interface of objective function More...

#include <objective.h>

Inheritance diagram for xgboost::ObjFunction:

Public Member Functions
	~ObjFunction () override=default
	virtual destructor

virtual void	Configure (const std::vector< std::pair< std::string, std::string > > &args)=0
	Configure the objective with the specified parameters.

virtual void	GetGradient (const HostDeviceVector< bst_float > &preds, const MetaInfo &info, int iteration, HostDeviceVector< GradientPair > *out_gpair)=0
	Get gradient over each of predictions, given existing information.

virtual const char *	DefaultEvalMetric () const =0

virtual Json	DefaultMetricConfig () const
	Return the configuration for the default metric.

virtual void	PredTransform (HostDeviceVector< bst_float > *) const
	transform prediction values, this is only called when Prediction is called

virtual void	EvalTransform (HostDeviceVector< bst_float > *io_preds)
	transform prediction values, this is only called when Eval is called, usually it redirect to PredTransform

virtual bst_float	ProbToMargin (bst_float base_score) const
	transform probability value back to margin this is used to transform user-set base_score back to margin used by gradient boosting

virtual void	InitEstimation (MetaInfo const &info, linalg::Tensor< float, 1 > *base_score) const
	Make initialize estimation of prediction.

virtual struct ObjInfo	Task () const =0
	Return task of this objective.

virtual bst_target_t	Targets (MetaInfo const &info) const
	Return number of targets for input matrix.

virtual void	UpdateTreeLeaf (HostDeviceVector< bst_node_t > const &, MetaInfo const &, float, HostDeviceVector< float > const &, std::int32_t, RegTree *) const
	Update the leaf values after a tree is built.

Public Member Functions inherited from xgboost::Configurable
virtual void	LoadConfig (Json const &in)=0
	Load configuration from JSON object.

virtual void	SaveConfig (Json *out) const =0
	Save configuration to JSON object.

Static Public Member Functions
static constexpr float	DefaultBaseScore ()

static ObjFunction *	Create (const std::string &name, Context const *ctx)
	Create an objective function according to name.

Protected Attributes
Context const *	ctx_

Detailed Description

interface of objective function

Member Function Documentation

◆ Configure()

virtual void xgboost::ObjFunction::Configure ( const std::vector< std::pair< std::string, std::string > > & args )

pure virtual

Configure the objective with the specified parameters.

Parameters

args	arguments to the objective function.

Implemented in xgboost::obj::LambdaRankObj< Loss, Cache >, xgboost::obj::LambdaRankObj< LambdaRankMAP, ltr::MAPCache >, xgboost::obj::LambdaRankObj< LambdaRankNDCG, ltr::NDCGCache >, xgboost::obj::LambdaRankObj< LambdaRankPairwise, ltr::RankingCache >, xgboost::obj::MyLogistic, and xgboost::obj::RegLossObjOneAPI< Loss >.

◆ Create()

ObjFunction * xgboost::ObjFunction::Create	(	const std::string &	name,
		Context const *	ctx
	)

static

Create an objective function according to name.

Parameters

ctx	Pointer to runtime parameters.
name	Name of the objective.

◆ DefaultEvalMetric()

virtual const char * xgboost::ObjFunction::DefaultEvalMetric ( ) const

pure virtual

Returns: the default evaluation metric for the objective

Implemented in xgboost::obj::MyLogistic, xgboost::obj::RegLossObjOneAPI< Loss >, xgboost::obj::LambdaRankNDCG, xgboost::obj::LambdaRankMAP, and xgboost::obj::LambdaRankPairwise.

◆ DefaultMetricConfig()

virtual Json xgboost::ObjFunction::DefaultMetricConfig ( ) const

inlinevirtual

Return the configuration for the default metric.

Reimplemented in xgboost::obj::LambdaRankNDCG.

