eVision.EasyDeepLearning.EWeightedPrecisionRecallPoint Class Reference

Weighted precision and recall for a given threshold. More...

Public Member Functions
None	__init__ (self)
	Default constructor.
None	__init__ (self, eVision.EasyDeepLearning.EWeightedPrecisionRecallPoint other)
	Copy constructor.
None	__init__ (self, float Threshold)
None	__init__ (self, float Threshold, float Recall)
None	__init__ (self, float Threshold, float Recall, float Precision)
None	__init__ (self, float Threshold, float Recall, float Precision, float FScore)

Protected Member Functions
float	_Threshold__getter (self)
	\cond
	_Threshold__setter (self, float value)
	Threshold of the metric point.
float	_Recall__getter (self)
	\cond
	_Recall__setter (self, float value)
	Weighted recall corresponding to the threshold.
float	_Precision__getter (self)
	\cond
	_Precision__setter (self, float value)
	Weighted precision corresponding to the threshold.
float	_FScore__getter (self)
	\cond
	_FScore__setter (self, float value)
	Weighted F-Score corresponding to the threshold.

Properties
	Threshold = property(_Threshold__getter, _Threshold__setter)
	\endcond
	Recall = property(_Recall__getter, _Recall__setter)
	\endcond
	Precision = property(_Precision__getter, _Precision__setter)
	\endcond
	FScore = property(_FScore__getter, _FScore__setter)
	\endcond

Detailed Description

Weighted precision and recall for a given threshold.

Remarks:

License(s): BasicTypes

Constructor & Destructor Documentation

__init__() [1/6]

None eVision.EasyDeepLearning.EWeightedPrecisionRecallPoint.__init__

(

self

)

Default constructor.

__init__() [2/6]

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

Copy constructor.

Parameters

other

Weighted metric point to copy

__init__() [3/6]

None eVision.EasyDeepLearning.EWeightedPrecisionRecallPoint.__init__	(		self,
		float	Threshold )

Parameters

Threshold

Threshold of the metric point.

__init__() [4/6]

None eVision.EasyDeepLearning.EWeightedPrecisionRecallPoint.__init__	(		self,
		float	Threshold,
		float	Recall )

Parameters

Threshold	Threshold of the metric point.
Recall	Weighted recall corresponding to the threshold.

The weighted recall is the weighted average of the recall for each label. The recall for a label is the number of correctly predicted element divided by the number of ground truth elements of that label.

__init__() [5/6]

None eVision.EasyDeepLearning.EWeightedPrecisionRecallPoint.__init__	(		self,
		float	Threshold,
		float	Recall,
		float	Precision )

Parameters

Threshold	Threshold of the metric point.
Recall	Weighted recall corresponding to the threshold.

The weighted recall is the weighted average of the recall for each label. The recall for a label is the number of correctly predicted element divided by the number of ground truth elements of that label.

Parameters

Precision

Weighted precision corresponding to the threshold.

The weighted precision is the weighted average of the precision for each label. The precision for a label is the number of correctly predicted element divided by the number of elements predicted for that label (correctly or wrongly).

__init__() [6/6]

None eVision.EasyDeepLearning.EWeightedPrecisionRecallPoint.__init__	(		self,
		float	Threshold,
		float	Recall,
		float	Precision,
		float	FScore )

Parameters

Threshold	Threshold of the metric point.
Recall	Weighted recall corresponding to the threshold.

The weighted recall is the weighted average of the recall for each label. The recall for a label is the number of correctly predicted element divided by the number of ground truth elements of that label.

Parameters

Precision

Weighted precision corresponding to the threshold.

The weighted precision is the weighted average of the precision for each label. The precision for a label is the number of correctly predicted element divided by the number of elements predicted for that label (correctly or wrongly).

Parameters

FScore

Weighted F-Score corresponding to the threshold.

The weighted F-Score is the harmonic mean of the weighted recall and weighted precision.

Member Function Documentation

_FScore__getter()

float eVision.EasyDeepLearning.EWeightedPrecisionRecallPoint._FScore__getter ( self )

protected

\cond

Weighted F-Score corresponding to the threshold.

The weighted F-Score is the harmonic mean of the weighted recall and weighted precision.

_FScore__setter()

eVision.EasyDeepLearning.EWeightedPrecisionRecallPoint._FScore__setter ( self, float value )

protected

Weighted F-Score corresponding to the threshold.

The weighted F-Score is the harmonic mean of the weighted recall and weighted precision.

_Precision__getter()

float eVision.EasyDeepLearning.EWeightedPrecisionRecallPoint._Precision__getter ( self )

protected

\cond

Weighted precision corresponding to the threshold.

The weighted precision is the weighted average of the precision for each label. The precision for a label is the number of correctly predicted element divided by the number of elements predicted for that label (correctly or wrongly).

_Precision__setter()

eVision.EasyDeepLearning.EWeightedPrecisionRecallPoint._Precision__setter ( self, float value )

protected

Weighted precision corresponding to the threshold.

The weighted precision is the weighted average of the precision for each label. The precision for a label is the number of correctly predicted element divided by the number of elements predicted for that label (correctly or wrongly).

_Recall__getter()

float eVision.EasyDeepLearning.EWeightedPrecisionRecallPoint._Recall__getter ( self )

protected

\cond

Weighted recall corresponding to the threshold.

The weighted recall is the weighted average of the recall for each label. The recall for a label is the number of correctly predicted element divided by the number of ground truth elements of that label.

_Recall__setter()

eVision.EasyDeepLearning.EWeightedPrecisionRecallPoint._Recall__setter ( self, float value )

protected

Weighted recall corresponding to the threshold.

The weighted recall is the weighted average of the recall for each label. The recall for a label is the number of correctly predicted element divided by the number of ground truth elements of that label.

_Threshold__getter()

float eVision.EasyDeepLearning.EWeightedPrecisionRecallPoint._Threshold__getter ( self )

protected

\cond

Threshold of the metric point.

_Threshold__setter()

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

protected

Threshold of the metric point.

Property Documentation

FScore

eVision.EasyDeepLearning.EWeightedPrecisionRecallPoint.FScore = property(_FScore__getter, _FScore__setter)

static

\endcond

Weighted F-Score corresponding to the threshold.

The weighted F-Score is the harmonic mean of the weighted recall and weighted precision.

Precision

eVision.EasyDeepLearning.EWeightedPrecisionRecallPoint.Precision = property(_Precision__getter, _Precision__setter)

static

\endcond

Weighted precision corresponding to the threshold.

The weighted precision is the weighted average of the precision for each label. The precision for a label is the number of correctly predicted element divided by the number of elements predicted for that label (correctly or wrongly).

Recall

eVision.EasyDeepLearning.EWeightedPrecisionRecallPoint.Recall = property(_Recall__getter, _Recall__setter)

static

\endcond

Weighted recall corresponding to the threshold.

The weighted recall is the weighted average of the recall for each label. The recall for a label is the number of correctly predicted element divided by the number of ground truth elements of that label.

Threshold

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

static

\endcond

Threshold of the metric point.