def main():
try:
# retrive command line options
shortopts = "m:i:n:d:k:l:t:o:vh?"
longopts = [
"methods=", "inputfile=", "nTrees=", "maxDepth=", "mass=",
"varListKey=", "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
nTrees = DEFAULT_NTREES
mDepth = DEFAULT_MDEPTH
mass = DEFAULT_MASS
varListKey = DEFAULT_VARLISTKEY
verbose = True
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 ("-d", "--maxDepth"):
mDepth = a
elif o in ("-k", "--mass"):
mass = a
elif o in ("-l", "--varListKey"):
varListKey = a
elif o in ("-i", "--inputfile"):
infname = a
elif o in ("-n", "--nTrees"):
nTrees = 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
varList = varsList.varList[varListKey]
nVars = str(len(varList)) + 'vars'
Note = methods + '_' + varListKey + '_' + nVars + '_mDepth' + mDepth + '_M' + mass
outfname = "weights/TMVA_" + Note + ".root"
# Print methods
mlist = methods.replace(' ', ',').split(',')
print "=== TMVAClassification: 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( "./" )
# 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" )
factory = TMVA.Factory(
"TMVAClassification", outputFile,
"!V:!Silent:Color:DrawProgressBar:Transformations=I;: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 = "weights/" + Note
# 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" )]
for iVar in varList:
if iVar[0] == 'NJets_singleLepCalc':
factory.AddVariable(iVar[0], iVar[1], iVar[2], 'I')
else:
factory.AddVariable(iVar[0], iVar[1], iVar[2], '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
inputDir = varsList.inputDir
print 'mass point ' + mass
infname = "ChargedHiggs_HplusTB_HplusToTB_M-%s_13TeV_amcatnlo_pythia8_hadd.root" % (
mass)
iFileSig = TFile.Open(inputDir + infname)
sigChain = iFileSig.Get("ljmet")
# os.exits(1)
#BDT machinary
factory.AddSignalTree(sigChain)
bkg_list = []
bkg_trees_list = []
hist_list = []
weightsList = []
for i in range(len(varsList.bkg)):
bkg_list.append(TFile.Open(inputDir + varsList.bkg[i]))
print inputDir + varsList.bkg[i]
bkg_trees_list.append(bkg_list[i].Get("ljmet"))
bkg_trees_list[i].GetEntry(0)
if bkg_trees_list[i].GetEntries() == 0:
continue
factory.AddBackgroundTree(bkg_trees_list[i], 1)
signalWeight = 1 #0.0159/sigChain.GetEntries() #xs (pb)
# ====== register trees ====================================================
# 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");
#factory.SetBackgroundWeightExpression( "weight" )
factory.SetSignalWeightExpression(weightStrS)
factory.SetBackgroundWeightExpression(weightStrB)
# 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(cutStrS)
mycutBkg = TCut(cutStrB)
# 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:nTest_Signal=10:nTest_Background=100:SplitMode=Random:NormMode=NumEvents:!V" )
"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
# bdtSetting for "BDT"
bdtSetting = '!H:!V:NTrees=%s:MaxDepth=%s' % (nTrees, mDepth)
bdtSetting += ':MinNodeSize=2.5%:BoostType=AdaBoost:AdaBoostBeta=0.5:UseBaggedBoost:BaggedSampleFraction=0.5:SeparationType=GiniIndex:nCuts=20'
bdtSetting += ':IgnoreNegWeightsInTraining=True'
# bdtSetting for "BDTMitFisher"
bdtFSetting = '!H:!V:NTrees=%s' % nTrees
bdtFSetting += ':MinNodeSize=2.5%:UseFisherCuts:MaxDepth=3:BoostType=AdaBoost:AdaBoostBeta=0.5:SeparationType=GiniIndex:nCuts=20'
bdtFSetting += ':IgnoreNegWeightsInTraining=True'
# bdtSetting for "BDTG"
bdtGSetting = '!H:!V:NTrees=%s:MaxDepth=%s' % (nTrees, mDepth)
