def get_significance(label, bins):
if "binary" in args.method:
score = "sig_prediction"
min_score = 0
max_score = 1
elif "DNNmulti" in args.method:
score = "(ggH_prediction+VBF_prediction+(1-DY_prediction)+(1-ttbar_prediction))"
min_score = 1
max_score = 3
elif "BDTmva" in args.method:
score = "MVA"
min_score = -1
max_score = 1
elif "Rapidity" in args.method:
score = "max_abs_eta_mu"
min_score = 0
max_score = 2.4
# print "Will use method", args.method
# print " min score =", min_score
# print " max score =", max_score
step = (args.max_var - args.min_var)/float(args.nSteps)
print "Rescaling cut boundaries:"
new_bins = []
for ii in range(len(bins)):
new_bins.append(min_score + bins[ii]*step)
print bins, " --> ", new_bins
categories = {}
for ii in range(len(new_bins)-1):
cat_name = "cat%i"%ii
cut = "(%s>%f)&(%s<%f)"%(score, new_bins[ii], score, new_bins[ii+1])
categories[cat_name] = cut
print "Categories ready:"
print categories
print "Creating datacards..."
create_datacard(categories, args.sig_input_path, args.data_input_path, args.data_tree, args.output_path, "datacard_"+label, "workspace_"+label, nuis=args.nuis, res_unc_val=args.res_unc_val, scale_unc_val=args.scale_unc_val, smodel=args.smodel, method=args.method, lumi=args.lumi)
os.system('ls')
os.system('combine -M Significance --expectSignal=1 -t -1 -n %s -d datacard_%s.txt'%(label, label))
os.system('rm datacard_%s.txt'%label)
os.system('rm workspace_%s.root'%label)
os.system('ls')
significance = 0
tree = ROOT.TChain("limit")
tree.Add("higgsCombine%s.Significance.mH120.root"%label)
for iev, event in enumerate(tree):
significance = event.limit
os.system('rm higgsCombine%s.Significance.mH120.root'%label)
print "Expected significance =", significance
return significance
def get_significance(self, label, bins1, bins2):
new_bins1 = []
for i in range(len(bins1)):
new_bins1.append(self.min_var1 + bins1[i] * self.step1)
new_bins2 = []
for i in range(len(bins2)):
new_bins2.append(self.min_var2 + bins2[i] * self.step2)
self.log(" Rescaled cut boundaries:")
self.log(" 1st variable: %s --> %s" % (', '.join(
[str(b) for b in bins1]), ', '.join([str(b) for b in new_bins1])))
self.log(" 2nd variable: %s --> %s" % (', '.join(
[str(b) for b in bins2]), ', '.join([str(b) for b in new_bins2])))
self.log(" Categories ready:")
categories_for_combine = {}
for ii in range(len(new_bins1) - 1):
for jj in range(len(new_bins2) - 1):
cat_name = "cat_%i_%i" % (ii, jj)
cut = "(%s>%f)&(%s<%f)&(%s>%f)&(%s<%f)" % (
self.var1, new_bins1[ii], self.var1, new_bins1[ii + 1],
self.var2, new_bins2[ii], self.var2, new_bins2[ii + 1])
categories_for_combine[cat_name] = cut
self.log(" %s: %s" % (cat_name, cut))
self.log(" Creating datacards... Please wait...")
success = create_datacard(categories_for_combine,
args.sig_input_path,
args.data_input_path,
args.data_tree,
args.output_path,
"datacard_" + label,
"workspace_" + label,
nuis=args.nuis,
res_unc_val=args.res_unc_val,
scale_unc_val=args.scale_unc_val,
smodel=args.smodel,
method=args.method,
lumi=args.lumi)
if not success:
print "Datacards were not created (might be not enough events in the category). Set significance to 0."
