eVision.EasyDeepLearning.EROCPoint Class Reference

The structure representing a point on the ROC (Receiver Operating Characteristic) curve. More...

Public Member Functions
None	__init__ (self)
	Constructs a eVision.EasyDeepLearning.EROCPoint.
None	__init__ (self, float threshold, int truePositiveCount, int positiveCount, int falsePositiveCount, int negativeCount)
	Constructs a eVision.EasyDeepLearning.EROCPoint.
None	__init__ (self, float TPR)
None	__init__ (self, float TPR, int TP)
None	__init__ (self, float TPR, int TP, int P)
None	__init__ (self, float TPR, int TP, int P, float FPR)
None	__init__ (self, float TPR, int TP, int P, float FPR, int FP)
None	__init__ (self, float TPR, int TP, int P, float FPR, int FP, int N)
None	__init__ (self, float TPR, int TP, int P, float FPR, int FP, int N, float Threshold)
None	__init__ (self, eVision.EasyDeepLearning.EROCPoint other)
None	Load (self, str path)
	Loads the ROC point.
None	Load (self, eVision.ESerializer|None serializer)
	Loads the ROC point.
None	Save (self, str path)
	Saves the ROC point.
None	Save (self, eVision.ESerializer|None serializer)
	Saves the ROC point.

Protected Member Functions
float	_TPR__getter (self)
	\cond
	_TPR__setter (self, float value)
	The True Positive Rate: it is the number of defective images classified as defective, normalized by the number of defective images.
int	_TP__getter (self)
	\cond
	_TP__setter (self, int value)
	The True Positive count: it is the number of defective images classified as defective.
int	_P__getter (self)
	\cond
	_P__setter (self, int value)
	The Positive count: it is the number of defective images.
float	_FPR__getter (self)
	\cond
	_FPR__setter (self, float value)
	The False Positive Rate: it is the number of good image classified as defective, normalized by the number of good images.
int	_FP__getter (self)
	\cond
	_FP__setter (self, int value)
	The False Positive count: it is the number of good image classified as defective.
int	_N__getter (self)
	\cond
	_N__setter (self, int value)
	The Negative count: it is the number of good images.
float	_Threshold__getter (self)
	\cond
	_Threshold__setter (self, float value)
	The threshold associated with the true and false positive rates (see eVision.EasyDeepLearning.EUnsupervisedSegmenter.ClassificationThreshold).

Properties
	TPR = property(_TPR__getter, _TPR__setter)
	\endcond
	TP = property(_TP__getter, _TP__setter)
	\endcond
	P = property(_P__getter, _P__setter)
	\endcond
	FPR = property(_FPR__getter, _FPR__setter)
	\endcond
	FP = property(_FP__getter, _FP__setter)
	\endcond
	N = property(_N__getter, _N__setter)
	\endcond
	Threshold = property(_Threshold__getter, _Threshold__setter)
	\endcond

Detailed Description

The structure representing a point on the ROC (Receiver Operating Characteristic) curve.

Remarks:

License(s): None

Constructor & Destructor Documentation

__init__() [1/10]

None eVision.EasyDeepLearning.EROCPoint.__init__

(

self

)

Constructs a eVision.EasyDeepLearning.EROCPoint.

__init__() [2/10]

None eVision.EasyDeepLearning.EROCPoint.__init__	(		self,
		float	threshold,
		int	truePositiveCount,
		int	positiveCount,
		int	falsePositiveCount,
		int	negativeCount )

Constructs a eVision.EasyDeepLearning.EROCPoint.

Parameters

threshold	Threshold corresponding to the ROC point.
truePositiveCount	Number of defective images classified as defective.
positiveCount	Total number of defective images.
falsePositiveCount	Number of good images classified as defective.
negativeCount	Total number of good images.

__init__() [3/10]

None eVision.EasyDeepLearning.EROCPoint.__init__	(		self,
		float	TPR )

Parameters

TPR	The True Positive Rate: it is the number of defective images classified as defective, normalized by the number of defective images.

The TPR can be NaN (Not A Number) if the metrics were computed using only good images.

__init__() [4/10]

None eVision.EasyDeepLearning.EROCPoint.__init__	(		self,
		float	TPR,
		int	TP )

Parameters

TPR	The True Positive Rate: it is the number of defective images classified as defective, normalized by the number of defective images.

The TPR can be NaN (Not A Number) if the metrics were computed using only good images.

