double getChisq(RooAbsData &dat, RooAbsPdf &pdf, RooRealVar &var, bool prt=false) {
// Find total number of events
double nEvt;
double nTot=0.0;
for(int j=0; j<dat.numEntries(); j++) {
dat.get(j);
nEvt=dat.weight();
nTot+=nEvt;
}
// Find chi-squared equivalent 2NLL
//RooRealVar *var=(RooRealVar*)(pdf.getParameters(*dat)->find("CMS_hgg_mass"));
double totNLL=0.0;
double prbSum=0.0;
for(int j=0; j<dat.numEntries(); j++) {
double m=dat.get(j)->getRealValue(var.GetName());
if ( m < var.getMin() || m > var.getMax()) continue;
// Find probability density and hence probability
var.setVal(m);
double prb = var.getBinWidth(0)*pdf.getVal(var);
prbSum+=prb;
dat.get(j);
nEvt=dat.weight();
double mubin=nTot*prb;
double contrib(0.);
if (nEvt < 1) contrib = mubin;
else contrib=mubin-nEvt+nEvt*log(nEvt/mubin);
totNLL+=contrib;
if(prt) cout << "Bin " << j << " prob = " << prb << " nEvt = " << nEvt << ", mu = " << mubin << " contribution " << contrib << endl;
}
totNLL*=2.0;
if(prt) cout << pdf.GetName() << " nTot = " << nTot << " 2NLL constant = " << totNLL << endl;
return totNLL;
}
void setup(ModelConfig* mcInWs) {
RooAbsPdf* combPdf = mcInWs->GetPdf();
RooArgSet mc_obs = *mcInWs->GetObservables();
RooArgSet mc_globs = *mcInWs->GetGlobalObservables();
RooArgSet mc_nuis = *mcInWs->GetNuisanceParameters();
// pair the nuisance parameter to the global observable
RooArgSet mc_nuis_tmp = mc_nuis;
RooArgList nui_list;
RooArgList glob_list;
RooArgSet constraint_set_tmp(*combPdf->getAllConstraints(mc_obs, mc_nuis_tmp, false));
RooArgSet constraint_set;
int counter_tmp = 0;
unfoldConstraints(constraint_set_tmp, constraint_set, mc_obs, mc_nuis_tmp, counter_tmp);
TIterator* cIter = constraint_set.createIterator();
RooAbsArg* arg;
while ((arg = (RooAbsArg*)cIter->Next())) {
RooAbsPdf* pdf = (RooAbsPdf*)arg;
if (!pdf) continue;
// pdf->Print();
TIterator* nIter = mc_nuis.createIterator();
RooRealVar* thisNui = NULL;
RooAbsArg* nui_arg;
while ((nui_arg = (RooAbsArg*)nIter->Next())) {
if (pdf->dependsOn(*nui_arg)) {
thisNui = (RooRealVar*)nui_arg;
break;
}
}
delete nIter;
// need this incase the observable isn't fundamental.
// in this case, see which variable is dependent on the nuisance parameter and use that.
RooArgSet* components = pdf->getComponents();
// components->Print();
components->remove(*pdf);
if (components->getSize()) {
TIterator* itr1 = components->createIterator();
RooAbsArg* arg1;
while ((arg1 = (RooAbsArg*)itr1->Next())) {
TIterator* itr2 = components->createIterator();
RooAbsArg* arg2;
while ((arg2 = (RooAbsArg*)itr2->Next())) {
if (arg1 == arg2) continue;
if (arg2->dependsOn(*arg1)) {
components->remove(*arg1);
}
}
delete itr2;
}
delete itr1;
}
if (components->getSize() > 1) {
cout << "ERROR::Couldn't isolate proper nuisance parameter" << endl;
return;
}
else if (components->getSize() == 1) {
thisNui = (RooRealVar*)components->first();
}
TIterator* gIter = mc_globs.createIterator();
RooRealVar* thisGlob = NULL;
RooAbsArg* glob_arg;
while ((glob_arg = (RooAbsArg*)gIter->Next())) {
if (pdf->dependsOn(*glob_arg)) {
thisGlob = (RooRealVar*)glob_arg;
break;
}
}
delete gIter;
if (!thisNui || !thisGlob) {
cout << "WARNING::Couldn't find nui or glob for constraint: " << pdf->GetName() << endl;
//return;
continue;
}
// cout << "Pairing nui: " << thisNui->GetName() << ", with glob: " << thisGlob->GetName() << ", from constraint: " << pdf->GetName() << endl;
nui_list.add(*thisNui);
glob_list.add(*thisGlob);
if (string(pdf->ClassName()) == "RooPoisson") {
double minVal = max(0.0, thisGlob->getVal() - 8*sqrt(thisGlob->getVal()));
double maxVal = max(10.0, thisGlob->getVal() + 8*sqrt(thisGlob->getVal()));
thisNui->setRange(minVal, maxVal);
thisGlob->setRange(minVal, maxVal);
}
else if (string(pdf->ClassName()) == "RooGaussian") {
thisNui->setRange(-7, 7);
thisGlob->setRange(-10, 10);
}
// thisNui->Print();
// thisGlob->Print();
}
delete cIter;
}
void splitws(string inFolderName, double mass, string channel) {
cout << "Splitting workspace in " << channel << endl;
int flatInterpCode = 4;
int shapeInterpCode = 4;
bool do2011 = 0;
if (inFolderName.find("2011") != string::npos) do2011 = 1;
bool conditionalAsimov = 0;
bool doData = 1;
//if (inFolderName.find("_blind_") != string::npos) {
//conditionalAsimov = 0;
//}
//else {
//conditionalAsimov = 1;
//}
set<string> channelNames;
if (channel == "01j") {
channelNames.insert("em_signalLike1_0j"+string(!do2011?"_2012":""));
channelNames.insert("em_signalLike2_0j"+string(!do2011?"_2012":""));
channelNames.insert("me_signalLike1_0j"+string(!do2011?"_2012":""));
channelNames.insert("me_signalLike2_0j"+string(!do2011?"_2012":""));
channelNames.insert("SF_AfrecSR_0j"+string(!do2011?"_2012":""));
channelNames.insert("SF_ASR_0j"+string(!do2011?"_2012":""));
channelNames.insert("OF_AfrecSR_0j"+string(!do2011?"_2012":""));
channelNames.insert("OF_ASR_0j"+string(!do2011?"_2012":""));
channelNames.insert("SF_CfrecZpeak_0j"+string(!do2011?"_2012":""));
channelNames.insert("SF_CZpeak_0j"+string(!do2011?"_2012":""));
channelNames.insert("OF_CfrecZpeak_0j"+string(!do2011?"_2012":""));
channelNames.insert("OF_CZpeak_0j"+string(!do2011?"_2012":""));
channelNames.insert("OF_mainControl_0j"+string(!do2011?"_2012":""));
channelNames.insert("em_signalLike1_1j"+string(!do2011?"_2012":""));
channelNames.insert("em_signalLike2_1j"+string(!do2011?"_2012":""));
channelNames.insert("me_signalLike1_1j"+string(!do2011?"_2012":""));
channelNames.insert("me_signalLike2_1j"+string(!do2011?"_2012":""));
channelNames.insert("SF_AfrecSR_1j"+string(!do2011?"_2012":""));
channelNames.insert("SF_ASR_1j"+string(!do2011?"_2012":""));
channelNames.insert("OF_AfrecSR_1j"+string(!do2011?"_2012":""));
channelNames.insert("OF_ASR_1j"+string(!do2011?"_2012":""));
channelNames.insert("SF_CfrecZpeak_1j"+string(!do2011?"_2012":""));
