Hi @jalopezg @moneta glad you can help!
I copied my code here since I am not able to upload file yet.
#!/usr/bin/env python
# @(#)root/tmva $Id$
# --------------------------------------------
# Standard python import
import os,sys # exit
import time # time accounting
import getopt # command line parser
import ROOT as r
import varsList
# --------------------------------------------
#weight and cut strings below are used for both background and signals!
weightStrC = "triggerXSF*pileupWeight*lepIdSF*EGammaGsfSF*isoSF*L1NonPrefiringProb_CommonCalc*MCWeight_MultiLepCalc/abs(MCWeight_MultiLepCalc)"
weightStrS = weightStrC+"*xsecEff"
#### NJetsCSVwithSF_MultiLepCalc
cutStrC = "(NJets_JetSubCalc >= 6 && NJetsCSV_MultiLepCalc >= 2) && ((leptonPt_MultiLepCalc > 20 && isElectron) || (leptonPt_MultiLepCalc > 20 && isMuon)) && (corr_met_MultiLepCalc > 60) && (MT_lepMet > 60) && (minDR_lepJet > 0.4) && (AK4HT > 500) && (DataPastTriggerX == 1) && (MCPastTriggerX == 1)"
cutStrS = cutStrC+" && ( isTraining == 1 || isTraining == 2 )"
# Default settings for command line arguments
DEFAULT_OUTFNAME = "weights/TMVA.root"
DEFAULT_INFNAME = "180"
DEFAULT_TREESIG = "TreeS"
DEFAULT_TREEBKG = "TreeB"
DEFAULT_METHODS = "BDT"
# "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,BDTF,RuleFit"
DEFAULT_NTREES = "100"
DEFAULT_MDEPTH = "2"#str(len(varList))
DEFAULT_VARLISTKEY = "BigComb"
#print "Usage: python %s [options]" % sys.argv[2]
# Print usage help
def usage():
print " "
print "Usage: python %s [options]" % sys.argv[0]
print " -m | --methods : gives methods to be run (default: all methods)"
print " -i | --inputfile : name of input ROOT file (default: '%s')" % DEFAULT_INFNAME
print " -o | --outputfile : name of output ROOT file containing results (default: '%s')" % DEFAULT_OUTFNAME
print " -n | --nTrees : amount of trees for BDT study (default: '%s')" %DEFAULT_NTREES
print " -d | --maxDepth : maximum depth for BDT study (default: '%s')" %DEFAULT_MDEPTH
print " -l | --varListKey : BDT input variable list (default: '%s')" %DEFAULT_VARLISTKEY
print " -t | --inputtrees : input ROOT Trees for signal and background (default: '%s %s')" \
% (DEFAULT_TREESIG, DEFAULT_TREEBKG)
print " -v | --verbose"
print " -? | --usage : print this help message"
print " -h | --help : print this help message"
print " "
# Main routine
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
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 ("-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 = '_6j_year2016_NJetsCSV_multi'
Note=methods+'_'+varListKey+'_'+nVars+'_mDepth'+mDepth+note
outfname = "dataset2021/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)
# Import TMVA classes from ROOT
from ROOT import TMVA
# Output file
outputFile = TFile( outfname, 'RECREATE' )
factory = TMVA.Factory( "TMVAMulticlass", outputFile,
"!V:!Silent:Color:DrawProgressBar:Transformations=I;D;P;G,D:AnalysisType=multiclass" )
loader = TMVA.DataLoader("dataset2021")
(TMVA.gConfig().GetIONames()).fWeightFileDir = "weights/"+Note
for iVar in varList:
if iVar[0]=='NJets_JetSubCalc': loader.AddVariable(iVar[0],iVar[1],iVar[2],'I')
else: loader.AddVariable(iVar[0],iVar[1],iVar[2],'F')
inputDir = varsList.inputDir
infname = "TTTT_TuneCP5_PSweights_13TeV-amcatnlo-pythia8_correctnPartonsInBorn_hadd.root" # 2016 2017
iFileSig = TFile.Open(inputDir+infname)
sigTree = iFileSig.Get("ljmet")
loader.AddTree(sigTree, "Signal")
## multiple bkg classes
bkg1File = TFile.Open(inputDir+ varsList.bkg1)
bkg1Tree = bkg1File.Get("ljmet")
bkg2File = TFile.Open(inputDir+ varsList.bkg2)
bkg2Tree = bkg2File.Get("ljmet")
loader.AddTree(bkg1Tree, "ttbar")
loader.AddTree(bkg2Tree, "ttH")
loader.SetWeightExpression( weightStrS )
mycutSig = TCut( cutStrS )
loader.PrepareTrainingAndTestTree( mycutSig,
"SplitMode=Random:NormMode=NumEvents:!V" ) # nEvents
# 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'
#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=="BDT": factory.BookMethod( loader, TMVA.Types.kBDT, "BDT",bdtSetting)
if methods=="BDTG": factory.BookMethod( loader, 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)
# --------------------------------------------------------------------------------------------------
# Train MVAs
print "train all method"
factory.TrainAllMethods()
print "test all method"
# Test MVAs
factory.TestAllMethods()
# Evaluate MVAs
factory.EvaluateAllMethods()
# Save the output.
outputFile.Close()
if not gROOT.IsBatch(): TMVA.TMVAGui( outfname )
print "DONE"
if __name__ == "__main__":
main()