◆ EvalTransform()

virtual void xgboost::ObjFunction::EvalTransform ( HostDeviceVector< bst_float > * io_preds )

inlinevirtual

transform prediction values, this is only called when Eval is called, usually it redirect to PredTransform

Parameters

io_preds prediction values, saves to this vector as well

◆ GetGradient()

virtual void xgboost::ObjFunction::GetGradient	(	const HostDeviceVector< bst_float > &	preds,
		const MetaInfo &	info,
		int	iteration,
		HostDeviceVector< GradientPair > *	out_gpair
	)

pure virtual

Get gradient over each of predictions, given existing information.

Parameters

preds	prediction of current round
info	information about labels, weights, groups in rank
iteration	current iteration number.
out_gpair	output of get gradient, saves gradient and second order gradient in

Implemented in xgboost::obj::RegLossObjOneAPI< Loss >.

◆ InitEstimation()

void xgboost::ObjFunction::InitEstimation	(	MetaInfo const &	info,
		linalg::Tensor< float, 1 > *	base_score
	)		const

virtual

Make initialize estimation of prediction.

Parameters

info	MetaInfo that contains label.
base_score	Output estimation.

◆ PredTransform()

virtual void xgboost::ObjFunction::PredTransform ( HostDeviceVector< bst_float > * ) const

inlinevirtual

transform prediction values, this is only called when Prediction is called

Parameters

io_preds prediction values, saves to this vector as well

Reimplemented in xgboost::obj::MyLogistic.

◆ ProbToMargin()

virtual bst_float xgboost::ObjFunction::ProbToMargin ( bst_float base_score ) const

inlinevirtual

transform probability value back to margin this is used to transform user-set base_score back to margin used by gradient boosting

Returns: transformed value

Reimplemented in xgboost::obj::MyLogistic, and xgboost::obj::RegLossObjOneAPI< Loss >.

◆ Targets()

virtual bst_target_t xgboost::ObjFunction::Targets ( MetaInfo const & info ) const

inlinevirtual

Return number of targets for input matrix.

Right now XGBoost supports only multi-target regression.

Reimplemented in xgboost::obj::LambdaRankObj< Loss, Cache >, xgboost::obj::LambdaRankObj< LambdaRankMAP, ltr::MAPCache >, xgboost::obj::LambdaRankObj< LambdaRankNDCG, ltr::NDCGCache >, and xgboost::obj::LambdaRankObj< LambdaRankPairwise, ltr::RankingCache >.

◆ Task()

virtual struct ObjInfo xgboost::ObjFunction::Task ( ) const

pure virtual

Return task of this objective.

Implemented in xgboost::obj::MyLogistic, xgboost::obj::LambdaRankObj< Loss, Cache >, xgboost::obj::LambdaRankObj< LambdaRankMAP, ltr::MAPCache >, xgboost::obj::LambdaRankObj< LambdaRankNDCG, ltr::NDCGCache >, and xgboost::obj::LambdaRankObj< LambdaRankPairwise, ltr::RankingCache >.

◆ UpdateTreeLeaf()

virtual void xgboost::ObjFunction::UpdateTreeLeaf	(	HostDeviceVector< bst_node_t > const &	,
		MetaInfo const &	,
		float	,
		HostDeviceVector< float > const &	,
		std::int32_t	,
		RegTree *
	)		const

inlinevirtual

Update the leaf values after a tree is built.

Needed for objectives with 0 hessian.

Note that the leaf update is not well defined for distributed training as XGBoost computes only an average of quantile between workers. This breaks when some leaf have no sample assigned in a local worker.

Parameters

position	The leaf index for each rows.
info	MetaInfo providing labels and weights.
learning_rate	The learning rate for current iteration.
prediction	Model prediction after transformation.
group_idx	The group index for this tree, 0 when it's not multi-target or multi-class.
p_tree	Tree that needs to be updated.

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

External/xgboost/include/xgboost/objective.h
External/xgboost/src/objective/objective.cc

Public Member Functions

Static Public Member Functions

Protected Attributes

Detailed Description

Member Function Documentation

◆ Configure()

◆ Create()

◆ DefaultEvalMetric()

◆ DefaultMetricConfig()

◆ EvalTransform()

◆ GetGradient()

◆ InitEstimation()

◆ PredTransform()

◆ ProbToMargin()

◆ Targets()

◆ Task()

◆ UpdateTreeLeaf()