channelNames.insert("SF_CZpeak_1j"+string(!do2011?"_2012":""));
channelNames.insert("OF_CfrecZpeak_1j"+string(!do2011?"_2012":""));
channelNames.insert("OF_CZpeak_1j"+string(!do2011?"_2012":""));
channelNames.insert("OF_mainControl_1j"+string(!do2011?"_2012":""));
channelNames.insert("OF_topbox_1j"+string(!do2011?"_2012":""));
}
else if (channel == "0j") {
channelNames.insert("em_signalLike1_0j"+string(!do2011?"_2012":""));
channelNames.insert("em_signalLike2_0j"+string(!do2011?"_2012":""));
channelNames.insert("me_signalLike1_0j"+string(!do2011?"_2012":""));
channelNames.insert("me_signalLike2_0j"+string(!do2011?"_2012":""));
channelNames.insert("SF_AfrecSR_0j"+string(!do2011?"_2012":""));
channelNames.insert("SF_ASR_0j"+string(!do2011?"_2012":""));
channelNames.insert("OF_AfrecSR_0j"+string(!do2011?"_2012":""));
channelNames.insert("OF_ASR_0j"+string(!do2011?"_2012":""));
channelNames.insert("SF_CfrecZpeak_0j"+string(!do2011?"_2012":""));
channelNames.insert("SF_CZpeak_0j"+string(!do2011?"_2012":""));
channelNames.insert("OF_CfrecZpeak_0j"+string(!do2011?"_2012":""));
channelNames.insert("OF_CZpeak_0j"+string(!do2011?"_2012":""));
channelNames.insert("OF_mainControl_0j"+string(!do2011?"_2012":""));
}
else if (channel == "1j") {
channelNames.insert("em_signalLike1_1j"+string(!do2011?"_2012":""));
channelNames.insert("em_signalLike2_1j"+string(!do2011?"_2012":""));
channelNames.insert("me_signalLike1_1j"+string(!do2011?"_2012":""));
channelNames.insert("me_signalLike2_1j"+string(!do2011?"_2012":""));
channelNames.insert("SF_AfrecSR_1j"+string(!do2011?"_2012":""));
channelNames.insert("SF_ASR_1j"+string(!do2011?"_2012":""));
channelNames.insert("OF_AfrecSR_1j"+string(!do2011?"_2012":""));
channelNames.insert("OF_ASR_1j"+string(!do2011?"_2012":""));
channelNames.insert("SF_CfrecZpeak_1j"+string(!do2011?"_2012":""));
channelNames.insert("SF_CZpeak_1j"+string(!do2011?"_2012":""));
channelNames.insert("OF_CfrecZpeak_1j"+string(!do2011?"_2012":""));
channelNames.insert("OF_CZpeak_1j"+string(!do2011?"_2012":""));
channelNames.insert("OF_mainControl_1j"+string(!do2011?"_2012":""));
channelNames.insert("OF_topbox_1j"+string(!do2011?"_2012":""));
}
else if (channel == "OF01j") {
channelNames.insert("em_signalLike1_0j"+string(!do2011?"_2012":""));
channelNames.insert("em_signalLike2_0j"+string(!do2011?"_2012":""));
channelNames.insert("me_signalLike1_0j"+string(!do2011?"_2012":""));
channelNames.insert("me_signalLike2_0j"+string(!do2011?"_2012":""));
channelNames.insert("OF_mainControl_0j"+string(!do2011?"_2012":""));
channelNames.insert("OF_sscr_0j"+string(!do2011?"_2012":""));
channelNames.insert("em_signalLike1_1j"+string(!do2011?"_2012":""));
channelNames.insert("em_signalLike2_1j"+string(!do2011?"_2012":""));
channelNames.insert("me_signalLike1_1j"+string(!do2011?"_2012":""));
channelNames.insert("me_signalLike2_1j"+string(!do2011?"_2012":""));
channelNames.insert("OF_mainControl_1j"+string(!do2011?"_2012":""));
channelNames.insert("OF_topbox_1j"+string(!do2011?"_2012":""));
channelNames.insert("OF_sscr_1j"+string(!do2011?"_2012":""));
}
else if (channel == "OF0j") {
channelNames.insert("em_signalLike1_0j"+string(!do2011?"_2012":""));
channelNames.insert("em_signalLike2_0j"+string(!do2011?"_2012":""));
channelNames.insert("me_signalLike1_0j"+string(!do2011?"_2012":""));
channelNames.insert("me_signalLike2_0j"+string(!do2011?"_2012":""));
channelNames.insert("OF_mainControl_0j"+string(!do2011?"_2012":""));
channelNames.insert("OF_sscr_0j"+string(!do2011?"_2012":""));
}
else if (channel == "OF1j") {
channelNames.insert("em_signalLike1_1j"+string(!do2011?"_2012":""));
channelNames.insert("em_signalLike2_1j"+string(!do2011?"_2012":""));
channelNames.insert("me_signalLike1_1j"+string(!do2011?"_2012":""));
channelNames.insert("me_signalLike2_1j"+string(!do2011?"_2012":""));
channelNames.insert("OF_mainControl_1j"+string(!do2011?"_2012":""));
channelNames.insert("OF_topbox_1j"+string(!do2011?"_2012":""));
channelNames.insert("OF_sscr_1j"+string(!do2011?"_2012":""));
}
else if (channel == "SF01j") {
channelNames.insert("SF_AfrecSR_0j"+string(!do2011?"_2012":""));
channelNames.insert("SF_ASR_0j"+string(!do2011?"_2012":""));
channelNames.insert("OF_AfrecSR_0j"+string(!do2011?"_2012":""));
channelNames.insert("OF_ASR_0j"+string(!do2011?"_2012":""));
channelNames.insert("SF_CfrecZpeak_0j"+string(!do2011?"_2012":""));
channelNames.insert("SF_CZpeak_0j"+string(!do2011?"_2012":""));
channelNames.insert("OF_CfrecZpeak_0j"+string(!do2011?"_2012":""));
channelNames.insert("OF_CZpeak_0j"+string(!do2011?"_2012":""));
channelNames.insert("OF_mainControl_0j"+string(!do2011?"_2012":""));
channelNames.insert("SF_AfrecSR_1j"+string(!do2011?"_2012":""));
channelNames.insert("SF_ASR_1j"+string(!do2011?"_2012":""));
channelNames.insert("OF_AfrecSR_1j"+string(!do2011?"_2012":""));
channelNames.insert("OF_ASR_1j"+string(!do2011?"_2012":""));
channelNames.insert("SF_CfrecZpeak_1j"+string(!do2011?"_2012":""));
channelNames.insert("SF_CZpeak_1j"+string(!do2011?"_2012":""));
channelNames.insert("OF_CfrecZpeak_1j"+string(!do2011?"_2012":""));
channelNames.insert("OF_CZpeak_1j"+string(!do2011?"_2012":""));
channelNames.insert("OF_mainControl_1j"+string(!do2011?"_2012":""));
channelNames.insert("OF_topbox_1j"+string(!do2011?"_2012":""));
}
else if (channel == "SF0j") {
channelNames.insert("SF_AfrecSR_0j"+string(!do2011?"_2012":""));
channelNames.insert("SF_ASR_0j"+string(!do2011?"_2012":""));
channelNames.insert("OF_AfrecSR_0j"+string(!do2011?"_2012":""));
channelNames.insert("OF_ASR_0j"+string(!do2011?"_2012":""));
channelNames.insert("SF_CfrecZpeak_0j"+string(!do2011?"_2012":""));
channelNames.insert("SF_CZpeak_0j"+string(!do2011?"_2012":""));
channelNames.insert("OF_CfrecZpeak_0j"+string(!do2011?"_2012":""));
channelNames.insert("OF_CZpeak_0j"+string(!do2011?"_2012":""));