bdtGSetting += ':MinNodeSize=2.5%:BoostType=Grad:Shrinkage=0.10:UseBaggedBoost:BaggedSampleFraction=0.5:nCuts=20'
bdtGSetting += ':Pray' #Pray takes into account the effect of negative bins in BDTG
#bdtGSetting += ':IgnoreNegWeightsInTraining=True'
# bdtSetting for "BDTB"
bdtBSetting = '!H:!V:NTrees=%s' % nTrees
bdtBSetting += ':MinNodeSize=2.5%:BoostType=Bagging:SeparationType=GiniIndex:nCuts=20'
bdtBSetting += ':IgnoreNegWeightsInTraining=True'
# bdtSetting for "BDTD"
bdtDSetting = '!H:!V:NTrees=%s' % nTrees
bdtDSetting += ':MinNodeSize=2.5%:MaxDepth=3:BoostType=AdaBoost:SeparationType=GiniIndex:nCuts=20:VarTransform=Decorrelate'
bdtDSetting += ':IgnoreNegWeightsInTraining=True'
#Note also that explicitly setting *nEventsMin* so far OVERWRITES the option recomeded ^[[0m
#BOOKING AN ALGORITHM
if methods == "BDT": factory.BookMethod(TMVA.Types.kBDT, "BDT", bdtSetting)
if methods == "BDTG":
factory.BookMethod(TMVA.Types.kBDT, "BDTG", bdtGSetting)
if methods == "BDTMitFisher":
factory.BookMethod(TMVA.Types.kBDT, "BDTMitFisher", bdtFSetting)
if methods == "BDTB":
factory.BookMethod(TMVA.Types.kBDT, "BDTB", bdtBSetting)
if methods == "BDTD":
factory.BookMethod(TMVA.Types.kBDT, "BDTD", bdtDSetting)
# --------------------------------------------------------------------------------------------------
# ---- 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"
# save plots:
os.chdir('weights/' + Note)
#TMVA.mvaeffs( "../../"+outfname ) #Classifier Cut Efficiencies
gROOT.SetBatch(1)
TMVA.efficiencies(
"../../" + outfname
) #Classifier Background Rejection vs Signal Efficiency (ROC curve)
#TMVA.efficiencies( "weights/TMVA_BDTG_APR9_33vars_mDepth3_MLow.root", 3 ) #Classifier 1/(Backgr. Efficiency) vs Signal Efficiency (ROC curve)
TMVA.mvas("../../" + outfname,
0) #Classifier Output Distributions (test sample)
TMVA.correlations(
"../../" + outfname) #Input Variable Linear Correlation Coefficients
TMVA.variables("../../" + outfname) #Input variables (training sample)
#TMVA.mvas( "../../"+outfname ) #Classifier Output Distributions (test and training samples superimposed)
#gROOT.ProcessLine( ".x ../../mvas.C(\"%s\",3)" % ("../../"+outfname) ) #Classifier Output Distributions (test and training samples superimposed)
if not gROOT.IsBatch(): TMVA.TMVAGui("../../" + outfname)
# os.chdir('plots')
# try: os.system('convert CorrelationMatrixS.eps CorrelationMatrixS_'+Note+'.png')
# except: pass
# try: os.system('convert CorrelationMatrixB.eps CorrelationMatrixB_'+Note+'.png')
# except: pass
# #try: os.system('convert invBeffvsSeff.eps invBeffvsSeff_'+Note+'.png')
# #except: pass
# try: os.system('convert mva_'+Note.split('_')[0]+'.eps mva_'+Note+'.png')
# except: pass
# try: os.system('convert mvaeffs_'+Note.split('_')[0]+'.eps mvaeffs_'+Note+'.png')
# except: pass
# try: os.system('convert overtrain_'+Note.split('_')[0]+'.eps overtrain_'+Note+'.png')
# except: pass
# try: os.system('convert rejBvsS.eps rejBvsS_'+Note+'.png')
# except: pass
# try: os.system('convert variables_id_c1.eps variables_id_c1_'+Note+'.png')
# except: pass
# try: os.system('convert variables_id_c2.eps variables_id_c2_'+Note+'.png')
# except: pass
# try: os.system('convert variables_id_c3.eps variables_id_c3_'+Note+'.png')
# except: pass
# try: os.system('convert variables_id_c4.eps variables_id_c4_'+Note+'.png')
# except: pass
# try: os.system('convert variables_id_c5.eps variables_id_c5_'+Note+'.png')
# except: pass
# try: os.system('convert variables_id_c6.eps variables_id_c6_'+Note+'.png')
# except: pass
# os.system('rm *.eps')
print "DONE"
