xgboost::tree::QuantileHistMaker Class Reference

construct a tree using quantized feature values More...

Inheritance diagram for xgboost::tree::QuantileHistMaker:

Public Member Functions
	QuantileHistMaker (Context const *ctx, ObjInfo const *task)
void	Configure (Args const &args) override
	Initialize the updater with given arguments.
void	LoadConfig (Json const &in) override
	Load configuration from JSON object.
void	SaveConfig (Json *p_out) const override
	Save configuration to JSON object.
char const *	Name () const override
void	Update (TrainParam const *param, HostDeviceVector< GradientPair > *gpair, DMatrix *p_fmat, common::Span< HostDeviceVector< bst_node_t > > out_position, const std::vector< RegTree * > &trees) override
	perform update to the tree models
bool	UpdatePredictionCache (const DMatrix *data, linalg::MatrixView< float > out_preds) override
	determines whether updater has enough knowledge about a given dataset to quickly update prediction cache its training data and performs the update if possible.
bool	HasNodePosition () const override
	Wether the out_position in Update is valid. This determines whether adaptive tree can be used.
Public Member Functions inherited from xgboost::TreeUpdater
	TreeUpdater (const Context *ctx)
	~TreeUpdater () override=default
	virtual destructor
virtual bool	CanModifyTree () const
	Whether this updater can be used for updating existing trees.

Additional Inherited Members
Static Public Member Functions inherited from xgboost::TreeUpdater
static TreeUpdater *	Create (const std::string &name, Context const *ctx, ObjInfo const *task)
	Create a tree updater given name.
Protected Attributes inherited from xgboost::TreeUpdater
Context const *	ctx_ = nullptr

Detailed Description

construct a tree using quantized feature values

Member Function Documentation

Configure()

void xgboost::tree::QuantileHistMaker::Configure ( Args const &  args )

inlineoverridevirtual

Initialize the updater with given arguments.

Parameters

args	arguments to the objective function.

Implements xgboost::TreeUpdater.

HasNodePosition()

bool xgboost::tree::QuantileHistMaker::HasNodePosition ( ) const

inlineoverridevirtual

Wether the out_position in Update is valid. This determines whether adaptive tree can be used.

Reimplemented from xgboost::TreeUpdater.

LoadConfig()

void xgboost::tree::QuantileHistMaker::LoadConfig ( Json const &  in )

inlineoverridevirtual

Load configuration from JSON object.

Parameters

in	JSON object containing the configuration

Implements xgboost::Configurable.

Name()

char const * xgboost::tree::QuantileHistMaker::Name ( ) const

inlineoverridevirtual

Implements xgboost::TreeUpdater.

SaveConfig()

void xgboost::tree::QuantileHistMaker::SaveConfig ( Json *  out ) const

inlineoverridevirtual

Save configuration to JSON object.

Parameters

out	pointer to output JSON object

Implements xgboost::Configurable.

Update()

void xgboost::tree::QuantileHistMaker::Update ( TrainParam const *  param, HostDeviceVector< GradientPair > *  gpair, DMatrix *  data, common::Span< HostDeviceVector< bst_node_t > >  out_position, const std::vector< RegTree * > &  out_trees  )

inlineoverridevirtual

perform update to the tree models

Parameters

param	Hyper-parameter for constructing trees.
gpair	the gradient pair statistics of the data
data	The data matrix passed to the updater.
out_position	The leaf index for each row. The index is negated if that row is removed during sampling. So the 3th node is ~3.
out_trees	references the trees to be updated, updater will change the content of trees note: all the trees in the vector are updated, with the same statistics, but maybe different random seeds, usually one tree is passed in at a time, there can be multiple trees when we train random forest style model

Implements xgboost::TreeUpdater.

UpdatePredictionCache()

bool xgboost::tree::QuantileHistMaker::UpdatePredictionCache ( const DMatrix *  , linalg::MatrixView< float >    )

inlineoverridevirtual

determines whether updater has enough knowledge about a given dataset to quickly update prediction cache its training data and performs the update if possible.

Parameters

data	data matrix
out_preds	prediction cache to be updated

Returns

boolean indicating whether updater has capability to update the prediction cache. If true, the prediction cache will have been updated by the time this function returns.

Reimplemented from xgboost::TreeUpdater.

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

• External/xgboost/src/tree/updater_quantile_hist.cc