channelNames.insert("OF_mainControl_0j"+string(!do2011?"_2012":""));
}
else if (channel == "SF1j") {
channelNames.insert("SF_AfrecSR_1j"+string(!do2011?"_2012":""));
channelNames.insert("SF_ASR_1j"+string(!do2011?"_2012":""));
channelNames.insert("OF_AfrecSR_1j"+string(!do2011?"_2012":""));
channelNames.insert("OF_ASR_1j"+string(!do2011?"_2012":""));
channelNames.insert("SF_CfrecZpeak_1j"+string(!do2011?"_2012":""));
channelNames.insert("SF_CZpeak_1j"+string(!do2011?"_2012":""));
channelNames.insert("OF_CfrecZpeak_1j"+string(!do2011?"_2012":""));
channelNames.insert("OF_CZpeak_1j"+string(!do2011?"_2012":""));
channelNames.insert("OF_mainControl_1j"+string(!do2011?"_2012":""));
channelNames.insert("OF_topbox_1j"+string(!do2011?"_2012":""));
}
else if (channel == "2j") {
channelNames.insert("em_signalLike1_2j"+string(!do2011?"_2012":""));
channelNames.insert("ee_signalLike1_2j"+string(!do2011?"_2012":""));
channelNames.insert("SF_topbox_2j"+string(!do2011?"_2012":""));
}
else if (channel == "OF2j") {
channelNames.insert("em_signalLike1_2j"+string(!do2011?"_2012":""));
channelNames.insert("SF_topbox_2j"+string(!do2011?"_2012":""));
}
else if (channel == "SF2j") {
channelNames.insert("ee_signalLike1_2j"+string(!do2011?"_2012":""));
channelNames.insert("SF_topbox_2j"+string(!do2011?"_2012":""));
}
else if (channel == "OF") {
channelNames.insert("em_signalLike1_0j"+string(!do2011?"_2012":""));
channelNames.insert("em_signalLike2_0j"+string(!do2011?"_2012":""));
channelNames.insert("me_signalLike1_0j"+string(!do2011?"_2012":""));
channelNames.insert("me_signalLike2_0j"+string(!do2011?"_2012":""));
channelNames.insert("OF_mainControl_0j"+string(!do2011?"_2012":""));
channelNames.insert("em_signalLike1_1j"+string(!do2011?"_2012":""));
channelNames.insert("em_signalLike2_1j"+string(!do2011?"_2012":""));
channelNames.insert("me_signalLike1_1j"+string(!do2011?"_2012":""));
channelNames.insert("me_signalLike2_1j"+string(!do2011?"_2012":""));
channelNames.insert("OF_mainControl_1j"+string(!do2011?"_2012":""));
channelNames.insert("OF_topbox_1j"+string(!do2011?"_2012":""));
channelNames.insert("em_signalLike1_2j"+string(!do2011?"_2012":""));
channelNames.insert("SF_topbox_2j"+string(!do2011?"_2012":""));
}
else if (channel == "SF") {
channelNames.insert("SF_AfrecSR_0j"+string(!do2011?"_2012":""));
channelNames.insert("SF_ASR_0j"+string(!do2011?"_2012":""));
channelNames.insert("OF_AfrecSR_0j"+string(!do2011?"_2012":""));
channelNames.insert("OF_ASR_0j"+string(!do2011?"_2012":""));
channelNames.insert("SF_CfrecZpeak_0j"+string(!do2011?"_2012":""));
channelNames.insert("SF_CZpeak_0j"+string(!do2011?"_2012":""));
channelNames.insert("OF_CfrecZpeak_0j"+string(!do2011?"_2012":""));
channelNames.insert("OF_CZpeak_0j"+string(!do2011?"_2012":""));
channelNames.insert("OF_mainControl_0j"+string(!do2011?"_2012":""));
channelNames.insert("SF_AfrecSR_1j"+string(!do2011?"_2012":""));
channelNames.insert("SF_ASR_1j"+string(!do2011?"_2012":""));
channelNames.insert("OF_AfrecSR_1j"+string(!do2011?"_2012":""));
channelNames.insert("OF_ASR_1j"+string(!do2011?"_2012":""));
channelNames.insert("SF_CfrecZpeak_1j"+string(!do2011?"_2012":""));
channelNames.insert("SF_CZpeak_1j"+string(!do2011?"_2012":""));
channelNames.insert("OF_CfrecZpeak_1j"+string(!do2011?"_2012":""));
channelNames.insert("OF_CZpeak_1j"+string(!do2011?"_2012":""));
channelNames.insert("OF_mainControl_1j"+string(!do2011?"_2012":""));
channelNames.insert("OF_topbox_1j"+string(!do2011?"_2012":""));
channelNames.insert("ee_signalLike1_2j"+string(!do2011?"_2012":""));
channelNames.insert("SF_topbox_2j"+string(!do2011?"_2012":""));
}
else {
cout << "Channel " << channel << " not defined. Please check!" << endl;
exit(1);
}
// bool fix = 1;
stringstream inFileName;
inFileName << "workspaces/" << inFolderName << "/" << mass << ".root";
TFile f(inFileName.str().c_str());
RooWorkspace* w = (RooWorkspace*)f.Get("combWS");
if (!w) w = (RooWorkspace*)f.Get("combined");
RooDataSet* data = (RooDataSet*)w->data("combData");
if (!data) data = (RooDataSet*)w->data("obsData");
ModelConfig* mc = (ModelConfig*)w->obj("ModelConfig");
RooRealVar* weightVar = w->var("weightVar");
RooRealVar* mu = (RooRealVar*)mc->GetParametersOfInterest()->first();
if (!mu) mu = w->var("SigXsecOverSM");
const RooArgSet* mc_obs = mc->GetObservables();
const RooArgSet* mc_nuis = mc->GetNuisanceParameters();
const RooArgSet* mc_globs = mc->GetGlobalObservables();
const RooArgSet* mc_poi = mc->GetParametersOfInterest();
RooArgSet nuis = *mc_nuis;
RooArgSet antiNuis = *mc_nuis;
RooArgSet globs = *mc_globs;
RooArgSet antiGlobs = *mc_globs;
RooArgSet allParams;
RooSimultaneous* simPdf = (RooSimultaneous*)mc->GetPdf();
RooCategory* cat = (RooCategory*)&simPdf->indexCat();
RooArgSet nuis_tmp = nuis;
RooArgSet fullConstraints = *simPdf->getAllConstraints(*mc_obs,nuis_tmp,false);
vector<string> foundChannels;
vector<string> skippedChannels;
cout << "Getting constraints" << endl;
map<string, RooDataSet*> data_map;
map<string, RooAbsPdf*> pdf_map;
RooCategory* decCat = new RooCategory("dec_channel","dec_channel");
// int i = 0;
TIterator* catItr = cat->typeIterator();
RooCatType* type;
RooArgSet allConstraints;
while ((type = (RooCatType*)catItr->Next())) {
RooAbsPdf* pdf = simPdf->getPdf(type->GetName());
string typeName(type->GetName());
if (channelNames.size() && channelNames.find(typeName) == channelNames.end()) {
skippedChannels.push_back(typeName);
continue;
}
cout << "On channel " << type->GetName() << endl;
foundChannels.push_back(typeName);
decCat->defineType(type->GetName());
// pdf->getParameters(*data)->Print("v");
RooArgSet nuis_tmp1 = nuis;
RooArgSet nuis_tmp2 = nuis;
RooArgSet* constraints = pdf->getAllConstraints(*mc_obs, nuis_tmp1, true);
constraints->Print();
allConstraints.add(*constraints);
}
catItr->Reset();
