interface of gradient boosting model. More...

#include <gbm.h>

Inheritance diagram for xgboost::GradientBooster:

Collaboration diagram for xgboost::GradientBooster:

Public Member Functions
	~GradientBooster () override=default
	virtual destructor More...

virtual void	Configure (const std::vector< std::pair< std::string, std::string > > &cfg)=0
	Set the configuration of gradient boosting. User must call configure once before InitModel and Training. More...

virtual void	Load (dmlc::Stream *fi)=0
	load model from stream More...

virtual void	Save (dmlc::Stream *fo) const =0
	save model to stream. More...

virtual void	Slice (bst_layer_t, bst_layer_t, bst_layer_t, GradientBooster , bool ) const
	Slice a model using boosting index. The slice m:n indicates taking all trees that were fit during the boosting rounds m, (m+1), (m+2), ..., (n-1). More...

virtual std::int32_t	BoostedRounds () const =0
	Return number of boosted rounds. More...

virtual bool	ModelFitted () const =0
	Whether the model has already been trained. When tree booster is chosen, then returns true when there are existing trees. More...

virtual void	DoBoost (DMatrix p_fmat, linalg::Matrix< GradientPair > in_gpair, PredictionCacheEntry , ObjFunction const obj)=0
	perform update to the model(boosting) More...

virtual void	PredictBatch (DMatrix dmat, PredictionCacheEntry out_preds, bool training, bst_layer_t begin, bst_layer_t end)=0
	Generate predictions for given feature matrix. More...

virtual void	InplacePredict (std::shared_ptr< DMatrix >, float, PredictionCacheEntry *, bst_layer_t, bst_layer_t) const
	Inplace prediction. More...

virtual void	PredictInstance (const SparsePage::Inst &inst, std::vector< bst_float > *out_preds, unsigned layer_begin, unsigned layer_end)=0
	online prediction function, predict score for one instance at a time NOTE: use the batch prediction interface if possible, batch prediction is usually more efficient than online prediction This function is NOT threadsafe, make sure you only call from one thread More...

virtual void	PredictLeaf (DMatrix dmat, HostDeviceVector< bst_float > out_preds, unsigned layer_begin, unsigned layer_end)=0
	predict the leaf index of each tree, the output will be nsample * ntree vector this is only valid in gbtree predictor More...

virtual void	PredictContribution (DMatrix dmat, HostDeviceVector< float > out_contribs, bst_layer_t layer_begin, bst_layer_t layer_end, bool approximate=false)=0
	feature contributions to individual predictions; the output will be a vector of length (nfeats + 1) * num_output_group * nsample, arranged in that order More...

virtual void	PredictInteractionContributions (DMatrix dmat, HostDeviceVector< float > out_contribs, bst_layer_t layer_begin, bst_layer_t layer_end, bool approximate)=0

virtual std::vector< std::string >	DumpModel (const FeatureMap &fmap, bool with_stats, std::string format) const =0
	dump the model in the requested format More...

virtual void	FeatureScore (std::string const &importance_type, common::Span< int32_t const > trees, std::vector< bst_feature_t > features, std::vector< float > scores) const =0

virtual bool	UseGPU () const =0
	Whether the current booster uses GPU. More...

Public Member Functions inherited from xgboost::Model
virtual	~Model ()=default

virtual void	LoadModel (Json const &in)=0
	load the model from a JSON object More...

virtual void	SaveModel (Json *out) const =0
	saves the model config to a JSON object More...

Public Member Functions inherited from xgboost::Configurable
virtual	~Configurable ()=default

virtual void	LoadConfig (Json const &in)=0
	Load configuration from JSON object. More...

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

Static Public Member Functions
static GradientBooster *	Create (const std::string &name, Context const ctx, LearnerModelParam const learner_model_param)
	create a gradient booster from given name More...

Protected Member Functions
	GradientBooster (Context const *ctx)

Protected Attributes
Context const *	ctx_

Detailed Description

interface of gradient boosting model.

