def reportMeanAccyracyOfEpoch(self):
trainOrValString = "TRAINING" if self.training0orValidation1 == 0 else "VALIDATION"
logStr = trainOrValString + ": Epoch #" + str(self.epoch)
# Report the multi-class accuracy first.
self.log.print3(
"( >>>>>>>>>>>>>>>>>>>> Reporting Accuracy over whole epoch <<<<<<<<<<<<<<<<<<<<<<<<<<<<"
)
meanEmpiricalAccOfEp = getMeanOfListExclNA(
self.meanEmpiricalAccuracyOfEachSubep, self.NA_PATTERN)
self.log.print3(
logStr + ", Overall:\t mean accuracy of epoch:\t" +
strFl4fNA(meanEmpiricalAccOfEp, self.NA_PATTERN) +
"\t=> Correctly-Classified-Voxels/All-Predicted-Voxels")
if self.training0orValidation1 == 0: # During training, also report the mean value of the Cost Function:
meanCostOfEp = getMeanOfListExclNA(self.meanCostOfEachSubep,
self.NA_PATTERN)
self.log.print3(logStr + ", Overall:\t mean cost of epoch: \t" +
strFl5fNA(meanCostOfEp, self.NA_PATTERN))
self.log.print3(logStr +
", Overall:\t mean accuracy of each subepoch:\t" +
strListFl4fNA(self.meanEmpiricalAccuracyOfEachSubep,
self.NA_PATTERN))
if self.training0orValidation1 == 0:
self.log.print3(
logStr + ", Overall:\t mean cost of each subepoch: \t" +
strListFl5fNA(self.meanCostOfEachSubep, self.NA_PATTERN))
# Report for each class.
for class_i in range(self.numberOfClasses):
classString = "Class-" + str(class_i)
extraDescription = "[Whole Foreground (Pos) Vs Background (Neg)]" if class_i == 0 else "[This Class (Pos) Vs All Others (Neg)]"
self.log.print3(
">>>>>>>>>>>> Reporting Accuracy over whole epoch for " +
classString + " >>>>>>>>> " + extraDescription +
" <<<<<<<<<<<<<")
if class_i != 0:
meanAccPerSubep = [
self.listPerSubepPerClassMeanAccSensSpecDsc[subep_i]
[class_i][0] for subep_i in range(
len(self.listPerSubepPerClassMeanAccSensSpecDsc))
]
meanSensPerSubep = [
self.listPerSubepPerClassMeanAccSensSpecDsc[subep_i]
[class_i][1] for subep_i in range(
len(self.listPerSubepPerClassMeanAccSensSpecDsc))
]
meanPrecPerSubep = [
self.listPerSubepPerClassMeanAccSensSpecDsc[subep_i]
[class_i][2] for subep_i in range(
len(self.listPerSubepPerClassMeanAccSensSpecDsc))
]
meanSpecPerSubep = [
self.listPerSubepPerClassMeanAccSensSpecDsc[subep_i]
[class_i][3] for subep_i in range(
len(self.listPerSubepPerClassMeanAccSensSpecDsc))
]
meanDscPerSubep = [
self.listPerSubepPerClassMeanAccSensSpecDsc[subep_i]
[class_i][4] for subep_i in range(
len(self.listPerSubepPerClassMeanAccSensSpecDsc))
]
else: # Foreground Vs Background
meanAccPerSubep = [
self.listPerSubepForegrMeanAccSensSpecDsc[subep_i][0]
for subep_i in range(
len(self.listPerSubepForegrMeanAccSensSpecDsc))
]
meanSensPerSubep = [
self.listPerSubepForegrMeanAccSensSpecDsc[subep_i][1]
for subep_i in range(
len(self.listPerSubepForegrMeanAccSensSpecDsc))
]
meanPrecPerSubep = [
self.listPerSubepForegrMeanAccSensSpecDsc[subep_i][2]
for subep_i in range(
len(self.listPerSubepForegrMeanAccSensSpecDsc))
]
meanSpecPerSubep = [
self.listPerSubepForegrMeanAccSensSpecDsc[subep_i][3]
for subep_i in range(
len(self.listPerSubepForegrMeanAccSensSpecDsc))
]
meanDscPerSubep = [
self.listPerSubepForegrMeanAccSensSpecDsc[subep_i][4]
for subep_i in range(
len(self.listPerSubepForegrMeanAccSensSpecDsc))
