Inheritance diagram for LightGBM::GOSS:

Public Member Functions
	GOSS ()
	Constructor.

void	Init (const Config config, const Dataset train_data, const ObjectiveFunction objective_function, const std::vector< const Metric > &training_metrics) override
	Initialization logic.

void	ResetTrainingData (const Dataset train_data, const ObjectiveFunction objective_function, const std::vector< const Metric * > &training_metrics) override
	Reset the training data.

void	ResetConfig (const Config *config) override
	Reset Boosting Config.

void	ResetGoss ()

data_size_t	BaggingHelper (Random &cur_rand, data_size_t start, data_size_t cnt, data_size_t buffer, data_size_t buffer_right)

void	Bagging (int iter) override
	Implement bagging logic.

Public Member Functions inherited from LightGBM::GBDT
	GBDT ()
	Constructor.

	~GBDT ()
	Destructor.

void	MergeFrom (const Boosting *other) override
	Merge model from other boosting object. Will insert to the front of current boosting object.

void	ShuffleModels (int start_iter, int end_iter) override
	Shuffle Existing Models.

void	AddValidDataset (const Dataset valid_data, const std::vector< const Metric > &valid_metrics) override
	Adding a validation dataset.

void	Train (int snapshot_freq, const std::string &model_output_path) override
	Perform a full training procedure.

void	RefitTree (const std::vector< std::vector< int > > &tree_leaf_prediction) override
	Update the tree output by new training data.

virtual bool	TrainOneIter (const score_t gradients, const score_t hessians) override
	Training logic.

void	RollbackOneIter () override
	Rollback one iteration.

int	GetCurrentIteration () const override
	Get current iteration.

bool	NeedAccuratePrediction () const override
	Can use early stopping for prediction or not.

std::vector< double >	GetEvalAt (int data_idx) const override
	Get evaluation result at data_idx data.

virtual const double *	GetTrainingScore (int64_t *out_len) override
	Get current training score.

virtual int64_t	GetNumPredictAt (int data_idx) const override
	Get size of prediction at data_idx data.

void	GetPredictAt (int data_idx, double out_result, int64_t out_len) override
	Get prediction result at data_idx data.

int	NumPredictOneRow (int num_iteration, bool is_pred_leaf, bool is_pred_contrib) const override
	Get number of prediction for one data.

void	PredictRaw (const double features, double output, const PredictionEarlyStopInstance *earlyStop) const override
	Prediction for one record, not sigmoid transform.

void	PredictRawByMap (const std::unordered_map< int, double > &features, double output, const PredictionEarlyStopInstance early_stop) const override

void	Predict (const double features, double output, const PredictionEarlyStopInstance *earlyStop) const override
	Prediction for one record, sigmoid transformation will be used if needed.

void	PredictByMap (const std::unordered_map< int, double > &features, double output, const PredictionEarlyStopInstance early_stop) const override

void	PredictLeafIndex (const double features, double output) const override
	Prediction for one record with leaf index.

void	PredictLeafIndexByMap (const std::unordered_map< int, double > &features, double *output) const override

void	PredictContrib (const double features, double output, const PredictionEarlyStopInstance *earlyStop) const override
	Feature contributions for the model's prediction of one record.

std::string	DumpModel (int start_iteration, int num_iteration) const override
	Dump model to json format string.

std::string	ModelToIfElse (int num_iteration) const override
	Translate model to if-else statement.

bool	SaveModelToIfElse (int num_iteration, const char *filename) const override
	Translate model to if-else statement.

virtual bool	SaveModelToFile (int start_iteration, int num_iterations, const char *filename) const override
	Save model to file.

std::string	SaveModelToString (int num_iterations)
	Save model to string.

virtual std::string	SaveModelToString (int start_iteration, int num_iterations) const override

bool	LoadModelFromString (std::string str)
	Restore from a serialized buffer.

bool	LoadModelFromString (const char *buffer, size_t len) override

std::vector< double >	FeatureImportance (int num_iteration, int importance_type) const override
	Calculate feature importances.

int	MaxFeatureIdx () const override
	Get max feature index of this model.

std::vector< std::string >	FeatureNames () const override
	Get feature names of this model.

int	LabelIdx () const override
	Get index of label column.

int	NumberOfTotalModel () const override
	Get number of weak sub-models.

int	NumModelPerIteration () const override
	Get number of tree per iteration.

int	NumberOfClasses () const override
	Get number of classes.

void	InitPredict (int num_iteration, bool is_pred_contrib) override
	Initial work for the prediction.

double	GetLeafValue (int tree_idx, int leaf_idx) const override

void	SetLeafValue (int tree_idx, int leaf_idx, double val) override

virtual const char *	SubModelName () const override
	Get Type name of this boosting object.

Public Member Functions inherited from LightGBM::Boosting
virtual	~Boosting ()
	virtual destructor

std::string	SaveModelToString (int num_iterations)
	Save model to string.

bool	LoadModelFromString (std::string str)
	Restore from a serialized string.

Boosting &	operator= (const Boosting &)=delete
	Disable copy.

	Boosting (const Boosting &)=delete
	Disable copy.

Additional Inherited Members
Static Public Member Functions inherited from LightGBM::Boosting
static bool	LoadFileToBoosting (Boosting boosting, const char filename)

static Boosting *	CreateBoosting (const std::string &type, const char *filename)
	Create boosting object.