return 0
os.system(
'combine -M Significance --expectSignal=1 -t -1 -n %s -d datacard_%s.txt'
% (label, label))
os.system('rm datacard_%s.txt' % label)
os.system('rm workspace_%s.root' % label)
significance = 0
tree = ROOT.TChain("limit")
tree.Add("higgsCombine%s.Significance.mH120.root" % label)
for iev, event in enumerate(tree):
significance = event.limit
os.system('rm higgsCombine%s.Significance.mH120.root' % label)
return significance
parser = argparse.ArgumentParser(description='')
parser.add_argument('--sig_in_path',
action='store',
dest='sig_input_path',
help='Input path')
parser.add_argument('--data_in_path',
action='store',
dest='data_input_path',
help='Input path')
parser.add_argument('--sig_tree',
action='store',
dest='sig_tree',
help='Tree name')
parser.add_argument('--data_tree',
action='store',
dest='data_tree',
help='Tree name')
parser.add_argument('--out_path',
action='store',
dest='output_path',
help='Output path')
parser.add_argument('--lumi',
action='store',
dest='lumi',
help='Integrated luminosity')
args = parser.parse_args()
create_datacard([0, 2.4], args.sig_input_path, args.sig_tree,
args.data_input_path, args.data_tree, args.output_path,
"datacard", "workspace", args.lumi)
help='Output path')
parser.add_argument('--lumi',
action='store',
dest='lumi',
help='Integrated luminosity')
parser.add_argument('--nuis',
action='store_true',
dest='nuis',
help='Nuisances')
parser.add_argument('--nuis_val',
action='store',
dest='nuis_val',
help='Resolution uncertainty')
args = parser.parse_args()
args = parser.parse_args()
for i in range(17, 18):
for j in range(i):
bins = [0.0, (j + 1) / 10.0, (i + 1) / 10.0, 2.4]
create_datacard(bins,
args.sig_input_path,
args.sig_tree,
args.data_input_path,
args.data_tree,
args.output_path,
"datacard_3cat_%i_%i" % (j + 1, i + 1),
"workspace_3cat_%i_%i" % (j + 1, i + 1),
args.lumi,
nuis=args.nuis,
nuis_val=args.nuis_val)
def get_significance(label, bins):
global memorized
global additional_cut
new_bins = []
for i in range(len(bins)):
new_bins.append(args.min_var + bins[i] * step)
log(" Rescaled cut boundaries:")
log(" %s --> %s" %
(', '.join([str(b)
for b in bins]), ', '.join([str(b) for b in new_bins])))
log(" Categories ready:")
categories = {}
for i in range(len(new_bins) - 1):
cat_name = "cat%i" % i
cut = "(%s>%f)&(%s<%f)&(%s)" % (score, new_bins[i], score,
new_bins[i + 1], additional_cut)
categories[cat_name] = cut
log(" %s: %s" % (cat_name, cut))
log(" Creating datacards... Please wait...")
if "UCSD" in args.method:
try:
success = create_datacard_ucsd(categories,
sig_2016 + sig_2017 + sig_2018,
bkg_2016 + bkg_2017 + bkg_2018,
args.output_path,
"datacard_" + label,
"workspace_" + label)
except:
"There was an error. Setting significance to 0."
return 0
else:
success = create_datacard(categories,
args.sig_input_path,
args.data_input_path,
args.data_tree,
args.output_path,
"datacard_" + label,
"workspace_" + label,
nuis=args.nuis,
res_unc_val=args.res_unc_val,
scale_unc_val=args.scale_unc_val,
smodel=args.smodel,
method=args.method,
lumi=args.lumi)
if not success:
return 0
os.system(
'combine -M Significance --expectSignal=1 -t -1 -n %s -d datacard_%s.txt --LoadLibrary /home/dkondra/Hmumu_analysis/Hmumu_ML/cut_optimization/dynamic_categorization/lib/RooDCBShape_cxx.so'
% (label, label))
os.system('rm datacard_%s.txt' % label)
os.system('rm workspace_%s.root' % label)
significance = 0
tree = ROOT.TChain("limit")
tree.Add("higgsCombine%s.Significance.mH120.root" % label)
for iev, event in enumerate(tree):
significance = event.limit
os.system('rm higgsCombine%s.Significance.mH120.root' % label)
log("Expected significance %f calculated for bins %s" %
(significance, ', '.join([str(b) for b in bins])))
bins_str = ""
for ii in range(len(bins) - 1):
bins_str = bins_str + "%f_" % bins[ii]
bins_str = bins_str + "%f" % bins[len(bins) - 1]
memorized[bins_str] = significance
log("Memorizing result as " + bins_str)
log(" ")
log("############################################")
log(" ")
return significance
"(%s>%f)&(%s<%f)"%(score, mva_cuts[2], score, mva_cuts[3]), #cat8
"(%s>%f)&(%s<%f)"%(score, mva_cuts[2], score, mva_cuts[3]), #cat9
"(%s>%f)&(%s<%f)"%(score, mva_cuts[3], score, mva_cuts[4]), #cat10
"(%s>%f)&(%s<%f)"%(score, mva_cuts[3], score, mva_cuts[4]), #cat11
"(%s>%f)&(%s<%f)"%(score, mva_cuts[3], score, mva_cuts[4]), #cat12
"(%s>%f)&(%s<%f)"%(score, mva_cuts[4], score, mva_cuts[5]), #cat13
"(%s>%f)"%(score, mva_cuts[5]), #cat14
]
