Namespaces
namespace	detail
	Copyright 2022-2023 by XGBoost Contributors.

Data Structures
class	FitIntercept

class	LambdaRankMAP

class	LambdaRankNDCG

class	LambdaRankObj
	Base class for pair-wise learning to rank. More...

class	LambdaRankPairwise
	The RankNet loss. More...

struct	LinearSquareLoss

struct	LinearSquareLossOneAPI

struct	LogisticClassification

struct	LogisticClassificationOneAPI

struct	LogisticRaw

struct	LogisticRawOneAPI

struct	LogisticRegression

struct	LogisticRegressionOneAPI

class	MyLogistic

struct	MyLogisticParam

class	RegLossObjOneAPI

struct	RegLossParamOneAPI

struct	SquaredLogError

struct	SquaredLogErrorOneAPI

Functions
	DMLC_REGISTER_PARAMETER (MyLogisticParam)

	describe ("User defined logistic regression plugin") .set_body([]()

float	SigmoidOneAPI (float x)
	calculate the sigmoid of the input.

	DMLC_REGISTRY_FILE_TAG (regression_obj_oneapi)

	DMLC_REGISTER_PARAMETER (RegLossParamOneAPI)

	describe ("Regression with squared error with DPC++ backend.") .set_body([]()

	describe ("Regression with root mean squared logarithmic error with DPC++ backend.") .set_body([]()

	describe ("Logistic regression for probability regression task with DPC++ backend.") .set_body([]()

	describe ("Logistic regression for binary classification task with DPC++ backend.") .set_body([]()

	describe ("Logistic regression for classification, output score " "before logistic transformation with DPC++ backend.") .set_body([]()

void	UpdateTreeLeaf (Context const ctx, HostDeviceVector< bst_node_t > const &position, std::int32_t group_idx, MetaInfo const &info, float learning_rate, HostDeviceVector< float > const &predt, float alpha, RegTree p_tree)

	DMLC_REGISTRY_FILE_TAG (aft_obj)

	DMLC_REGISTRY_FILE_TAG (hinge_obj)

void	CheckInitInputs (MetaInfo const &info)

	describe ("LambdaRank with NDCG loss as objective") .set_body([]()

	describe ("LambdaRank with RankNet loss as objective") .set_body([]()

	describe ("LambdaRank with MAP loss as objective.") .set_body([]()

	DMLC_REGISTRY_FILE_TAG (lambdarank_obj)

double constexpr	Eps64 ()

template<bool exp>
XGBOOST_DEVICE double	DeltaNDCG (float y_high, float y_low, std::size_t rank_high, std::size_t rank_low, double inv_IDCG, common::Span< double const > discount)

XGBOOST_DEVICE double	DeltaMAP (float y_high, float y_low, std::size_t rank_high, std::size_t rank_low, common::Span< double const > n_rel, common::Span< double const > acc)

template<bool unbiased, typename Delta >
XGBOOST_DEVICE GradientPair	LambdaGrad (linalg::VectorView< float const > labels, common::Span< float const > predts, common::Span< size_t const > sorted_idx, std::size_t rank_high, std::size_t rank_low, Delta delta, linalg::VectorView< double const > t_plus, linalg::VectorView< double const > t_minus, double *p_cost)

XGBOOST_DEVICE GradientPair	Repulse (GradientPair pg)

template<typename Op >
void	MakePairs (Context const *ctx, std::int32_t iter, std::shared_ptr< ltr::RankingCache > const cache, bst_group_t g, linalg::VectorView< float const > g_label, common::Span< std::size_t const > g_rank, Op op)

	DMLC_REGISTRY_FILE_TAG (multiclass_obj)

	DMLC_REGISTRY_LINK_TAG (regression_obj)

	DMLC_REGISTRY_LINK_TAG (quantile_obj)

	DMLC_REGISTRY_LINK_TAG (hinge_obj)

	DMLC_REGISTRY_LINK_TAG (multiclass_obj)

	DMLC_REGISTRY_LINK_TAG (lambdarank_obj)

	DMLC_REGISTRY_FILE_TAG (quantile_obj)

	DMLC_REGISTRY_FILE_TAG (regression_obj)

	TEST (LambdaRank, NDCGJsonIO)

void	TestNDCGGPair (Context const *ctx)

	TEST (LambdaRank, NDCGGPair)

void	TestUnbiasedNDCG (Context const *ctx)

	TEST (LambdaRank, UnbiasedNDCG)

void	InitMakePairTest (Context const ctx, MetaInfo out_info, HostDeviceVector< float > *out_predt)
	Initialize test data for make pair tests.

	TEST (LambdaRank, MakePair)

void	TestMAPStat (Context const *ctx)

	TEST (LambdaRank, MAPStat)

void	TestMAPGPair (Context const *ctx)

	TEST (LambdaRank, MAPGPair)

void	TestPairWiseGPair (Context const *ctx)

	TEST (LambdaRank, Pairwise)

void	TestNDCGJsonIO (Context const *ctx)

Detailed Description

Vocabulary explanation:

There are two different lists we need to handle in the objective, first is the list of labels (relevance degree) provided by the user. Its order has no particular meaning when bias estimation is NOT used. Another one is generated by our model, sorted index based on prediction scores. rank_high refers to the position index of the model rank list that is higher than rank_low, while idx_high refers to where does the rank_high sample comes from. Simply put, rank_high indexes into the rank list obtained from the model, while idx_high indexes into the user provided sample list.

Function Documentation

◆ MakePairs()

template<typename Op >

void xgboost::obj::MakePairs	(	Context const *	ctx,
		std::int32_t	iter,
		std::shared_ptr< ltr::RankingCache > const	cache,
		bst_group_t	g,
		linalg::VectorView< float const >	g_label,
		common::Span< std::size_t const >	g_rank,
		Op	op
	)

Parameters

Construct pairs on CPU

Template Parameters

Op	Functor for upgrading a pair of gradients.

Parameters

ctx	The global context.
iter	The boosting iteration.
cache	ltr cache.
g	The current query group
g_label	label The labels for the current query group
g_rank	Sorted index of model scores for the current query group.
op	A callable that accepts two index for a pair of documents. The index is for the ranked list (labels sorted according to model scores).

◆ SigmoidOneAPI()

float xgboost::obj::SigmoidOneAPI ( float x )

inline

calculate the sigmoid of the input.

Parameters

x	input parameter

Returns: the transformed value.

Namespaces

Data Structures

Functions

Detailed Description

Function Documentation

◆ MakePairs()

◆ SigmoidOneAPI()