File partition.hpp¶
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class IPartition¶
- #include <partition.hpp>
Defines an interface for all classes that provide access to the indices of training examples that have been split into a training set and a holdout set.
Subclassed by BiPartition, SinglePartition
Public Functions
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inline virtual ~IPartition()¶
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virtual std::unique_ptr<IStoppingCriterion> createStoppingCriterion(const IStoppingCriterionFactory &factory) = 0¶
Creates and returns a new instance of the class
IStoppingCriterion
, based on the type of this partition.- Parameters:
factory – A reference to an object of type
IStoppingCriterionFactory
that should be used to create the instance- Returns:
An unique pointer to an object of type
IStoppingCriterion
that has been created
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virtual std::unique_ptr<IInstanceSampling> createInstanceSampling(const IInstanceSamplingFactory &factory, const IRowWiseLabelMatrix &labelMatrix, IStatistics &statistics) = 0¶
Creates and returns a new instance of the class
IInstanceSampling
, based on the type of this partition.- Parameters:
factory – A reference to an object of type
IInstanceSamplingFactory
that should be used to create the instancelabelMatrix – A reference to an object of type
IRowWiseLabelMatrix
that provides row-wise access to the labels of individual training examplesstatistics – A reference to an object of type
IStatistics
that provides access to the statistics which serve as a basis for learning rules
- Returns:
An unique pointer to an object of type
IInstanceSampling
that has been created
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virtual Quality evaluateOutOfSample(const IFeatureSubspace &featureSubspace, const CoverageMask &coverageMask, const IPrediction &head) = 0¶
Calculates and returns a numerical score that assesses the quality of a rule’s prediction for all examples that do not belong to the current sample and are marked as covered according to a given object of type
CoverageMask
.- Parameters:
featureSubspace – A reference to an object of type
IFeatureSubspace
that should be used to evaluate the predictioncoverageMask – A reference to an object of type
CoverageMask
that keeps track of the examples that are covered by the rulehead – A reference to an object of type
IPrediction
that stores the scores that are predicted by the rule
- Returns:
An object of type
Quality
that stores the calculated quality
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virtual void recalculatePrediction(const IFeatureSubspace &featureSubspace, const CoverageMask &coverageMask, IPrediction &head) = 0¶
Recalculates and updates a rule’s prediction based on all examples in the training set that are marked as covered according to a given object of type
CoverageMask
.- Parameters:
featureSubspace – A reference to an object of type
IFeatureSubspace
that should be used to recalculate the predictioncoverageMask – A reference to an object of type
CoverageMask
that keeps track of the examples that are covered by the rulehead – A reference to an object of type
IPrediction
to be updated
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virtual std::unique_ptr<IMarginalProbabilityCalibrationModel> fitMarginalProbabilityCalibrationModel(const IMarginalProbabilityCalibrator &probabilityCalibrator, const IRowWiseLabelMatrix &labelMatrix, const IStatistics &statistics) = 0¶
Fits and returns a model for the calibration of marginal probabilities, based on the type of this partition.
- Parameters:
probabilityCalibrator – A reference to an object of type
IMarginalProbabilityCalibrator
that should be used to fit the calibration modellabelMatrix – A reference to an object of type
IRowWiseLabelMatrix
that provides row-wise access to the labels of the training examplesstatistics – A reference to an object of type
IStatistics
that provides access to statistics about the labels of the training examples
- Returns:
An unique pointer to an object of type
IMarginalProbabilityCalibrationModel
that has been fit
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virtual std::unique_ptr<IJointProbabilityCalibrationModel> fitJointProbabilityCalibrationModel(const IJointProbabilityCalibrator &probabilityCalibrator, const IRowWiseLabelMatrix &labelMatrix, const IStatistics &statistics) = 0¶
Fits and returns a model for the calibration of joint probabilities, based on the type of this partition.
- Parameters:
probabilityCalibrator – A reference to an object of type
IJointProbabilityCalibrator
that should be used to fit the calibration modellabelMatrix – A reference to an object of type
IRowWiseLabelMatrix
that provides row-wise access to the labels of the training examplesstatistics – A reference to an object of type
IStatistics
that provides access to statistics about the labels of the training examples
- Returns:
An unique pointer to an object of type
IJointProbabilityCalibrationModel
that has been fit
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inline virtual ~IPartition()¶