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(data['allTestEvents'],
setup,
reader,
allMethods[classifier]['config'],
cut + '&&type==1&&mstop==300&&mlsp==270',
-999,
weightFunc=getScaledWeight)
yieldS = getYield(data['allTestEvents'],
setup,
reader,
allMethods[classifier]['config'],
cut + '&&type==1&&mstop==300&&mlsp==270',
thresh,
weightFunc=getScaledWeight)
yieldB = getYield(data['allTestEvents'],
setup,
reader,
allMethods[classifier]['config'],
cut + '&&type==0',
thresh,
weightFunc=getScaledWeight)
# yieldS = getYieldFromChain(cSignal, cut, weight = "weight")
# yieldB = getYieldFromChain(cBkg, cut, weight = "weight")
if yieldS <= 0. or yieldB <= 0.: return -999.
fom = yieldS / sqrt(yieldB + (relSysErr * yieldB)**2)
if verbose:
print "thresh", thresh, "fom:", fom, 'cut', cut, 'sigEff:', yieldS / yieldStot
if resType == "fom":
return fom
else:
return {
'fom': fom,
'yieldS': yieldS,
"yieldB": yieldB,
'sigEff': yieldS / yieldStot
}
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(
data["allTestEvents"],
setup,
reader,
allMethods[classifier]["config"],
cut + "&&type==1&&mstop==300&&mlsp==270",
-999,
weightFunc=getScaledWeight,
)
yieldS = getYield(
data["allTestEvents"],
setup,
reader,
allMethods[classifier]["config"],
cut + "&&type==1&&mstop==300&&mlsp==270",
thresh,
weightFunc=getScaledWeight,
)
yieldB = getYield(
data["allTestEvents"],
setup,
reader,
allMethods[classifier]["config"],
cut + "&&type==0",
thresh,
weightFunc=getScaledWeight,
)
# yieldS = getYieldFromChain(cSignal, cut, weight = "weight")
# yieldB = getYieldFromChain(cBkg, cut, weight = "weight")
if yieldS <= 0.0 or yieldB <= 0.0:
return -999.0
fom = yieldS / sqrt(yieldB + (relSysErr * yieldB) ** 2)
if verbose:
print "thresh", thresh, "fom:", fom, "cut", cut, "sigEff:", yieldS / yieldStot
if resType == "fom":
return fom
else:
return {"fom": fom, "yieldS": yieldS, "yieldB": yieldB, "sigEff": yieldS / yieldStot}
def getExpExcl(thresh, retType=None):
bkg = lumiFac*getYield(data['simu'], setup, reader, allMethods[methodName]['config'], setup["preselection"]+"&&type==0", thresh, weight)
sig = lumiFac*getYield(data['simu'], setup, reader, allMethods[methodName]['config'], setup["preselection"]+"&&type==1&&osetMgl=="+str(mgl)+"&&osetMN=="+str(mN), thresh, weight)
c = cardFileWriter()
c.addBin('Bin0', ['bkg'], 'Bin0')
c.specifyObservation('Bin0', int(bkg))
c.specifyExpectation('Bin0', 'bkg', bkg)
c.specifyExpectation('Bin0', 'signal', sig)
c.addUncertainty('globalUnc', 'lnN')
c.specifyUncertainty('globalUnc', 'Bin0', 'bkg', 1.2)
if bkg==0. or sig==0.:
res={'0.500':float('nan')}
else:
res= c.calcLimit('opt.txt',options=opt)
print "#########################", methodName, "##################################"
print "Now at thresh",thresh, "bkg/sig", bkg,"/", sig,
print "Results",res
print "################################################################"
if not retType:
return res['0.500']
else:
return res
def getSigEffDev(thresh): sig = lumiFac*getYield(data['simu'], setup, reader, allMethods[methodName]['config'], setup["preselection"]+"&&type==1&&osetMgl=="+str(mgl)+"&&osetMN=="+str(mN), thresh, weight) res= sig/float(sigInc) print "Optimizing MVA cut for targetSigEff of ",targetSigEff,". Testing threshold",thresh,"found sig eff.:", res return abs(res-targetSigEff)
nev = data['simu'].GetEntries('osetMgl=='+str(mgl)+'&&osetMN=='+str(mN))
if nev==0:
print "No events in dataset for mgl/mN ",mgl, "/",mN
continue
from cardFileWriter import cardFileWriter
# targetBkg=3.
for methodName in [ 'MLP21']:#, 'myCut']:
import numpy as np
from scipy import optimize
# def getBkgDev(thresh):
# res= getYield(data['simu'], setup, reader, allMethods[methodName]['config'], setup["preselection"]+"&&type==0", thresh, 'weightLumi')
# print "Optimizing MVA cut for bkg estimation of ",targetBkg,". Testing threshold",thresh,"found bkg exp.:",res
# return abs(res-targetBkg)
# x0 = np.array([0.9])
# optThresh = optimize.fmin(getBkgDev, x0)
sigInc = lumiFac*getYield(data['simu'], setup, reader, allMethods[methodName]['config'], setup["preselection"]+"&&type==1&&osetMgl=="+str(mgl)+"&&osetMN=="+str(mN), -1, weight)
def getSigEffDev(thresh):
sig = lumiFac*getYield(data['simu'], setup, reader, allMethods[methodName]['config'], setup["preselection"]+"&&type==1&&osetMgl=="+str(mgl)+"&&osetMN=="+str(mN), thresh, weight)
res= sig/float(sigInc)
print "Optimizing MVA cut for targetSigEff of ",targetSigEff,". Testing threshold",thresh,"found sig eff.:", res
return abs(res-targetSigEff)
# x0 = np.array([0.9])
# optThresh = optimize.fmin(getSigEffDev, x0)
if (model=="T1tttt" or model=="T1tttt-madgraph") and mgl<=950 and mN<=400:
opt = "--rMax 10.0"
else:
opt = ""
def getExpExcl(thresh, retType=None):
