120 lines
4.5 KiB
C++
120 lines
4.5 KiB
C++
#ifndef XGBOOST_REG_EVAL_H
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#define XGBOOST_REG_EVAL_H
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/*!
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* \file xgboost_reg_eval.h
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* \brief evaluation metrics for regression and classification
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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 <vector>
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#include <algorithm>
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#include "../utils/xgboost_utils.h"
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#include "../utils/xgboost_omp.h"
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namespace xgboost{
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namespace regression{
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/*! \brief evaluator that evaluates the loss metrics */
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struct IEvaluator{
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/*!
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* \brief evaluate a specific metric
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* \param preds prediction
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* \param labels label
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*/
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virtual float Eval( const std::vector<float> &preds,
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const std::vector<float> &labels ) const= 0;
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/*! \return name of metric */
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virtual const char *Name( void ) const= 0;
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};
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/*! \brief RMSE */
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struct EvalRMSE : public IEvaluator{
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virtual float Eval( const std::vector<float> &preds,
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const std::vector<float> &labels ) const{
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const unsigned ndata = static_cast<unsigned>( preds.size() );
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float sum = 0.0;
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#pragma omp parallel for reduction(+:sum) schedule( static )
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for( unsigned i = 0; i < ndata; ++ i ){
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float diff = preds[i] - labels[i];
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sum += diff * diff;
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}
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return sqrtf( sum / ndata );
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}
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virtual const char *Name( void ) const{
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return "rmse";
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}
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};
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/*! \brief Error */
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struct EvalError : public IEvaluator{
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virtual float Eval( const std::vector<float> &preds,
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const std::vector<float> &labels ) const{
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const unsigned ndata = static_cast<unsigned>( preds.size() );
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unsigned nerr = 0;
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#pragma omp parallel for reduction(+:nerr) schedule( static )
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for( unsigned i = 0; i < ndata; ++ i ){
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if( preds[i] > 0.5f ){
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if( labels[i] < 0.5f ) nerr += 1;
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}else{
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if( labels[i] > 0.5f ) nerr += 1;
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}
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}
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return static_cast<float>(nerr) / ndata;
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}
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virtual const char *Name( void ) const{
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return "error";
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}
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};
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/*! \brief Error */
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struct EvalLogLoss : public IEvaluator{
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virtual float Eval( const std::vector<float> &preds,
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const std::vector<float> &labels ) const{
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const unsigned ndata = static_cast<unsigned>( preds.size() );
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unsigned nerr = 0;
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#pragma omp parallel for reduction(+:nerr) schedule( static )
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for( unsigned i = 0; i < ndata; ++ i ){
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const float y = labels[i];
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const float py = preds[i];
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nerr -= y * std::log(py) + (1.0f-y)*std::log(1-py);
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}
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return static_cast<float>(nerr) / ndata;
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}
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virtual const char *Name( void ) const{
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return "negllik";
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}
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};
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};
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namespace regression{
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/*! \brief a set of evaluators */
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struct EvalSet{
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public:
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inline void AddEval( const char *name ){
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if( !strcmp( name, "rmse") ) evals_.push_back( &rmse_ );
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if( !strcmp( name, "error") ) evals_.push_back( &error_ );
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if( !strcmp( name, "logloss") ) evals_.push_back( &logloss_ );
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}
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inline void Init( void ){
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std::sort( evals_.begin(), evals_.end() );
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evals_.resize( std::unique( evals_.begin(), evals_.end() ) - evals_.begin() );
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}
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inline void Eval( FILE *fo, const char *evname,
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const std::vector<float> &preds,
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const std::vector<float> &labels ) const{
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for( size_t i = 0; i < evals_.size(); ++ i ){
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float res = evals_[i]->Eval( preds, labels );
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fprintf( fo, "\t%s-%s:%f", evname, evals_[i]->Name(), res );
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}
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}
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private:
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EvalRMSE rmse_;
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EvalError error_;
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EvalLogLoss logloss_;
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std::vector<const IEvaluator*> evals_;
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};
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};
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};
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#endif
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