File predictor_binary_gfm.hpp¶

namespace boosting

class IGfmBinaryPredictorConfig¶

#include <predictor_binary_gfm.hpp>

Defines an interface for all classes that allow to configure a predictor that predicts whether individual labels of given query examples are relevant or irrelevant by discretizing the regression scores or probability estimates that are predicted for each label according to the general F-measure maximizer (GFM).

Subclassed by boosting::GfmBinaryPredictorConfig

Public Functions

inline virtual ~IGfmBinaryPredictorConfig()¶

virtual bool isProbabilityCalibrationModelUsed() const = 0¶

Returns whether a model for the calibration of probabilities is used, if available, or not.

Returns:: True, if a model for the calibration of probabilities is used, if available, false otherwise

virtual IGfmBinaryPredictorConfig &setUseProbabilityCalibrationModel(bool useProbabilityCalibrationModel) = 0¶

Sets whether a model for the calibration of probabilities should be used, if available, or not.

Parameters:: useProbabilityCalibrationModel – True, if a model for the calibration of probabilities should be used, if available, false otherwise
Returns:: A reference to an object of type IGfmBinaryPredictorConfig that allows further configuration of the predictor

class GfmBinaryPredictorConfig : public boosting::IGfmBinaryPredictorConfig, public IBinaryPredictorConfig ¶

#include <predictor_binary_gfm.hpp>

Allows to configure a predictor that predicts whether individual labels of given query examples are relevant or irrelevant by discretizing the regression scores or probability estimates that are predicted for each label according to the general F-measure maximizer (GFM).

Public Functions

GfmBinaryPredictorConfig(const std::unique_ptr<ILossConfig> &lossConfigPtr, const std::unique_ptr<IMultiThreadingConfig> &multiThreadingConfigPtr)¶

Parameters:

lossConfigPtr – A reference to an unique pointer that stores the configuration of the loss function
multiThreadingConfigPtr – A reference to an unique pointer that stores the configuration of the multi-threading behavior that should be used to predict for several query examples in parallel

virtual bool isProbabilityCalibrationModelUsed() const override¶

Returns whether a model for the calibration of probabilities is used, if available, or not.

Returns:: True, if a model for the calibration of probabilities is used, if available, false otherwise

virtual IGfmBinaryPredictorConfig &setUseProbabilityCalibrationModel(bool useProbabilityCalibrationModel) override¶

Sets whether a model for the calibration of probabilities should be used, if available, or not.

Parameters:: useProbabilityCalibrationModel – True, if a model for the calibration of probabilities should be used, if available, false otherwise
Returns:: A reference to an object of type IGfmBinaryPredictorConfig that allows further configuration of the predictor

std::unique_ptr<IBinaryPredictorFactory> createPredictorFactory(const IRowWiseFeatureMatrix &featureMatrix, uint32 numLabels) const override¶