dataloader, TMVA.Types.kBDT, "BDTG",
"!H:!V:NTrees=1000:MinNodeSize=2.5%:BoostType=Grad:Shrinkage=0.20:UseBaggedBoost:BaggedSampleFraction=0.5:nCuts=20:MaxDepth=6"
)
factory.BookMethod(
dataloader, TMVA.Types.kBDT, "BDT",
"!H:!V:NTrees=1000:MinNodeSize=2.5%:MaxDepth=6:BoostType=AdaBoost:AdaBoostBeta=0.3:UseBaggedBoost:BaggedSampleFraction=0.3:SeparationType=GiniIndex:nCuts=20"
)
factory.BookMethod(
dataloader, TMVA.Types.kBDT, "BDTB",
"!H:!V:NTrees=1000:BoostType=Bagging:SeparationType=GiniIndex:nCuts=20")
factory.BookMethod(
dataloader, TMVA.Types.kBDT, "BDTD",
"!H:!V:NTrees=1000:MinNodeSize=2.5%:MaxDepth=6:BoostType=AdaBoost:AdaBoostBeta=0.7:SeparationType=GiniIndex:nCuts=20:VarTransform=Decorrelate"
)
# Run training, test and evaluation
factory.TrainAllMethods()
factory.TestAllMethods()
factory.EvaluateAllMethods()
# Save output
print("""
==> Wrote root file: {} \n
==> TMVAClassification is done!\n
""").format(output.GetName())
TMVA.TMVAGui(output)
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 "=== TMVAClassification: 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)
# Import TMVA classes from ROOT
from ROOT import 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;D;P;G,D:AnalysisType=Classification" )
# Set verbosity
factory.SetVerbose( verbose )
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
# 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" )]
dataloader.AddVariable( "myvar1 := var1+var2", 'F' )
dataloader.AddVariable( "myvar2 := var1-var2", "Expression 2", "", 'F' )
dataloader.AddVariable( "var3", "Variable 3", "units", 'F' )
dataloader.AddVariable( "var4", "Variable 4", "units", '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
#dataloader.AddSpectator( "spec1 := var1*2", "Spectator 1", "units", 'F' );
#dataloader.AddSpectator( "spec2 := var1*3", "Spectator 2", "units", 'F' );
# Read input data
if gSystem.AccessPathName( infname ) != 0: gSystem.Exec( "wget http://root.cern.ch/files/" + infname )
input = TFile.Open( 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
dataloader.AddSignalTree ( signal, signalWeight )
dataloader.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");
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( "" )
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
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+5:TestRate=5:!UseRegulator" )
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=1000:MinNodeSize=1.5%:BoostType=Grad:Shrinkage=0.10:UseBaggedBoost:BaggedSampleFraction=0.5:nCuts=20:MaxDepth=2" )
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:UseBaggedBoost:BaggedSampleFraction=0.5:SeparationType=GiniIndex:nCuts=20" )
if "BDTB" in mlist:
factory.BookMethod( dataloader, TMVA.Types.kBDT, "BDTB",
"!H:!V:NTrees=400:BoostType=Bagging:SeparationType=GiniIndex:nCuts=20" )
if "BDTD" in mlist:
factory.BookMethod( dataloader, TMVA.Types.kBDT, "BDTD",
"!H:!V:NTrees=400:MinNodeSize=5%:MaxDepth=3:BoostType=AdaBoost:SeparationType=GiniIndex:nCuts=20: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
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
TMVA.TMVAGui(outfname)
# keep the ROOT thread running
gApplication.Run()
def launch_gui(self):
'''Launch the TMVA GUI.'''
