eVision::EasyDeepLearning::EClassificationMetrics Class Reference

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

#include <eVision/EasyDeepLearning/EasyClassify/EClassificationMetrics.h>

Collaboration diagram for eVision::EasyDeepLearning::EClassificationMetrics:

Public Member Functions
	EClassificationMetrics ()
	Constructs an EClassificationMetrics object.
	EClassificationMetrics (const eVision::EasyDeepLearning::EClassificationMetrics &other)
	Copy constructor of an EClassificationMetrics object.
virtual	~EClassificationMetrics () throw ()
eVision::EasyDeepLearning::EClassificationMetrics &	operator= (const eVision::EasyDeepLearning::EClassificationMetrics &other)
	Assignment operator.
void	Load (const std::string &path)
	Loads a classification metric. The given ESerializer must have been created for reading.
void	Load (eVision::ESerializer *serializer)
	Loads a classification metric. The given ESerializer must have been created for reading.
void	Save (const std::string &path)
	Saves a classification metric. The given ESerializer must have been created for writing.
void	Save (eVision::ESerializer *serializer)
	Saves a classification metric. The given ESerializer must have been created for writing.
bool	IsValid () const
	Indicates whether the object contains at least one classification result.
void	AddResult (const eVision::EasyDeepLearning::EClassificationResult &result, const std::string &groundtruthLabel)
	Adds the given result with the corresponding ground truth label to the metrics.
void	AddMetrics (const eVision::EasyDeepLearning::EClassificationMetrics &other)
	Adds the other metrics to the current metrics of this object.
float	GetAccuracy () const
	The accuracy of the classifier.
float	GetWeightedAccuracy (const std::vector< float > &weights) const
	The label weighted accuracy.
float	GetWeightedAccuracy (const eVision::EasyDeepLearning::EDataset &dataset) const
	The label weighted accuracy.
float	GetBalancedAccuracy () const
	The balanced accuracy.
float	GetLabelAccuracy (const std::string &label) const
	The accuracy of the classifier for a given label.
float	GetError () const
	The error of the classifier.
float	GetWeightedError (const std::vector< float > &weights) const
	The label weighted error.
float	GetWeightedError (const eVision::EasyDeepLearning::EDataset &dataset) const
	The label weighted error.
float	GetBalancedError () const
	The balanced error.
float	GetLabelError (const std::string &label) const
	The error of the classifier for a given label.
bool	CanComputeWeightedError () const
	Whether the object can be used to get weighted, balanced, and label errors.
uint32_t	GetConfusion (const std::string &trueClass, const std::string &predictedClass) const
	Confusion value of one label with another.
	EClassificationMetrics (CppWrapperInternal::InternalConstructor)

Public Attributes
CppWrapperInternal::WrappedClass	wrappedClass_

Detailed Description

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

A metric is a value summarizing a collection of classification results (EClassificationResult). New results can be added to the object individually with EClassificationMetrics::AddResult or collectively with EClassificationMetrics::AddMetrics.

EClassificationMetrics contains the following metrics:

• the accuracy (see EClassificationMetrics::GetAccuracy)
• the error (see EClassificationMetrics::GetError)

License(s): BasicTypes

Constructor & Destructor Documentation

EClassificationMetrics() [1/3]

eVision::EasyDeepLearning::EClassificationMetrics::EClassificationMetrics

(

)

Constructs an EClassificationMetrics object.

EClassificationMetrics() [2/3]

eVision::EasyDeepLearning::EClassificationMetrics::EClassificationMetrics

(

const eVision::EasyDeepLearning::EClassificationMetrics &

other

)

Copy constructor of an EClassificationMetrics object.

Parameters

other

Reference to the EClassificationMetrics object that should be copied

~EClassificationMetrics()

eVision::EasyDeepLearning::EClassificationMetrics::~EClassificationMetrics ( ) throw (  )

virtual

EClassificationMetrics() [3/3]

eVision::EasyDeepLearning::EClassificationMetrics::EClassificationMetrics

(

CppWrapperInternal::InternalConstructor

)

Member Function Documentation

AddMetrics()

void eVision::EasyDeepLearning::EClassificationMetrics::AddMetrics

(

const eVision::EasyDeepLearning::EClassificationMetrics &

other

)

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

Parameters

other

Classification metrics

AddResult()

void eVision::EasyDeepLearning::EClassificationMetrics::AddResult	(	const eVision::EasyDeepLearning::EClassificationResult &	result,
		const std::string &	groundtruthLabel )

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

Parameters

result	A reference to an EClassificationResult object.
groundtruthLabel	The ground truth label corresponding to the result

CanComputeWeightedError()

bool eVision::EasyDeepLearning::EClassificationMetrics::CanComputeWeightedError

(

)

const

Whether the object can be used to get weighted, balanced, and label errors.

GetAccuracy()

float eVision::EasyDeepLearning::EClassificationMetrics::GetAccuracy

(

)

const

The accuracy of the classifier.

The accuracy is the number of images that were correctly classified (also called the true positives) over the total number of images that was used to evaluate the classifier.

GetBalancedAccuracy()

float eVision::EasyDeepLearning::EClassificationMetrics::GetBalancedAccuracy

(

)

const

The balanced accuracy.

The balanced accuracy is the label weighted accuracy with the same weight for each labels.

GetBalancedError()

float eVision::EasyDeepLearning::EClassificationMetrics::GetBalancedError

(

)

const

The balanced error.

