Collection of metrics used to evaluate the state of an EUnsupervisedSegmenter. More...

#include <eVision/EasyDeepLearning/EasySegment/EUnsupervisedSegmenterMetrics.h>

Inheritance diagram for eVision::EasyDeepLearning::EUnsupervisedSegmenterMetrics:

Collaboration diagram for eVision::EasyDeepLearning::EUnsupervisedSegmenterMetrics:

Public Member Functions
	EUnsupervisedSegmenterMetrics ()
	Constructs an EUnsupervisedSegmenterMetrics object.
	EUnsupervisedSegmenterMetrics (const eVision::EasyDeepLearning::EUnsupervisedSegmenterMetrics &other)
	Copy constructor of an EUnsupervisedSegmenterMetrics object.
virtual	~EUnsupervisedSegmenterMetrics () throw ()
eVision::EasyDeepLearning::EUnsupervisedSegmenterMetrics &	operator= (const eVision::EasyDeepLearning::EUnsupervisedSegmenterMetrics &other)
	Assignment operator.
bool	IsTotallyUnsupervised () const
	Whether this metrics has results from only good images (true) or from both good and defective images (false).
bool	IsValid () const
	Indicates whether the object contains at least one result.
virtual void	Load (const std::string &path)
	Loads an unsupervised segmentation metric. The given ESerializer must have been created for reading.
virtual void	Load (eVision::ESerializer *serializer)
	Loads an unsupervised segmentation metric. The given ESerializer must have been created for reading.
virtual void	Save (const std::string &path)
	Saves an unsupervised segmentation metric. The given ESerializer must have been created for writing.
virtual void	Save (eVision::ESerializer *serializer)
	Saves an unsupervised segmentation metric. The given ESerializer must have been created for writing.
void	AddResult (const eVision::EasyDeepLearning::EUnsupervisedSegmenterResult &result, bool isGoodImage)
	Adds the given result with the corresponding ground truth label to the metrics.
void	AddErrorResult (const eVision::EasyDeepLearning::EUnsupervisedSegmenterResult &result)
	Adds the given result to the metric for error computation. The result must be computed from a good image that was corrupted during training.
void	RemoveResult (const eVision::EasyDeepLearning::EUnsupervisedSegmenterResult &result, bool isGoodImage)
	Removes the given result with the corresponding ground truth label to the metrics.
void	RemoveErrorResult (const eVision::EasyDeepLearning::EUnsupervisedSegmenterResult &result)
	Removes the given result from the metric for error computation. The result must be computed from a good image that was corrupted during training.
void	AddMetrics (const eVision::EasyDeepLearning::EUnsupervisedSegmenterMetrics &other)
	Adds the other metrics to the current metrics of this object.
float	GetBestWeightedAccuracy (float goodWeight, float badWeight) const
	Best achievable weighted accuracy.
float	GetBestWeightedAccuracy (const eVision::EasyDeepLearning::EDataset &dataset) const
	Best achievable weighted accuracy.
float	GetBestWeightedAccuracyClassificationThreshold (float goodWeight, float badWeight) const
	Classification threshold giving the best achievable weighted accuracy (see EUnsupervisedSegmenterMetrics::GetBestWeightedAccuracy).
float	GetBestWeightedAccuracyClassificationThreshold (const eVision::EasyDeepLearning::EDataset &dataset) const
	Classification threshold giving the best achievable weighted accuracy (see EUnsupervisedSegmenterMetrics::GetBestWeightedAccuracy).
float	GetError () const
	The error of the segmenter.
float	GetAverageScoreOnGoodImages () const
	Gets the average score of good images.
float	GetAverageScoreOnDefectiveImages () const
	Gets the average score of defective images.
	EUnsupervisedSegmenterMetrics (CppWrapperInternal::InternalConstructor)
Public Member Functions inherited from eVision::EasyDeepLearning::EDeepLearningDefectDetectionMetrics
	EDeepLearningDefectDetectionMetrics ()
	Constructs an empty EDeepLearningDefectDetectionMetrics object.
	EDeepLearningDefectDetectionMetrics (const eVision::EasyDeepLearning::EDeepLearningDefectDetectionMetrics &other)
	Copy constructor.
virtual	~EDeepLearningDefectDetectionMetrics () throw ()
eVision::EasyDeepLearning::EDeepLearningDefectDetectionMetrics &	operator= (const eVision::EasyDeepLearning::EDeepLearningDefectDetectionMetrics &other)
	Copy operator.
bool	IsDefectDetectionMetricsValid () const
	Whether the defect detection metrics are valid or not. Defect detection metrics are completely valid when the metrics has results for at least one good and one defective images. Some metrics might be valid with only defective or good results.
int	GetNumDefectiveSample () const
	Number of defective sample added to the metric.
int	GetNumGoodSample () const
	Number of good sample added to the metric.
float	GetAccuracy (int index) const
	The accuracy of the segmenter.
float	GetAccuracy () const
	The accuracy of the segmenter.
float	GetBestAccuracy () const
	Best achievable accuracy.
float	GetBestBalancedAccuracy () const
	Best achievable balanced accuracy.
float	GetBestWeightedAccuracy (float goodWeight, float badWeight) const
	Best achievable weighted accuracy.
float	GetBestAccuracyClassificationThreshold () const
	Classification threshold giving the best achievable accuracy (see EDeepLearningDefectDetectionMetrics::GetBestAccuracy).
float	GetBestBalancedAccuracyClassificationThreshold () const
	Classification threshold giving the best achievable balanced accuracy (see EDeepLearningDefectDetectionMetrics::GetBestBalancedAccuracy).
float	GetBestWeightedAccuracyClassificationThreshold (float goodWeight, float badWeight) const
	Classification threshold giving the best achievable weighted accuracy (see EDeepLearningDefectDetectionMetrics::GetBestWeightedAccuracy).
uint32_t	GetConfusion (eVision::EasyDeepLearning::EConfusionMatrixElement element, int index) const
	Confusion value of one label with another.
uint32_t	GetConfusion (eVision::EasyDeepLearning::EConfusionMatrixElement element) const
	Confusion value of one label with another.
float	GetAreaUnderROCCurve () const
	The area under ROC curve (AUC) of the classifier (see EDeepLearningDefectDetectionMetrics::GetROCPoint). It's value is between 0 and 1.
float	GetAveragePrecision () const
	Average precision.
float	GetBestFScore () const
	Best F1-Score.
float	GetBestFScoreThreshold () const
	Classification threshold that yields the best F1-Score.
float	GetRecall (int index) const
	Recall for the current threshold.
float	GetRecall () const
	Recall for the current threshold.
float	GetPrecision (int index) const
	Precision for the current threshold.
float	GetPrecision () const
	Precision for the current threshold.
float	GetFScore (int index) const
	The F1-Score for the current threshold.
float	GetFScore () const
	The F1-Score for the current threshold.
int	GetPrecisionRecallCurveIndex () const
	Index in the precision/recall curve for the current EDeepLearningDefectDetectionMetrics::GetClassificationThreshold.
int	GetNumPrecisionRecallCurvePoint () const
	Number of point in the precision/recall curve.
eVision::EasyDeepLearning::EROCPoint	GetPrecisionRecallCurvePoint (int index) const
	Get a point on the precision/recall curve.
eVision::EasyDeepLearning::EROCPoint	GetPrecisionRecallCurvePoint () const
	Get a point on the precision/recall curve.
int	GetNumberOfClassifiers () const
	Number of different possible classifiers.
eVision::EasyDeepLearning::EROCPoint	GetROCPoint (int index) const
	ROC (Receiver Operating Characteristic) point.
eVision::EasyDeepLearning::EROCPoint	GetROCPoint () const
	ROC (Receiver Operating Characteristic) point.
float	GetClassificationThreshold () const
	Classification threshold.
void	SetClassificationThreshold (float threshold)
	Classification threshold.
	EDeepLearningDefectDetectionMetrics (CppWrapperInternal::InternalConstructor)