self.cd_weightsdir()
return TMVA.TMVAGui(self.outputfile)
def main():
# Default settings for command line arguments
DEFAULT_OUTFNAME = "TMVAXi2.root"
DEFAULT_INFNAME = "MC_Xic0_2015_filtered.root"
DEFAULT_TREESIG = "DecayTree"
DEFAULT_TREEBKG = "DecayTree"
DEFAULT_METHODS = "Cuts,CutsD,CutsPCA,CutsGA,CutsSA,Likelihood,LikelihoodD,LikelihoodPCA,LikelihoodKDE,LikelihoodMIX,PDERS,PDERSD,PDERSPCA,PDEFoam,PDEFoamBoost,KNN,LD,Fisher,FisherG,BoostedFisher,HMatrix,FDA_GA,FDA_SA,FDA_MC,FDA_MT,FDA_GAMT,FDA_MCMT,MLP,MLPBFGS,MLPBNN,CFMlpANN,TMlpANN,SVM,BDT,BDTD,BDTG,BDTB,RuleFit"
import argparse
argparser = argparse.ArgumentParser()
argparser.add_argument("-m",
"--methods",
default=repr(DEFAULT_METHODS.split(',')))
argparser.add_argument("-o", "--outputfile", default=DEFAULT_OUTFNAME)
argparser.add_argument('--variables')
argparser.add_argument('-s', '--spectators', default='()')
argparser.add_argument('--signalfile', default=DEFAULT_INFNAME)
argparser.add_argument('--signaltree', default=DEFAULT_TREESIG)
argparser.add_argument('--signalsel', default='')
argparser.add_argument('--signalweight', default='')
argparser.add_argument('--bkgfile', default=DEFAULT_INFNAME)
argparser.add_argument('--bkgtree', default=DEFAULT_TREEBKG)
argparser.add_argument('--bkgsel', default='')
argparser.add_argument('--bkgweight', default='')
argparser.add_argument('--factoryname', default="TMVAClassification")
argparser.add_argument('-v',
'--verbose',
action='store_true',
default=False)
argparser.add_argument('--weightsdir', default='weights')
argparser.add_argument('--datasetname', default='dataset')
args = argparser.parse_args()
weightsdir = args.weightsdir
ROOT.TMVA.Config.Instance().GetIONames().fWeightFileDir = weightsdir
# Print methods
mlist = eval(args.methods)
print "=== TMVAClassification: 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
if os.path.exists('./TMVAlogon.C'):
gROOT.Macro("./TMVAlogon.C")
# Import TMVA classes from ROOT
from ROOT import TMVA
# Output file
outputFile = TFile(args.outputfile, '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(
args.factoryname, outputFile,
"!V:!Silent:Color:!DrawProgressBar:Transformations=I;D;P;G,D:AnalysisType=Classification"
)
# Set verbosity
factory.SetVerbose(args.verbose)