Constructor & Destructor Documentation

◆ GradientBooster()

xgboost::GradientBooster::GradientBooster ( Context const * ctx )

inlineexplicitprotected

◆ ~GradientBooster()

xgboost::GradientBooster::~GradientBooster ( )

overridedefault

virtual destructor

Member Function Documentation

◆ BoostedRounds()

virtual std::int32_t xgboost::GradientBooster::BoostedRounds ( ) const

pure virtual

Return number of boosted rounds.

◆ Configure()

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

pure virtual

Set the configuration of gradient boosting. User must call configure once before InitModel and Training.

Parameters

cfg	configurations on both training and model parameters.

◆ Create()

static GradientBooster* xgboost::GradientBooster::Create	(	const std::string &	name,
		Context const *	ctx,
		LearnerModelParam const *	learner_model_param
	)

static

create a gradient booster from given name

Parameters

name	name of gradient booster
generic_param	Pointer to runtime parameters
learner_model_param	pointer to global model parameters

Returns: The created booster.

◆ DoBoost()

virtual void xgboost::GradientBooster::DoBoost	(	DMatrix *	p_fmat,
		linalg::Matrix< GradientPair > *	in_gpair,
		PredictionCacheEntry *	,
		ObjFunction const *	obj
	)

pure virtual

perform update to the model(boosting)

Parameters

p_fmat	feature matrix that provide access to features
in_gpair	address of the gradient pair statistics of the data
prediction	The output prediction cache entry that needs to be updated. the booster may change content of gpair
obj	The objective function used for boosting.

◆ DumpModel()

virtual std::vector<std::string> xgboost::GradientBooster::DumpModel	(	const FeatureMap &	fmap,
		bool	with_stats,
		std::string	format
	)		const

pure virtual

dump the model in the requested format

Parameters

fmap	feature map that may help give interpretations of feature
with_stats	extra statistics while dumping model
format	the format to dump the model in

Returns: a vector of dump for boosters.

◆ FeatureScore()

virtual void xgboost::GradientBooster::FeatureScore	(	std::string const &	importance_type,
		common::Span< int32_t const >	trees,
		std::vector< bst_feature_t > *	features,
		std::vector< float > *	scores
	)		const

pure virtual

◆ InplacePredict()

virtual void xgboost::GradientBooster::InplacePredict	(	std::shared_ptr< DMatrix >	,
		float	,
		PredictionCacheEntry *	,
		bst_layer_t	,
		bst_layer_t
	)		const

inlinevirtual

Inplace prediction.

Parameters

	p_fmat	A proxy DMatrix that contains the data and related.
	missing	Missing value in the data.
[in,out]	out_preds	The output preds.
	begin	(Optional) Beginning of boosted tree layer used for prediction.
	end	(Optional) End of booster layer. 0 means do not limit trees.

◆ Load()

virtual void xgboost::GradientBooster::Load ( dmlc::Stream * fi )

pure virtual

load model from stream

Parameters

fi	input stream.

◆ ModelFitted()

virtual bool xgboost::GradientBooster::ModelFitted ( ) const

pure virtual

Whether the model has already been trained. When tree booster is chosen, then returns true when there are existing trees.

◆ PredictBatch()

virtual void xgboost::GradientBooster::PredictBatch	(	DMatrix *	dmat,
		PredictionCacheEntry *	out_preds,
		bool	training,
		bst_layer_t	begin,
		bst_layer_t	end
	)

pure virtual

Generate predictions for given feature matrix.

Parameters

dmat	The feature matrix.
out_preds	output vector to hold the predictions
training	Whether the prediction value is used for training. For dart booster drop out is performed during training.
begin	Beginning of boosted tree layer used for prediction.
end	End of booster layer. 0 means do not limit trees.

