for mwp in mu_wp[algo]:
roc_curve = ROC('-'.join([algo, ewp, mwp]),
signal_path,
misid_path=bkgr_path)
p = Plot(roc_curve.returnGraph(),
algo,
'-'.join([algo, ewp, mwp]),
'efficiency',
'misid',
y_log=True,
extra_text=extra_text)
output_path = os.path.join(
os.getcwd(), 'data', 'Results', 'compareTauID',
reco_names[args.includeReco], d,
signal_name + '-' + bkgr_name, algo)
p.drawGraph(output_dir=output_path)
curves.append(
ROC('-'.join([algo, 'None', 'None']),
signal_path,
misid_path=bkgr_path).returnGraph())
ordered_names.append(algo)
p = Plot(curves,
ordered_names,
'ROC',
'efficiency',
'misid',
y_log=True,
extra_text=extra_text)
output_path = os.path.join(os.getcwd(), 'data', 'Results',
'compareTauID', args.year,
reco_names[args.includeReco], d,
input_signal = glob.glob(os.getcwd()+'/data/compareLightLeptonId/'+args.signal+'/*ROC-'+args.flavor+'.root')
bkgr_prefix = os.getcwd()+'/data/compareLightLeptonId/'+args.bkgr
from HNL.Plotting.plot import Plot
output_dir = makePathTimeStamped(os.getcwd()+'/data/Results/compareLightLeptonId/ROC/'+args.signal+'-'+args.bkgr)
curves = []
ordered_f_names = []
for f_path in input_signal:
f_name = f_path.rsplit('/', 1)[1].split('.')[0]
ordered_f_names.append(f_name)
roc_curve = ROC(f_name.split('-')[0], 1., '', f_path, misid_path =bkgr_prefix + '/'+f_name+'.root')
curves.append(roc_curve.returnGraph())
p = Plot(curves, ordered_f_names, args.signal+'_'+args.flavor, 'efficiency', 'misid', y_log=True)
p.drawGraph(output_dir = output_dir)
from HNL.Tools.efficiency import Efficiency
from HNL.Tools.helpers import rootFileContent
from ROOT import TFile
var = ['pt', 'eta']
inputFiles = {'efficiency' : glob.glob(os.getcwd()+'/data/compareLightLeptonId/'+args.signal+'/efficiency-'+str(args.flavor)+'.root'), 'fakerate': glob.glob(os.getcwd()+'/data/compareLightLeptonId/'+args.bkgr+'/fakerate-'+str(args.flavor)+'.root')}
for eff_name in ['efficiency', 'fakerate']:
for f in inputFiles[eff_name]:
print f
rf = TFile(f)
key_names = [k[0] for k in rootFileContent(rf)]
filtered_key_names = {fk.rsplit('-', 1)[0].split('/')[-1] for fk in key_names}
list_of_eff = {}
for fk in filtered_key_names: #algo
list_of_eff[fk] = {}
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'
mnames = sortByOtherList(mnames, ROC_integrals)
mnames.reverse()
pdf = FPDF()
for mname in mnames:
pdf.add_page()
pdf.image(
os.path.join(
out_file_name.rsplit('/', 1)[0], 'kBDT', 'plots',
'roc_' + mname + '.png'), 50., 50., 100., 100.)
pdf.image(
os.path.join(
out_file_name.rsplit('/', 1)[0], 'kBDT', 'plots',
'overtrain_' + mname + '.png'), 50., 150., 100., 100.)