def isExist(dsid):
#to see the file if exists
exist = 1
if gSystem.AccessPathName("%s%s" % (prepath, dsid), kFileExists) == False:
exist = 1
else:
exist = 0
return exist
def Skim(dsid, treename):
#to see the file if exists
if gSystem.AccessPathName("%s%s" % (prepath, dsid), kFileExists) == False:
f1=TFile("%s%s" % (prepath, dsid))
tree=f1.Get(treename)
#skim here
outputname="%s%s" % (outpath, dsid)
f2=TFile("%s%s" % (outpath, dsid),"update")
outtree = tree.CopyTree("passEventCleaning && (onelep_type||dilep_type||trilep_type||quadlep_type) && ((RunYear==2015 && (HLT_mu20_iloose_L1MU15 || HLT_mu50 || HLT_e24_lhmedium_L1EM20VH || HLT_e60_lhmedium || HLT_e120_lhloose )) || (RunYear==2016 && ( HLT_mu26_ivarmedium || HLT_mu50 || HLT_e26_lhtight_nod0_ivarloose || HLT_e60_lhmedium_nod0 || HLT_e140_lhloose_nod0))|| (RunYear==2017 && (HLT_mu26_ivarmedium || HLT_mu50 || HLT_e26_lhtight_nod0_ivarloose || HLT_e60_lhmedium_nod0 ||HLT_e140_lhloose_nod0 ))||(RunYear==2015&&(HLT_2e12_lhloose_L12EM10VH||HLT_e17_lhloose_mu14||HLT_mu18_mu8noL1)) ||(RunYear==2016&&(HLT_2e17_lhvloose_nod0||HLT_e17_lhloose_nod0_mu14||HLT_mu22_mu8noL1))||(RunYear==2017&&(HLT_2e17_lhvloose_nod0||HLT_e17_lhloose_nod0_mu14||HLT_mu22_mu8noL1))) && onelep_type>0 && ( abs(lep_ID_0)==13 ||((abs(lep_ID_0)==11)&&lep_isTightLH_0)) &&lep_isTrigMatch_0 && lep_Pt_0>27e3 && lep_isolationFixedCutLoose_0 && nTaus_OR_Pt25==2 &&tau_tight_0 && tau_tight_1 && nJets_OR_T>=3 && nJets_OR_T_MV2c10_70>=1 && tau_tagWeightBin_1<4 && tau_tagWeightBin_0 <4") #skip MVA1l2tau_weight>0.5 tau_charge_0*tau_charge_1<0
outtree.Write()
f2.Close()
else:
print "Error: %s does not exists" % dsid
def parse_drawer_options(options):
# Create outdir if necessary
if not options.outdir.endswith("/"):
options.outdir += "/"
if gSystem.AccessPathName(options.outdir):
gSystem.mkdir(options.outdir)
# Make input file list
if not options.infile.endswith(".root") and not options.infile.endswith(
".txt"):
raise ValueError("infile must be .root file or .txt file")
if options.infile.endswith(".root"):
# Create a temporary file with one line
with tempfile.NamedTemporaryFile() as infile:
infile.write(options.infile)
infile.flush()
options.tfilecoll = TFileCollection("fc", "", infile.name)
else:
options.tfilecoll = TFileCollection("fc", "", options.infile)
# Batch mode
if options.batch:
gROOT.SetBatch(True)
# Trigger tower parameter space
if options.tower != 99:
ieta = options.tower / 8
iphi = options.tower % 8
options.etamin = -2.2 + (4.4 / 6) * ieta
options.etamax = -2.2 + (4.4 / 6) * (ieta + 1)
if iphi < 4:
options.phimin = (2 * pi / 8) * iphi
options.phimax = (2 * pi / 8) * (iphi + 1)
else:
options.phimin = -2 * pi - pi + (2 * pi / 8) * iphi
options.phimax = -2 * pi - pi + (2 * pi / 8) * (iphi + 1)
options.ptmin = 2.
options.ptmax = 2000.
options.vzmin = -15.
options.vzmax = +15.
def main():
try:
# retrive command line options
shortopts = "w:m:i:j:f:g:t:o:a:vgh?"
longopts = ["weight_fold=", "methods=", "inputfilesig=", "inputfilebkg=", "friendinputfilesig=", "friendinputfilebkg=", "inputtrees=", "outputfile=", "verbose", "gui", "help", "usage"]
opts, args = getopt.getopt( sys.argv[1:], shortopts, longopts )
except getopt.GetoptError:
# print help information and exit:
print "ERROR: unknown options in argument %s" % sys.argv[1:]
usage()
sys.exit(1)
infnameSig = DEFAULT_INFNAMESIG
infnameBkg = DEFAULT_INFNAMEBKG
friendfnameSig = DEFAULT_FRIENDNAMESIG
friendfnameBkg = DEFAULT_FRIENDNAMEBKG
treeNameSig = DEFAULT_TREESIG
treeNameBkg = DEFAULT_TREEBKG
outfname = DEFAULT_OUTFNAME
methods = DEFAULT_METHODS
weight_fold = "weights"
verbose = False
gui = False
addedcuts = ""
for o, a in opts:
if o in ("-?", "-h", "--help", "--usage"):
usage()
sys.exit(0)
elif o in ("-w", "--weight_fold"):
weight_fold = a
elif o in ("-m", "--methods"):
methods = a
elif o in ("-i", "--inputfilesig"):
infnameSig = a
elif o in ("-j", "--inputfilebkg"):
infnameBkg = a
elif o in ("-f", "--friendinputfilesig"):
friendfnameSig = a
elif o in ("-g", "--friendinputfilebkg"):
friendfnameBkg = a
elif o in ("-o", "--outputfile"):
outfname = a
elif o in ("-a", "--addedcuts"):
addedcuts = a
elif o in ("-t", "--inputtrees"):
a.strip()
trees = a.rsplit( ' ' )
trees.sort()
trees.reverse()
if len(trees)-trees.count('') != 2:
print "ERROR: need to give two trees (each one for signal and background)"
print trees
sys.exit(1)
treeNameSig = trees[0]
treeNameBkg = trees[1]
elif o in ("-v", "--verbose"):
verbose = True
elif o in ("-g", "--gui"):
gui = True
# Print methods
mlist = methods.replace(' ',',').split(',')
print "=== TMVAClassification: use method(s)..."
for m in mlist:
if m.strip() != '':
print "=== - <%s>" % m.strip()
# Print the file
print "Using file " + infnameSig + " for signal..."
print "Using file " + infnameBkg + " for background..."
# Import ROOT classes
from ROOT import gSystem, gROOT, gApplication, TFile, TTree, TCut
# check ROOT version, give alarm if 5.18
print "ROOT version is " + str(gROOT.GetVersionCode())
if gROOT.GetVersionCode() >= 332288 and gROOT.GetVersionCode() < 332544:
print "*** You are running ROOT version 5.18, which has problems in PyROOT such that TMVA"
print "*** does not run properly (function calls with enums in the argument are ignored)."
print "*** Solution: either use CINT or a C++ compiled version (see TMVA/macros or TMVA/examples),"
print "*** or use another ROOT version (e.g., ROOT 5.19)."
sys.exit(1)
# Logon not automatically loaded through PyROOT (logon loads TMVA library) load also GUI
gROOT.SetMacroPath( "./" )
## SO I TAKE DEFAULT FORM ROOT# gROOT.Macro ( "./TMVAlogon.C" )
#! gROOT.LoadMacro ( "./TMVAGui.C" )
# Import TMVA classes from ROOT
from ROOT import TMVA
# Output file
outputFile = TFile( outfname, 'RECREATE' )
# Create instance of TMVA factory (see TMVA/macros/TMVAClassification.C for more factory options)
# All TMVA output can be suppressed by removing the "!" (not) in
# front of the "Silent" argument in the option string
factory = TMVA.Factory( "TMVAClassification", outputFile,
"!V:!Silent:Color:DrawProgressBar:Transformations=I;D;P;G,D:AnalysisType=Classification" )
# Set verbosity
factory.SetVerbose( verbose )
# If you wish to modify default settings
# (please check "src/Config.h" to see all available global options)
# gConfig().GetVariablePlotting()).fTimesRMS = 8.0
(TMVA.gConfig().GetIONames()).fWeightFileDir = weight_fold;
# Define the input variables that shall be used for the classifier training
# note that you may also use variable expressions, such as: "3*var1/var2*abs(var3)"
# [all types of expressions that can also be parsed by TTree::Draw( "expression" )]
factory.AddVariable( "dR_l1l2", "dR_l1l2", "", 'F' )
factory.AddVariable( "dR_b1b2", "dR_b1b2", "", 'F' )
factory.AddVariable( "dR_bl", "dR_bl", "", 'F' )
factory.AddVariable( "dR_l1l2b1b2", "dR_l1l2b1b2", "", 'F' )
factory.AddVariable( "MINdR_bl", "MINdR_bl", "", 'F' )
factory.AddVariable( "dphi_l1l2b1b2", "dphi_l1l2b1b2", "", 'F' )
factory.AddVariable( "mass_l1l2", "mass_l1l2", "", 'F' )
factory.AddVariable( "mass_b1b2", "mass_b1b2", "", 'F' )
factory.AddVariable( "mass_trans", "mass_trans", "", 'F' )
factory.AddVariable( "MT2", "MT2", "", 'F' )
factory.AddVariable( "pt_b1b2", "pt_b1b2", "", 'F' )
#factory.AddVariable( "MMC_h2mass_MaxBin", "MMC_h2mass_MaxBin", "", 'F' )
#factory.AddVariable( "MMC_h2mass_RMS", "MMC_h2mass_RMS", "", 'F' )
#factory.AddVariable( "MMC_h2mass_prob", "MMC_h2mass_prob", "", 'F' )
# You can add so-called "Spectator variables", which are not used in the MVA training,
# but will appear in the final "TestTree" produced by TMVA. This TestTree will contain the
# input variables, the response values of all trained MVAs, and the spectator variables
# factory.AddSpectator( "spec1:=var1*2", "Spectator 1", "units", 'F' )
# factory.AddSpectator( "spec2:=var1*3", "Spectator 2", "units", 'F' )
# Read input data
if gSystem.AccessPathName( infnameSig ) != 0 or gSystem.AccessPathName( infnameBkg ): gSystem.Exec( "wget http://root.cern.ch/files/" + infname )
inputSig = TFile.Open( infnameSig )
inputBkg = TFile.Open( infnameBkg )
# Get the signal and background trees for training
signal = inputSig.Get( treeNameSig )
background = inputBkg.Get( treeNameBkg )
##signal.AddFriend( "eleIDdir/isoT1 = eleIDdir/T1", friendfnameSig )
##background.AddFriend( "eleIDdir/isoT1 = eleIDdir/T1", friendfnameBkg )
# Global event weights (see below for setting event-wise weights)
signalWeight = 1.
backgroundWeight = 1.
