LightGBM::RegressionL2loss Class Reference

Objective function for regression. More...

#include <regression_objective.hpp>

Inheritance diagram for LightGBM::RegressionL2loss:

Public Member Functions
	RegressionL2loss (const Config &config)
	RegressionL2loss (const std::vector< std::string > &strs)
void	Init (const Metadata &metadata, data_size_t num_data) override
	Initialize.
void	GetGradients (const double *score, score_t *gradients, score_t *hessians) const override
	calculating first order derivative of loss function
const char *	GetName () const override
void	ConvertOutput (const double *input, double *output) const override
std::string	ToString () const override
bool	IsConstantHessian () const override
double	BoostFromScore (int) const override
Public Member Functions inherited from LightGBM::ObjectiveFunction
virtual	~ObjectiveFunction ()
	virtual destructor
virtual bool	IsRenewTreeOutput () const
virtual double	RenewTreeOutput (double ori_output, const double *, const data_size_t *, const data_size_t *, data_size_t) const
virtual double	RenewTreeOutput (double ori_output, double, const data_size_t *, const data_size_t *, data_size_t) const
virtual bool	ClassNeedTrain (int) const
virtual bool	SkipEmptyClass () const
virtual int	NumModelPerIteration () const
virtual int	NumPredictOneRow () const
virtual bool	NeedAccuratePrediction () const
	The prediction should be accurate or not. True will disable early stopping for prediction.
ObjectiveFunction &	operator= (const ObjectiveFunction &)=delete
	Disable copy.
	ObjectiveFunction (const ObjectiveFunction &)=delete
	Disable copy.

Protected Attributes
bool	sqrt_
data_size_t	num_data_
	Number of data.
const label_t *	label_
	Pointer of label.
const label_t *	weights_
	Pointer of weights.
std::vector< label_t >	trans_label_

Additional Inherited Members
Static Public Member Functions inherited from LightGBM::ObjectiveFunction
static LIGHTGBM_EXPORT ObjectiveFunction *	CreateObjectiveFunction (const std::string &type, const Config &config)
	Create object of objective function.
static LIGHTGBM_EXPORT ObjectiveFunction *	CreateObjectiveFunction (const std::string &str)
	Load objective function from string object.

Detailed Description

Objective function for regression.

Member Function Documentation

BoostFromScore()

double LightGBM::RegressionL2loss::BoostFromScore ( int  ) const

inlineoverridevirtual

Reimplemented from LightGBM::ObjectiveFunction.

ConvertOutput()

void LightGBM::RegressionL2loss::ConvertOutput ( const double *  input, double *  output  ) const

inlineoverridevirtual

Reimplemented from LightGBM::ObjectiveFunction.

GetGradients()

void LightGBM::RegressionL2loss::GetGradients ( const double *  score, score_t *  gradients, score_t *  hessians  ) const

inlineoverridevirtual

calculating first order derivative of loss function

Parameters

score

prediction score in this round \gradients Output gradients \hessians Output hessians

Implements LightGBM::ObjectiveFunction.

Reimplemented in LightGBM::RegressionPoissonLoss, LightGBM::RegressionQuantileloss, LightGBM::RegressionMAPELOSS, and LightGBM::RegressionTweedieLoss.

GetName()

const char * LightGBM::RegressionL2loss::GetName ( ) const

inlineoverridevirtual

Implements LightGBM::ObjectiveFunction.

Init()

void LightGBM::RegressionL2loss::Init ( const Metadata &  metadata, data_size_t  num_data  )

inlineoverridevirtual

Initialize.

Parameters

metadata	Label data
num_data	Number of data

Implements LightGBM::ObjectiveFunction.

Reimplemented in LightGBM::RegressionPoissonLoss, and LightGBM::RegressionMAPELOSS.

IsConstantHessian()

bool LightGBM::RegressionL2loss::IsConstantHessian ( ) const

inlineoverridevirtual

Reimplemented from LightGBM::ObjectiveFunction.

ToString()

std::string LightGBM::RegressionL2loss::ToString ( ) const

inlineoverridevirtual

Implements LightGBM::ObjectiveFunction.

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The documentation for this class was generated from the following file:

• External/LightGBM_2.2.3/LightGBM-2.2.3/src/objective/regression_objective.hpp