while ((type = (RooCatType*)catItr->Next())) {
RooAbsPdf* pdf = simPdf->getPdf(type->GetName());
string typeName(type->GetName());
cout << "Considering type " << typeName << endl;
if (channelNames.size() && channelNames.find(typeName) == channelNames.end()) continue;
cout << "On channel " << type->GetName() << endl;
RooArgSet nuis_tmp1 = nuis;
RooArgSet nuis_tmp2 = nuis;
RooArgSet* constraints = pdf->getAllConstraints(*mc_obs, nuis_tmp1, true);
cout << "Adding pdf to map: " << typeName << " = " << pdf->GetName() << endl;
pdf_map[typeName] = pdf;
RooProdPdf prod("prod","prod",*constraints);
RooArgSet* params = pdf->getParameters(*data);
antiNuis.remove(*params);
antiGlobs.remove(*params);
allParams.add(*params);
// cout << type->GetName() << endl;
}
// return;
RooArgSet decNuis;
TIterator* nuiItr = mc_nuis->createIterator();
TIterator* parItr = allParams.createIterator();
RooAbsArg* nui, *par;
while ((par = (RooAbsArg*)parItr->Next())) {
nuiItr->Reset();
while ((nui = (RooAbsArg*)nuiItr->Next())) {
if (par == nui) decNuis.add(*nui);
}
}
RooArgSet decGlobs;
TIterator* globItr = mc_globs->createIterator();
parItr->Reset();
RooAbsArg* glob;
while ((par = (RooAbsArg*)parItr->Next())) {
globItr->Reset();
while ((glob = (RooAbsArg*)globItr->Next())) {
if (par == glob) decGlobs.add(*glob);
}
}
// antiNuis.Print();
// nuis.Print();
// globs.Print();
// i = 0;
TList* datalist = data->split(*cat, true);
TIterator* dataItr = datalist->MakeIterator();
RooAbsData* ds;
while ((ds = (RooAbsData*)dataItr->Next())) {
string typeName(ds->GetName());
if (channelNames.size() && channelNames.find(typeName) == channelNames.end()) continue;
cout << "Adding dataset to map: " << ds->GetName() << endl;
data_map[string(ds->GetName())] = (RooDataSet*)ds;
cout << ds->GetName() << endl;
}
RooSimultaneous* decPdf = new RooSimultaneous("decPdf","decPdf",pdf_map,*decCat);
RooArgSet decObs = *decPdf->getObservables(data);
// decObs.add(*(RooAbsArg*)weightVar);
decObs.add(*(RooAbsArg*)decCat);
decObs.Print();
nuis.remove(antiNuis);
globs.remove(antiGlobs);
// nuis.Print("v");
RooDataSet* decData = new RooDataSet("obsData","obsData",RooArgSet(decObs,*(RooAbsArg*)weightVar),Index(*decCat),Import(data_map),WeightVar(*weightVar));
decData->Print();
RooArgSet poi(*(RooAbsArg*)mu);
RooWorkspace decWS("combined");
ModelConfig decMC("ModelConfig",&decWS);
decMC.SetPdf(*decPdf);
decMC.SetObservables(decObs);
decMC.SetNuisanceParameters(decNuis);
decMC.SetGlobalObservables(decGlobs);
decMC.SetParametersOfInterest(poi);
decMC.Print();
decWS.import(*decPdf);
decWS.import(decMC);
decWS.import(*decData);
// decWS.Print();
ModelConfig* mcInWs = (ModelConfig*)decWS.obj("ModelConfig");
decPdf = (RooSimultaneous*)mcInWs->GetPdf();
// setup(mcInWs);
// return;
mcInWs->GetNuisanceParameters()->Print("v");
mcInWs->GetGlobalObservables()->Print("v");
// decData->tree()->Scan("*");
// Make asimov data
RooArgSet funcs = decWS.allFunctions();
TIterator* it = funcs.createIterator();
TObject* tempObj = 0;
while((tempObj=it->Next()))
{
FlexibleInterpVar* flex = dynamic_cast<FlexibleInterpVar*>(tempObj);
if(flex) {
flex->setAllInterpCodes(flatInterpCode);
}
PiecewiseInterpolation* piece = dynamic_cast<PiecewiseInterpolation*>(tempObj);
if(piece) {
piece->setAllInterpCodes(shapeInterpCode);
}
}
RooDataSet* dataInWs = (RooDataSet*)decWS.data("obsData");
makeAsimovData(mcInWs, conditionalAsimov && doData, &decWS, mcInWs->GetPdf(), dataInWs, 0);
makeAsimovData(mcInWs, conditionalAsimov && doData, &decWS, mcInWs->GetPdf(), dataInWs, 1);
makeAsimovData(mcInWs, conditionalAsimov && doData, &decWS, mcInWs->GetPdf(), dataInWs, 2);
system(("mkdir -vp workspaces/"+inFolderName+"_"+channel).c_str());
stringstream outFileName;
outFileName << "workspaces/" << inFolderName << "_" << channel << "/" << mass << ".root";
cout << "Exporting" << endl;
decWS.writeToFile(outFileName.str().c_str());
cout << "\nIncluded the following channels: " << endl;
for (int i=0;i<(int)foundChannels.size();i++) {
cout << "-> " << foundChannels[i] << endl;
}
cout << "\nSkipping the following channels: " << endl;
for (int i=0;i<(int)skippedChannels.size();i++) {
cout << "-> " << skippedChannels[i] << endl;
}
cout << "Done" << endl;
// decPdf->fitTo(*decData, Hesse(0), Minos(0), PrintLevel(0));
}
// internal routine to run the inverter
HypoTestInverterResult *
RooStats::HypoTestInvTool::RunInverter(RooWorkspace * w,
const char * modelSBName, const char * modelBName,
const char * dataName, int type, int testStatType,
bool useCLs, int npoints, double poimin, double poimax,
int ntoys,
bool useNumberCounting,
const char * nuisPriorName ){
std::cout << "Running HypoTestInverter on the workspace " << w->GetName() << std::endl;
w->Print();
RooAbsData * data = w->data(dataName);
if (!data) {
Error("StandardHypoTestDemo","Not existing data %s",dataName);
return 0;
}
else
std::cout << "Using data set " << dataName << std::endl;
if (mUseVectorStore) {
RooAbsData::setDefaultStorageType(RooAbsData::Vector);
data->convertToVectorStore() ;
}
// get models from WS
// get the modelConfig out of the file
ModelConfig* bModel = (ModelConfig*) w->obj(modelBName);
ModelConfig* sbModel = (ModelConfig*) w->obj(modelSBName);
if (!sbModel) {
Error("StandardHypoTestDemo","Not existing ModelConfig %s",modelSBName);
return 0;
}
// check the model
if (!sbModel->GetPdf()) {
Error("StandardHypoTestDemo","Model %s has no pdf ",modelSBName);
return 0;
}
if (!sbModel->GetParametersOfInterest()) {
Error("StandardHypoTestDemo","Model %s has no poi ",modelSBName);
return 0;
}
if (!sbModel->GetObservables()) {
Error("StandardHypoTestInvDemo","Model %s has no observables ",modelSBName);
return 0;
}
if (!sbModel->GetSnapshot() ) {
Info("StandardHypoTestInvDemo","Model %s has no snapshot - make one using model poi",modelSBName);