◆ PredictContribution()

virtual void xgboost::GradientBooster::PredictContribution	(	DMatrix *	dmat,
		HostDeviceVector< float > *	out_contribs,
		bst_layer_t	layer_begin,
		bst_layer_t	layer_end,
		bool	approximate = `false`
	)

pure virtual

feature contributions to individual predictions; the output will be a vector of length (nfeats + 1) * num_output_group * nsample, arranged in that order

Parameters

dmat	feature matrix
out_contribs	output vector to hold the contributions
layer_begin	Beginning of boosted tree layer used for prediction.
layer_end	End of booster layer. 0 means do not limit trees.
approximate	use a faster (inconsistent) approximation of SHAP values

◆ PredictInstance()

virtual void xgboost::GradientBooster::PredictInstance	(	const SparsePage::Inst &	inst,
		std::vector< bst_float > *	out_preds,
		unsigned	layer_begin,
		unsigned	layer_end
	)

pure virtual

online prediction function, predict score for one instance at a time NOTE: use the batch prediction interface if possible, batch prediction is usually more efficient than online prediction This function is NOT threadsafe, make sure you only call from one thread

Parameters

inst	the instance you want to predict
out_preds	output vector to hold the predictions
layer_begin	Beginning of boosted tree layer used for prediction.
layer_end	End of booster layer. 0 means do not limit trees.

See also: Predict

◆ PredictInteractionContributions()

virtual void xgboost::GradientBooster::PredictInteractionContributions	(	DMatrix *	dmat,
		HostDeviceVector< float > *	out_contribs,
		bst_layer_t	layer_begin,
		bst_layer_t	layer_end,
		bool	approximate
	)

pure virtual

◆ PredictLeaf()

virtual void xgboost::GradientBooster::PredictLeaf	(	DMatrix *	dmat,
		HostDeviceVector< bst_float > *	out_preds,
		unsigned	layer_begin,
		unsigned	layer_end
	)

pure virtual

predict the leaf index of each tree, the output will be nsample * ntree vector this is only valid in gbtree predictor

Parameters

dmat	feature matrix
out_preds	output vector to hold the predictions
layer_begin	Beginning of boosted tree layer used for prediction.
layer_end	End of booster layer. 0 means do not limit trees.

◆ Save()

virtual void xgboost::GradientBooster::Save ( dmlc::Stream * fo ) const

pure virtual

save model to stream.

Parameters

fo	output stream

◆ Slice()

virtual void xgboost::GradientBooster::Slice	(	bst_layer_t	,
		bst_layer_t	,
		bst_layer_t	,
		GradientBooster *	,
		bool *
	)		const

inlinevirtual

Slice a model using boosting index. The slice m:n indicates taking all trees that were fit during the boosting rounds m, (m+1), (m+2), ..., (n-1).

Parameters

begin	Beginning of boosted tree layer used for prediction.
end	End of booster layer. 0 means do not limit trees.
out	Output gradient booster

◆ UseGPU()

virtual bool xgboost::GradientBooster::UseGPU ( ) const

pure virtual

Whether the current booster uses GPU.

Member Data Documentation

◆ ctx_

Context const* xgboost::GradientBooster::ctx_

protected

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

/home/docs/checkouts/readthedocs.org/user_builds/xgboost/checkouts/stable/include/xgboost/gbm.h

Public Member Functions

Static Public Member Functions

Protected Member Functions

Protected Attributes

Detailed Description

Constructor & Destructor Documentation

◆ GradientBooster()

◆ ~GradientBooster()

Member Function Documentation

◆ BoostedRounds()

◆ Configure()

◆ Create()

◆ DoBoost()

◆ DumpModel()

◆ FeatureScore()

◆ InplacePredict()

◆ Load()

◆ ModelFitted()

◆ PredictBatch()

◆ PredictContribution()

◆ PredictInstance()

◆ PredictInteractionContributions()

◆ PredictLeaf()

◆ Save()

◆ Slice()

◆ UseGPU()

Member Data Documentation

◆ ctx_