#I don't think there's a general answer to this. The safest 'default'
#is to use the envent weight such that you have equal amounts of signal
#and background
#for the training, otherwise for example: if you look for a rare
#signal and you use the weight to scale the number of events according
#to the expected ratio of signal and background
#according to the luminosity... the classifier sees hardly any signal
#events and "thinks" .. Oh I just classify everything background and do
#a good job!
#
#One can try to 'optimize' the training a bit more in either 'high
#purity' or 'high efficiency' by choosing different weights, but as I
#said, there's no fixed rule. You'd have
#to 'try' and see if you get better restults by playing with the weights.
# ====== register trees ====================================================
#
# the following method is the prefered one:
# you can add an arbitrary number of signal or background trees
factory.AddSignalTree ( signal, signalWeight )
factory.AddBackgroundTree( background, backgroundWeight )
# To give different trees for training and testing, do as follows:
# factory.AddSignalTree( signalTrainingTree, signalTrainWeight, "Training" )
# factory.AddSignalTree( signalTestTree, signalTestWeight, "Test" )
# Use the following code instead of the above two or four lines to add signal and background
# training and test events "by hand"
# NOTE that in this case one should not give expressions (such as "var1+var2") in the input
# variable definition, but simply compute the expression before adding the event
#
# # --- begin ----------------------------------------------------------
#
# ... *** please lookup code in TMVA/macros/TMVAClassification.C ***
#
# # --- end ------------------------------------------------------------
#
# ====== end of register trees ==============================================
# Set individual event weights (the variables must exist in the original TTree)
# for signal : factory.SetSignalWeightExpression ("weight1*weight2");
# for background: factory.SetBackgroundWeightExpression("weight1*weight2");
# Apply additional cuts on the signal and background sample.
# example for cut: mycut = TCut( "abs(var1)<0.5 && abs(var2-0.5)<1" )
#mycutSig = TCut( "nu1and2_diBaxis_t>-900 && met_diBaxis_t>-900&& hasb1jet && hasb2jet && hasMET && hasGenMET && hasdRljet && hastwomuons" )
mycutSig = TCut( addedcuts )
#mycutBkg = TCut( "event_n%2!=0 && " + addedcuts )
mycutBkg = TCut( addedcuts )
#mycutBkg = TCut( "nu1and2_diBaxis_t>-900 && met_diBaxis_t>-900&& hasb1jet && hasb2jet && hasMET && hasGenMET && hasdRljet && hastwomuons" )
print mycutSig
# Here, the relevant variables are copied over in new, slim trees that are
# used for TMVA training and testing
# "SplitMode=Random" means that the input events are randomly shuffled before
# splitting them into training and test samples
factory.PrepareTrainingAndTestTree( mycutSig, mycutBkg,
"nTrain_Signal=0:nTrain_Background=0:SplitMode=Random:NormMode=NumEvents:!V" )
# --------------------------------------------------------------------------------------------------
# ---- Book MVA methods
#
# please lookup the various method configuration options in the corresponding cxx files, eg:
# src/MethoCuts.cxx, etc, or here: http://tmva.sourceforge.net/optionRef.html
# it is possible to preset ranges in the option string in which the cut optimisation should be done:
# "...:CutRangeMin[2]=-1:CutRangeMax[2]=1"...", where [2] is the third input variable
# Cut optimisation
if "Cuts" in mlist:
factory.BookMethod( TMVA.Types.kCuts, "Cuts",
"!H:!V:FitMethod=MC:EffSel:VarProp[0]=FMax:VarProp[1]=FMin" )
if "CutsD" in mlist:
factory.BookMethod( TMVA.Types.kCuts, "CutsD",
"!H:!V:FitMethod=MC:EffSel:SampleSize=200000:VarProp=FSmart:VarTransform=Decorrelate" )
if "CutsPCA" in mlist:
factory.BookMethod( TMVA.Types.kCuts, "CutsPCA",
"!H:!V:FitMethod=MC:EffSel:SampleSize=200000:VarProp=FSmart:VarTransform=PCA" )
if "CutsGA" in mlist:
factory.BookMethod( TMVA.Types.kCuts, "CutsGA",
"H:!V:FitMethod=GA:EffSel:Steps=30:Cycles=3:PopSize=400:SC_steps=10:SC_rate=5:SC_factor=0.95:VarProp[0]=FMin:VarProp[1]=FMax" )
if "CutsSA" in mlist:
factory.BookMethod( TMVA.Types.kCuts, "CutsSA",
"!H:!V:FitMethod=SA:EffSel:MaxCalls=150000:KernelTemp=IncAdaptive:InitialTemp=1e+6:MinTemp=1e-6:Eps=1e-10:UseDefaultScale" )
# Likelihood ("naive Bayes estimator")
if "Likelihood" in mlist:
factory.BookMethod( TMVA.Types.kLikelihood, "Likelihood",
"H:!V:!TransformOutput:PDFInterpol=Spline2:NSmoothSig[0]=20:NSmoothBkg[0]=20:NSmoothBkg[1]=10:NSmooth=1:NAvEvtPerBin=50" )
# Decorrelated likelihood
if "LikelihoodD" in mlist:
factory.BookMethod( TMVA.Types.kLikelihood, "LikelihoodD",
"!H:!V:TransformOutput:PDFInterpol=Spline2:NSmoothSig[0]=20:NSmoothBkg[0]=20:NSmooth=5:NAvEvtPerBin=50:VarTransform=Decorrelate" )
# PCA-transformed likelihood
if "LikelihoodPCA" in mlist:
factory.BookMethod( TMVA.Types.kLikelihood, "LikelihoodPCA",
"!H:!V:!TransformOutput:PDFInterpol=Spline2:NSmoothSig[0]=20:NSmoothBkg[0]=20:NSmooth=5:NAvEvtPerBin=50:VarTransform=PCA" )
# Use a kernel density estimator to approximate the PDFs
if "LikelihoodKDE" in mlist:
factory.BookMethod( TMVA.Types.kLikelihood, "LikelihoodKDE",
"!H:!V:!TransformOutput:PDFInterpol=KDE:KDEtype=Gauss:KDEiter=Adaptive:KDEFineFactor=0.3:KDEborder=None:NAvEvtPerBin=50" )
# Use a variable-dependent mix of splines and kernel density estimator
if "LikelihoodMIX" in mlist:
factory.BookMethod( TMVA.Types.kLikelihood, "LikelihoodMIX",
"!H:!V:!TransformOutput:PDFInterpolSig[0]=KDE:PDFInterpolBkg[0]=KDE:PDFInterpolSig[1]=KDE:PDFInterpolBkg[1]=KDE:PDFInterpolSig[2]=Spline2:PDFInterpolBkg[2]=Spline2:PDFInterpolSig[3]=Spline2:PDFInterpolBkg[3]=Spline2:KDEtype=Gauss:KDEiter=Nonadaptive:KDEborder=None:NAvEvtPerBin=50" )
# Test the multi-dimensional probability density estimator
# here are the options strings for the MinMax and RMS methods, respectively:
# "!H:!V:VolumeRangeMode=MinMax:DeltaFrac=0.2:KernelEstimator=Gauss:GaussSigma=0.3" );
# "!H:!V:VolumeRangeMode=RMS:DeltaFrac=3:KernelEstimator=Gauss:GaussSigma=0.3" );
if "PDERS" in mlist:
factory.BookMethod( TMVA.Types.kPDERS, "PDERS",
"!H:!V:NormTree=T:VolumeRangeMode=Adaptive:KernelEstimator=Gauss:GaussSigma=0.3:NEventsMin=400:NEventsMax=600" )
if "PDERSD" in mlist:
factory.BookMethod( TMVA.Types.kPDERS, "PDERSD",
"!H:!V:VolumeRangeMode=Adaptive:KernelEstimator=Gauss:GaussSigma=0.3:NEventsMin=400:NEventsMax=600:VarTransform=Decorrelate" )
if "PDERSPCA" in mlist:
