( const FMatrixS::Line &feat, int buffer_index = -1, unsigned rid = 0 ){
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return base_model.Predict(feat,buffer_index,rid);
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}
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float Predict( const std::vector<float> &feat,
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const std::vector<bool> &funknown,
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int buffer_index = -1,
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unsigned rid = 0 ){
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return base_model.Predict(feat,funknown,buffer_index,rid);
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}
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struct GBRTParam{
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/*! \brief path of input training data */
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const char* train_file_path;
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GBRTParam( void ){
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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("train_file_path", name ) ) train_file_path = val;
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}
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};
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private:
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inline float FirstOrderGradient(float pred_transform,float label){
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return label - pred_transform;
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}
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inline float SecondOrderGradient(float pred_transform){
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return pred_transform * ( 1 - pred_transform );
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}
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inline float Logistic(float x){
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return 1.0/(1.0 + exp(-x));
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}
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GBMBaseModel base_model;
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GBRTParam param;
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};
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#endif
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@ -13,6 +13,7 @@
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// implementations of boosters
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#include "tree/xgboost_svdf_tree.hpp"
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#include "linear/xgboost_linear.hpp"
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#include "../regression/xgboost_reg.h"
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namespace xgboost{
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namespace booster{
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154
regression/xgboost_reg.h
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154
regression/xgboost_reg.h
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#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 "xgboost_regdata.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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/*!
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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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std::vector<const DMatrix *> evals,
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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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//TODO: assign buffer index
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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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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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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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utils::Assert( fi.Read( &mparam, sizeof(ModelParam) ) != 0 );
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base_model.LoadModel( fi );
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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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fo.Write( &mparam, sizeof(ModelParam) );
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base_model.SaveModel( fo );
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}
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/*!
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* \brief update the model for one iteration
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*/
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inline void UpdateOneIter( void ){
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//TODO
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}
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/*! \brief predict the results, given data */
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inline void Predict( std::vector<float> &preds, const DMatrix &data ){
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//TODO
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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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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 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 0: return predt - label;
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case 1: 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 0: return 1.0f;
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case 1: 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 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 0: return x;
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case 1: 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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private:
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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<const DMatrix *> evals_;
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std::vector<std::string> evname_;
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};
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};
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};
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#endif
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