# Training strategies
training0 = TString("LearningRate=1e-1,Momentum=0.9,Repetitions=1,"
"ConvergenceSteps=2,BatchSize=256,TestRepetitions=10,"
"WeightDecay=1e-4,Regularization=L2,"
"DropConfig=0.0+0.5+0.5+0.5, Multithreading=True")
training1 = TString("LearningRate=1e-2,Momentum=0.9,Repetitions=1,"
"ConvergenceSteps=2,BatchSize=256,TestRepetitions=10,"
"WeightDecay=1e-4,Regularization=L2,"
"DropConfig=0.0+0.0+0.0+0.0, Multithreading=True")
trainingStrategyString = TString("TrainingStrategy=")
trainingStrategyString += training0 + TString("|") + training1
# General Options
dnnOptions = TString("!H:!V:ErrorStrategy=CROSSENTROPY:VarTransform=N:"
"WeightInitialization=XAVIERUNIFORM")
dnnOptions.Append(":")
dnnOptions.Append(layoutString)
dnnOptions.Append(":")
dnnOptions.Append(trainingStrategyString)
# Standard implementation, no dependencies.
stdOptions = dnnOptions + ":Architecture=CPU"
factory.BookMethod(loader, TMVA.Types.kDNN, "DNN", stdOptions)
#factory.BookMethod(loader, TMVA.Types.kDNN, "DNN","Architecture=CPU")
##Boosted Decision Trees
#factory.BookMethod(loader,TMVA.Types.kBDT, "BDT","!V:NTrees=200:MinNodeSize=2.5%:MaxDepth=2:BoostType=AdaBoost:AdaBoostBeta=0.5:UseBaggedBoost:BaggedSampleFraction=0.5:SeparationType=GiniIndex:nCuts=20" )
factory.BookMethod(loader, TMVA.Types.kBDT, "BDT")
def main():
name = str(options.name)
#inputFile_sig = TFile.Open("/home/net3/afortman/projects/hotpot/oct_sim/layertests/hazel_both_smearf_1M_35ns_e100_split.root")
#inputFile_bkg = TFile.Open("/home/net3/afortman/projects/hotpot/oct_sim/layertests/hazel_bkg_smearf_"+name+"_train.root")
inputFile_sig = TFile.Open(
"/home/net3/afortman/projects/hotpot/oct_sim/hazel_sig_smear0.root")
inputFile_bkg = TFile.Open(
"/home/net3/afortman/projects/hotpot/oct_sim/hazel_bkg_smear0.root")
outputFile = TFile.Open("TMVAOutput_" + name + ".root", "RECREATE")
factory = TMVA.Factory(
"TMVAClassification", outputFile,
"!V:!Silent:Color:!DrawProgressBar:AnalysisType=Classification")
loader = TMVA.DataLoader("dataset0")
#loader.AddVariable("EventNumHazel",'I')
#loader.AddVariable("EventNumGingko",'I')
#loader.AddVariable("trigger_gingko",'I')
#loader.AddVariable("iroad_x",'I')
#loader.AddVariable("iroad_u",'I')
#loader.AddVariable("iroad_v",'I')
loader.AddVariable("Hit_plane0", 'I')
loader.AddVariable("Hit_plane1", 'I')
loader.AddVariable("Hit_plane2", 'I')
loader.AddVariable("Hit_plane3", 'I')
loader.AddVariable("Hit_plane4", 'I')
loader.AddVariable("Hit_plane5", 'I')
loader.AddVariable("Hit_plane6", 'I')
loader.AddVariable("Hit_plane7", 'I')
#loader.AddVariable("Hit_n",'I')
#loader.AddVariable("dtheta")
#loader.AddVariable("chi2")
#tsignal = inputFile_sig.Get("hazel_train")
tsignal = inputFile_sig.Get("hazel")
tbackground = inputFile_bkg.Get("hazel")
loader.AddSignalTree(tsignal)
loader.AddBackgroundTree(tbackground)
#loader.PrepareTrainingAndTestTree(TCut(""),"nTrain_Signal=1000:nTrain_Background=1000:SplitMode=Random:NormMode=NumEvents:!V")
######### for restriction to 8 layers
#loader.PrepareTrainingAndTestTree(TCut("Hit_n==8"),"nTest_Signal=1000:nTest_Background=1000:SplitMode=Random:NormMode=NumEvents:!V")
loader.PrepareTrainingAndTestTree(
TCut(""),
"nTest_Signal=1000:nTest_Background=1000:SplitMode=Random:NormMode=NumEvents:!V"
)
#loader.PrepareTrainingAndTestTree(TCut(""),"SplitMode=Random:NormMode=NumEvents:!V")
# General layout
layoutString = TString("Layout=TANH|128,TANH|128,TANH|128,LINEAR")
# Training strategies
training0 = TString("LearningRate=1e-1,Momentum=0.9,Repetitions=1,"
"ConvergenceSteps=2,BatchSize=256,TestRepetitions=10,"
"WeightDecay=1e-4,Regularization=L2,"
"DropConfig=0.0+0.5+0.5+0.5, Multithreading=True")
training1 = TString("LearningRate=1e-2,Momentum=0.9,Repetitions=1,"
"ConvergenceSteps=2,BatchSize=256,TestRepetitions=10,"
"WeightDecay=1e-4,Regularization=L2,"
"DropConfig=0.0+0.0+0.0+0.0, Multithreading=True")
trainingStrategyString = TString("TrainingStrategy=")
trainingStrategyString += training0 + TString("|") + training1
# General Options
dnnOptions = TString("!H:!V:ErrorStrategy=CROSSENTROPY:VarTransform=N:"
"WeightInitialization=XAVIERUNIFORM")
dnnOptions.Append(":")
dnnOptions.Append(layoutString)
dnnOptions.Append(":")
dnnOptions.Append(trainingStrategyString)
# Standard implementation, no dependencies.
stdOptions = dnnOptions + ":Architecture=CPU"
factory.BookMethod(loader, TMVA.Types.kDNN, "DNN", stdOptions)
#factory.BookMethod(loader, TMVA.Types.kDNN, "DNN","Architecture=CPU")
##Boosted Decision Trees
#factory.BookMethod(loader,TMVA.Types.kBDT, "BDT","!V:NTrees=200:MinNodeSize=2.5%:MaxDepth=2:BoostType=AdaBoost:AdaBoostBeta=0.5:UseBaggedBoost:BaggedSampleFraction=0.5:SeparationType=GiniIndex:nCuts=20" )
factory.BookMethod(loader, TMVA.Types.kBDT, "BDT")
##Multi-Layer Perceptron (Neural Network)
factory.BookMethod(
loader, TMVA.Types.kMLP, "MLP",
"!H:!V:NeuronType=tanh:VarTransform=N:NCycles=100:HiddenLayers=N+5:TestRate=5:!UseRegulator"
)
#factory.BookMethod(loader, TMVA.Types.kKNN, "kNN" );
# CPU implementation, using BLAS
#cpuOptions = dnnOptions + ":Architecture=CPU"
#factory.BookMethod(loader, TMVA.Types.kDNN, "DNN CPU", cpuOptions)
factory.TrainAllMethods()
factory.TestAllMethods()
factory.EvaluateAllMethods()
c = factory.GetROCCurve(loader)
c.Draw()
c.SaveAs("dataset0/roc_TMVA_smear0.pdf")
