Inheritance diagram for xgboost::gbm::GBTree:

Public Member Functions
	GBTree (LearnerModelParam const booster_config, Context const ctx)

void	Configure (Args const &cfg) override
	Set the configuration of gradient boosting. User must call configure once before InitModel and Training.

void	UpdateTreeLeaf (DMatrix const p_fmat, HostDeviceVector< float > const &predictions, ObjFunction const obj, std::int32_t group_idx, std::vector< HostDeviceVector< bst_node_t > > const &node_position, std::vector< std::unique_ptr< RegTree > > *p_trees)
	Optionally update the leaf value.

void	DoBoost (DMatrix p_fmat, HostDeviceVector< GradientPair > in_gpair, PredictionCacheEntry predt, ObjFunction const obj) override
	Carry out one iteration of boosting.

bool	UseGPU () const override
	Whether the current booster uses GPU.

GBTreeTrainParam const &	GetTrainParam () const

void	Load (dmlc::Stream *fi) override
	load model from stream

void	Save (dmlc::Stream *fo) const override
	save model to stream.

void	LoadConfig (Json const &in) override
	Load configuration from JSON object.

void	SaveConfig (Json *p_out) const override
	Save configuration to JSON object.

void	SaveModel (Json *p_out) const override
	saves the model config to a JSON object

void	LoadModel (Json const &in) override
	load the model from a JSON object

void	Slice (bst_layer_t begin, bst_layer_t end, bst_layer_t step, GradientBooster out, bool out_of_bound) const override
	Slice a model using boosting index.

std::int32_t	BoostedRounds () const override
	Return number of boosted rounds.

bool	ModelFitted () const override
	Whether the model has already been trained.

void	PredictBatchImpl (DMatrix p_fmat, PredictionCacheEntry out_preds, bool is_training, bst_layer_t layer_begin, bst_layer_t layer_end) const

void	PredictBatch (DMatrix p_fmat, PredictionCacheEntry out_preds, bool training, bst_layer_t layer_begin, bst_layer_t layer_end) override
	Generate predictions for given feature matrix.

void	InplacePredict (std::shared_ptr< DMatrix > p_m, float missing, PredictionCacheEntry *out_preds, bst_layer_t layer_begin, bst_layer_t layer_end) const override
	Inplace prediction.

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

void	PredictInstance (const SparsePage::Inst &inst, std::vector< bst_float > *out_preds, uint32_t layer_begin, uint32_t layer_end) override

void	PredictLeaf (DMatrix p_fmat, HostDeviceVector< bst_float > out_preds, uint32_t layer_begin, uint32_t layer_end) override

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

void	PredictInteractionContributions (DMatrix p_fmat, HostDeviceVector< float > out_contribs, bst_layer_t layer_begin, bst_layer_t layer_end, bool approximate) override

std::vector< std::string >	DumpModel (const FeatureMap &fmap, bool with_stats, std::string format) const override
	dump the model in the requested format

Public Member Functions inherited from xgboost::GradientBooster
	~GradientBooster () override=default
	virtual destructor

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

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

Protected Member Functions
void	BoostNewTrees (HostDeviceVector< GradientPair > gpair, DMatrix p_fmat, int bst_group, std::vector< HostDeviceVector< bst_node_t > > out_position, std::vector< std::unique_ptr< RegTree > > ret)

std::unique_ptr< Predictor > const &	GetPredictor (bool is_training, HostDeviceVector< float > const out_pred=nullptr, DMatrix f_dmat=nullptr) const

virtual void	CommitModel (TreesOneIter &&new_trees)

Protected Member Functions inherited from xgboost::GradientBooster
	GradientBooster (Context const *ctx)

Protected Attributes
GBTreeModel	model_

GBTreeTrainParam	tparam_

tree::TrainParam	tree_param_

bool	specified_updater_ {false}

std::vector< std::unique_ptr< TreeUpdater > >	updaters_

std::unique_ptr< Predictor >	cpu_predictor_

std::unique_ptr< Predictor >	gpu_predictor_ {nullptr}

common::Monitor	monitor_

Protected Attributes inherited from xgboost::GradientBooster
Context const *	ctx_

Additional Inherited Members
Static Public Member Functions inherited from xgboost::GradientBooster
static GradientBooster *	Create (const std::string &name, Context const ctx, LearnerModelParam const learner_model_param)
	create a gradient booster from given name

Member Function Documentation

◆ BoostedRounds()

std::int32_t xgboost::gbm::GBTree::BoostedRounds ( ) const

inlineoverridevirtual

Return number of boosted rounds.