factory.BookMethod( TMVA.Types.kPDERS, "PDERSPCA",
"!H:!V:VolumeRangeMode=Adaptive:KernelEstimator=Gauss:GaussSigma=0.3:NEventsMin=400:NEventsMax=600:VarTransform=PCA" )
# Multi-dimensional likelihood estimator using self-adapting phase-space binning
if "PDEFoam" in mlist:
factory.BookMethod( TMVA.Types.kPDEFoam, "PDEFoam",
"!H:!V:SigBgSeparate=F:TailCut=0.001:VolFrac=0.0666:nActiveCells=500:nSampl=2000:nBin=5:Nmin=100:Kernel=None:Compress=T" )
if "PDEFoamBoost" in mlist:
factory.BookMethod( TMVA.Types.kPDEFoam, "PDEFoamBoost",
"!H:!V:Boost_Num=30:Boost_Transform=linear:SigBgSeparate=F:MaxDepth=4:UseYesNoCell=T:DTLogic=MisClassificationError:FillFoamWithOrigWeights=F:TailCut=0:nActiveCells=500:nBin=20:Nmin=400:Kernel=None:Compress=T" )
# K-Nearest Neighbour classifier (KNN)
if "KNN" in mlist:
factory.BookMethod( TMVA.Types.kKNN, "KNN",
"H:nkNN=20:ScaleFrac=0.8:SigmaFact=1.0:Kernel=Gaus:UseKernel=F:UseWeight=T:!Trim" )
# H-Matrix (chi2-squared) method
if "HMatrix" in mlist:
factory.BookMethod( TMVA.Types.kHMatrix, "HMatrix", "!H:!V" )
# Linear discriminant (same as Fisher discriminant)
if "LD" in mlist:
factory.BookMethod( TMVA.Types.kLD, "LD", "H:!V:VarTransform=None:CreateMVAPdfs:PDFInterpolMVAPdf=Spline2:NbinsMVAPdf=50:NsmoothMVAPdf=10" )
# Fisher discriminant (same as LD)
if "Fisher" in mlist:
factory.BookMethod( TMVA.Types.kFisher, "Fisher", "H:!V:Fisher:CreateMVAPdfs:PDFInterpolMVAPdf=Spline2:NbinsMVAPdf=50:NsmoothMVAPdf=10" )
# Fisher with Gauss-transformed input variables
if "FisherG" in mlist:
factory.BookMethod( TMVA.Types.kFisher, "FisherG", "H:!V:VarTransform=Gauss" )
# Composite classifier: ensemble (tree) of boosted Fisher classifiers
if "BoostedFisher" in mlist:
factory.BookMethod( TMVA.Types.kFisher, "BoostedFisher",
"H:!V:Boost_Num=20:Boost_Transform=log:Boost_Type=AdaBoost:Boost_AdaBoostBeta=0.2" )
# Function discrimination analysis (FDA) -- test of various fitters - the recommended one is Minuit (or GA or SA)
if "FDA_MC" in mlist:
factory.BookMethod( TMVA.Types.kFDA, "FDA_MC",
"H:!V:Formula=(0)+(1)*x0+(2)*x1+(3)*x2+(4)*x3:ParRanges=(-1,1)(-10,10);(-10,10);(-10,10);(-10,10):FitMethod=MC:SampleSize=100000:Sigma=0.1" );
if "FDA_GA" in mlist:
factory.BookMethod( TMVA.Types.kFDA, "FDA_GA",
"H:!V:Formula=(0)+(1)*x0+(2)*x1+(3)*x2+(4)*x3:ParRanges=(-1,1)(-10,10);(-10,10);(-10,10);(-10,10):FitMethod=GA:PopSize=300:Cycles=3:Steps=20:Trim=True:SaveBestGen=1" );
if "FDA_SA" in mlist:
factory.BookMethod( TMVA.Types.kFDA, "FDA_SA",
"H:!V:Formula=(0)+(1)*x0+(2)*x1+(3)*x2+(4)*x3:ParRanges=(-1,1)(-10,10);(-10,10);(-10,10);(-10,10):FitMethod=SA:MaxCalls=15000:KernelTemp=IncAdaptive:InitialTemp=1e+6:MinTemp=1e-6:Eps=1e-10:UseDefaultScale" );
if "FDA_MT" in mlist:
factory.BookMethod( TMVA.Types.kFDA, "FDA_MT",
"H:!V:Formula=(0)+(1)*x0+(2)*x1+(3)*x2+(4)*x3:ParRanges=(-1,1)(-10,10);(-10,10);(-10,10);(-10,10):FitMethod=MINUIT:ErrorLevel=1:PrintLevel=-1:FitStrategy=2:UseImprove:UseMinos:SetBatch" );
if "FDA_GAMT" in mlist:
factory.BookMethod( TMVA.Types.kFDA, "FDA_GAMT",
"H:!V:Formula=(0)+(1)*x0+(2)*x1+(3)*x2+(4)*x3:ParRanges=(-1,1)(-10,10);(-10,10);(-10,10);(-10,10):FitMethod=GA:Converger=MINUIT:ErrorLevel=1:PrintLevel=-1:FitStrategy=0:!UseImprove:!UseMinos:SetBatch:Cycles=1:PopSize=5:Steps=5:Trim" );
if "FDA_MCMT" in mlist:
factory.BookMethod( TMVA.Types.kFDA, "FDA_MCMT",
"H:!V:Formula=(0)+(1)*x0+(2)*x1+(3)*x2+(4)*x3:ParRanges=(-1,1)(-10,10);(-10,10);(-10,10);(-10,10):FitMethod=MC:Converger=MINUIT:ErrorLevel=1:PrintLevel=-1:FitStrategy=0:!UseImprove:!UseMinos:SetBatch:SampleSize=20" );
# TMVA ANN: MLP (recommended ANN) -- all ANNs in TMVA are Multilayer Perceptrons
if "MLP" in mlist:
factory.BookMethod( TMVA.Types.kMLP, "MLP", "H:!V:NeuronType=tanh:VarTransform=N:NCycles=600:HiddenLayers=N+5:TestRate=5:!UseRegulator" )
if "MLPBFGS" in mlist:
factory.BookMethod( TMVA.Types.kMLP, "MLPBFGS", "H:!V:NeuronType=tanh:VarTransform=N:NCycles=600:HiddenLayers=N+5:TestRate=5:TrainingMethod=BFGS:!UseRegulator" )
if "MLPBNN" in mlist:
factory.BookMethod( TMVA.Types.kMLP, "MLPBNN", "H:!V:NeuronType=tanh:VarTransform=N:NCycles=600:HiddenLayers=N+5:TestRate=5:TrainingMethod=BFGS:UseRegulator" ) # BFGS training with bayesian regulators
# CF(Clermont-Ferrand)ANN
if "CFMlpANN" in mlist:
factory.BookMethod( TMVA.Types.kCFMlpANN, "CFMlpANN", "!H:!V:NCycles=2000:HiddenLayers=N+1,N" ) # n_cycles:#nodes:#nodes:...
# Tmlp(Root)ANN
if "TMlpANN" in mlist:
factory.BookMethod( TMVA.Types.kTMlpANN, "TMlpANN", "!H:!V:NCycles=200:HiddenLayers=N+1,N:LearningMethod=BFGS:ValidationFraction=0.3" ) #n_cycles:#nodes:#nodes:...
# Support Vector Machine
if "SVM" in mlist:
factory.BookMethod( TMVA.Types.kSVM, "SVM", "Gamma=0.25:Tol=0.001:VarTransform=Norm" )
# Boosted Decision Trees
if "BDTG" in mlist:
factory.BookMethod( TMVA.Types.kBDT, "BDTG",
"!H:!V:NTrees=1000:BoostType=Grad:Shrinkage=0.30:UseBaggedGrad:GradBaggingFraction=0.6:SeparationType=GiniIndex:nCuts=20:NNodesMax=5" )
if "BDT" in mlist:
factory.BookMethod( TMVA.Types.kBDT, "BDT",
"!H:!V:NTrees=850:nEventsMin=150:MaxDepth=3:BoostType=AdaBoost:AdaBoostBeta=0.5:SeparationType=GiniIndex:nCuts=20:PruneMethod=NoPruning" )
if "BDTB" in mlist:
factory.BookMethod( TMVA.Types.kBDT, "BDTB",
"!H:!V:NTrees=400:BoostType=Bagging:SeparationType=GiniIndex:nCuts=20:PruneMethod=NoPruning" )
if "BDTD" in mlist:
factory.BookMethod( TMVA.Types.kBDT, "BDTD",
"!H:!V:NTrees=400:nEventsMin=400:MaxDepth=3:BoostType=AdaBoost:SeparationType=GiniIndex:nCuts=20:PruneMethod=NoPruning:VarTransform=Decorrelate" )
# RuleFit -- TMVA implementation of Friedman's method
if "RuleFit" in mlist:
factory.BookMethod( TMVA.Types.kRuleFit, "RuleFit",
"H:!V:RuleFitModule=RFTMVA:Model=ModRuleLinear:MinImp=0.001:RuleMinDist=0.001:NTrees=20:fEventsMin=0.01:fEventsMax=0.5:GDTau=-1.0:GDTauPrec=0.01:GDStep=0.01:GDNSteps=10000:GDErrScale=1.02" )
# --------------------------------------------------------------------------------------------------
# ---- Now you can tell the factory to train, test, and evaluate the MVAs.
# Train MVAs
factory.TrainAllMethods()
# Test MVAs
factory.TestAllMethods()
# Evaluate MVAs
factory.EvaluateAllMethods()
# Save the output.
outputFile.Close()
print "=== wrote root file %s\n" % outfname
print "=== TMVAClassification is done!\n"
# open the GUI for the result macros
if( gui ):
gROOT.ProcessLine( "TMVAGui(\"%s\")" % outfname )
# keep the ROOT thread running
gApplication.Run()
def main():
try:
# retrive command line options
shortopts = "m:i:t:o:vh?"
