def ApplyTransferFactorModel(transferfactor, data_3b, data_4b, tag, category,
valflag, srflag):
############################################################################
##Let's slice data one more time to have the inputs for the bdt reweighting
############################################################################
variables = ['HH_m']
original, original_weights = data.preparedataforprediction(
data_3b, transferfactor, variables)
target, target_weights = data.preparedataforprediction(
data_4b, 1, variables)
########################################
## Check the single bin, done just for the validation signal region as we are currently blinded
########################################
data.getmodeltransferfactorweights(original, original_weights, target,
target_weights, transferfactor, valflag,
srflag)
return original_weights
def CreatePredictionModel(reweightermodel, transferfactor, data_3b, category):
############################################################################
##Let's slice data one more time to have the inputs for the bdt reweighting#
############################################################################
if category == 'GGF':
variables = [
'H1_pt', 'H2_pt', 'H1_eta', 'H2_eta', 'nJet', 'H1_bb_deltaR',
'H2_bb_deltaR', 'H1_bb_deltaPhi', 'H2_bb_deltaPhi'
]
elif category == 'VBF':
variables = [
'H1_pt', 'H2_pt', 'H1_eta', 'H2_eta', 'nJet', 'JJ_j1_qgl',
'JJ_j2_qgl', 'JJ_m', 'j1j2_deltaEta'
]
else:
variables = ['H1_pt', 'H2_pt', 'H1_eta', 'H2_eta', 'nJet']
original, original_weights = data.preparedataforprediction(
data_3b, transferfactor, variables)
########################################3############
##Folding Gradient Boosted Reweighter (and DQM plots)
#####################################################
folding_weights = data.getmodelweights(original, original_weights,
reweightermodel, transferfactor)
return folding_weights
def ApplyReweightingModel(reweightermodel, transferfactor, data_3b, data_4b,
tag, bkgclassifierparams, category, valflag, srflag):
############################################################################
##Let's slice data one more time to have the inputs for the bdt reweighting
############################################################################
if category == 'GGF' or category == 'GGF1' or category == 'GGF2':
variables = [
'H1_b1_ptRegressed', 'H1_b2_ptRegressed', 'H2_b1_ptRegressed',
'H2_b2_ptRegressed', 'H1_m', 'H2_m', 'HH_m', 'H1_pt', 'H2_pt',
'h1h2_deltaEta', 'H1_bb_deltaR', 'H2_bb_deltaR',
'abs_costh_H1_ggfcm', 'HH_btag_b3_bres', 'abs_costh_H1_b1_h1cm',
'sum_3b_bscore', 'sum_4b_pt', 'HH_pt'
]
elif category == 'VBF' or category == 'VBF1' or category == 'VBF2':
variables = [
'H1_b1_ptRegressed', 'H1_b2_ptRegressed', 'H2_b1_ptRegressed',
'H2_b2_ptRegressed', 'H1_m', 'H2_m', 'HH_m', 'H1_pt', 'H2_pt',
'h1h2_deltaEta', 'h1h2_deltaPhi', 'JJ_m', 'j1j2_deltaEta',
'GGFKiller'
]
else:
variables = ['HH_m']
original, original_weights = data.preparedataforprediction(
data_3b, transferfactor, variables)
target, target_weights = data.preparedataforprediction(
data_4b, 1, variables)
########################################3############
##Folding Gradient Boosted Reweighter (and DQM plots)
#####################################################
folding_weights = data.getmodelweights(original, original_weights, target,
target_weights, reweightermodel,
transferfactor, valflag, srflag)
########################################
## KS Test (as the developers of the method do), done just for the validation signal region as we are currently blinded
########################################
#if valflag==True and srflag==True:
############################################################################
##Individual and global tests of the validation signal region
############################################################################
#plotter.Draw1DHistosComparison(original, target, variables, original_weights,True,"%s_val%s_%ssr_originalprediction"%(tag,category,category) )
#plotter.Draw1DHistosComparison(original, target, variables, folding_weights,True,"%s_val%s_%ssr_modelprediction"%(tag,category,category) )
#ksresult_original = bdtreweighter.ks_test(original, target, variables, original_weights)
#ksresult_model = bdtreweighter.ks_test(original, target, variables, folding_weights)
#bdtreweighter.ks_comparison(variables,ksresult_original,ksresult_model)
#bdtreweighter.discrimination_test(original,target,original_weights,bkgclassifierparams,"%s_val%s_%ssr"%(tag,category,category),"originalprediction")
#bdtreweighter.discrimination_test(original,target,folding_weights,bkgclassifierparams,"%s_val%s_%ssr"%(tag,category,category),"modelprediction")
############################################################################
##Let's slice data one more time to have the observables tested
############################################################################
#if category == 'GGF1':
# observable = ['GGFMVA1']
# hfmt = [50,0,1]
#elif category == 'GGF2':
# observable = ['GGFMVA2']
# hfmt = [50,0,1]
#else:
# observable = ['HH_m']
# hfmt = [80,200,1000]
#ori_obs,ori_weights = data.preparedataforprediction(data_3b,transferfactor,observable)
#tar_obs,tar_weights = data.preparedataforprediction(data_4b,1,observable)
#plotter.Draw1DHisto(ori_obs, tar_obs, observable[0], folding_weights, hfmt, False,"%s_%s_model"%(tag,category) )
#del ori_obs,tar_obs,ori_weights,tar_weights
return folding_weights