File feature_subspace.hpp

class IFeatureSubspace
#include <feature_subspace.hpp>

Defines an interface for all classes that provide access a subspace of the feature space that includes the training examples covered by a rule.

Public Functions

inline virtual ~IFeatureSubspace()
virtual std::unique_ptr<IFeatureSubspace> copy() const = 0

Creates and returns a copy of this object.

Returns:

An unique pointer to an object of type IFeatureSubspace that has been created

virtual std::unique_ptr<IRuleRefinement> createRuleRefinement(const CompleteIndexVector &outputIndices, uint32 featureIndex) = 0

Creates and returns a new instance of the type IRuleRefinement that allows to find the best refinement of a rule that covers all examples included in this subspace and predicts for all available outputs.

Parameters:

  • outputIndices – A reference to an object of type CompleteIndexVector that provides access to the indices of the outputs for which the existing rule predicts

  • featureIndex – The index of the feature that should be considered when searching for refinements

Returns:

An unique pointer to an object of type IRuleRefinement that has been created

virtual std::unique_ptr<IRuleRefinement> createRuleRefinement(const PartialIndexVector &outputIndices, uint32 featureIndex) = 0

Creates and returns a new instance of the type IRuleRefinement that allows to find the best refinement of a rule that covers all examples included in this subspace and predicts for a subset of the available outputs.

Parameters:

  • outputIndices – A reference to an object of type PartialIndexVector that provides access to the indices of the outputs for which the existing rule predicts

  • featureIndex – The index of the feature that should be considered when searching for refinements

Returns:

An unique pointer to an object of type IRuleRefinement that has been created

virtual void filterSubspace(const Condition &condition) = 0

Filters the subspace such that it only includes those training examples that statisfy a specific condition.

Parameters:

condition – A reference to an object of type Condition

virtual void resetSubspace() = 0

Resets the subspace. This reverts the effects of all previous calls to the function filterSubspace.

virtual const CoverageMask &getCoverageMask() const = 0

Returns an object of type CoverageMask that keeps track of the training examples that are included in this subspace.

Returns:

A reference to an object of type CoverageMask that keeps track of the training examples that are included in this subspace

virtual Quality evaluateOutOfSample(const SinglePartition &partition, const CoverageMask &coverageMask, const IPrediction &head) const = 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 instance sub-sample and are marked as covered according to a given CoverageMask.

For calculating the quality, only examples that belong to the training set and are not included in the current instance sub-sample, i.e., only examples with zero weights, are considered and assigned equally distributed weights.

Parameters:

  • partition – A reference to an object of type SinglePartition that provides access to the indices of the training examples that belong to the training set

  • coverageMask – A reference to an object of type CoverageMask that keeps track of the examples that are covered by the rule

  • head – 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

virtual Quality evaluateOutOfSample(const BiPartition &partition, const CoverageMask &coverageMask, const IPrediction &head) const = 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 instance sub-sample and are marked as covered according to a given CoverageMask.

For calculating the quality, only examples that belong to the training set and are not included in the current instance sub-sample, i.e., only examples with zero weights, are considered and assigned equally distributed weights.

Parameters:

  • partition – A reference to an object of type BiPartition that provides access to the indices of the training examples that belong to the training set

  • coverageMask – A reference to an object of type CoverageMask that keeps track of the examples that are covered by the rule

  • head – 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

virtual void recalculatePrediction(const SinglePartition &partition, const CoverageMask &coverageMask, IPrediction &head) const = 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 CoverageMask.

When calculating the updated prediction, the weights of the individual training examples are ignored and equally distributed weights are used instead.

Parameters:

  • partition – A reference to an object of type SinglePartition that provides access to the indices of the training examples that belong to the training set

  • coverageMask – A reference to an object of type CoverageMask that keeps track of the examples that are covered by the rule

  • head – A reference to an object of type IPrediction to be updated

virtual void recalculatePrediction(const BiPartition &partition, const CoverageMask &coverageMask, IPrediction &head) const = 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 CoverageMask.

When calculating the updated prediction, the weights of the individual training examples are ignored and equally distributed weights are used instead.

Parameters:

  • partition – A reference to an object of type BiPartition that provides access to the indices of the training examples that belong to the training set

  • coverageMask – A reference to an object of type CoverageMask that keeps track of the examples that are covered by the rule

  • head – A reference to an object of type IPrediction to be updated

virtual void applyPrediction(const IPrediction &prediction) = 0

Updates the statistics that correspond to the training examples included in this subspace based on the prediction of a rule.

Parameters:

prediction – A reference to an object of type IPrediction that stores the prediction of the rule

virtual void revertPrediction(const IPrediction &prediction) = 0

Reverts the statistics that correspond to the training examples included in this subspace based on the predictions of a rule.

Parameters:

prediction – A reference to an object of type IPrediction that stores the prediction of the rule