Parameters

TP	The True Positive count: it is the number of defective images classified as defective.

__init__() [5/10]

None eVision.EasyDeepLearning.EROCPoint.__init__	(		self,
		float	TPR,
		int	TP,
		int	P )

Parameters

TPR	The True Positive Rate: it is the number of defective images classified as defective, normalized by the number of defective images.

The TPR can be NaN (Not A Number) if the metrics were computed using only good images.

Parameters

TP	The True Positive count: it is the number of defective images classified as defective.
P	The Positive count: it is the number of defective images.

__init__() [6/10]

None eVision.EasyDeepLearning.EROCPoint.__init__	(		self,
		float	TPR,
		int	TP,
		int	P,
		float	FPR )

Parameters

TPR	The True Positive Rate: it is the number of defective images classified as defective, normalized by the number of defective images.

The TPR can be NaN (Not A Number) if the metrics were computed using only good images.

Parameters

TP	The True Positive count: it is the number of defective images classified as defective.
P	The Positive count: it is the number of defective images.
FPR	The False Positive Rate: it is the number of good image classified as defective, normalized by the number of good images.

__init__() [7/10]

None eVision.EasyDeepLearning.EROCPoint.__init__	(		self,
		float	TPR,
		int	TP,
		int	P,
		float	FPR,
		int	FP )

Parameters

TPR	The True Positive Rate: it is the number of defective images classified as defective, normalized by the number of defective images.

The TPR can be NaN (Not A Number) if the metrics were computed using only good images.

Parameters

TP	The True Positive count: it is the number of defective images classified as defective.
P	The Positive count: it is the number of defective images.
FPR	The False Positive Rate: it is the number of good image classified as defective, normalized by the number of good images.
FP	The False Positive count: it is the number of good image classified as defective.

__init__() [8/10]

None eVision.EasyDeepLearning.EROCPoint.__init__	(		self,
		float	TPR,
		int	TP,
		int	P,
		float	FPR,
		int	FP,
		int	N )

Parameters

TPR	The True Positive Rate: it is the number of defective images classified as defective, normalized by the number of defective images.

The TPR can be NaN (Not A Number) if the metrics were computed using only good images.

Parameters

TP	The True Positive count: it is the number of defective images classified as defective.
P	The Positive count: it is the number of defective images.
FPR	The False Positive Rate: it is the number of good image classified as defective, normalized by the number of good images.
FP	The False Positive count: it is the number of good image classified as defective.
N	The Negative count: it is the number of good images.

__init__() [9/10]

None eVision.EasyDeepLearning.EROCPoint.__init__	(		self,
		float	TPR,
		int	TP,
		int	P,
		float	FPR,
		int	FP,
		int	N,
		float	Threshold )

Parameters

TPR	The True Positive Rate: it is the number of defective images classified as defective, normalized by the number of defective images.

The TPR can be NaN (Not A Number) if the metrics were computed using only good images.

Parameters

TP	The True Positive count: it is the number of defective images classified as defective.
P	The Positive count: it is the number of defective images.
FPR	The False Positive Rate: it is the number of good image classified as defective, normalized by the number of good images.
FP	The False Positive count: it is the number of good image classified as defective.
N	The Negative count: it is the number of good images.
Threshold	The threshold associated with the true and false positive rates (see eVision.EasyDeepLearning.EUnsupervisedSegmenter.ClassificationThreshold).

__init__() [10/10]

None eVision.EasyDeepLearning.EROCPoint.__init__	(		self,
		eVision.EasyDeepLearning.EROCPoint	other )

Parameters

other

Other instance

Member Function Documentation

_FP__getter()

int eVision.EasyDeepLearning.EROCPoint._FP__getter ( self )

protected

\cond

The False Positive count: it is the number of good image classified as defective.

_FP__setter()

eVision.EasyDeepLearning.EROCPoint._FP__setter ( self, int value )

protected

The False Positive count: it is the number of good image classified as defective.

_FPR__getter()

float eVision.EasyDeepLearning.EROCPoint._FPR__getter ( self )

protected

\cond

The False Positive Rate: it is the number of good image classified as defective, normalized by the number of good images.

_FPR__setter()

eVision.EasyDeepLearning.EROCPoint._FPR__setter ( self, float value )

protected

The False Positive Rate: it is the number of good image classified as defective, normalized by the number of good images.

_N__getter()

int eVision.EasyDeepLearning.EROCPoint._N__getter ( self )

protected

\cond

The Negative count: it is the number of good images.