# If you wish to modify default settings
# (please check "src/Config.h" to see all available global options)
# gConfig().GetVariablePlotting()).fTimesRMS = 8.0
# 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" )]
# For ROOT v6 compatibility.
root6 = not hasattr(factory, 'AddVariable')
if root6:
dataloader = ROOT.TMVA.DataLoader(args.datasetname)
else:
dataloader = factory
for var in eval(args.variables):
if not isinstance(var, (tuple, list)):
var = (var, )
try:
dataloader.AddVariable(*var)
except:
print 'Failed to call dataloader.AddVariable with args', var
raise
# dataloader.AddVariable( "myvar1 := var1+var2", 'F' )
# dataloader.AddVariable( "myvar2 := var1-var2", "Expression 2", "", 'F' )
# dataloader.AddVariable( "var3", "Variable 3", "units", 'F' )
# dataloader.AddVariable( "var4", "Variable 4", "units", '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
for var in eval(args.spectators):
if not isinstance(var, (tuple, list)):
var = (var, )
try:
dataloader.AddSpectator(*var)
except:
print 'Failed to call dataloader.AddSpectator with args', var
raise
# dataloader.AddSpectator( "spec1:=var1*2", "Spectator 1", "units", 'F' )
# dataloader.AddSpectator( "spec2:=var1*3", "Spectator 2", "units", 'F' )
# Read input data
# if gSystem.AccessPathName( infname ) != 0: gSystem.Exec( "wget http://root.cern.ch/files/" + infname )
# input = TFile.Open( infname )
# # Get the signal and background trees for training
# signal = input.Get( treeNameSig )
# background = input.Get( treeNameBkg )
signalfile = TFile.Open(args.signalfile)
if signalfile.IsZombie():
raise OSError("Couldn't find signal file " + repr(args.signalfile))
signal = signalfile.Get(args.signaltree)
if not signal:
raise ValueError("Couldn't find signal TTree " +
repr(args.signaltree) + " in file " +
repr(args.signalfile))
bkgfile = TFile.Open(args.bkgfile)
if bkgfile.IsZombie():
raise OSError("Couldn't find bkg file " + repr(args.bkgfile))
background = bkgfile.Get(args.bkgtree)
if not background:
raise ValueError("Couldn't find bkg TTree " + repr(args.bkgtree) +
" in file " + repr(args.bkgfile))
# 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");
if args.signalweight:
dataloader.SetSignalWeightExpression(args.signalweight)
if args.bkgweight:
dataloader.SetBackgroundWeightExpression(args.bkgweight)
# 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(args.signalsel)
mycutBkg = TCut(args.bkgsel)
# 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 root6:
# Bit of an ugly hack, but does the job.
factory._BookMethod = factory.BookMethod
# Don't know why 'self' isn't passed here?
def BookMethod(*args):
factory._BookMethod(dataloader, *args)
factory.BookMethod = BookMethod
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: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(
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:MinNodeSize=1.5%:BoostType=Grad:Shrinkage=0.10:UseBaggedGrad:GradBaggingFraction=0.5:nCuts=20:MaxDepth=2"
)
if "BDT" in mlist:
factory.BookMethod(
TMVA.Types.kBDT, "BDT",
"!H:!V:NTrees=850:MinNodeSize=2.5%:MaxDepth=3:BoostType=AdaBoost:AdaBoostBeta=0.5:SeparationType=GiniIndex:nCuts=20"
)
if "BDTB" in mlist:
factory.BookMethod(
TMVA.Types.kBDT, "BDTB",
"!H:!V:NTrees=400:BoostType=Bagging:SeparationType=GiniIndex:nCuts=20"
)
if "BDTD" in mlist:
factory.BookMethod(
TMVA.Types.kBDT, "BDTD",
"!H:!V:NTrees=400:MinNodeSize=5%:MaxDepth=3:BoostType=AdaBoost:SeparationType=GiniIndex:nCuts=20: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" % outputFile.GetName()
print "=== TMVAClassification is done!\n"
# open the GUI for the result macros
if not ROOT.gROOT.IsBatch():
if hasattr(TMVA, 'TMVAGui'):
TMVA.TMVAGui(outputFile.GetName())
raw_input('Hit enter to quit.')
elif 'ROOTSYS' in os.environ:
tmvaguipath = os.path.join(os.environ['ROOTSYS'], 'tutorials',
'tmva')
if os.path.exists(os.path.join(tmvaguipath, 'TMVAGui.C')):
gROOT.SetMacroPath(tmvaguipath)
gROOT.LoadMacro("TMVAGui.C")
try:
gROOT.ProcessLine("TMVAGui(\"%s\")" % outputFile.GetName())
raw_input('Hit enter to quit.')
except RuntimeError:
print "Couldn't run TMVAGui!"
outputfilename = outputFile.GetName()
weightsfiles = dict(
(m,
os.path.join(weightsdir, args.factoryname + '_' + m + '.weights.xml'))
for m in mlist)
classfiles = dict(
(m, os.path.join(weightsdir, args.factoryname + '_' + m + '.class.C'))
for m in mlist)
# keep the ROOT thread running (this makes the function hang).
#gApplication.Run()
# TMVA disables unused branches when copying the trees then doesn't change them back.
background.SetBranchStatus('*', 1)
signal.SetBranchStatus('*', 1)
if 'signalfile' in locals():
signalfile.Close()
if 'bkgfile' in locals():
bkgfile.Close()
return locals()
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)
