class observableContainer:
# -------------------------------------
def __init__(self,
signame,
bkgname,
variables,
cuts,
label,
treeName,
weightloc='weights',
MVAMethod="BDTG"):
self._f_sig = ROOT.TFile(signame)
self._f_bkg = ROOT.TFile(bkgname)
self._t_sig = self._f_sig.Get(treeName)
self._t_bkg = self._f_bkg.Get(treeName)
self._discVariables = variables
#,"MHT","nJets_30"];
#tmva cut
self._cutstring = "("
cutctr = 0
for cut in cuts:
self._cutstring += "(" + cut[0] + " > " + str(
cut[1]) + ") && (" + cut[0] + " < " + str(cut[2]) + ")"
if cutctr < len(cuts) - 1: self._cutstring += "&&"
cutctr += 1
self._cutstring += ")"
print "cutstring = ", self._cutstring
# self._spectatorVariables = ["lheWeight"];
self._spectatorVariables = []
self._trees = [self._t_sig, self._t_bkg]
self._bdt = TMVAhelper("MVA_" + label, self._discVariables,
self._trees, self._spectatorVariables,
weightloc)
print ";;weightloc = ", weightloc
# -------------------------------------
def doTraining(self):
self._bdt.train(self._cutstring)
def readMVA(self, method="BDTG"):
# read in weight files (one can train and loop at different times)
self._bdt.read(method)
def evaluateMVA(self, val, method="BDTG"):
return self._bdt.evaluate(val, method)
class observableContainer:
# -------------------------------------
def __init__(self,signame,bkgname,variables,cuts,label,treeName,weightloc='weights',MVAMethod="BDTG"):
self._f_sig = ROOT.TFile(signame);
self._f_bkg = ROOT.TFile(bkgname);
self._t_sig = self._f_sig.Get(treeName);
self._t_bkg = self._f_bkg.Get(treeName);
self._discVariables = variables;#,"MHT","nJets_30"];
#tmva cut
self._cutstring = "(";
cutctr = 0;
for cut in cuts:
self._cutstring += "(" + cut[0] + " > " + str(cut[1]) + ") && (" + cut[0] + " < " + str(cut[2]) + ")"
if cutctr < len(cuts) - 1: self._cutstring += "&&";
cutctr+=1;
self._cutstring += ")";
print "cutstring = ", self._cutstring;
# self._spectatorVariables = ["lheWeight"];
self._spectatorVariables = [];
self._trees = [self._t_sig,self._t_bkg];
self._bdt = TMVAhelper("MVA_"+label,self._discVariables,self._trees,self._spectatorVariables,weightloc);
print ";;weightloc = ",weightloc
# -------------------------------------
def doTraining(self):
self._bdt.train(self._cutstring);
def readMVA(self,method="BDTG"):
# read in weight files (one can train and loop at different times)
self._bdt.read(method);
def evaluateMVA(self,val,method="BDTG"):
return self._bdt.evaluate(val,method);
obsctr = 0;
for j in range(len(obs2Corr)):
for k in range(j+1,len(obs2Corr)):
h_2D[a][obsctr].Fill( getattr(t,obs2Corr[j])[0], getattr(t,obs2Corr[k])[0] );
tmpList = [];
for l in range(len(h_bdts_vars[obsctr])):
tmpList.append( getattr(t,h_bdts_vars[obsctr][l])[0] );
bdtOutput = bdtClasses[obsctr].evaluate( tmpList );
h_bdts[a][obsctr].Fill(bdtOutput);
obsctr += 1;
tmpList = [];
for j in range(len(obs2Corr)):
tmpList.append( getattr(t,obs2Corr[j])[0] );
h_bdtall[a].Fill( tmva_all.evaluate( tmpList ) );
## =====================================================================================
del f;
del t;
#######-----------------------------------------------
####### plot
#######-----------------------------------------------
## =====================================================================================
## 1D stuff
makeCanvas(h_pt1, types, "jpt1", True);
makeCanvas(h_pt2, types, "jpt2", True);
makeCanvas(h_eta1, types, "jeta1", True);
if options.tmvaname == "Wq" or options.tmvaname == "Wg":
for j in range(len(obs2Corr)):
if not (obs2Corr[j] in varFor3Vars):
tmpList = [];
for l in range(len(h_bdts_vars[obsctr])):
tmpList.append( getattr(t,h_bdts_vars[obsctr][l])[0] );
#print "obsctr = ", obsctr,", total length = ", len(bdtClasses)
bdtOutput = bdtClasses[obsctr].evaluate( tmpList, tmvamethod );
h_bdts[a][obsctr].Fill(bdtOutput);
obsctr += 1;
tmpList = [];
for j in range(len(obs2Corr_all)):
tmpList.append( getattr(t,obs2Corr_all[j])[0] );
h_bdtall[a].Fill( tmva_all.evaluate( tmpList, tmvamethod ) );
aOvars[a][len(obs2Corr)][0] = tmva_all.evaluate( tmpList, tmvamethod );
## =====================================================================================
otrees[a].Fill();
del f;
del t;
# OFILE.cd();
# for a in range(len(otrees)): otrees[a].Write();
# OFILE.Close();
#######-----------------------------------------------
####### plot
#######-----------------------------------------------