_N__setter()

eVision.EasyDeepLearning.EROCPoint._N__setter ( self, int value )

protected

The Negative count: it is the number of good images.

_P__getter()

int eVision.EasyDeepLearning.EROCPoint._P__getter ( self )

protected

\cond

The Positive count: it is the number of defective images.

_P__setter()

eVision.EasyDeepLearning.EROCPoint._P__setter ( self, int value )

protected

The Positive count: it is the number of defective images.

_Threshold__getter()

float eVision.EasyDeepLearning.EROCPoint._Threshold__getter ( self )

protected

\cond

The threshold associated with the true and false positive rates (see eVision.EasyDeepLearning.EUnsupervisedSegmenter.ClassificationThreshold).

_Threshold__setter()

eVision.EasyDeepLearning.EROCPoint._Threshold__setter ( self, float value )

protected

The threshold associated with the true and false positive rates (see eVision.EasyDeepLearning.EUnsupervisedSegmenter.ClassificationThreshold).

_TP__getter()

int eVision.EasyDeepLearning.EROCPoint._TP__getter ( self )

protected

\cond

The True Positive count: it is the number of defective images classified as defective.

_TP__setter()

eVision.EasyDeepLearning.EROCPoint._TP__setter ( self, int value )

protected

The True Positive count: it is the number of defective images classified as defective.

_TPR__getter()

float eVision.EasyDeepLearning.EROCPoint._TPR__getter ( self )

protected

\cond

The True Positive Rate: it is the number of defective images classified as defective, normalized by the number of defective images.

The TPR can be NaN (Not A Number) if the metrics were computed using only good images.

_TPR__setter()

eVision.EasyDeepLearning.EROCPoint._TPR__setter ( self, float value )

protected

The True Positive Rate: it is the number of defective images classified as defective, normalized by the number of defective images.

The TPR can be NaN (Not A Number) if the metrics were computed using only good images.

Load() [1/2]

None eVision.EasyDeepLearning.EROCPoint.Load	(		self,
		eVision.ESerializer | None	serializer )

Loads the ROC point.

The given eVision.ESerializer must have been created for reading.

Parameters

serializer

The serializer.

Load() [2/2]

None eVision.EasyDeepLearning.EROCPoint.Load	(		self,
		str	path )

Loads the ROC point.

The given eVision.ESerializer must have been created for reading.

Parameters

path	The file path.

Save() [1/2]

None eVision.EasyDeepLearning.EROCPoint.Save	(		self,
		eVision.ESerializer | None	serializer )

Saves the ROC point.

The given eVision.ESerializer must have been created for writing.

Parameters

serializer

The serializer.

Save() [2/2]

None eVision.EasyDeepLearning.EROCPoint.Save	(		self,
		str	path )

Saves the ROC point.

The given eVision.ESerializer must have been created for writing.

Parameters

path	The file path.

Property Documentation

FP

eVision.EasyDeepLearning.EROCPoint.FP = property(_FP__getter, _FP__setter)

static

\endcond

The False Positive count: it is the number of good image classified as defective.

FPR

eVision.EasyDeepLearning.EROCPoint.FPR = property(_FPR__getter, _FPR__setter)

static

\endcond

The False Positive Rate: it is the number of good image classified as defective, normalized by the number of good images.

N

eVision.EasyDeepLearning.EROCPoint.N = property(_N__getter, _N__setter)

static

\endcond

The Negative count: it is the number of good images.

P

eVision.EasyDeepLearning.EROCPoint.P = property(_P__getter, _P__setter)

static

\endcond

The Positive count: it is the number of defective images.

Threshold

eVision.EasyDeepLearning.EROCPoint.Threshold = property(_Threshold__getter, _Threshold__setter)

static

\endcond

The threshold associated with the true and false positive rates (see eVision.EasyDeepLearning.EUnsupervisedSegmenter.ClassificationThreshold).

TP

eVision.EasyDeepLearning.EROCPoint.TP = property(_TP__getter, _TP__setter)

static

\endcond

The True Positive count: it is the number of defective images classified as defective.

TPR

eVision.EasyDeepLearning.EROCPoint.TPR = property(_TPR__getter, _TPR__setter)

static

\endcond

The True Positive Rate: it is the number of defective images classified as defective, normalized by the number of defective images.

The TPR can be NaN (Not A Number) if the metrics were computed using only good images.