# categories_inclusive = {"cat0": "1"}
# create_datacard(categories_inclusive, args.sig_input_path, args.data_input_path, args.data_tree, args.output_path, "datacard_dnn_option%s_inclusive"%args.option, "workspace_dnn_option%s_inclusive"%args.option, nuis=args.nuis, res_unc_val=args.res_unc_val, scale_unc_val=args.scale_unc_val, smodel=args.smodel, method=args.method, lumi=lumi)
# categories_full = {}
# for i in range(len(eta_cut_full)):
# categories_full["cat%i"%i] = "(%s)&(%s)"%(eta_cut_full[i], mva_cut_full[i])
# create_datacard(categories_full, args.sig_input_path, args.data_input_path, args.data_tree, args.output_path, "datacard_dnn_option%s_full"%args.option, "workspace_dnn_option%s_full"%args.option, nuis=args.nuis, res_unc_val=args.res_unc_val, scale_unc_val=args.scale_unc_val, smodel=args.smodel, method=args.method, lumi=lumi)
categories_mva = {}
for i in range(len(mva_cut)):
categories_mva["cat%i"%i] = "(%s)"%(mva_cut[i]) # only mva categorization
create_datacard(categories_mva, args.sig_input_path, args.data_input_path, args.data_tree, args.output_path, "datacard_dnn_option%s_mva"%args.option, "workspace_dnn_option%s_mva"%args.option, nuis=args.nuis, res_unc_val=args.res_unc_val, scale_unc_val=args.scale_unc_val, smodel=args.smodel, method=args.method, lumi=lumi)
# categories_eta = {}
# for i in range(len(eta_cut)):
# categories_eta["cat%i"%i] = "(%s)"%(eta_cut[i]) # only eta categorization
# create_datacard(categories_eta, args.sig_input_path, args.data_input_path, args.data_tree, args.output_path, "datacard_dnn_option%s_eta"%args.option, "workspace_dnn_option%s_eta"%args.option, nuis=args.nuis, res_unc_val=args.res_unc_val, scale_unc_val=args.scale_unc_val, smodel=args.smodel, method=args.method, lumi=lumi)
parser.add_argument('--nSteps', action='store', dest='nSteps', help='nSteps', type=int)
parser.add_argument('--nIter', action='store', dest='nIter', help='nIter', type=int)
parser.add_argument('--lumi', action='store', dest='lumi', help='lumi', type=float)
parser.add_argument('--penalty', action='store', dest='penalty', help='penalty', type=float)
args = parser.parse_args()
eta_categories = {
"eta0": "(max_abs_eta_mu>0)&(max_abs_eta_mu<0.9)",
"eta1": "(max_abs_eta_mu>0.9)&(max_abs_eta_mu<1.9)",
"eta2": "(max_abs_eta_mu>1.9)&(max_abs_eta_mu<2.4)"
}
if args.option is "0": # inclusive
create_datacard({"cat0": "1"}, args.sig_input_path, args.data_input_path, args.data_tree, args.output_path, "datacard_inclusive", "workspace_inclusive", nuis=args.nuis, res_unc_val=args.res_unc_val, scale_unc_val=args.scale_unc_val, smodel=args.smodel, method=args.method, lumi=args.lumi)
create_datacard(eta_categories, args.sig_input_path, args.data_input_path, args.data_tree, args.output_path, "datacard_inclusive_eta", "workspace_inclusive_eta", nuis=args.nuis, res_unc_val=args.res_unc_val, scale_unc_val=args.scale_unc_val, smodel=args.smodel, method=args.method, lumi=args.lumi)
sys.exit()
step = (args.max_var - args.min_var)/float(args.nSteps)
def bins_to_illustration(min, max, bins):
result = ""
for iii in range(min, max):
if (iii in bins):
result = result+"| "
result = result+"%i "%iii
result = result+"| "
return result
def get_significance(label, bins):
dest='nuis',
help='Nuisances')
parser.add_argument('--nuis_val',
action='store',
dest='res_unc_val',
help='Resolution uncertainty')
parser.add_argument('--scale_unc_val',
action='store',
dest='scale_unc_val',
help='Scale uncertainty')
parser.add_argument('--smodel',
action='store',
dest='smodel',
help='Signal model')
args = parser.parse_args()
for i in range(23):
bins = [0, (i + 1) / 10.0, 2.4]
create_datacard(bins,
args.sig_input_path,
args.sig_tree,
args.data_input_path,
args.data_tree,
args.output_path,
"datacard_2cat_%i" % (i + 1),
"workspace_2cat_%i" % (i + 1),
args.lumi,
nuis=args.nuis,
res_unc_val=args.res_unc_val,
scale_unc_val=args.scale_unc_val,
smodel=args.smodel)
args = parser.parse_args()
second_cut_options = {
# "1p8": 1.8,
"1p9": 1.9,
"2p0": 2.0,
}
scan_options = ["Bscan", "Oscan", "Escan"]
for key, value in second_cut_options.iteritems():
for scan in scan_options:
if "O" in scan:
for i in range(int((value - 1) * 10)):
bins = [0, 0.9, (i + 10) / 10.0, value, 2.4]
create_datacard(bins, args.input_path,
args.output_path + "_%s_%s/" % (key, scan),
"datacard_0p9_%i_%s" % ((i + 10), key),
"workspace_0p9_%i_%s" % ((i + 10), key))
if "E" in scan:
for i in range(23 - int((value) * 10)):
bins = [0, 0.9, value, i / 10.0 + value + 0.1, 2.4]
create_datacard(
bins, args.input_path,
args.output_path + "_%s_%s/" % (key, scan),
"datacard_0p9_%i_%s" % ((i + 1 + value * 10), key),
"workspace_0p9_%i_%s" % ((i + 1 + value * 10), key))
if "B" in scan:
for i in range(8):
bins = [0, (i + 1) / 10.0, 0.9, value, 2.4]
create_datacard(bins, args.input_path,
args.output_path + "_%s_%s/" % (key, scan),
"datacard_0p9_%i_%s" % ((i + 1), key),