Additional Inherited Members
Public Attributes inherited from eVision::EasyDeepLearning::EDeepLearningDefectDetectionMetrics
CppWrapperInternal::WrappedClass	wrappedClass_

Detailed Description

Collection of metrics used to evaluate the state of an EUnsupervisedSegmenter.

A metric is a value summarizing the quality of a collection of unsupervised segmentation results (see EUnsupervisedSegmenterResult) with respect to their ground truth. New results can be added to the object individually with EUnsupervisedSegmenterMetrics::AddResult or collectively with EUnsupervisedSegmenterMetrics::AddMetrics.

EUnsupervisedSegmenterMetrics contains two types of metrics: unsupervised metrics that are computed only on good images and supervised/defect detection metrics that are computed on both good and bad images. The defect detection metrics are accessible only when results for bad images were added to the object. When supervised metrics are accessible, EUnsupervisedSegmenterMetrics::IsTotallyUnsupervised is false.

There is only one unsupervised metric: the error (see EUnsupervisedSegmenterMetrics::GetError). See EDeepLearningDefectDetectionMetrics for a description of the defect detection metrics.

License(s): BasicTypes

Constructor & Destructor Documentation

◆ EUnsupervisedSegmenterMetrics() [1/3]

eVision::EasyDeepLearning::EUnsupervisedSegmenterMetrics::EUnsupervisedSegmenterMetrics ( )

Constructs an EUnsupervisedSegmenterMetrics object.

