def MakePlots(self, OutPreOther, OutPreSame):
""" Plotting the ROC, Score of the Model """
AucOthers, AucSames = [], []
PlotService.RocCurve("./",OutPreOther,self.DataSet,self.ModelNames)
if(len(self.ModelNames) == 1):
AucOther, AucSame = PlotService.Score("./",OutPreOther[0],OutPreSame[0],self.DataSet,self.ModelNames[0])
elif(len(self.ModelNames) > 1):
for i,Name in enumerate(self.ModelNames):
AucOther, AucSame = PlotService.Score("./",OutPreOther[i],OutPreSame[i],self.DataSet,Name)
AucOthers.append(AucOther)
AucSames.append(AucSame)
return AucOthers, AucSames
def PreTrainedFNN(self):
LOutPreOther = []
for Name in self.ModelNames:
OutPreOther, OutPreSame = self.GetOutPreFromFile(Name)
PreLabelsOther, PreLabelsSame = self.PredictClasses(
OutPreOther, OutPreSame)
#self.MakeConfusionMatrix(Name, PreLabelsOther, PreLabelsSame)
Classnum = 13
PlotService.MultiScore("./plots/", OutPreOther[:, Classnum],
OutPreSame[:, Classnum], self.DataSet, Name,
Classnum) #Score for Sig
PlotService.Score("./plots/", OutPreOther[:, Classnum],
OutPreSame[:, Classnum], self.DataSet, Name)
#Fit(OutPreOther)
LOutPreOther.append(OutPreOther[:, 0])
PlotService.RocCurve("./plots/", LOutPreOther, self.DataSet,
self.ModelNames)
def EvaluateFNN(self):
LOutPreOther = []
for Name in self.ModelNames:
if ("BDT" in Name): #Import the BDT Scores as comperison
OutPreOther, OutPreSame = self.GetOutPreFromFile(Name)
else:
OutPreOther, OutPreSame = self.GetOutPreFromRoc(Name)
PreLabelsOther, PreLabelsSame = self.PredictClasses(
OutPreOther, OutPreSame)
self.MakeConfusionMatrix(Name, PreLabelsOther, PreLabelsSame)
PlotService.Score("./plots/", OutPreOther[:, 0], OutPreSame[:, 0],
self.DataSet, Name)
PlotService.MultiScore("./plots/", OutPreOther[:, 0],
OutPreSame[:, 0], self.DataSet, Name,
0) #Score for Sig
LOutPreOther.append(OutPreOther[:, 0])
PlotService.RocCurve("./plots/", LOutPreOther, self.DataSet,
self.ModelNames)