xgboost::obj::RegLossObjOneAPI< Loss > Class Template Reference

Inheritance diagram for xgboost::obj::RegLossObjOneAPI< Loss >:

Public Member Functions
void	Configure (const std::vector< std::pair< std::string, std::string > > &args) override
	Configure the objective with the specified parameters.
void	GetGradient (const HostDeviceVector< bst_float > &preds, const MetaInfo &info, int iter, HostDeviceVector< GradientPair > *out_gpair) override
	Get gradient over each of predictions, given existing information.
const char *	DefaultEvalMetric () const override
void	PredTransform (HostDeviceVector< float > *io_preds) override
float	ProbToMargin (float base_score) const override
	transform probability value back to margin this is used to transform user-set base_score back to margin used by gradient boosting
void	SaveConfig (Json *p_out) const override
	Save configuration to JSON object.
void	LoadConfig (Json const &in) override
	Load configuration from JSON object.
Public Member Functions inherited from xgboost::ObjFunction
	~ObjFunction () override=default
	virtual destructor
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 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.

Protected Attributes
HostDeviceVector< int >	label_correct_
RegLossParamOneAPI	param_
cl::sycl::queue	qu_
Protected Attributes inherited from xgboost::ObjFunction
Context const *	ctx_

Additional Inherited Members
Static Public Member Functions inherited from xgboost::ObjFunction
static constexpr float	DefaultBaseScore ()
static ObjFunction *	Create (const std::string &name, Context const *ctx)
	Create an objective function according to name.

Member Function Documentation

Configure()

template<typename Loss >

void xgboost::obj::RegLossObjOneAPI< Loss >::Configure ( const std::vector< std::pair< std::string, std::string > > &  args )

inlineoverridevirtual

Configure the objective with the specified parameters.

Parameters

args	arguments to the objective function.

Implements xgboost::ObjFunction.

DefaultEvalMetric()

template<typename Loss >

const char * xgboost::obj::RegLossObjOneAPI< Loss >::DefaultEvalMetric ( ) const

inlineoverridevirtual

Returns

the default evaluation metric for the objective

Implements xgboost::ObjFunction.

GetGradient()

template<typename Loss >

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

inlineoverridevirtual

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

Implements xgboost::ObjFunction.

LoadConfig()

template<typename Loss >

void xgboost::obj::RegLossObjOneAPI< Loss >::LoadConfig ( Json const &  in )

inlineoverridevirtual

Load configuration from JSON object.

Parameters

in	JSON object containing the configuration

Implements xgboost::Configurable.

ProbToMargin()

template<typename Loss >

float xgboost::obj::RegLossObjOneAPI< Loss >::ProbToMargin ( float  base_score ) const

inlineoverridevirtual

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 from xgboost::ObjFunction.

SaveConfig()

template<typename Loss >

void xgboost::obj::RegLossObjOneAPI< Loss >::SaveConfig ( Json *  out ) const

inlineoverridevirtual

Save configuration to JSON object.

Parameters

out	pointer to output JSON object

Implements xgboost::Configurable.

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

• External/xgboost/plugin/updater_oneapi/regression_obj_oneapi.cc