◆ EUnsupervisedSegmenterMetrics() [2/3]

eVision::EasyDeepLearning::EUnsupervisedSegmenterMetrics::EUnsupervisedSegmenterMetrics ( const eVision::EasyDeepLearning::EUnsupervisedSegmenterMetrics & other )

Copy constructor of an EUnsupervisedSegmenterMetrics object.

Parameters

other Reference to the EUnsupervisedSegmenterMetrics object that should be copied

◆ ~EUnsupervisedSegmenterMetrics()

eVision::EasyDeepLearning::EUnsupervisedSegmenterMetrics::~EUnsupervisedSegmenterMetrics	(		)
throw	(	)

virtual

◆ EUnsupervisedSegmenterMetrics() [3/3]

eVision::EasyDeepLearning::EUnsupervisedSegmenterMetrics::EUnsupervisedSegmenterMetrics ( CppWrapperInternal::InternalConstructor )

Member Function Documentation

◆ AddErrorResult()

void eVision::EasyDeepLearning::EUnsupervisedSegmenterMetrics::AddErrorResult ( const eVision::EasyDeepLearning::EUnsupervisedSegmenterResult & result )

Adds the given result to the metric for error computation. The result must be computed from a good image that was corrupted during training.

Parameters

result A reference to an EUnsupervisedSegmenterResult object.

◆ AddMetrics()

void eVision::EasyDeepLearning::EUnsupervisedSegmenterMetrics::AddMetrics ( const eVision::EasyDeepLearning::EUnsupervisedSegmenterMetrics & other )

Adds the other metrics to the current metrics of this object.

Parameters

other Unsupervised segmenter metrics

◆ AddResult()

void eVision::EasyDeepLearning::EUnsupervisedSegmenterMetrics::AddResult	(	const eVision::EasyDeepLearning::EUnsupervisedSegmenterResult &	result,
		bool	isGoodImage )

Adds the given result with the corresponding ground truth label to the metrics.

Parameters

result	A reference to an EUnsupervisedSegmenterResult object.
isGoodImage	True if the ground truth of this result is good, else false.

◆ GetAverageScoreOnDefectiveImages()

float eVision::EasyDeepLearning::EUnsupervisedSegmenterMetrics::GetAverageScoreOnDefectiveImages ( ) const

Gets the average score of defective images.

◆ GetAverageScoreOnGoodImages()

float eVision::EasyDeepLearning::EUnsupervisedSegmenterMetrics::GetAverageScoreOnGoodImages ( ) const

Gets the average score of good images.

◆ GetBestWeightedAccuracy() [1/2]

float eVision::EasyDeepLearning::EUnsupervisedSegmenterMetrics::GetBestWeightedAccuracy ( const eVision::EasyDeepLearning::EDataset & dataset ) const

Best achievable weighted accuracy.

The weighted accuracy is the weighted average of the true positive rate and the true negative rate (which is equal to 1 minus the false positive rate). See EROCPoint.

The classification threshold corresponding to this accuracy is given by EUnsupervisedSegmenterMetrics::GetBestWeightedAccuracyClassificationThreshold.

Parameters

dataset Dataset to get the label weight from.

When using a dataset as the source for the label weights, the good weight is the weight of the "good" label and the bad weight is the sum of the weights of all the other labels.

◆ GetBestWeightedAccuracy() [2/2]

float eVision::EasyDeepLearning::EUnsupervisedSegmenterMetrics::GetBestWeightedAccuracy	(	float	goodWeight,
		float	badWeight ) const

Best achievable weighted accuracy.

The weighted accuracy is the weighted average of the true positive rate and the true negative rate (which is equal to 1 minus the false positive rate). See EROCPoint.

The classification threshold corresponding to this accuracy is given by EUnsupervisedSegmenterMetrics::GetBestWeightedAccuracyClassificationThreshold.

Parameters

goodWeight	weight of the good label.
badWeight	weight of the bad label.

When using a dataset as the source for the label weights, the good weight is the weight of the "good" label and the bad weight is the sum of the weights of all the other labels.

◆ GetBestWeightedAccuracyClassificationThreshold() [1/2]

float eVision::EasyDeepLearning::EUnsupervisedSegmenterMetrics::GetBestWeightedAccuracyClassificationThreshold ( const eVision::EasyDeepLearning::EDataset & dataset ) const

Classification threshold giving the best achievable weighted accuracy (see EUnsupervisedSegmenterMetrics::GetBestWeightedAccuracy).

Parameters

dataset Dataset to get the label weight from.

◆ GetBestWeightedAccuracyClassificationThreshold() [2/2]

float eVision::EasyDeepLearning::EUnsupervisedSegmenterMetrics::GetBestWeightedAccuracyClassificationThreshold	(	float	goodWeight,
		float	badWeight ) const

Classification threshold giving the best achievable weighted accuracy (see EUnsupervisedSegmenterMetrics::GetBestWeightedAccuracy).