longopts = ["methods=", "inputfile=", "inputtrees=", "outputfile=", "verbose", "help", "usage"]
opts, args = getopt.getopt( sys.argv[1:], shortopts, longopts )
except getopt.GetoptError:
# print help information and exit:
print "ERROR: unknown options in argument %s" % sys.argv[1:]
usage()
sys.exit(1)
infname = DEFAULT_INFNAME
treeNameSig = DEFAULT_TREESIG
treeNameBkg = DEFAULT_TREEBKG
outfname = DEFAULT_OUTFNAME
methods = DEFAULT_METHODS
verbose = False
for o, a in opts:
if o in ("-?", "-h", "--help", "--usage"):
usage()
sys.exit(0)
elif o in ("-m", "--methods"):
methods = a
elif o in ("-i", "--inputfile"):
infname = a
elif o in ("-o", "--outputfile"):
outfname = a
elif o in ("-t", "--inputtrees"):
a.strip()
trees = a.rsplit( ' ' )
trees.sort()
trees.reverse()
if len(trees)-trees.count('') != 2:
print "ERROR: need to give two trees (each one for signal and background)"
print trees
sys.exit(1)
treeNameSig = trees[0]
treeNameBkg = trees[1]
elif o in ("-v", "--verbose"):
verbose = True
# Print methods
mlist = methods.replace(' ',',').split(',')
print "=== TMVAnalysis: use method(s)..."
for m in mlist:
if m.strip() != '':
print "=== - <%s>" % m.strip()
# Import ROOT classes
from ROOT import gSystem, gROOT, gApplication, TFile, TTree, TCut
# check ROOT version, give alarm if 5.18
if gROOT.GetVersionCode() >= 332288 and gROOT.GetVersionCode() < 332544:
print "*** You are running ROOT version 5.18, which has problems in PyROOT such that TMVA"
print "*** does not run properly (function calls with enums in the argument are ignored)."
print "*** Solution: either use CINT or a C++ compiled version (see TMVA/macros or TMVA/examples),"
print "*** or use another ROOT version (e.g., ROOT 5.19)."
sys.exit(1)
# Logon not automatically loaded through PyROOT (logon loads TMVA library) load also GUI
gROOT.SetMacroPath( "../macros/" )
gROOT.Macro ( "../macros/TMVAlogon.C" )
gROOT.LoadMacro ( "../macros/TMVAGui.C" )
# Import TMVA classes from ROOT
from ROOT import TMVA
# Output file
outputFile = TFile( outfname, 'RECREATE' )
# Create instance of TMVA factory (see TMVA/macros/TMVAnalysis.C for more factory options)
# All TMVA output can be suppressed by removing the "!" (not) in
# front of the "Silent" argument in the option string
factory = TMVA.Factory( "TMVAnalysis", outputFile, "!V:!Silent:Color" )
# Set verbosity
factory.SetVerbose( verbose )
# Define the input variables that shall be used for the classifier training
# note that you may also use variable expressions, such as: "3*var1/var2*abs(var3)"
# [all types of expressions that can also be parsed by TTree::Draw( "expression" )]
factory.AddVariable( "var1+var2", 'F' )
factory.AddVariable( "var1-var2", 'F' )
factory.AddVariable( "var3", 'F' )
factory.AddVariable( "var4", 'F' )
# Read input data
if not gSystem.AccessPathName( infname ):
input = TFile( infname )
else:
print "ERROR: could not access data file %s\n" % infname
# Get the signal and background trees for training
signal = input.Get( treeNameSig )
background = input.Get( treeNameBkg )
# Global event weights (see below for setting event-wise weights)
signalWeight = 1.0
backgroundWeight = 1.0
# ====== register trees ====================================================
#
# the following method is the prefered one:
# you can add an arbitrary number of signal or background trees
factory.AddSignalTree ( signal, signalWeight )
factory.AddBackgroundTree( background, backgroundWeight )
# To give different trees for training and testing, do as follows:
# factory.AddSignalTree( signalTrainingTree, signalTrainWeight, "Training" )
# factory.AddSignalTree( signalTestTree, signalTestWeight, "Test" )
# Use the following code instead of the above two or four lines to add signal and background
# training and test events "by hand"
# NOTE that in this case one should not give expressions (such as "var1+var2") in the input
# variable definition, but simply compute the expression before adding the event
#
# # --- begin ----------------------------------------------------------
#
# ... *** please lookup code in TMVA/macros/TMVAnalysis.C ***
#
# # --- end ------------------------------------------------------------
#
# ====== end of register trees ==============================================
# This would set individual event weights (the variables defined in the
# expression need to exist in the original TTree)
# for signal : factory.SetSignalWeightExpression("weight1*weight2")
# for background: factory.SetBackgroundWeightExpression("weight1*weight2")
# Apply additional cuts on the signal and background sample.
# example for cut: mycut = TCut( "abs(var1)<0.5 && abs(var2-0.5)<1" )
mycutSig = TCut( "" )
mycutBkg = TCut( "" )
# Here, the relevant variables are copied over in new, slim trees that are
# used for TMVA training and testing
# "SplitMode=Random" means that the input events are randomly shuffled before
# splitting them into training and test samples
factory.PrepareTrainingAndTestTree( mycutSig, mycutBkg,
"NSigTrain=3000:NBkgTrain=3000:SplitMode=Random:NormMode=NumEvents:!V" )
# ... and alternative call to use a different number of signal and background training/test event is:
# factory.PrepareTrainingAndTestTree( mycut, "NSigTrain=3000:NBkgTrain=3000:NSigTest=3000:NBkgTest=3000:SplitMode=Random:!V" )
# --------------------------------------------------------------------------------------------------
# Cut optimisation
if "Cuts" in mlist:
factory.BookMethod( TMVA.Types.kCuts, "Cuts",
"!H:!V:FitMethod=MC:EffSel:SampleSize=200000:VarProp=FSmart" )
if "CutsD" in mlist:
factory.BookMethod( TMVA.Types.kCuts, "CutsD",
"!H:!V:FitMethod=MC:EffSel:SampleSize=200000:VarProp=FSmart:VarTransform=Decorrelate" )
if "CutsPCA" in mlist:
factory.BookMethod( TMVA.Types.kCuts, "CutsPCA",
"!H:!V:FitMethod=MC:EffSel:SampleSize=200000:VarProp=FSmart:VarTransform=PCA" )
if "CutsGA" in mlist:
factory.BookMethod( TMVA.Types.kCuts, "CutsGA",
"H:!V:FitMethod=GA:EffSel:Steps=30:Cycles=3:PopSize=100:SC_steps=10:SC_rate=5:SC_factor=0.95:VarProp=FSmart" )
if "CutsSA" in mlist:
factory.BookMethod( TMVA.Types.kCuts, "CutsSA",
"H:!V:FitMethod=SA:EffSel:MaxCalls=150000:KernelTemperature=IncreasingAdaptive:InitialTemperature=1e+6:MinTemperature=1e-6:Eps=1e-10:UseDefaultScale" )
# Likelihood
if "Likelihood" in mlist:
factory.BookMethod( TMVA.Types.kLikelihood, "Likelihood",
"!H:!V:!TransformOutput:PDFInterpol=Spline2:NSmoothSig[0]=10:NSmoothBkg[0]=10:NSmoothBkg[1]=10:NSmooth=10:NAvEvtPerBin=50" )
# test the decorrelated likelihood
if "LikelihoodD" in mlist:
factory.BookMethod( TMVA.Types.kLikelihood, "LikelihoodD",
"!H:!V:!TransformOutput:PDFInterpol=Spline2:NSmoothSig[0]=10:NSmoothBkg[0]=10:NSmooth=5:NAvEvtPerBin=50:VarTransform=Decorrelate" )
if "LikelihoodPCA" in mlist:
factory.BookMethod( TMVA.Types.kLikelihood, "LikelihoodPCA",
"!H:!V:!TransformOutput:PDFInterpol=Spline2:NSmoothSig[0]=10:NSmoothBkg[0]=10:NSmooth=5:NAvEvtPerBin=50:VarTransform=PCA" )
# test the new kernel density estimator
if "LikelihoodKDE" in mlist:
factory.BookMethod( TMVA.Types.kLikelihood, "LikelihoodKDE",
"!H:!V:!TransformOutput:PDFInterpol=KDE:KDEtype=Gauss:KDEiter=Nonadaptive:KDEborder=None:NAvEvtPerBin=50" )
# test the mixed splines and kernel density estimator (depending on which variable)
if "LikelihoodMIX" in mlist:
