xgboost::LearnerIO Class Reference

Inheritance diagram for xgboost::LearnerIO:

Public Member Functions
	LearnerIO (std::vector< std::shared_ptr< DMatrix > > cache)
void	LoadModel (Json const &in) override
	load the model from a JSON object
void	SaveModel (Json *p_out) const override
	saves the model config to a JSON object
void	LoadModel (dmlc::Stream *fi) override
void	SaveModel (dmlc::Stream *fo) const override
void	Save (dmlc::Stream *fo) const override
	saves the model to a stream
void	Load (dmlc::Stream *fi) override
	load the model from a stream
Public Member Functions inherited from xgboost::LearnerConfiguration
	LearnerConfiguration (std::vector< std::shared_ptr< DMatrix > > cache)
void	Configure () override
	Configure Learner based on set parameters.
void	CheckModelInitialized () const
void	LoadConfig (Json const &in) override
	Load configuration from JSON object.
void	SaveConfig (Json *p_out) const override
	Save configuration to JSON object.
void	SetParam (const std::string &key, const std::string &value) override
	Set parameter for booster.
void	SetParams (std::vector< std::pair< std::string, std::string > > const &args) override
	Set multiple parameters at once.
uint32_t	GetNumFeature () const override
	Get the number of features of the booster.
void	SetAttr (const std::string &key, const std::string &value) override
	Set additional attribute to the Booster.
bool	GetAttr (const std::string &key, std::string *out) const override
	Get attribute from the booster. The property will be saved along the booster.
bool	DelAttr (const std::string &key) override
	Delete an attribute from the booster.
void	SetFeatureNames (std::vector< std::string > const &fn) override
	Set the feature names for current booster.
void	GetFeatureNames (std::vector< std::string > *fn) const override
	Get the feature names for current booster.
void	SetFeatureTypes (std::vector< std::string > const &ft) override
	Set the feature types for current booster.
void	GetFeatureTypes (std::vector< std::string > *p_ft) const override
	Get the feature types for current booster.
std::vector< std::string >	GetAttrNames () const override
	Get a vector of attribute names from the booster.
const std::map< std::string, std::string > &	GetConfigurationArguments () const override
	Get configuration arguments currently stored by the learner.
Context const *	Ctx () const override
	Return the context object of this Booster.
Public Member Functions inherited from xgboost::Learner
	~Learner () override
	virtual destructor
virtual void	UpdateOneIter (int iter, std::shared_ptr< DMatrix > train)=0
	update the model for one iteration With the specified objective function.
virtual void	BoostOneIter (int iter, std::shared_ptr< DMatrix > train, HostDeviceVector< GradientPair > *in_gpair)=0
	Do customized gradient boosting with in_gpair. in_gair can be mutated after this call.
virtual std::string	EvalOneIter (int iter, const std::vector< std::shared_ptr< DMatrix > > &data_sets, const std::vector< std::string > &data_names)=0
	evaluate the model for specific iteration using the configured metrics.
virtual void	Predict (std::shared_ptr< DMatrix > data, bool output_margin, HostDeviceVector< bst_float > *out_preds, bst_layer_t layer_begin, bst_layer_t layer_end, bool training=false, bool pred_leaf=false, bool pred_contribs=false, bool approx_contribs=false, bool pred_interactions=false)=0
	get prediction given the model.
virtual void	InplacePredict (std::shared_ptr< DMatrix > p_m, PredictionType type, float missing, HostDeviceVector< float > **out_preds, bst_layer_t layer_begin, bst_layer_t layer_end)=0
	Inplace prediction.
virtual void	CalcFeatureScore (std::string const &importance_type, common::Span< int32_t const > trees, std::vector< bst_feature_t > *features, std::vector< float > *scores)=0
	Calculate feature score. See doc in C API for outputs.
virtual int32_t	BoostedRounds () const =0
virtual std::uint32_t	Groups () const =0
	Get the number of output groups from the model.
virtual Learner *	Slice (bst_layer_t begin, bst_layer_t end, bst_layer_t step, bool *out_of_bound)=0
	Slice the model.
virtual std::vector< std::string >	DumpModel (const FeatureMap &fmap, bool with_stats, std::string format)=0
	dump the model in the requested format
virtual XGBAPIThreadLocalEntry &	GetThreadLocal () const =0
Public Member Functions inherited from dmlc::Serializable
virtual	~Serializable ()
	virtual destructor

Additional Inherited Members
Static Public Member Functions inherited from xgboost::Learner
static Learner *	Create (const std::vector< std::shared_ptr< DMatrix > > &cache_data)
	Create a new instance of learner.
Protected Member Functions inherited from xgboost::LearnerConfiguration
void	ConfigureModelParamWithoutBaseScore ()
void	InitBaseScore (DMatrix const *p_fmat)
	Calculate the base_score based on input data.
Protected Attributes inherited from xgboost::LearnerConfiguration
std::atomic< bool >	need_configuration_
std::map< std::string, std::string >	cfg_
std::map< std::string, std::string >	attributes_
std::vector< std::string >	feature_names_
std::vector< std::string >	feature_types_
common::Monitor	monitor_
LearnerModelParamLegacy	mparam_
LearnerModelParam	learner_model_param_
LearnerTrainParam	tparam_
PredictionContainer	prediction_container_
std::vector< std::string >	metric_names_
Protected Attributes inherited from xgboost::Learner
std::unique_ptr< ObjFunction >	obj_
	objective function
std::unique_ptr< GradientBooster >	gbm_
	The gradient booster used by the model.
std::vector< std::unique_ptr< Metric > >	metrics_
	The evaluation metrics used to evaluate the model.
Context	ctx_
	Training parameter.
Static Protected Attributes inherited from xgboost::LearnerConfiguration
static std::string const	kEvalMetric {"eval_metric"}

Member Function Documentation

Load()

void xgboost::LearnerIO::Load ( dmlc::Stream *  fi )

inlineoverridevirtual

load the model from a stream

Parameters

fi	stream where to load the model from

Implements dmlc::Serializable.

LoadModel() [1/2]

void xgboost::LearnerIO::LoadModel ( dmlc::Stream *  fi )

inlineoverridevirtual

Implements xgboost::Learner.

LoadModel() [2/2]

void xgboost::LearnerIO::LoadModel ( Json const &  in )

inlineoverridevirtual

load the model from a JSON object

Parameters

in	JSON object where to load the model from

Implements xgboost::Learner.

Save()

void xgboost::LearnerIO::Save ( dmlc::Stream *  fo ) const

inlineoverridevirtual

saves the model to a stream

Parameters

fo	stream where to save the model to

Implements dmlc::Serializable.

SaveModel() [1/2]

void xgboost::LearnerIO::SaveModel ( dmlc::Stream *  fo ) const

inlineoverridevirtual

Implements xgboost::Learner.

SaveModel() [2/2]

void xgboost::LearnerIO::SaveModel ( Json *  out ) const

inlineoverridevirtual

saves the model config to a JSON object

Parameters

out	JSON container where to save the model to

Implements xgboost::Learner.

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

• External/xgboost/src/learner.cc