"config": m,
}
# cSignal = ROOT.TChain("Events")
# cSignal.Add("/data/schoef/monoJetTuples_v3/copy/stop300lsp270FastSim/histo_stop300lsp270FastSim.root")
#
# cBkg = ROOT.TChain("Events")
# cBkg.Add("/data/schoef/monoJetTuples_v3/copy/W1JetsToLNu/histo_W1JetsToLNu.root")
# cBkg.Add("/data/schoef/monoJetTuples_v3/copy/W2JetsToLNu/histo_W2JetsToLNu.root")
# cBkg.Add("/data/schoef/monoJetTuples_v3/copy/W3JetsToLNu/histo_W3JetsToLNu.root")
# cBkg.Add("/data/schoef/monoJetTuples_v3/copy/W4JetsToLNu/histo_W4JetsToLNu.root")
def getScaledWeight(c):
return c.GetLeaf("weight").GetValue() * c.GetLeaf("testSampleScaleFac").GetValue()
data = constructDataset(setup, None, None, False)
# limits={}
# resFile = "/data/schoef/nnAnalysis/MVA_Analyzer/limits/"+prefix+postfix+"_"+'limits.pkl'
if overWrite or not os.path.isfile(resFile):
import numpy as np
from scipy import optimize
classifier = "BDT_maxDepth1"
def getFom(thresh, classifier, relSysErr=0.05, lepCharge=0, verbose=False, resType="fom"):
cut = setup["preselection"]
if lepCharge == -1:
cut += "&&softIsolatedMuCharge==-1"
if lepCharge == 1:
cut += "&&softIsolatedMuCharge==+1"
yieldStot = getYield(
ksres = {}
for seed in range(1):
print 'y'
prepreprefix = 'RA4NNAnalysis_T1tttt_'+str(mgl)+"_"+str(mN)+"_BkgMix_"+str(seed)
preprefix = prepreprefix
prefix = preprefix
for mode in ["Ele", "Mu"]:
signalModel = getT1ttttSample(mgl, mN, mode)
backgroundModel = getBkgSample("TTJets-PowHeg", mode)
if True:
setup['dataFile'] = '/data/'+nfsUser+'/RA4NNAnalysis/datasets/'+mode+"_"+prefix+'.root'
if (not overWriteData and ( os.path.isfile(setup['dataFile']))) :
print "File",setup['dataFile'],"found->do load!"
data = constructDataset(setup, None, None, overWriteData)
else:
weightForSampleComposition = "weight"
signal = ROOT.TChain('Events')
for b in signalModel['bins']:
fstring = signalModel['dirname']+'/'+b+'/*.root'
signal.Add(fstring)
signalSampleSize = signal.GetEntries(setup['preselection'])
signal.Draw(">>eList", setup['preselection'])
signalModel['eListPreselection'] = ROOT.gDirectory.Get("eList").Clone('eListPreselectionSignal')
print "Added bin ",b,"to signal, now",signal.GetEntries()," after preselection:",signalSampleSize,signalModel['eListPreselection'].GetN()
background = ROOT.TChain('Events')
counter=0
backgroundModel['rangeInChain'] = {}
setup['TMVAFactoryOptions'] = ["!V","!Silent","Color","DrawProgressBar","Transformations=I;D;P;G,D","AnalysisType=Classification"]
setup['plotTransformations'] = ['Id', 'Deco', 'PCA', 'Gauss_Deco']
setup['makeCorrelationScatterPlots'] = False
setup['plotMVAEffs'] = False #needs active X-forwarding since a QT Object is involved
setup['plotMVAEffs'] = False #needs active X-forwarding since a QT Object is involved
setup['fomPlotZoomCoordinates'] = [0, 0.95, 0.2, 1.0]
setup['mvaInputVars'] = ["mT", "type1phiMet", "mt2w","nbtags","njets",'minDeltaPhi', 'deltaPhi']
prefix = '_'.join(setup['mvaInputVars'])
prefix = preprefix+prefix
setup['TMVAOutputFile'] = '/data/'+nfsUser+'/RA4NNAnalysis/MVA_Analyzer/'+prefix+'.root'
setup['weightDir'] ='/data/'+nfsUser+'/RA4NNAnalysis/MVA_Analyzer/'+prefix+'/'
data = constructDataset(setup, None, None, False)
if not data:
print "Could not load dataset -> do nothing"
if data and (overWriteTMVAFrameWork or not os.path.isfile(setup['TMVAOutputFile'])):
setup['fom_plot_vars'] = []#[['mT', [0,1000] , ROOT.kGreen], ['type1phiMet', [0,1000] , ROOT.kMagenta], ['mt2w',[0,1000],ROOT.kGreen+4],['nbtags',[0,1000],ROOT.kYellow]]
setup['plotDir'] = '/afs/hephy.at/user/'+afsUser[0]+'/'+afsUser+'/www/'+localPlotDir
setup['plotSubDir'] = prefix
methodCutOpt['type']=ROOT.TMVA.Types.kCuts
methodCutOpt['name']='myCut'
methodCutOpt['lineColor']=ROOT.kRed
methodCutOpt['niceName']='cutOptimized'
methodCutOpt['options'] =('!H','!V','VarTransform=None','CreateMVAPdfs=True','FitMethod=GA','EffMethod=EffSel','VarProp=NotEnforced','CutRangeMin=-1','CutRangeMax=-1')
addNeurons = [2,1]
def getLSPMassFromFilename(c):
return getMassesFromFilename(c)[1]
ksres = {}
for seed in range(1):
prepreprefix = 'MonoJet_'+signalModel['name']+"_BkgMix_"+str(seed)
if True:
preprefix = prepreprefix
prefix = preprefix
setup['dataFile'] = '/data/'+nfsUser+'/MonoJetNNAnalysis/datasets/'+prefix+'.root'
if (not overWriteData and ( os.path.isfile(setup['dataFile']))) :
print "File",setup['dataFile'],"found->do load!"
data = constructDataset(setup, None, None, overWriteData)
else:
setup['preselection'] = 'isrJetPt>110&&isrJetBTBVetoPassed&&softIsolatedMuPt>5&&nHardElectrons+nHardMuons==0&&njet60<=2&&type1phiMet>150'
setup["sigMVAWeightFac"] = 1.#background.GetEntries(setup['preselection'])/float(signal.GetEntries(setup['preselection']))
setup["bkgMVAWeightFac"] = 1.
# setup['weightForMVA'] = {'weight':1., 'sigFac':float(sigScale)/100., 'bkgFac':1}
setup['weightForMVA'] = {'weight':1., 'sigFac':1., 'bkgFac':1}
#If changing between met and type1phiMet the formula for deltaPhi (if used) has to be changed!
setup['varsFromInputData'] = ['type1phiMet', 'isrJetPt', 'isrJetBTBVetoPassed', 'softIsolatedMuPt', 'softIsolatedMuEta','ht', 'nHardElectrons', 'nHardMuons', 'njet60/I','njet/I', 'weight', 'isrJetPt']
setup['varsFromInputSignal'] = [\
['mstop/I', getStopMassFromFilename],\
['mlsp/I', getLSPMassFromFilename]]
from monoJetFuncs import cosDeltaPhiLepW, softIsolatedMT
from math import acos
setup['varsCalculated'] = [\