sbModel->SetSnapshot( *sbModel->GetParametersOfInterest() );
}
// case of no systematics
// remove nuisance parameters from model
if (noSystematics) {
const RooArgSet * nuisPar = sbModel->GetNuisanceParameters();
if (nuisPar && nuisPar->getSize() > 0) {
std::cout << "StandardHypoTestInvDemo" << " - Switch off all systematics by setting them constant to their initial values" << std::endl;
RooStats::SetAllConstant(*nuisPar);
}
if (bModel) {
const RooArgSet * bnuisPar = bModel->GetNuisanceParameters();
if (bnuisPar)
RooStats::SetAllConstant(*bnuisPar);
}
}
if (!bModel || bModel == sbModel) {
Info("StandardHypoTestInvDemo","The background model %s does not exist",modelBName);
Info("StandardHypoTestInvDemo","Copy it from ModelConfig %s and set POI to zero",modelSBName);
bModel = (ModelConfig*) sbModel->Clone();
bModel->SetName(TString(modelSBName)+TString("_with_poi_0"));
RooRealVar * var = dynamic_cast<RooRealVar*>(bModel->GetParametersOfInterest()->first());
if (!var) return 0;
double oldval = var->getVal();
var->setVal(0);
bModel->SetSnapshot( RooArgSet(*var) );
var->setVal(oldval);
}
else {
if (!bModel->GetSnapshot() ) {
Info("StandardHypoTestInvDemo","Model %s has no snapshot - make one using model poi and 0 values ",modelBName);
RooRealVar * var = dynamic_cast<RooRealVar*>(bModel->GetParametersOfInterest()->first());
if (var) {
double oldval = var->getVal();
var->setVal(0);
bModel->SetSnapshot( RooArgSet(*var) );
var->setVal(oldval);
}
else {
Error("StandardHypoTestInvDemo","Model %s has no valid poi",modelBName);
return 0;
}
}
}
// check model has global observables when there are nuisance pdf
// for the hybrid case the globobs are not needed
if (type != 1 ) {
bool hasNuisParam = (sbModel->GetNuisanceParameters() && sbModel->GetNuisanceParameters()->getSize() > 0);
bool hasGlobalObs = (sbModel->GetGlobalObservables() && sbModel->GetGlobalObservables()->getSize() > 0);
if (hasNuisParam && !hasGlobalObs ) {
// try to see if model has nuisance parameters first
RooAbsPdf * constrPdf = RooStats::MakeNuisancePdf(*sbModel,"nuisanceConstraintPdf_sbmodel");
if (constrPdf) {
Warning("StandardHypoTestInvDemo","Model %s has nuisance parameters but no global observables associated",sbModel->GetName());
Warning("StandardHypoTestInvDemo","\tThe effect of the nuisance parameters will not be treated correctly ");
}
}
}
// run first a data fit
const RooArgSet * poiSet = sbModel->GetParametersOfInterest();
RooRealVar *poi = (RooRealVar*)poiSet->first();
std::cout << "StandardHypoTestInvDemo : POI initial value: " << poi->GetName() << " = " << poi->getVal() << std::endl;
// fit the data first (need to use constraint )
TStopwatch tw;
bool doFit = initialFit;
if (testStatType == 0 && initialFit == -1) doFit = false; // case of LEP test statistic
if (type == 3 && initialFit == -1) doFit = false; // case of Asymptoticcalculator with nominal Asimov
double poihat = 0;
if (minimizerType.size()==0) minimizerType = ROOT::Math::MinimizerOptions::DefaultMinimizerType();
else
ROOT::Math::MinimizerOptions::SetDefaultMinimizer(minimizerType.c_str());
Info("StandardHypoTestInvDemo","Using %s as minimizer for computing the test statistic",
ROOT::Math::MinimizerOptions::DefaultMinimizerType().c_str() );
if (doFit) {
// do the fit : By doing a fit the POI snapshot (for S+B) is set to the fit value
// and the nuisance parameters nominal values will be set to the fit value.
// This is relevant when using LEP test statistics
Info( "StandardHypoTestInvDemo"," Doing a first fit to the observed data ");
RooArgSet constrainParams;
if (sbModel->GetNuisanceParameters() ) constrainParams.add(*sbModel->GetNuisanceParameters());
RooStats::RemoveConstantParameters(&constrainParams);
tw.Start();
RooFitResult * fitres = sbModel->GetPdf()->fitTo(*data,InitialHesse(false), Hesse(false),
Minimizer(minimizerType.c_str(),"Migrad"), Strategy(0), PrintLevel(mPrintLevel), Constrain(constrainParams), Save(true) );
if (fitres->status() != 0) {
Warning("StandardHypoTestInvDemo","Fit to the model failed - try with strategy 1 and perform first an Hesse computation");
fitres = sbModel->GetPdf()->fitTo(*data,InitialHesse(true), Hesse(false),Minimizer(minimizerType.c_str(),"Migrad"), Strategy(1), PrintLevel(mPrintLevel+1), Constrain(constrainParams), Save(true) );
}
if (fitres->status() != 0)
Warning("StandardHypoTestInvDemo"," Fit still failed - continue anyway.....");
poihat = poi->getVal();
std::cout << "StandardHypoTestInvDemo - Best Fit value : " << poi->GetName() << " = "
<< poihat << " +/- " << poi->getError() << std::endl;
std::cout << "Time for fitting : "; tw.Print();
//save best fit value in the poi snapshot
sbModel->SetSnapshot(*sbModel->GetParametersOfInterest());
std::cout << "StandardHypoTestInvo: snapshot of S+B Model " << sbModel->GetName()
<< " is set to the best fit value" << std::endl;
}
// print a message in case of LEP test statistics because it affects result by doing or not doing a fit
if (testStatType == 0) {
if (!doFit)
Info("StandardHypoTestInvDemo","Using LEP test statistic - an initial fit is not done and the TS will use the nuisances at the model value");
else
Info("StandardHypoTestInvDemo","Using LEP test statistic - an initial fit has been done and the TS will use the nuisances at the best fit value");
}
// build test statistics and hypotest calculators for running the inverter
SimpleLikelihoodRatioTestStat slrts(*sbModel->GetPdf(),*bModel->GetPdf());
// null parameters must includes snapshot of poi plus the nuisance values
RooArgSet nullParams(*sbModel->GetSnapshot());
if (sbModel->GetNuisanceParameters()) nullParams.add(*sbModel->GetNuisanceParameters());
if (sbModel->GetSnapshot()) slrts.SetNullParameters(nullParams);
RooArgSet altParams(*bModel->GetSnapshot());
if (bModel->GetNuisanceParameters()) altParams.add(*bModel->GetNuisanceParameters());