Implements xgboost::GradientBooster.

◆ Configure()

void xgboost::gbm::GBTree::Configure ( Args const & cfg )

overridevirtual

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

Parameters

cfg	configurations on both training and model parameters.

Implements xgboost::GradientBooster.

◆ DoBoost()

void xgboost::gbm::GBTree::DoBoost	(	DMatrix *	p_fmat,
		HostDeviceVector< GradientPair > *	in_gpair,
		PredictionCacheEntry *	predt,
		ObjFunction const *	obj
	)

overridevirtual

Carry out one iteration of boosting.

Implements xgboost::GradientBooster.

◆ DumpModel()

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

inlineoverridevirtual

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.

Implements xgboost::GradientBooster.

◆ FeatureScore()

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

inlineoverridevirtual

Implements xgboost::GradientBooster.

◆ InplacePredict()

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

overridevirtual

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.

Reimplemented from xgboost::GradientBooster.

◆ Load()

void xgboost::gbm::GBTree::Load ( dmlc::Stream * fi )

inlineoverridevirtual

load model from stream

Parameters

fi	input stream.

Implements xgboost::GradientBooster.

◆ LoadConfig()

void xgboost::gbm::GBTree::LoadConfig ( Json const & in )

overridevirtual

Load configuration from JSON object.

Parameters

in	JSON object containing the configuration

Implements xgboost::Configurable.

◆ LoadModel()

void xgboost::gbm::GBTree::LoadModel ( Json const & in )

overridevirtual

load the model from a JSON object

Parameters

in	JSON object where to load the model from

Implements xgboost::Model.

◆ ModelFitted()

bool xgboost::gbm::GBTree::ModelFitted ( ) const

inlineoverridevirtual

Whether the model has already been trained.

When tree booster is chosen, then returns true when there are existing trees.

Implements xgboost::GradientBooster.

◆ PredictBatch()

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

overridevirtual

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.

Implements xgboost::GradientBooster.

◆ PredictContribution()

void xgboost::gbm::GBTree::PredictContribution	(	DMatrix *	dmat,
		HostDeviceVector< float > *	out_contribs,
		bst_layer_t	layer_begin,
		bst_layer_t	layer_end,
		bool	approximate
	)

inlineoverridevirtual

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

Implements xgboost::GradientBooster.

◆ PredictInteractionContributions()

void xgboost::gbm::GBTree::PredictInteractionContributions	(	DMatrix *	p_fmat,
		HostDeviceVector< float > *	out_contribs,
		bst_layer_t	layer_begin,
		bst_layer_t	layer_end,
		bool	approximate
	)

inlineoverridevirtual

Implements xgboost::GradientBooster.

◆ Save()

void xgboost::gbm::GBTree::Save ( dmlc::Stream * fo ) const

inlineoverridevirtual

save model to stream.

Parameters

fo	output stream

Implements xgboost::GradientBooster.

◆ SaveConfig()

void xgboost::gbm::GBTree::SaveConfig ( Json * out ) const

overridevirtual

Save configuration to JSON object.

Parameters

out	pointer to output JSON object

Implements xgboost::Configurable.

◆ SaveModel()

void xgboost::gbm::GBTree::SaveModel ( Json * out ) const

overridevirtual

saves the model config to a JSON object

Parameters

out	JSON container where to save the model to

Implements xgboost::Model.

◆ Slice()

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

overridevirtual

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

Reimplemented from xgboost::GradientBooster.

◆ UseGPU()

bool xgboost::gbm::GBTree::UseGPU ( ) const

inlineoverridevirtual

Whether the current booster uses GPU.

Implements xgboost::GradientBooster.

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

External/xgboost/src/gbm/gbtree.h
External/xgboost/src/gbm/gbtree.cc

Public Member Functions

Protected Member Functions

Protected Attributes

Additional Inherited Members

Member Function Documentation

◆ BoostedRounds()

◆ Configure()

◆ DoBoost()

◆ DumpModel()

◆ FeatureScore()

◆ InplacePredict()

◆ Load()

◆ LoadConfig()

◆ LoadModel()

◆ ModelFitted()

◆ PredictBatch()

◆ PredictContribution()

◆ PredictInteractionContributions()

◆ Save()

◆ SaveConfig()

◆ SaveModel()

◆ Slice()

◆ UseGPU()