]
meanAccOfEp = getMeanOfListExclNA(meanAccPerSubep, self.NA_PATTERN)
meanSensOfEp = getMeanOfListExclNA(meanSensPerSubep,
self.NA_PATTERN)
meanPrecOfEp = getMeanOfListExclNA(meanPrecPerSubep,
self.NA_PATTERN)
meanSpecOfEp = getMeanOfListExclNA(meanSpecPerSubep,
self.NA_PATTERN)
meanDscOfEp = getMeanOfListExclNA(meanDscPerSubep, self.NA_PATTERN)
logStrClass = logStr + ", " + classString + ":"
self.log.print3(logStrClass + "\t mean accuracy of epoch:\t" +
strFl4fNA(meanAccOfEp, self.NA_PATTERN) +
"\t=> (TruePos+TrueNeg)/All-Predicted-Voxels")
self.log.print3(logStrClass + "\t mean sensitivity of epoch:\t" +
strFl4fNA(meanSensOfEp, self.NA_PATTERN) +
"\t=> TruePos/RealPos")
self.log.print3(logStrClass + "\t mean precision of epoch:\t" +
strFl4fNA(meanPrecOfEp, self.NA_PATTERN) +
"\t=> TruePos/(TruePos+FalsePos)")
self.log.print3(logStrClass + "\t mean specificity of epoch:\t" +
strFl4fNA(meanSpecOfEp, self.NA_PATTERN) +
"\t=> TrueNeg/RealNeg")
self.log.print3(logStrClass + "\t mean Dice of epoch: \t" +
strFl4fNA(meanDscOfEp, self.NA_PATTERN))
#Visualised in my scripts:
self.log.print3(logStrClass +
"\t mean accuracy of each subepoch:\t" +
strListFl4fNA(meanAccPerSubep, self.NA_PATTERN))
self.log.print3(logStrClass +
"\t mean sensitivity of each subepoch:\t" +
strListFl4fNA(meanSensPerSubep, self.NA_PATTERN))
self.log.print3(logStrClass +
"\t mean precision of each subepoch:\t" +
strListFl4fNA(meanPrecPerSubep, self.NA_PATTERN))
self.log.print3(logStrClass +
"\t mean specificity of each subepoch:\t" +
strListFl4fNA(meanSpecPerSubep, self.NA_PATTERN))
self.log.print3(logStrClass +
"\t mean Dice of each subepoch: \t" +
strListFl4fNA(meanDscPerSubep, self.NA_PATTERN))
self.log.print3(
">>>>>>>>>>>>>>>>>>>>>>>>> End Of Accuracy Report at the end of Epoch <<<<<<<<<<<<<<<<<<<<<<<<<<<<<<"
)
self.log.print3(
">>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>><<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<"
)
def reportMeanAccyracyOfEpoch(self) :
trainOrValString = "TRAINING" if self.training0orValidation1 == 0 else "VALIDATION"
logStr = trainOrValString + ": Epoch #" + str(self.epoch)
# Report the multi-class accuracy first.
self.log.print3( "( >>>>>>>>>>>>>>>>>>>> Reporting Accuracy over whole epoch <<<<<<<<<<<<<<<<<<<<<<<<<<<<" )
meanEmpiricalAccOfEp = getMeanOfListExclNA(self.meanEmpiricalAccuracyOfEachSubep, self.NA_PATTERN)
self.log.print3( logStr + ", Overall:\t mean accuracy of epoch:\t" + strFl4fNA(meanEmpiricalAccOfEp, self.NA_PATTERN)+"\t=> Correctly-Classified-Voxels/All-Predicted-Voxels")
if self.training0orValidation1 == 0 : # During training, also report the mean value of the Cost Function:
meanCostOfEp = getMeanOfListExclNA(self.meanCostOfEachSubep, self.NA_PATTERN)
self.log.print3( logStr + ", Overall:\t mean cost of epoch: \t" + strFl5fNA(meanCostOfEp, self.NA_PATTERN) )
self.log.print3( logStr + ", Overall:\t mean accuracy of each subepoch:\t"+ strListFl4fNA(self.meanEmpiricalAccuracyOfEachSubep, self.NA_PATTERN) )
if self.training0orValidation1 == 0 :
self.log.print3( logStr + ", Overall:\t mean cost of each subepoch: \t" + strListFl5fNA(self.meanCostOfEachSubep, self.NA_PATTERN) )