if (bModel->GetSnapshot()) slrts.SetAltParameters(altParams);
// ratio of profile likelihood - need to pass snapshot for the alt
RatioOfProfiledLikelihoodsTestStat
ropl(*sbModel->GetPdf(), *bModel->GetPdf(), bModel->GetSnapshot());
ropl.SetSubtractMLE(false);
if (testStatType == 11) ropl.SetSubtractMLE(true);
ropl.SetPrintLevel(mPrintLevel);
ropl.SetMinimizer(minimizerType.c_str());
ProfileLikelihoodTestStat profll(*sbModel->GetPdf());
if (testStatType == 3) profll.SetOneSided(true);
if (testStatType == 4) profll.SetSigned(true);
profll.SetMinimizer(minimizerType.c_str());
profll.SetPrintLevel(mPrintLevel);
profll.SetReuseNLL(mOptimize);
slrts.SetReuseNLL(mOptimize);
ropl.SetReuseNLL(mOptimize);
if (mOptimize) {
profll.SetStrategy(0);
ropl.SetStrategy(0);
ROOT::Math::MinimizerOptions::SetDefaultStrategy(0);
}
if (mMaxPoi > 0) poi->setMax(mMaxPoi); // increase limit
MaxLikelihoodEstimateTestStat maxll(*sbModel->GetPdf(),*poi);
NumEventsTestStat nevtts;
AsymptoticCalculator::SetPrintLevel(mPrintLevel);
// create the HypoTest calculator class
HypoTestCalculatorGeneric * hc = 0;
if (type == 0) hc = new FrequentistCalculator(*data, *bModel, *sbModel);
else if (type == 1) hc = new HybridCalculator(*data, *bModel, *sbModel);
// else if (type == 2 ) hc = new AsymptoticCalculator(*data, *bModel, *sbModel, false, mAsimovBins);
// else if (type == 3 ) hc = new AsymptoticCalculator(*data, *bModel, *sbModel, true, mAsimovBins); // for using Asimov data generated with nominal values
else if (type == 2 ) hc = new AsymptoticCalculator(*data, *bModel, *sbModel, false );
else if (type == 3 ) hc = new AsymptoticCalculator(*data, *bModel, *sbModel, true ); // for using Asimov data generated with nominal values
else {
Error("StandardHypoTestInvDemo","Invalid - calculator type = %d supported values are only :\n\t\t\t 0 (Frequentist) , 1 (Hybrid) , 2 (Asymptotic) ",type);
return 0;
}
// set the test statistic
TestStatistic * testStat = 0;
if (testStatType == 0) testStat = &slrts;
if (testStatType == 1 || testStatType == 11) testStat = &ropl;
if (testStatType == 2 || testStatType == 3 || testStatType == 4) testStat = &profll;
if (testStatType == 5) testStat = &maxll;
if (testStatType == 6) testStat = &nevtts;
if (testStat == 0) {
Error("StandardHypoTestInvDemo","Invalid - test statistic type = %d supported values are only :\n\t\t\t 0 (SLR) , 1 (Tevatron) , 2 (PLR), 3 (PLR1), 4(MLE)",testStatType);
return 0;
}
ToyMCSampler *toymcs = (ToyMCSampler*)hc->GetTestStatSampler();
if (toymcs && (type == 0 || type == 1) ) {
// look if pdf is number counting or extended
if (sbModel->GetPdf()->canBeExtended() ) {
if (useNumberCounting) Warning("StandardHypoTestInvDemo","Pdf is extended: but number counting flag is set: ignore it ");
}
else {
// for not extended pdf
if (!useNumberCounting ) {
int nEvents = data->numEntries();
Info("StandardHypoTestInvDemo","Pdf is not extended: number of events to generate taken from observed data set is %d",nEvents);
toymcs->SetNEventsPerToy(nEvents);
}
else {
Info("StandardHypoTestInvDemo","using a number counting pdf");
toymcs->SetNEventsPerToy(1);
}
}
toymcs->SetTestStatistic(testStat);
if (data->isWeighted() && !mGenerateBinned) {
Info("StandardHypoTestInvDemo","Data set is weighted, nentries = %d and sum of weights = %8.1f but toy generation is unbinned - it would be faster to set mGenerateBinned to true\n",data->numEntries(), data->sumEntries());
}
toymcs->SetGenerateBinned(mGenerateBinned);
toymcs->SetUseMultiGen(mOptimize);
if (mGenerateBinned && sbModel->GetObservables()->getSize() > 2) {
Warning("StandardHypoTestInvDemo","generate binned is activated but the number of ovservable is %d. Too much memory could be needed for allocating all the bins",sbModel->GetObservables()->getSize() );
}
// set the random seed if needed
if (mRandomSeed >= 0) RooRandom::randomGenerator()->SetSeed(mRandomSeed);
}
// specify if need to re-use same toys
if (reuseAltToys) {
hc->UseSameAltToys();
}
if (type == 1) {
HybridCalculator *hhc = dynamic_cast<HybridCalculator*> (hc);
assert(hhc);
hhc->SetToys(ntoys,ntoys/mNToysRatio); // can use less ntoys for b hypothesis
// remove global observables from ModelConfig (this is probably not needed anymore in 5.32)
bModel->SetGlobalObservables(RooArgSet() );
sbModel->SetGlobalObservables(RooArgSet() );
// check for nuisance prior pdf in case of nuisance parameters
if (bModel->GetNuisanceParameters() || sbModel->GetNuisanceParameters() ) {
// fix for using multigen (does not work in this case)
toymcs->SetUseMultiGen(false);
ToyMCSampler::SetAlwaysUseMultiGen(false);
RooAbsPdf * nuisPdf = 0;
if (nuisPriorName) nuisPdf = w->pdf(nuisPriorName);
// use prior defined first in bModel (then in SbModel)
if (!nuisPdf) {
Info("StandardHypoTestInvDemo","No nuisance pdf given for the HybridCalculator - try to deduce pdf from the model");
if (bModel->GetPdf() && bModel->GetObservables() )
nuisPdf = RooStats::MakeNuisancePdf(*bModel,"nuisancePdf_bmodel");
else
nuisPdf = RooStats::MakeNuisancePdf(*sbModel,"nuisancePdf_sbmodel");
}
if (!nuisPdf ) {
if (bModel->GetPriorPdf()) {
nuisPdf = bModel->GetPriorPdf();
Info("StandardHypoTestInvDemo","No nuisance pdf given - try to use %s that is defined as a prior pdf in the B model",nuisPdf->GetName());
}
else {
Error("StandardHypoTestInvDemo","Cannnot run Hybrid calculator because no prior on the nuisance parameter is specified or can be derived");
return 0;
}
}
assert(nuisPdf);
Info("StandardHypoTestInvDemo","Using as nuisance Pdf ... " );
nuisPdf->Print();
const RooArgSet * nuisParams = (bModel->GetNuisanceParameters() ) ? bModel->GetNuisanceParameters() : sbModel->GetNuisanceParameters();
RooArgSet * np = nuisPdf->getObservables(*nuisParams);
if (np->getSize() == 0) {
Warning("StandardHypoTestInvDemo","Prior nuisance does not depend on nuisance parameters. They will be smeared in their full range");
