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)
def PreCutScore(OutPreOther, OutPreSame, DataSet, Name):
""" Plots a Score distribution after cutting on the signal Score distribution """
cut = 0.8
ScoreNum = 11
train, test = DataSet.GetInput("Even")
SigScoreOther = OutPreOther[:, 0] #Choosing the signal Score
SigScoreSame = OutPreSame[:, 0]
SecScoreOther = (OutPreOther[:, ScoreNum])[SigScoreOther > 0.8]
SecScoreSame = (OutPreSame[:, ScoreNum])[SigScoreSame > 0.8]
test = DIClasses.DISample(None, test.Weights[SigScoreOther > 0.8],
test.OutTrue[SigScoreOther > 0.8],
test.MultiClass[SigScoreOther > 0.8])
train = DIClasses.DISample(None, train.Weights[SigScoreSame > 0.8],
train.OutTrue[SigScoreSame > 0.8],
train.MultiClass[SigScoreSame > 0.8])
DataSet = DIClasses.DIDataSet(train, test, None, None)
PlotService.MultiScore("./plots/", SecScoreOther, SecScoreSame, DataSet,
Name, ScoreNum)