Parameters

goodWeight	Weight for the good label
badWeight	Weight for the bad label

◆ GetError()

float eVision::EasyDeepLearning::EUnsupervisedSegmenterMetrics::GetError ( ) const

The error of the segmenter.

This metric is only available in the metrics computed during a training which are accessible through EUnsupervisedSegmenter::GetValidationMetrics.

The error, which is also called the loss, is the quantity that is minimized during the training of the deep neural network.

◆ IsTotallyUnsupervised()

bool eVision::EasyDeepLearning::EUnsupervisedSegmenterMetrics::IsTotallyUnsupervised ( ) const

Whether this metrics has results from only good images (true) or from both good and defective images (false).

Some metrics are accessible only if EUnsupervisedSegmenterMetrics::IsTotallyUnsupervised is false.

◆ IsValid()

bool eVision::EasyDeepLearning::EUnsupervisedSegmenterMetrics::IsValid ( ) const

Indicates whether the object contains at least one result.

◆ Load() [1/2]

void eVision::EasyDeepLearning::EUnsupervisedSegmenterMetrics::Load ( const std::string & path )

virtual

Loads an unsupervised segmentation metric. The given ESerializer must have been created for reading.

Parameters

path	The file path.

Reimplemented from eVision::EasyDeepLearning::EDeepLearningDefectDetectionMetrics.

◆ Load() [2/2]

void eVision::EasyDeepLearning::EUnsupervisedSegmenterMetrics::Load ( eVision::ESerializer * serializer )

virtual

Loads an unsupervised segmentation metric. The given ESerializer must have been created for reading.

Parameters

serializer The serializer.

Reimplemented from eVision::EasyDeepLearning::EDeepLearningDefectDetectionMetrics.

◆ operator=()

eVision::EasyDeepLearning::EUnsupervisedSegmenterMetrics & eVision::EasyDeepLearning::EUnsupervisedSegmenterMetrics::operator= ( const eVision::EasyDeepLearning::EUnsupervisedSegmenterMetrics & other )

Assignment operator.

Parameters

other Reference to the EUnsupervisedSegmenterMetrics object used for the assignment

◆ RemoveErrorResult()

void eVision::EasyDeepLearning::EUnsupervisedSegmenterMetrics::RemoveErrorResult ( const eVision::EasyDeepLearning::EUnsupervisedSegmenterResult & result )

Removes the given result from the metric for error computation. The result must be computed from a good image that was corrupted during training.

Parameters

result A reference to an EUnsupervisedSegmenterResult object.

◆ RemoveResult()

void eVision::EasyDeepLearning::EUnsupervisedSegmenterMetrics::RemoveResult	(	const eVision::EasyDeepLearning::EUnsupervisedSegmenterResult &	result,
		bool	isGoodImage )

Removes the given result with the corresponding ground truth label to the metrics.

Parameters

result	A reference to an EUnsupervisedSegmenterResult object.
isGoodImage	True if the ground truth of this result is good, else false.

◆ Save() [1/2]

void eVision::EasyDeepLearning::EUnsupervisedSegmenterMetrics::Save ( const std::string & path )

virtual

Saves an unsupervised segmentation metric. The given ESerializer must have been created for writing.

Parameters

path	The file path.

Reimplemented from eVision::EasyDeepLearning::EDeepLearningDefectDetectionMetrics.

◆ Save() [2/2]

void eVision::EasyDeepLearning::EUnsupervisedSegmenterMetrics::Save ( eVision::ESerializer * serializer )

virtual

Saves an unsupervised segmentation metric. The given ESerializer must have been created for writing.

Parameters

serializer The serializer.

Reimplemented from eVision::EasyDeepLearning::EDeepLearningDefectDetectionMetrics.

Public Member Functions

Additional Inherited Members

Detailed Description

Constructor & Destructor Documentation

◆ EUnsupervisedSegmenterMetrics() [1/3]

◆ EUnsupervisedSegmenterMetrics() [2/3]

◆ ~EUnsupervisedSegmenterMetrics()

◆ EUnsupervisedSegmenterMetrics() [3/3]

Member Function Documentation

◆ AddErrorResult()

◆ AddMetrics()

◆ AddResult()

◆ GetAverageScoreOnDefectiveImages()

◆ GetAverageScoreOnGoodImages()

◆ GetBestWeightedAccuracy() [1/2]

◆ GetBestWeightedAccuracy() [2/2]

◆ GetBestWeightedAccuracyClassificationThreshold() [1/2]

◆ GetBestWeightedAccuracyClassificationThreshold() [2/2]

◆ GetError()

◆ IsTotallyUnsupervised()

◆ IsValid()

◆ Load() [1/2]

◆ Load() [2/2]

◆ operator=()

◆ RemoveErrorResult()

◆ RemoveResult()

◆ Save() [1/2]

◆ Save() [2/2]