Protected Member Functions inherited from LightGBM::GBDT
virtual bool	EvalAndCheckEarlyStopping ()
	Print eval result and check early stopping.

void	ResetBaggingConfig (const Config *config, bool is_change_dataset)
	reset config for bagging

data_size_t	BaggingHelper (Random &cur_rand, data_size_t start, data_size_t cnt, data_size_t *buffer)
	Helper function for bagging, used for multi-threading optimization.

virtual void	Boosting ()
	calculate the object function

virtual void	UpdateScore (const Tree *tree, const int cur_tree_id)
	updating score after tree was trained

virtual std::vector< double >	EvalOneMetric (const Metric metric, const double score) const
	eval results for one metric

std::string	OutputMetric (int iter)
	Print metric result of current iteration.

double	BoostFromAverage (int class_id, bool update_scorer)

Protected Attributes inherited from LightGBM::GBDT
int	iter_
	current iteration

const Dataset *	train_data_
	Pointer to training data.

std::unique_ptr< Config >	config_
	Config of gbdt.

std::unique_ptr< TreeLearner >	tree_learner_
	Tree learner, will use this class to learn trees.

const ObjectiveFunction *	objective_function_
	Objective function.

std::unique_ptr< ScoreUpdater >	train_score_updater_
	Store and update training data's score.

std::vector< const Metric * >	training_metrics_
	Metrics for training data.

std::vector< std::unique_ptr< ScoreUpdater > >	valid_score_updater_
	Store and update validation data's scores.

std::vector< std::vector< const Metric * > >	valid_metrics_
	Metric for validation data.

int	early_stopping_round_
	Number of rounds for early stopping.

std::vector< std::vector< int > >	best_iter_
	Best iteration(s) for early stopping.

std::vector< std::vector< double > >	best_score_
	Best score(s) for early stopping.

std::vector< std::vector< std::string > >	best_msg_
	output message of best iteration

std::vector< std::unique_ptr< Tree > >	models_
	Trained models(trees)

int	max_feature_idx_
	Max feature index of training data.

std::vector< score_t >	gradients_
	First order derivative of training data.

std::vector< score_t >	hessians_
	Secend order derivative of training data.

std::vector< data_size_t >	bag_data_indices_
	Store the indices of in-bag data.

data_size_t	bag_data_cnt_
	Number of in-bag data.

std::vector< data_size_t >	tmp_indices_
	Store the indices of in-bag data.

data_size_t	num_data_
	Number of training data.

int	num_tree_per_iteration_
	Number of trees per iterations.

int	num_class_
	Number of class.

data_size_t	label_idx_
	Index of label column.

int	num_iteration_for_pred_
	number of used model

double	shrinkage_rate_
	Shrinkage rate for one iteration.

int	num_init_iteration_
	Number of loaded initial models.

std::vector< std::string >	feature_names_
	Feature names.

std::vector< std::string >	feature_infos_

int	num_threads_
	number of threads

std::vector< data_size_t >	offsets_buf_
	Buffer for multi-threading bagging.

std::vector< data_size_t >	left_cnts_buf_
	Buffer for multi-threading bagging.

std::vector< data_size_t >	right_cnts_buf_
	Buffer for multi-threading bagging.

std::vector< data_size_t >	left_write_pos_buf_
	Buffer for multi-threading bagging.

std::vector< data_size_t >	right_write_pos_buf_
	Buffer for multi-threading bagging.

std::unique_ptr< Dataset >	tmp_subset_

bool	is_use_subset_

std::vector< bool >	class_need_train_

bool	is_constant_hessian_

std::unique_ptr< ObjectiveFunction >	loaded_objective_

bool	average_output_

bool	need_re_bagging_

std::string	loaded_parameter_

Json	forced_splits_json_

Member Function Documentation

◆ Bagging()

void LightGBM::GOSS::Bagging ( int iter )

inlineoverridevirtual

Implement bagging logic.

Parameters

iter	Current interation

Reimplemented from LightGBM::GBDT.

◆ Init()

void LightGBM::GOSS::Init	(	const Config *	gbdt_config,
		const Dataset *	train_data,
		const ObjectiveFunction *	objective_function,
		const std::vector< const Metric * > &	training_metrics
	)

inlineoverridevirtual

Initialization logic.

Parameters

gbdt_config	Config for boosting
train_data	Training data
objective_function	Training objective function
training_metrics	Training metrics

Reimplemented from LightGBM::GBDT.

◆ ResetConfig()

void LightGBM::GOSS::ResetConfig ( const Config * gbdt_config )

inlineoverridevirtual

Reset Boosting Config.

Parameters

gbdt_config Config for boosting

Reimplemented from LightGBM::GBDT.

◆ ResetTrainingData()

void LightGBM::GOSS::ResetTrainingData	(	const Dataset *	train_data,
		const ObjectiveFunction *	objective_function,
		const std::vector< const Metric * > &	training_metrics
	)

inlineoverridevirtual

Reset the training data.

Parameters

train_data	New Training data
objective_function	Training objective function
training_metrics	Training metrics

Reimplemented from LightGBM::GBDT.

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

External/LightGBM_2.2.3/LightGBM-2.2.3/src/boosting/goss.hpp

Public Member Functions

Additional Inherited Members

Member Function Documentation

◆ Bagging()

◆ Init()

◆ ResetConfig()

◆ ResetTrainingData()