factory.BookMethod( TMVA.Types.kLikelihood, "LikelihoodMIX",
"!H:!V:!TransformOutput:PDFInterpol[0]=KDE:PDFInterpol[1]=KDE:PDFInterpol[2]=Spline2:PDFInterpol[3]=Spline2:KDEtype=Gauss:KDEiter=Nonadaptive:KDEborder=None:NAvEvtPerBin=50" )
# PDE - RS method
if "PDERS" in mlist:
factory.BookMethod( TMVA.Types.kPDERS, "PDERS",
"!H:!V:NormTree=T:VolumeRangeMode=Adaptive:KernelEstimator=Gauss:GaussSigma=0.3:NEventsMin=400:NEventsMax=600" )
# And the options strings for the MinMax and RMS methods, respectively:
# "!H:!V:VolumeRangeMode=MinMax:DeltaFrac=0.2:KernelEstimator=Gauss:GaussSigma=0.3"
# "!H:!V:VolumeRangeMode=RMS:DeltaFrac=3:KernelEstimator=Gauss:GaussSigma=0.3"
if "PDERSkNN" in mlist:
factory.BookMethod( TMVA.Types.kPDERS, "PDERSkNN",
"!H:!V:VolumeRangeMode=kNN:KernelEstimator=Gauss:GaussSigma=0.3:NEventsMin=400:NEventsMax=600" )
if "PDERSD" in mlist:
factory.BookMethod( TMVA.Types.kPDERS, "PDERSD",
"!H:!V:VolumeRangeMode=Adaptive:KernelEstimator=Gauss:GaussSigma=0.3:NEventsMin=400:NEventsMax=600:VarTransform=Decorrelate" )
if "PDERSPCA" in mlist:
factory.BookMethod( TMVA.Types.kPDERS, "PDERSPCA",
"!H:!V:VolumeRangeMode=Adaptive:KernelEstimator=Gauss:GaussSigma=0.3:NEventsMin=400:NEventsMax=600:VarTransform=PCA" )
# K-Nearest Neighbour class ifier (KNN)
if "KNN" in mlist:
factory.BookMethod( TMVA.Types.kKNN, "KNN",
"nkNN=400:TreeOptDepth=6:ScaleFrac=0.8:!UseKernel:!Trim" )
# H-Matrix (chi2-squared) method
if "HMatrix" in mlist:
factory.BookMethod( TMVA.Types.kHMatrix, "HMatrix", "!H:!V" )
# Fisher discriminant
if "Fisher" in mlist:
factory.BookMethod( TMVA.Types.kFisher, "Fisher",
"H:!V:!Normalise:CreateMVAPdfs:Fisher:NbinsMVAPdf=50:NsmoothMVAPdf=1" )
# Function discrimination analysis (FDA) -- test of various fitters - the recommended one is Minuit (or GA or SA)
if "FDA_MC" in mlist:
factory.BookMethod( TMVA.Types.kFDA, "FDA_MC",
"H:!V:Formula=(0)+(1)*x0+(2)*x1+(3)*x2+(4)*x3:ParRanges=(-1,1);(-10,10);(-10,10);(-10,10);(-10,10):FitMethod=MC:SampleSize=100000:Sigma=0.1" )
if "FDA_GA" in mlist:
factory.BookMethod( TMVA.Types.kFDA, "FDA_GA",
"H:!V:Formula=(0)+(1)*x0+(2)*x1+(3)*x2+(4)*x3:ParRanges=(-1,1);(-10,10);(-10,10);(-10,10);(-10,10):FitMethod=GA:PopSize=100:Cycles=3:Steps=20:Trim=True:SaveBestGen=0" )
if "FDA_MT" in mlist:
factory.BookMethod( TMVA.Types.kFDA, "FDA_MT",
"H:!V:Formula=(0)+(1)*x0+(2)*x1+(3)*x2+(4)*x3:ParRanges=(-1,1);(-10,10);(-10,10);(-10,10);(-10,10):FitMethod=MINUIT:ErrorLevel=1:PrintLevel=-1:FitStrategy=2:UseImprove:UseMinos:SetBatch" )
if "FDA_GAMT" in mlist:
factory.BookMethod( TMVA.Types.kFDA, "FDA_GAMT",
"H:!V:Formula=(0)+(1)*x0+(2)*x1+(3)*x2+(4)*x3:ParRanges=(-1,1);(-10,10);(-10,10);(-10,10);(-10,10):FitMethod=GA:Converger=MINUIT:ErrorLevel=1:PrintLevel=-1:FitStrategy=0:!UseImprove:!UseMinos:SetBatch:Cycles=1:PopSize=5:Steps=5:Trim" )
if "FDA_MCMT" in mlist:
factory.BookMethod( TMVA.Types.kFDA, "FDA_MCMT",
"H:!V:Formula=(0)+(1)*x0+(2)*x1+(3)*x2+(4)*x3:ParRanges=(-1,1);(-10,10);(-10,10);(-10,10);(-10,10):FitMethod=MC:Converger=MINUIT:ErrorLevel=1:PrintLevel=-1:FitStrategy=0:!UseImprove:!UseMinos:SetBatch:SampleSize=20" )
# TMVA ANN: MLP (recommended ANN) -- all ANNs in TMVA are Multilayer Perceptrons
if "MLP" in mlist:
factory.BookMethod( TMVA.Types.kMLP, "MLP", "H:!V:!Normalise:NeuronType=tanh:NCycles=200:HiddenLayers=N+1,N:TestRate=5" )
# CF(Clermont-Ferrand)ANN
if "CFMlpANN" in mlist:
factory.BookMethod( TMVA.Types.kCFMlpANN, "CFMlpANN", "!H:!V:NCycles=500:HiddenLayers=N+1,N" ) # n_cycles:#nodes:#nodes:...
# Tmlp(Root)ANN
if "TMlpANN" in mlist:
factory.BookMethod( TMVA.Types.kTMlpANN, "TMlpANN", "!H:!V:NCycles=200:HiddenLayers=N+1,N:LearningMethod=BFGS:ValidationFraction=0.3" ) # n_cycles:#nodes:#nodes:..
# Support Vector Machines using three d ifferent Kernel types (Gauss, polynomial and linear)
if "SVM_Gauss" in mlist:
factory.BookMethod( TMVA.Types.kSVM, "SVM_Gauss", "Sigma=2:C=1:Tol=0.001:Kernel=Gauss" )
if "SVM_Poly" in mlist:
factory.BookMethod( TMVA.Types.kSVM, "SVM_Poly", "Order=4:Theta=1:C=0.1:Tol=0.001:Kernel=Polynomial" )
if "SVM_Lin" in mlist:
factory.BookMethod( TMVA.Types.kSVM, "SVM_Lin", "!H:!V:Kernel=Linear:C=1:Tol=0.001" )
# Boosted Decision Trees (second one with decorrelation)
if "BDT" in mlist:
factory.BookMethod( TMVA.Types.kBDT, "BDT",
"!H:!V:NTrees=400:BoostType=AdaBoost:SeparationType=GiniIndex:nCuts=20:PruneMethod=CostComplexity:PruneStrength=1.5" )
if "BDTD" in mlist:
factory.BookMethod( TMVA.Types.kBDT, "BDTD",
"!H:!V:NTrees=400:BoostType=AdaBoost:SeparationType=GiniIndex:nCuts=20:PruneMethod=CostComplexity:PruneStrength=2.5:VarTransform=Decorrelate" )
# RuleFit -- TMVA implementation of Friedman's method
if "RuleFit" in mlist:
factory.BookMethod( TMVA.Types.kRuleFit, "RuleFit",
"H:!V:RuleFitModule=RFTMVA:Model=ModRuleLinear:MinImp=0.001:RuleMinDist=0.001:NTrees=20:fEventsMin=0.01:fEventsMax=0.5:GDTau=-1.0:GDTauPrec=0.01:GDStep=0.01:GDNSteps=10000:GDErrScale=1.02" )
# Friedman's RuleFit method, implementation by J. Friedman
if "RuleFitJF" in mlist:
factory.BookMethod( TMVA.Types.kRuleFit, "RuleFitJF",
"!V:RuleFitModule=RFFriedman:Model=ModRuleLinear:GDStep=0.01:GDNSteps=10000:GDErrScale=1.1:RFNendnodes=4" )
# --------------------------------------------------------------------------------------------------
# ---- Now you can tell the factory to train, test, and evaluate the MVAs.
# Train MVAs
factory.TrainAllMethods()
# Test MVAs
factory.TestAllMethods()
# Evaluate MVAs
factory.EvaluateAllMethods()
# Save the output.
outputFile.Close()
print "=== wrote root file %s\n" % outfname
print "=== TMVAnalysis is done!\n"
# open the GUI for the result macros
gROOT.ProcessLine( "TMVAGui(\"%s\")" % outfname )
# keep the ROOT thread running
gApplication.Run()
def TMVAClassification(methods,
sigfname,
bkgfname,
optname,
channel,
trees,
verbose=False): #="DecayTree,DecayTree"
# Print methods
mlist = methods.replace(' ', ',').split(',')
print "=== TMVAClassification: use method(s)..."
for m in mlist:
if m.strip() != '':
print "=== - <%s>" % m.strip()
# Define trees
trees = trees.split(",")
if len(trees) - trees.count('') != 2:
print "ERROR: need to give two trees (each one for signal and background)"
print trees
sys.exit(1)
treeNameSig = trees[0]
treeNameBkg = trees[1]
# Print output file and directory
outfname = "TMVA_%s_%s.root" % (channel, optname)
myWeightDirectory = "weights_%s_%s" % (channel, optname)
print "=== TMVAClassification: output will be written to:"
print "=== %s" % outfname
print "=== %s" % myWeightDirectory
# Import ROOT classes
from ROOT import gSystem, gROOT, gApplication, TFile, TTree, TCut
# check ROOT version, give alarm if 5.18
if gROOT.GetVersionCode() >= 332288 and gROOT.GetVersionCode() < 332544:
print "*** You are running ROOT version 5.18, which has problems in PyROOT such that TMVA"
print "*** does not run properly (function calls with enums in the argument are ignored)."
print "*** Solution: either use CINT or a C++ compiled version (see TMVA/macros or TMVA/examples),"
print "*** or use another ROOT version (e.g., ROOT 5.19)."
sys.exit(1)
# Logon not automatically loaded through PyROOT (logon loads TMVA library) load also GUI
#gROOT.SetMacroPath( "./" )
#gROOT.Macro ( "./tmva/test/TMVAlogon.C" )
#gROOT.LoadMacro ( "./tmva/test/TMVAGui.C" ) ###Is this really necessary??