# Report for each class.
for class_i in range(self.numberOfClasses) :
classString = "Class-"+str(class_i)
extraDescription = "[Whole Foreground (Pos) Vs Background (Neg)]" if class_i == 0 else "[This Class (Pos) Vs All Others (Neg)]"
self.log.print3( ">>>>>>>>>>>> Reporting Accuracy over whole epoch for " + classString + " >>>>>>>>> " + extraDescription + " <<<<<<<<<<<<<" )
if class_i != 0 :
meanAccPerSubep = [ self.listPerSubepPerClassMeanAccSensSpecDsc[subep_i][class_i][0] for subep_i in range(len(self.listPerSubepPerClassMeanAccSensSpecDsc)) ]
meanSensPerSubep = [ self.listPerSubepPerClassMeanAccSensSpecDsc[subep_i][class_i][1] for subep_i in range(len(self.listPerSubepPerClassMeanAccSensSpecDsc)) ]
meanPrecPerSubep = [ self.listPerSubepPerClassMeanAccSensSpecDsc[subep_i][class_i][2] for subep_i in range(len(self.listPerSubepPerClassMeanAccSensSpecDsc)) ]
meanSpecPerSubep = [ self.listPerSubepPerClassMeanAccSensSpecDsc[subep_i][class_i][3] for subep_i in range(len(self.listPerSubepPerClassMeanAccSensSpecDsc)) ]
meanDscPerSubep = [ self.listPerSubepPerClassMeanAccSensSpecDsc[subep_i][class_i][4] for subep_i in range(len(self.listPerSubepPerClassMeanAccSensSpecDsc)) ]
else : # Foreground Vs Background
meanAccPerSubep = [ self.listPerSubepForegrMeanAccSensSpecDsc[subep_i][0] for subep_i in range(len(self.listPerSubepForegrMeanAccSensSpecDsc)) ]
meanSensPerSubep = [ self.listPerSubepForegrMeanAccSensSpecDsc[subep_i][1] for subep_i in range(len(self.listPerSubepForegrMeanAccSensSpecDsc)) ]
meanPrecPerSubep = [ self.listPerSubepForegrMeanAccSensSpecDsc[subep_i][2] for subep_i in range(len(self.listPerSubepForegrMeanAccSensSpecDsc)) ]
meanSpecPerSubep = [ self.listPerSubepForegrMeanAccSensSpecDsc[subep_i][3] for subep_i in range(len(self.listPerSubepForegrMeanAccSensSpecDsc)) ]
meanDscPerSubep = [ self.listPerSubepForegrMeanAccSensSpecDsc[subep_i][4] for subep_i in range(len(self.listPerSubepForegrMeanAccSensSpecDsc)) ]
meanAccOfEp = getMeanOfListExclNA(meanAccPerSubep, self.NA_PATTERN)
meanSensOfEp = getMeanOfListExclNA(meanSensPerSubep, self.NA_PATTERN)
meanPrecOfEp = getMeanOfListExclNA(meanPrecPerSubep, self.NA_PATTERN)
meanSpecOfEp = getMeanOfListExclNA(meanSpecPerSubep, self.NA_PATTERN)
meanDscOfEp = getMeanOfListExclNA(meanDscPerSubep, self.NA_PATTERN)
logStrClass = logStr + ", " + classString + ":"
self.log.print3(logStrClass + "\t mean accuracy of epoch:\t"+ strFl4fNA(meanAccOfEp, self.NA_PATTERN) +"\t=> (TruePos+TrueNeg)/All-Predicted-Voxels")
self.log.print3(logStrClass + "\t mean sensitivity of epoch:\t"+ strFl4fNA(meanSensOfEp, self.NA_PATTERN) +"\t=> TruePos/RealPos")
self.log.print3(logStrClass + "\t mean precision of epoch:\t"+ strFl4fNA(meanPrecOfEp, self.NA_PATTERN) +"\t=> TruePos/(TruePos+FalsePos)")
self.log.print3(logStrClass + "\t mean specificity of epoch:\t"+ strFl4fNA(meanSpecOfEp, self.NA_PATTERN) +"\t=> TrueNeg/RealNeg")
self.log.print3(logStrClass + "\t mean Dice of epoch: \t"+ strFl4fNA(meanDscOfEp, self.NA_PATTERN) )
#Visualised in my scripts:
self.log.print3(logStrClass + "\t mean accuracy of each subepoch:\t"+ strListFl4fNA(meanAccPerSubep, self.NA_PATTERN) )
self.log.print3(logStrClass + "\t mean sensitivity of each subepoch:\t" + strListFl4fNA(meanSensPerSubep, self.NA_PATTERN) )
self.log.print3(logStrClass + "\t mean precision of each subepoch:\t" + strListFl4fNA(meanPrecPerSubep, self.NA_PATTERN) )
self.log.print3(logStrClass + "\t mean specificity of each subepoch:\t" + strListFl4fNA(meanSpecPerSubep, self.NA_PATTERN) )
self.log.print3(logStrClass + "\t mean Dice of each subepoch: \t" + strListFl4fNA(meanDscPerSubep, self.NA_PATTERN) )
self.log.print3( ">>>>>>>>>>>>>>>>>>>>>>>>> End Of Accuracy Report at the end of Epoch <<<<<<<<<<<<<<<<<<<<<<<<<<<<<<" )
self.log.print3( ">>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>><<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<" )