File learner_common.hpp¶

namespace boosting

class BoostedRuleLearnerConfigurator : public RuleLearnerConfigurator ¶

#include <learner_common.hpp>

Allows to configure the individual modules of a rule learner that makes use of gradient boosting, depending on an IBoostedRuleLearnerConfig.

Public Functions

inline BoostedRuleLearnerConfigurator(std::unique_ptr<IBoostedRuleLearnerConfig> configPtr, Blas::DdotFunction ddotFunction, Blas::DspmvFunction dspmvFunction, Lapack::DsysvFunction dsysvFunction)¶

Parameters:

configPtr – An unique pointer to an object of type IBoostedRuleLearnerConfig
ddotFunction – A function pointer to BLAS’ DDOT routine
dspmvFunction – A function pointer to BLAS’ DSPMV routine
dsysvFunction – A function pointer to LAPACK’S DSYSV routine

inline std::unique_ptr<IModelBuilderFactory> createModelBuilderFactory() const override¶

See also

RuleLearnerConfigurator::createRegressionStatisticsProviderFactory

Private Members

const std::unique_ptr<IBoostedRuleLearnerConfig> configPtr_¶

const Blas blas_¶

const Lapack lapack_¶

class BoostedRuleLearnerConfig : public RuleLearnerConfig, public virtual boosting::IBoostedRuleLearnerConfig ¶

#include <learner_common.hpp>

Allows to configure a rule learner that makes use of gradient boosting.

Public Functions

inline BoostedRuleLearnerConfig()¶

inline virtual ~BoostedRuleLearnerConfig() override¶

inline virtual Property<IHeadConfig> getHeadConfig() final override¶

Returns a Property that allows to access the IHeadConfig that stores configuration of the rule heads that should be induced by the rule learner.

Returns:: A reference to a Property that allows to access the IHeadConfig that stores the configuration of the rule heads

inline virtual ReadableProperty<IStatisticsConfig> getStatisticsConfig() const final override¶

Returns a ReadableProperty that allows to access the IStatisticsConfig that stores the configuration of the statistics that should be used by the rule learner.

Returns:: A ReadableProperty that allows to access the IStatisticsConfig that stores the configuration of the statistics

inline virtual SharedProperty<IClassificationStatisticsConfig> getClassificationStatisticsConfig() final override¶

Returns a SharedProperty that allows to access the IClassificationStatisticsConfig that stores the configuration of the statistics that should be used by the rule learner in classification problems.

Returns:: A SharedProperty that allows to access the IClassificationStatisticsConfig that stores the configuration of the statistics

inline virtual SharedProperty<IRegressionStatisticsConfig> getRegressionStatisticsConfig() final override¶

Returns a SharedProperty that allows to access the IRegressionStatisticsConfig that stores the configuration of the statistics that should be used by the rule learner in regression problems.

Returns:: A SharedProperty that allows to access the IRegressionStatisticsConfig that stores the configuration of the statistics

inline virtual Property<IRegularizationConfig> getL1RegularizationConfig() final override¶

Returns a Property that allows to access the IRegularizationConfig that stores the configuration of the L1 regularization term.

Returns:: A reference to an unique pointer of type IRegularizationConfig that stores the configuration of the L1 regularization term

inline virtual Property<IRegularizationConfig> getL2RegularizationConfig() final override¶

Returns a Property that allows to access the IRegularizationConfig that stores the configuration of the L2 regularization term.

Returns:: A Property that allows to access the IRegularizationConfig that stores the configuration of the L2 regularization term

inline virtual ReadableProperty<ILossConfig> getLossConfig() const final override¶

Returns a ReadableProperty that allows to access the ILossConfig that stores the configuration of the loss function.

Returns:: A ReadableProperty that allows to access the ILossConfig that stores the configuration of the loss function

inline virtual SharedProperty<IClassificationLossConfig> getClassificationLossConfig() final override¶

Returns a SharedProperty that allows to access the IClassificationLossConfig that stores the configuration of the loss function that should be used in classification problems.

Returns:: A SharedProperty that allows to access the IClassificationLossConfig that stores the configuration of the loss function that should be used in classification problems

inline virtual SharedProperty<IRegressionLossConfig> getRegressionLossConfig() final override¶

Returns a SharedProperty that allows to access the IRegressionLossConfig that stores the configuration of the loss function that should be used in regression problems.

Returns:: A SharedProperty that allows to access the IRegressionLossConfig that stores the configuration of the loss function that should be used in regression problems

inline virtual Property<ILabelBinningConfig> getLabelBinningConfig() final override¶

Returns a Property that allows to access the ILabelBinningConfig that stores the configuration of the method for the assignment of labels to bins.

Returns:: A Property that allows to access the ILabelBinningConfig that stores the configuration of the method for the assignment of labels to bins

Protected Attributes

std::unique_ptr<IHeadConfig> headConfigPtr_¶: An unique pointer that stores the configuration of the rule heads.

std::shared_ptr<IClassificationStatisticsConfig> classificationStatisticsConfigPtr_¶: A shared pointer that stores the configuration of the statistics that should be use in classification problems.

std::shared_ptr<IRegressionStatisticsConfig> regressionStatisticsConfigPtr_¶: A shared pointer that stores the configuration of the statistics that should be use in regression problems.

std::shared_ptr<IClassificationLossConfig> classificationLossConfigPtr_¶: A shared pointer that stores the configuration of the loss function that should be used in classification problems.

std::shared_ptr<IRegressionLossConfig> regressionLossConfigPtr_¶: A shared pointer that stores the configuration of the loss function that should be used in regression problems.

std::unique_ptr<IRegularizationConfig> l1RegularizationConfigPtr_¶: An unique pointer that stores the configuration of the L1 regularization term.

std::unique_ptr<IRegularizationConfig> l2RegularizationConfigPtr_¶: An unique pointer that stores the configuration of the L2 regularization term.

std::unique_ptr<ILabelBinningConfig> labelBinningConfigPtr_¶: An unique pointer that stores the configuration of the method that is used to assign labels to bins.