# Import TMVA classes from ROOT
from ROOT import TMVA
# Setup TMVA
TMVA.Tools.Instance()
# Output file
outputFile = TFile(outfname, 'RECREATE')
# Create instance of TMVA factory (see TMVA/macros/TMVAClassification.C for more factory options)
# All TMVA output can be suppressed by removing the "!" (not) in
# front of the "Silent" argument in the option string
factory = TMVA.Factory(
"TMVAClassification", outputFile,
"!V:!Silent:Color:DrawProgressBar:Transformations=I:AnalysisType=Classification"
)
# Set verbosity
factory.SetVerbose(verbose)
# Load data
dataloader = TMVA.DataLoader("dataset")
# If you wish to modify default settings
# (please check "src/Config.h" to see all available global options)
# gConfig().GetVariablePlotting()).fTimesRMS = 8.0
(TMVA.gConfig().GetIONames()).fWeightFileDir = myWeightDirectory
# Define the input variables that shall be used for the classifier training
# note that you may also use variable expressions, such as: "3*var1/var2*abs(var3)"
# [all types of expressions that can also be parsed by TTree::Draw( "expression" )]
print "*** Training on channel:"
print "*** %s" % channel
print "***"
'''
if channel == "1":
#dataloader.AddVariable( "pplus_ProbNNp", "Prob(p^{+})", "", 'F' );
#dataloader.AddVariable( "Kminus_ProbNNk", "Prob(K^{-})", "", 'F' );
dataloader.AddVariable( "pplus_PT", "P_{T}(p^{+})", "MeV", 'F' );
dataloader.AddVariable( "Kminus_PT", "P_{T}(K^{-})", "MeV", 'F' );
dataloader.AddVariable( "gamma_PT", "P_{T}(#gamma)", "MeV", 'F' );
dataloader.AddVariable( "Lambda_1520_0_PT", "P_{T}(#Lambda(1520))", "MeV", 'F' );
dataloader.AddVariable( "B_PT", "P_{T}(#Lambda_{b})", "MeV", 'F' );
dataloader.AddVariable( "beta:=(-gamma_P+Kminus_P+pplus_P)/(gamma_P+Kminus_P+pplus_P)","#beta", "MeV", 'F' );
dataloader.AddVariable( "MomCons1:=-B_P+gamma_P+Lambda_1520_0_P","P_{tot,1}", "MeV", 'F' );
dataloader.AddVariable( "MomCons2:=-Lambda_1520_0_P+Kminus_P+pplus_P","P_{tot,2}", "MeV", 'F' );
dataloader.AddVariable( "Sum_Kminus_p_eta:=atanh(pplus_PZ/pplus_P)+atanh(Kminus_PZ/Kminus_P)","#eta(K^{-})+#eta(p^{+})","MeV", 'F' );
dataloader.AddVariable( "Diff_Kminus_p_eta:=atanh(Kminus_PZ/Kminus_P)-atanh(pplus_PZ/pplus_P)","#eta(K^{-})-#eta(p^{+})","MeV", 'F' );
dataloader.AddVariable( "pplus_IPCHI2_OWNPV", "#chi^{2}_{IP}(p^{+})", "" , 'F' );
dataloader.AddVariable( "Kminus_IPCHI2_OWNPV", "#chi^{2}_{IP}(K^{-})", "" , 'F' );
dataloader.AddVariable( "B_IPCHI2_OWNPV", "#chi^{2}_{IP}(#Lambda_{b})", "" , 'F' );
#dataloader.AddVariable( "gamma_IPCHI2_OWNPV", "IP #chi^{2}(#gamma)", "" , 'F' );
#dataloader.AddVariable( "Lambda_1520_0_IP_OWNPV", "IP(#Lambda(1520))", "mm", 'F' );
#dataloader.AddVariable( "Lambda_1520_0_IPCHI2_OWNPV", "IP#chi^{2}(#Lambda(1520))", "", 'F' );
dataloader.AddVariable( "Lambda_1520_0_OWNPV_CHI2", "#chi^{2}_{vertex}(#Lambda(1520))", "" , 'F' );
dataloader.AddVariable( "B_OWNPV_CHI2", "#chi^{2}_{vertex}(#Lambda_{b})", "" , 'F' );
dataloader.AddVariable( "B_DIRA_OWNPV", "DIRA(#Lambda_{b})", "" , 'F' );
#dataloader.AddVariable( "Lambda_1520_0_FDCHI2_OWNPV", "FD #chi^{2}(#Lambda(1520))", "", 'F' );
dataloader.AddVariable( "B_FDCHI2_OWNPV", "#chi^{2}_{FD}(#Lambda_{b})", "", 'F' );
'''
if channel == "2":
dataloader.AddVariable("pplus_PT", "P_{T}(p^{+})", "MeV", 'F')
dataloader.AddVariable("Kminus_PT", "P_{T}(K^{-})", "MeV", 'F')
dataloader.AddVariable("gamma_PT", "P_{T}(#gamma)", "MeV", 'F')
dataloader.AddVariable("Lambda_1520_0_PT", "P_{T}(#Lambda*)", "MeV",
'F')
dataloader.AddVariable("B_PT", "P_{T}(#Lambda_{b})", "MeV", 'F')
dataloader.AddVariable(
"beta:=(-gamma_P+Kminus_P+pplus_P)/(gamma_P+Kminus_P+pplus_P)",
"#beta", "", 'F')
#ok
#dataloader.AddVariable( "MomCons1:=-B_P+gamma_P+Lambda_1520_0_P","P_{tot,1}", "MeV", 'F' );#BDT learned Mass check1
dataloader.AddVariable("MomCons2:=-Lambda_1520_0_P+Kminus_P+pplus_P",
"P_{tot,2}", "MeV", 'F')
#ok
#dataloader.AddVariable( "Sum_Kminus_p_eta:=atanh(pplus_PZ/pplus_P)+atanh(Kminus_PZ/Kminus_P)","#eta(K^{-})+#eta(p^{+})","", 'F' );#99correlationL_eta
dataloader.AddVariable(
"Diff_Kminus_p_eta:=atanh(Kminus_PZ/Kminus_P)-atanh(pplus_PZ/pplus_P)",
"#eta(K^{-})-#eta(p^{+})", "", 'F')
dataloader.AddVariable(
"Lambda_1520_0_eta:=atanh(Lambda_1520_0_PZ/Lambda_1520_0_P)",
"#eta(#Lambda*)", "", 'F')
dataloader.AddVariable("gamma_eta:=atanh(gamma_PZ/gamma_P)",
"#eta(#gamma)", "", 'F')
dataloader.AddVariable("pplus_IPCHI2_OWNPV", "#chi^{2}_{IP}(p^{+})",
"", 'F')
#dataloader.AddVariable( "Kminus_IPCHI2_OWNPV", "#chi^{2}_{IP}(K^{-})", "" , 'F' );
dataloader.AddVariable("B_IPCHI2_OWNPV", "#chi^{2}_{IP}(#Lambda_{b})",
"", 'F')
dataloader.AddVariable("Lambda_1520_0_IPCHI2_OWNPV",
"#chi^{2}_{IP}(#Lambda*)", "", 'F')
dataloader.AddVariable("Lambda_1520_0_OWNPV_CHI2",
"#chi^{2}_{vertex}(#Lambda*)", "", 'F')
dataloader.AddVariable("B_OWNPV_CHI2",
"#chi^{2}_{vertex}(#Lambda_{b})", "", 'F')
#dataloader.AddVariable( "B_BMassFit_chi2/B_BMassFit_nDOF", "#chi^{2}_{DTF}/n_{dof}", "" , 'F' );#BDT learned Mass check1
#dataloader.AddVariable( "B_PVFit_chi2/B_PVFit_nDOF", "#chi^{2}_{DTF}/n_{dof}", "" , 'F' );#put it out because array
#dataloader.AddVariable( "B_DIRA_OWNPV", "DIRA(#Lambda_{b})", "" , 'F' ); #not used by BDT
#dataloader.AddVariable( "Lambda_1520_0_DIRA_OWNPV", "DIRA(#Lambda*)", "" , 'F' ); #not used
#dataloader.AddVariable( "Lambda_1520_0_FDCHI2_OWNPV", "FD #chi^{2}(#Lambda*)", "", 'F' ); #not used
#dataloader.AddVariable( "B_FDCHI2_OWNPV", "#chi^{2}_{FD}(#Lambda_{b})", "", 'F' ); #not used
# Add Spectator Variables: not used for Training but written in final TestTree
#dataloader.AddSpectator( "B_M", "M(#Lambda_{b})", "MeV");
#dataloader.AddSpectator( "Lambda_1520_0_M", "M(#Lambda*)", "MeV");
# Read input data
if gSystem.AccessPathName(sigfname) != 0:
print "Can not find %s" % sigfname
if gSystem.AccessPathName(bkgfname) != 0:
print "Can not find %s" % bkgfname
inputSig = TFile.Open(sigfname)
inputBkg = TFile.Open(bkgfname)
# Get the signal and background trees for training
signal = inputSig.Get(treeNameSig)
background = inputBkg.Get(treeNameBkg)
# Global event weights (see below for setting event-wise weights)
signalWeight = 1.0
backgroundWeight = 1.0
# ====== register trees ====================================================
#
# the following method is the prefered one:
# you can add an arbitrary number of signal or background trees
dataloader.AddSignalTree(signal, signalWeight)
dataloader.AddBackgroundTree(background, backgroundWeight)
# To give different trees for training and testing, do as follows:
# dataloader.AddSignalTree( signalTrainingTree, signalTrainWeight, "Training" )
# dataloader.AddSignalTree( signalTestTree, signalTestWeight, "Test" )
# Use the following code instead of the above two or four lines to add signal and background
# training and test events "by hand"
# NOTE that in this case one should not give expressions (such as "var1+var2") in the input
# variable definition, but simply compute the expression before adding the event
#
# # --- begin ----------------------------------------------------------
#
# ... *** please lookup code in TMVA/macros/TMVAClassification.C ***
#
# # --- end ------------------------------------------------------------
#
# ====== end of register trees ==============================================
# Set individual event weights (the variables must exist in the original TTree)
# for signal : dataloader.SetSignalWeightExpression ("weight1*weight2");
# for background: dataloader.SetBackgroundWeightExpression("weight1*weight2");
#dataloader.SetBackgroundWeightExpression( "weight" )
