if args.masses is not None and chain.HNLmass not in args.masses: continue
#
# Create cutter to provide cut flow
#
cutter = Cutter(chain = chain)
#
# Loop over all events
#
ec = EventCategory(chain)
es = EventSelector(args.region, args.selection, object_selection_param, not args.genLevel, ec)
for entry in event_range:
chain.GetEntry(entry)
progress(entry - event_range[0], len(event_range))
cutter.cut(True, 'Total')
#
#Triggers
#
if args.selection == 'AN2017014':
if not cutter.cut(applyCustomTriggers(listOfTriggersAN2017014(chain)), 'pass_triggers'): continue
#
# Event selection
#
if not es.passedFilter(chain, chain, cutter): continue
nprompt = 0
if sample.name != 'Data':
for s in subdir:
mname = s.rsplit('/', 1)[-1]
mnames.append(mname)
mva_output = {mname: ROOT.vector('float')() for mname in mnames}
class_id = ROOT.vector('bool')()
weight = ROOT.vector('float')()
for i in xrange(len(test_tree['classID'])):
class_id.push_back(not test_tree['classID'][i])
weight.push_back(test_tree['weight'][i])
for mname in mnames:
mva_output[mname].push_back(test_tree[mname][i])
ROC_integrals = []
for iname, mname in enumerate(mnames):
progress(iname, len(mnames))
ROCs[mname] = ROOT.TMVA.ROCCurve(mva_output[mname], class_id, weight)
ROC_integrals.append(ROCs[mname].GetROCIntegral())
ROC_curve = ROCs[mname].GetROCCurve()
ROC_curve = fillRandGraph(ROC_curve, 10)
extra_text = [
extraTextFormat("AUC: " + str(ROCs[mname].GetROCIntegral()))
]
p = Plot(signal_hist=[ROC_curve],
tex_names=[mname],
name='roc_' + mname,
extra_text=extra_text)
p.drawGraph(output_dir=out_file_name.rsplit('/', 1)[0] + '/kBDT/plots',
draw_style="AP")
print 'Making pdf'