The balanced error is the label weighted error with the same weight for each labels.

If EClassificationMetrics::CanComputeWeightedError is false, this method is an alias for EClassificationMetrics::GetError.

GetConfusion()

uint32_t eVision::EasyDeepLearning::EClassificationMetrics::GetConfusion	(	const std::string &	trueClass,
		const std::string &	predictedClass ) const

Confusion value of one label with another.

The confusion value of a label with another is the number of images belonging to this label that are classified as belonging to the other label.

Parameters

trueClass	The label for which to obtain the confusion value
predictedClass	The label with which there is a confusion

GetError()

float eVision::EasyDeepLearning::EClassificationMetrics::GetError

(

)

const

The error of the classifier.

The error, which is also called the loss, is the quantity that is minimized during the training of the deep neural network. For classification, the error is the cross-entropy.

GetLabelAccuracy()

float eVision::EasyDeepLearning::EClassificationMetrics::GetLabelAccuracy

(

const std::string &

label

)

const

The accuracy of the classifier for a given label.

Parameters

label

The label of which we want the accuracy

The label accuracy is the number of images of a given label that were correctly classified (also called the true positives) over the total number of images of that label that was used to evaluate the classifier.

If there is no results for the given label, the method will throw an exception.

GetLabelError()

float eVision::EasyDeepLearning::EClassificationMetrics::GetLabelError

(

const std::string &

label

)

const

The error of the classifier for a given label.

Parameters

label

The label from which to get the error.

The label error corresponds to the error only for images of the given label.

If there is no results for the given label, the method will throw an exception. If EClassificationMetrics::CanComputeWeightedError is false, this method is an alias for EClassificationMetrics::GetError.

GetWeightedAccuracy() [1/2]

float eVision::EasyDeepLearning::EClassificationMetrics::GetWeightedAccuracy

(

const eVision::EasyDeepLearning::EDataset &

dataset

)

const

The label weighted accuracy.

Parameters

dataset

Dataset from which to use the label weights

The weighted accuracy is the weighted average of the label accuracies (see EClassificationMetrics::GetLabelAccuracy). If there is no results for a given label, it will be ignored in the weighted accuracy.

GetWeightedAccuracy() [2/2]

float eVision::EasyDeepLearning::EClassificationMetrics::GetWeightedAccuracy

(

const std::vector< float > &

weights

)

const

The label weighted accuracy.

Parameters

weights

Array of label weights for the labels of the classifier (see EDeepLearningTool::GetLabel)

The weighted accuracy is the weighted average of the label accuracies (see EClassificationMetrics::GetLabelAccuracy). If there is no results for a given label, it will be ignored in the weighted accuracy.

GetWeightedError() [1/2]

float eVision::EasyDeepLearning::EClassificationMetrics::GetWeightedError

(

const eVision::EasyDeepLearning::EDataset &

dataset

)

const

The label weighted error.

Parameters

dataset

Dataset from which to use the label weights

The weighted error is the weighted average of the label errors (see EClassificationMetrics::GetLabelError).

If there isn't any result for a given label, it will be ignored in the weighted error.

If EClassificationMetrics::CanComputeWeightedError is false, this method is an alias for EClassificationMetrics::GetError.

GetWeightedError() [2/2]

float eVision::EasyDeepLearning::EClassificationMetrics::GetWeightedError

(

const std::vector< float > &

weights

)

const

The label weighted error.

Parameters

weights

Array of label weights for the labels of the classifier (see EDeepLearningTool::GetLabel)

The weighted error is the weighted average of the label errors (see EClassificationMetrics::GetLabelError).

If there isn't any result for a given label, it will be ignored in the weighted error.

If EClassificationMetrics::CanComputeWeightedError is false, this method is an alias for EClassificationMetrics::GetError.

IsValid()

bool eVision::EasyDeepLearning::EClassificationMetrics::IsValid

(

)

const

Indicates whether the object contains at least one classification result.

Load() [1/2]

void eVision::EasyDeepLearning::EClassificationMetrics::Load

(

const std::string &

path

)

Loads a classification metric. The given ESerializer must have been created for reading.

Parameters

path	The file path.

Load() [2/2]

void eVision::EasyDeepLearning::EClassificationMetrics::Load

(

eVision::ESerializer *

serializer

)

Loads a classification metric. The given ESerializer must have been created for reading.

Parameters

serializer

The serializer.

operator=()

eVision::EasyDeepLearning::EClassificationMetrics & eVision::EasyDeepLearning::EClassificationMetrics::operator=

(

const eVision::EasyDeepLearning::EClassificationMetrics &

other

)

Assignment operator.

Parameters

other

Reference to the EClassificationMetrics object used for the assignment

Save() [1/2]

void eVision::EasyDeepLearning::EClassificationMetrics::Save

(

const std::string &

path

)

Saves a classification metric. The given ESerializer must have been created for writing.

Parameters

path	The file path.

Save() [2/2]

void eVision::EasyDeepLearning::EClassificationMetrics::Save

(

eVision::ESerializer *

serializer

)

Saves a classification metric. The given ESerializer must have been created for writing.

Parameters

serializer

The ESerializer object that is written to.

Member Data Documentation

wrappedClass_

CppWrapperInternal::WrappedClass eVision::EasyDeepLearning::EClassificationMetrics::wrappedClass_