}
delete np;
hhc->ForcePriorNuisanceAlt(*nuisPdf);
hhc->ForcePriorNuisanceNull(*nuisPdf);
}
}
else if (type == 2 || type == 3) {
if (testStatType == 3) ((AsymptoticCalculator*) hc)->SetOneSided(true);
if (testStatType != 2 && testStatType != 3)
Warning("StandardHypoTestInvDemo","Only the PL test statistic can be used with AsymptoticCalculator - use by default a two-sided PL");
}
else if (type == 0 || type == 1)
((FrequentistCalculator*) hc)->SetToys(ntoys,ntoys/mNToysRatio);
// Get the result
RooMsgService::instance().getStream(1).removeTopic(RooFit::NumIntegration);
HypoTestInverter calc(*hc);
calc.SetConfidenceLevel(0.95);
calc.UseCLs(useCLs);
calc.SetVerbose(true);
// can speed up using proof-lite
if (mUseProof && mNWorkers > 1) {
ProofConfig pc(*w, mNWorkers, "", kFALSE);
toymcs->SetProofConfig(&pc); // enable proof
}
if (npoints > 0) {
if (poimin > poimax) {
// if no min/max given scan between MLE and +4 sigma
poimin = int(poihat);
poimax = int(poihat + 4 * poi->getError());
}
std::cout << "Doing a fixed scan in interval : " << poimin << " , " << poimax << std::endl;
calc.SetFixedScan(npoints,poimin,poimax);
}
else {
//poi->setMax(10*int( (poihat+ 10 *poi->getError() )/10 ) );
std::cout << "Doing an automatic scan in interval : " << poi->getMin() << " , " << poi->getMax() << std::endl;
}
tw.Start();
HypoTestInverterResult * r = calc.GetInterval();
std::cout << "Time to perform limit scan \n";
tw.Print();
if (mRebuild) {
calc.SetCloseProof(1);
tw.Start();
SamplingDistribution * limDist = calc.GetUpperLimitDistribution(true,mNToyToRebuild);
std::cout << "Time to rebuild distributions " << std::endl;
tw.Print();
if (limDist) {
std::cout << "expected up limit " << limDist->InverseCDF(0.5) << " +/- "
<< limDist->InverseCDF(0.16) << " "
<< limDist->InverseCDF(0.84) << "\n";
//update r to a new updated result object containing the rebuilt expected p-values distributions
// (it will not recompute the expected limit)
if (r) delete r; // need to delete previous object since GetInterval will return a cloned copy
r = calc.GetInterval();
}
else
std::cout << "ERROR : failed to re-build distributions " << std::endl;
}
return r;
}
//put very small data entries in a binned dataset to avoid unphysical pdfs, specifically for H->ZZ->4l
RooDataSet* makeData(RooDataSet* orig, RooSimultaneous* simPdf, const RooArgSet* observables, RooRealVar* firstPOI, double mass, double& mu_min)
{
double max_soverb = 0;
mu_min = -10e9;
map<string, RooDataSet*> data_map;
firstPOI->setVal(0);
RooCategory* cat = (RooCategory*)&simPdf->indexCat();
TList* datalist = orig->split(*(RooAbsCategory*)cat, true);
TIterator* dataItr = datalist->MakeIterator();
RooAbsData* ds;
RooRealVar* weightVar = new RooRealVar("weightVar","weightVar",1);
while ((ds = (RooAbsData*)dataItr->Next()))
{
string typeName(ds->GetName());
cat->setLabel(typeName.c_str());
RooAbsPdf* pdf = simPdf->getPdf(typeName.c_str());
cout << "pdf: " << pdf << endl;
RooArgSet* obs = pdf->getObservables(observables);
cout << "obs: " << obs << endl;
RooArgSet obsAndWeight(*obs, *weightVar);
obsAndWeight.add(*cat);
stringstream datasetName;
datasetName << "newData_" << typeName;
RooDataSet* thisData = new RooDataSet(datasetName.str().c_str(),datasetName.str().c_str(), obsAndWeight, WeightVar(*weightVar));
RooRealVar* firstObs = (RooRealVar*)obs->first();
//int ibin = 0;
int nrEntries = ds->numEntries();
for (int ib=0;ib<nrEntries;ib++)
{
const RooArgSet* event = ds->get(ib);
const RooRealVar* thisObs = (RooRealVar*)event->find(firstObs->GetName());
firstObs->setVal(thisObs->getVal());
firstPOI->setVal(0);
double b = pdf->expectedEvents(*firstObs)*pdf->getVal(obs);
firstPOI->setVal(1);
double s = pdf->expectedEvents(*firstObs)*pdf->getVal(obs) - b;
if (s > 0)
{
mu_min = max(mu_min, -b/s);
double soverb = s/b;
if (soverb > max_soverb)
{
max_soverb = soverb;
cout << "Found new max s/b: " << soverb << " in pdf " << pdf->GetName() << " at m = " << thisObs->getVal() << endl;
}
}
if (b == 0 && s != 0)
{
cout << "Expecting non-zero signal and zero bg at m=" << firstObs->getVal() << " in pdf " << pdf->GetName() << endl;
}
if (s+b <= 0)
{
cout << "expecting zero" << endl;
continue;
}
double weight = ds->weight();
if ((typeName.find("ATLAS_H_4mu") != string::npos ||
typeName.find("ATLAS_H_4e") != string::npos ||
typeName.find("ATLAS_H_2mu2e") != string::npos ||
typeName.find("ATLAS_H_2e2mu") != string::npos) && fabs(firstObs->getVal() - mass) < 10 && weight == 0)
{
cout << "adding event: " << firstObs->getVal() << endl;
thisData->add(*event, pow(10., -9.));
}
else
{
//weight = max(pow(10.0, -9), weight);
thisData->add(*event, weight);
}
}
data_map[string(ds->GetName())] = (RooDataSet*)thisData;
}
RooDataSet* newData = new RooDataSet("newData","newData",RooArgSet(*observables, *weightVar),
Index(*cat), Import(data_map), WeightVar(*weightVar));
orig->Print();
newData->Print();
//newData->tree()->Scan("*");
return newData;
}
///
/// Fit to the minimum using an improve method.
/// The idea is to kill known minima. For this, we add a multivariate
/// Gaussian to the chi2 at the position of the minimum. Ideally it should
/// be a parabola shaped function, but the Gaussian is readily available.
/// A true parabola like in the original IMPROVE algorithm doesn't work
/// because apparently it affects the other regions of
/// the chi2 too much. There are two parameters to tune: the width of the
/// Gaussian, which is controlled by the error definition of the first fit,
/// and the height of the Gaussian, which is set to 16 (=4 sigma).
/// Three fits are performed: 1) initial fit, 2) fit with improved FCN,
/// 3) fit of the non-improved FCN using the result from step 2 as the start
/// parameters.
/// So far it is only available for the Prob method, via the probimprove
/// command line flag.