# Apply additional cuts on the signal and background sample.
# example for cut: mycut = TCut( "abs(var1)<0.5 && abs(var2-0.5)<1" )
mycutSig = TCut(
""
) #"pplus_ProbNNp>0.2 && Kminus_ProbNNk>0.2 && B_PT>4000 && Lambda_1520_0_PT>1500 && gamma_PT>3000 && pplus_PT>1000 && B_FDCHI2_OWNPV>100 && pplus_IPCHI2_OWNPV>50 && Kminus_IPCHI2_OWNPV>40")# B_BKGCAT==0 directly applied in root sample
#print(sigfname + str( mycutSig ) + treeNameSig)
mycutBkg = TCut(
"B_M>6120"
) #"pplus_ProbNNp>0.2 && Kminus_ProbNNk>0.2 && B_PT>4000 && Lambda_1520_0_PT>1500 && gamma_PT>3000 && pplus_PT>1000 && B_FDCHI2_OWNPV>100 && pplus_IPCHI2_OWNPV>50 && Kminus_IPCHI2_OWNPV>40 && B_M>6120")#(B_M>6120 || B_M<5120)" )
#print(bkgfname + str( mycutBkg ) + treeNameBkg)
# Here, the relevant variables are copied over in new, slim trees that are
# used for TMVA training and testing
# "SplitMode=Random" means that the input events are randomly shuffled before
# splitting them into training and test samples
dataloader.PrepareTrainingAndTestTree(
mycutSig, mycutBkg,
"nTrain_Signal=0:nTrain_Background=0:SplitMode=Random:NormMode=NumEvents:!V"
)
# --------------------------------------------------------------------------------------------------
# ---- Book MVA methods
#
# please lookup the various method configuration options in the corresponding cxx files, eg:
# src/MethoCuts.cxx, etc, or here: http://tmva.sourceforge.net/optionRef.html
# it is possible to preset ranges in the option string in which the cut optimisation should be done:
# "...:CutRangeMin[2]=-1:CutRangeMax[2]=1"...", where [2] is the third input variable
# Cut optimisation
if "Cuts" in mlist:
factory.BookMethod(
dataloader, TMVA.Types.kCuts, "Cuts",
"!H:!V:FitMethod=MC:EffSel:SampleSize=200000:VarProp=FSmart")
if "CutsD" in mlist:
factory.BookMethod(
dataloader, TMVA.Types.kCuts, "CutsD",
"!H:!V:FitMethod=MC:EffSel:SampleSize=200000:VarProp=FSmart:VarTransform=Decorrelate"
)
if "CutsPCA" in mlist:
factory.BookMethod(
dataloader, TMVA.Types.kCuts, "CutsPCA",
"!H:!V:FitMethod=MC:EffSel:SampleSize=200000:VarProp=FSmart:VarTransform=PCA"
)
if "CutsGA" in mlist:
factory.BookMethod(
dataloader, TMVA.Types.kCuts, "CutsGA",
"H:!V:FitMethod=GA:CutRangeMin[0]=-10:CutRangeMax[0]=10:VarProp[1]=FMax:EffSel:Steps=30:Cycles=3:PopSize=400:SC_steps=10:SC_rate=5:SC_factor=0.95"
)
if "CutsSA" in mlist:
factory.BookMethod(
dataloader, TMVA.Types.kCuts, "CutsSA",
"!H:!V:FitMethod=SA:EffSel:MaxCalls=150000:KernelTemp=IncAdaptive:InitialTemp=1e+6:MinTemp=1e-6:Eps=1e-10:UseDefaultScale"
)
# Likelihood ("naive Bayes estimator")
if "Likelihood" in mlist:
factory.BookMethod(
dataloader, TMVA.Types.kLikelihood, "Likelihood",
"H:!V:!TransformOutput:PDFInterpol=Spline2:NSmoothSig[0]=20:NSmoothBkg[0]=20:NSmoothBkg[1]=10:NSmooth=1:NAvEvtPerBin=50"
)
# Decorrelated likelihood
if "LikelihoodD" in mlist:
factory.BookMethod(
dataloader, TMVA.Types.kLikelihood, "LikelihoodD",
"!H:!V:TransformOutput:PDFInterpol=Spline2:NSmoothSig[0]=20:NSmoothBkg[0]=20:NSmooth=5:NAvEvtPerBin=50:VarTransform=Decorrelate"
)
# PCA-transformed likelihood
if "LikelihoodPCA" in mlist:
factory.BookMethod(
dataloader, TMVA.Types.kLikelihood, "LikelihoodPCA",
"!H:!V:!TransformOutput:PDFInterpol=Spline2:NSmoothSig[0]=20:NSmoothBkg[0]=20:NSmooth=5:NAvEvtPerBin=50:VarTransform=PCA"
)
# Use a kernel density estimator to approximate the PDFs
if "LikelihoodKDE" in mlist:
factory.BookMethod(
dataloader, TMVA.Types.kLikelihood, "LikelihoodKDE",
"!H:!V:!TransformOutput:PDFInterpol=KDE:KDEtype=Gauss:KDEiter=Adaptive:KDEFineFactor=0.3:KDEborder=None:NAvEvtPerBin=50"
)
# Use a variable-dependent mix of splines and kernel density estimator
if "LikelihoodMIX" in mlist:
factory.BookMethod(
dataloader, TMVA.Types.kLikelihood, "LikelihoodMIX",
"!H:!V:!TransformOutput:PDFInterpolSig[0]=KDE:PDFInterpolBkg[0]=KDE:PDFInterpolSig[1]=KDE:PDFInterpolBkg[1]=KDE:PDFInterpolSig[2]=Spline2:PDFInterpolBkg[2]=Spline2:PDFInterpolSig[3]=Spline2:PDFInterpolBkg[3]=Spline2:KDEtype=Gauss:KDEiter=Nonadaptive:KDEborder=None:NAvEvtPerBin=50"
)
# Test the multi-dimensional probability density estimator
# here are the options strings for the MinMax and RMS methods, respectively:
# "!H:!V:VolumeRangeMode=MinMax:DeltaFrac=0.2:KernelEstimator=Gauss:GaussSigma=0.3" );
# "!H:!V:VolumeRangeMode=RMS:DeltaFrac=3:KernelEstimator=Gauss:GaussSigma=0.3" );
if "PDERS" in mlist:
factory.BookMethod(
dataloader, TMVA.Types.kPDERS, "PDERS",
"!H:!V:NormTree=T:VolumeRangeMode=Adaptive:KernelEstimator=Gauss:GaussSigma=0.3:NEventsMin=400:NEventsMax=600"
)
if "PDERSD" in mlist:
factory.BookMethod(
dataloader, TMVA.Types.kPDERS, "PDERSD",
"!H:!V:VolumeRangeMode=Adaptive:KernelEstimator=Gauss:GaussSigma=0.3:NEventsMin=400:NEventsMax=600:VarTransform=Decorrelate"
)
if "PDERSPCA" in mlist:
factory.BookMethod(
dataloader, TMVA.Types.kPDERS, "PDERSPCA",
"!H:!V:VolumeRangeMode=Adaptive:KernelEstimator=Gauss:GaussSigma=0.3:NEventsMin=400:NEventsMax=600:VarTransform=PCA"
)
# Multi-dimensional likelihood estimator using self-adapting phase-space binning
if "PDEFoam" in mlist:
factory.BookMethod(
dataloader, TMVA.Types.kPDEFoam, "PDEFoam",
"!H:!V:SigBgSeparate=F:TailCut=0.001:VolFrac=0.0666:nActiveCells=500:nSampl=2000:nBin=5:Nmin=100:Kernel=None:Compress=T"
)
if "PDEFoamBoost" in mlist:
factory.BookMethod(
dataloader, TMVA.Types.kPDEFoam, "PDEFoamBoost",
"!H:!V:Boost_Num=30:Boost_Transform=linear:SigBgSeparate=F:MaxDepth=4:UseYesNoCell=T:DTLogic=MisClassificationError:FillFoamWithOrigWeights=F:TailCut=0:nActiveCells=500:nBin=20:Nmin=400:Kernel=None:Compress=T"
)
# K-Nearest Neighbour classifier (KNN)
if "KNN" in mlist:
factory.BookMethod(
dataloader, TMVA.Types.kKNN, "KNN",
"H:nkNN=20:ScaleFrac=0.8:SigmaFact=1.0:Kernel=Gaus:UseKernel=F:UseWeight=T:!Trim"
)
# H-Matrix (chi2-squared) method
if "HMatrix" in mlist:
factory.BookMethod(dataloader, TMVA.Types.kHMatrix, "HMatrix", "!H:!V")
# Linear discriminant (same as Fisher discriminant)
if "LD" in mlist:
factory.BookMethod(
dataloader, TMVA.Types.kLD, "LD",
"H:!V:VarTransform=None:CreateMVAPdfs:PDFInterpolMVAPdf=Spline2:NbinsMVAPdf=50:NsmoothMVAPdf=10"
)
# Fisher discriminant (same as LD)
if "Fisher" in mlist:
factory.BookMethod(
dataloader, TMVA.Types.kFisher, "Fisher",
"H:!V:Fisher:CreateMVAPdfs:PDFInterpolMVAPdf=Spline2:NbinsMVAPdf=50:NsmoothMVAPdf=10"
)
# Fisher with Gauss-transformed input variables
if "FisherG" in mlist:
factory.BookMethod(dataloader, TMVA.Types.kFisher, "FisherG",
"H:!V:VarTransform=Gauss")
# Composite classifier: ensemble (tree) of boosted Fisher classifiers
if "BoostedFisher" in mlist:
factory.BookMethod(
dataloader, TMVA.Types.kFisher, "BoostedFisher",
"H:!V:Boost_Num=20:Boost_Transform=log:Boost_Type=AdaBoost:Boost_AdaBoostBeta=0.2"
)
# Function discrimination analysis (FDA) -- test of various fitters - the recommended one is Minuit (or GA or SA)
if "FDA_MC" in mlist:
factory.BookMethod(
dataloader, TMVA.Types.kFDA, "FDA_MC",
"H:!V:Formula=(0)+(1)*x0+(2)*x1+(3)*x2+(4)*x3:ParRanges=(-1,1)(-10,10);(-10,10);(-10,10);(-10,10):FitMethod=MC:SampleSize=100000:Sigma=0.1"
)
if "FDA_GA" in mlist:
factory.BookMethod(
dataloader, TMVA.Types.kFDA, "FDA_GA",
"H:!V:Formula=(0)+(1)*x0+(2)*x1+(3)*x2+(4)*x3:ParRanges=(-1,1);(-10,10);(-10,10);(-10,10);(-10,10):FitMethod=GA:PopSize=300:Cycles=3:Steps=20:Trim=True:SaveBestGen=1"
)
if "FDA_SA" in mlist:
factory.BookMethod(
dataloader, TMVA.Types.kFDA, "FDA_SA",
"H:!V:Formula=(0)+(1)*x0+(2)*x1+(3)*x2+(4)*x3:ParRanges=(-1,1)(-10,10);(-10,10);(-10,10);(-10,10):FitMethod=SA:MaxCalls=15000:KernelTemp=IncAdaptive:InitialTemp=1e+6:MinTemp=1e-6:Eps=1e-10:UseDefaultScale"
)
if "FDA_MT" in mlist:
factory.BookMethod(
dataloader, TMVA.Types.kFDA, "FDA_MT",
"H:!V:Formula=(0)+(1)*x0+(2)*x1+(3)*x2+(4)*x3:ParRanges=(-1,1)(-10,10);(-10,10);(-10,10);(-10,10):FitMethod=MINUIT:ErrorLevel=1:PrintLevel=-1:FitStrategy=2:UseImprove:UseMinos:SetBatch"
)
if "FDA_GAMT" in mlist:
factory.BookMethod(
dataloader, TMVA.Types.kFDA, "FDA_GAMT",
"H:!V:Formula=(0)+(1)*x0+(2)*x1+(3)*x2+(4)*x3:ParRanges=(-1,1)(-10,10);(-10,10);(-10,10);(-10,10):FitMethod=GA:Converger=MINUIT:ErrorLevel=1:PrintLevel=-1:FitStrategy=0:!UseImprove:!UseMinos:SetBatch:Cycles=1:PopSize=5:Steps=5:Trim"
)
if "FDA_MCMT" in mlist:
factory.BookMethod(
dataloader, TMVA.Types.kFDA, "FDA_MCMT",
"H:!V:Formula=(0)+(1)*x0+(2)*x1+(3)*x2+(4)*x3:ParRanges=(-1,1)(-10,10);(-10,10);(-10,10);(-10,10):FitMethod=MC:Converger=MINUIT:ErrorLevel=1:PrintLevel=-1:FitStrategy=0:!UseImprove:!UseMinos:SetBatch:SampleSize=20"
)
# TMVA ANN: MLP (recommended ANN) -- all ANNs in TMVA are Multilayer Perceptrons
if "MLP" in mlist:
factory.BookMethod(
dataloader,
TMVA.Types.kMLP,
"MLP",
#"!H:!V:NeuronType=tanh:VarTransform=N:NCycles=600:HiddenLayers=N+3:TestRate=5:!UseRegulator" )#Try
"!H:!V:NeuronType=tanh:VarTransform=N:NCycles=600:HiddenLayers=N+5:TestRate=5:!UseRegulator"
) #Old
if "MLPBFGS" in mlist:
factory.BookMethod(
dataloader, TMVA.Types.kMLP, "MLPBFGS",
"H:!V:NeuronType=tanh:VarTransform=N:NCycles=600:HiddenLayers=N+5:TestRate=5:TrainingMethod=BFGS:!UseRegulator"
)
if "MLPBNN" in mlist:
factory.BookMethod(
dataloader, TMVA.Types.kMLP, "MLPBNN",
"H:!V:NeuronType=tanh:VarTransform=N:NCycles=600:HiddenLayers=N+5:TestRate=5:TrainingMethod=BFGS:UseRegulator"
) # BFGS training with bayesian regulators
# CF(Clermont-Ferrand)ANN
if "CFMlpANN" in mlist:
factory.BookMethod(dataloader, TMVA.Types.kCFMlpANN, "CFMlpANN",
"!H:!V:NCycles=2000:HiddenLayers=N+1,N"
) # n_cycles:#nodes:#nodes:...
# Tmlp(Root)ANN
if "TMlpANN" in mlist:
factory.BookMethod(
dataloader, TMVA.Types.kTMlpANN, "TMlpANN",
"!H:!V:NCycles=200:HiddenLayers=N+1,N:LearningMethod=BFGS:ValidationFraction=0.3"
) # n_cycles:#nodes:#nodes:...
# Support Vector Machine
if "SVM" in mlist:
factory.BookMethod(dataloader, TMVA.Types.kSVM, "SVM",
"Gamma=0.25:Tol=0.001:VarTransform=Norm")
# Boosted Decision Trees
if "BDTG" in mlist:
factory.BookMethod(
dataloader, TMVA.Types.kBDT, "BDTG",
"!H:!V:NTrees=600:BoostType=Grad:Shrinkage=0.1:UseBaggedGrad:GradBaggingFraction=0.73:SeparationType=GiniIndex:nCuts=15:MaxDepth=2"
) #Settings3
#"!H:!V:NTrees=300:BoostType=Grad:Shrinkage=0.11:UseBaggedGrad:GradBaggingFraction=0.73:SeparationType=GiniIndex:nCuts=17:MaxDepth=4" )#AnaNote
#"!H:!V:NTrees=1000:BoostType=Grad:Shrinkage=0.30:UseBaggedGrad:GradBaggingFraction=0.6:SeparationType=GiniIndex:nCuts=20:NNodesMax=5" )#Old
if "BDT" in mlist:
factory.BookMethod(
dataloader, TMVA.Types.kBDT, "BDT",
"!H:!V:NTrees=850:MinNodeSize=2.5%:MaxDepth=3:BoostType=AdaBoost:AdaBoostBeta=0.5:SeparationType=GiniIndex:nCuts=20:PruneMethod=NoPruning"
)
if "BDTB" in mlist:
factory.BookMethod(
dataloader, TMVA.Types.kBDT, "BDTB",
"!H:!V:NTrees=400:BoostType=Bagging:SeparationType=GiniIndex:nCuts=20:PruneMethod=NoPruning"
)
if "BDTD" in mlist:
factory.BookMethod(
dataloader, TMVA.Types.kBDT, "BDTD",
"!H:!V:NTrees=400:nEventsMin=400:MaxDepth=3:BoostType=AdaBoost:SeparationType=GiniIndex:nCuts=20:PruneMethod=NoPruning:VarTransform=Decorrelate"
)
# RuleFit -- TMVA implementation of Friedman's method
if "RuleFit" in mlist:
factory.BookMethod(
dataloader, TMVA.Types.kRuleFit, "RuleFit",
"H:!V:RuleFitModule=RFTMVA:Model=ModRuleLinear:MinImp=0.001:RuleMinDist=0.001:NTrees=20:fEventsMin=0.01:fEventsMax=0.5:GDTau=-1.0:GDTauPrec=0.01:GDStep=0.01:GDNSteps=10000:GDErrScale=1.02"
)
# --------------------------------------------------------------------------------------------------
# ---- Now you can tell the factory to train, test, and evaluate the MVAs.
# Train MVAs
print("FLAG 0")
factory.TrainAllMethods()
# Test MVAs
factory.TestAllMethods()
# Evaluate MVAs
factory.EvaluateAllMethods()
# Save the output.
outputFile.Close()
print "=== wrote root file %s\n" % outfname
print "=== TMVAClassification is done!\n"
# open the GUI for the result macros
if not gROOT.IsBatch(): TMVA.TMVAGui(outfname)