///
RooFitResult* Utils::fitToMinImprove(RooWorkspace *w, TString name)
{
TString parsName = "par_"+name;
TString obsName = "obs_"+name;
TString pdfName = "pdf_"+name;
int printlevel = -1;
RooMsgService::instance().setGlobalKillBelow(ERROR);
// step 1: find a minimum to start with
RooFitResult *r1 = 0;
{
RooFormulaVar ll("ll", "ll", "-2*log(@0)", RooArgSet(*w->pdf(pdfName)));
// RooFitResult* r1 = fitToMin(&ll, printlevel);
RooMinuit m(ll);
m.setPrintLevel(-2);
m.setNoWarn();
m.setErrorLevel(4.0); ///< define 2 sigma errors. This will make the hesse PDF 2 sigma wide!
int status = m.migrad();
r1 = m.save();
// if ( 102<RadToDeg(w->var("g")->getVal())&&RadToDeg(w->var("g")->getVal())<103 )
// {
// cout << "step 1" << endl;
// r1->Print("v");
// gStyle->SetPalette(1);
// float xmin = 0.;
// float xmax = 3.14;
// float ymin = 0.;
// float ymax = 0.2;
// TH2F* histo = new TH2F("histo", "histo", 100, xmin, xmax, 100, ymin, ymax);
// for ( int ix=0; ix<100; ix++ )
// for ( int iy=0; iy<100; iy++ )
// {
// float x = xmin + (xmax-xmin)*(double)ix/(double)100;
// float y = ymin + (ymax-ymin)*(double)iy/(double)100;
// w->var("d_dk")->setVal(x);
// w->var("r_dk")->setVal(y);
// histo->SetBinContent(ix+1,iy+1,ll.getVal());
// }
// newNoWarnTCanvas("c2");
// histo->GetZaxis()->SetRangeUser(0,20);
// histo->Draw("colz");
// setParameters(w, parsName, r1);
// }
}
// step 2: build and fit the improved fcn
RooFitResult *r2 = 0;
{
// create a Hesse PDF, import both PDFs into a new workspace,
// so that their parameters are linked
RooAbsPdf* hessePdf = r1->createHessePdf(*w->set(parsName));
if ( !hessePdf ) return r1;
// RooWorkspace* wImprove = (RooWorkspace*)w->Clone();
RooWorkspace* wImprove = new RooWorkspace();
wImprove->import(*w->pdf(pdfName));
wImprove->import(*hessePdf);
hessePdf = wImprove->pdf(hessePdf->GetName());
RooAbsPdf* fullPdf = wImprove->pdf(pdfName);
RooFormulaVar ll("ll", "ll", "-2*log(@0) +16*@1", RooArgSet(*fullPdf, *hessePdf));
// RooFitResult *r2 = fitToMin(&ll, printlevel);
RooMinuit m(ll);
m.setPrintLevel(-2);
m.setNoWarn();
m.setErrorLevel(1.0);
int status = m.migrad();
r2 = m.save();
// if ( 102<RadToDeg(w->var("g")->getVal())&&RadToDeg(w->var("g")->getVal())<103 )
// {
// cout << "step 3" << endl;
// r2->Print("v");
//
// gStyle->SetPalette(1);
// float xmin = 0.;
// float xmax = 3.14;
// float ymin = 0.;
// float ymax = 0.2;
// TH2F* histo = new TH2F("histo", "histo", 100, xmin, xmax, 100, ymin, ymax);
// for ( int ix=0; ix<100; ix++ )
// for ( int iy=0; iy<100; iy++ )
// {
// float x = xmin + (xmax-xmin)*(double)ix/(double)100;
// float y = ymin + (ymax-ymin)*(double)iy/(double)100;
// wImprove->var("d_dk")->setVal(x);
// wImprove->var("r_dk")->setVal(y);
// histo->SetBinContent(ix+1,iy+1,ll.getVal());
// }
// newNoWarnTCanvas("c7");
// histo->GetZaxis()->SetRangeUser(0,20);
// histo->Draw("colz");
// // setParameters(wImprove, parsName, r1);
// }
delete wImprove;
}
// step 3: use the fit result of the improved fit as
// start parameters for the nominal fcn
RooFitResult* r3;
{
setParameters(w, parsName, r2);
RooFormulaVar ll("ll", "ll", "-2*log(@0)", RooArgSet(*w->pdf(pdfName)));
RooMinuit m(ll);
m.setPrintLevel(-2);
m.setNoWarn();
m.setErrorLevel(1.0);
int status = m.migrad();
r3 = m.save();
// if ( 102<RadToDeg(w->var("g")->getVal())&&RadToDeg(w->var("g")->getVal())<103 )
// {
// cout << "step 3" << endl;
// r3->Print("v");
// }
}
// step 5: chose better minimum
// cout << r1->minNll() << " " << r3->minNll() << endl;
RooFitResult* r = 0;
if ( r1->minNll()<r3->minNll() )
{
delete r3;
r = r1;
}
else
{
delete r1;
r = r3;
// cout << "Utils::fitToMinImprove() : improved fit is better!" << endl;
}
RooMsgService::instance().setGlobalKillBelow(INFO);
// set to best parameters
setParameters(w, parsName, r);
return r;
}
void StandardHypoTestDemo(const char* infile = "",
const char* workspaceName = "combined",
const char* modelSBName = "ModelConfig",
const char* modelBName = "",
const char* dataName = "obsData",
int calcType = 0, // 0 freq 1 hybrid, 2 asymptotic
int testStatType = 3, // 0 LEP, 1 TeV, 2 LHC, 3 LHC - one sided
int ntoys = 5000,
bool useNC = false,
const char * nuisPriorName = 0)
{
/*
Other Parameter to pass in tutorial
apart from standard for filename, ws, modelconfig and data
type = 0 Freq calculator
type = 1 Hybrid calculator
type = 2 Asymptotic calculator
testStatType = 0 LEP
= 1 Tevatron
= 2 Profile Likelihood
= 3 Profile Likelihood one sided (i.e. = 0 if mu < mu_hat)
ntoys: number of toys to use
useNumberCounting: set to true when using number counting events
nuisPriorName: name of prior for the nnuisance. This is often expressed as constraint term in the global model
It is needed only when using the HybridCalculator (type=1)
If not given by default the prior pdf from ModelConfig is used.
extra options are available as global paramwters of the macro. They major ones are:
generateBinned generate binned data sets for toys (default is false) - be careful not to activate with
a too large (>=3) number of observables
nToyRatio ratio of S+B/B toys (default is 2)
printLevel
*/
// disable - can cause some problems
//ToyMCSampler::SetAlwaysUseMultiGen(true);
SimpleLikelihoodRatioTestStat::SetAlwaysReuseNLL(true);
ProfileLikelihoodTestStat::SetAlwaysReuseNLL(true);
RatioOfProfiledLikelihoodsTestStat::SetAlwaysReuseNLL(true);
//RooRandom::randomGenerator()->SetSeed(0);
// to change minimizers
// ROOT::Math::MinimizerOptions::SetDefaultStrategy(0);
// ROOT::Math::MinimizerOptions::SetDefaultMinimizer("Minuit2");
// ROOT::Math::MinimizerOptions::SetDefaultTolerance(1);
/////////////////////////////////////////////////////////////
// First part is just to access a user-defined file
// or create the standard example file if it doesn't exist
////////////////////////////////////////////////////////////
const char* filename = "";
if (!strcmp(infile,""))
filename = "results/example_combined_GaussExample_model.root";
else
filename = infile;
// Check if example input file exists
TFile *file = TFile::Open(filename);
// if input file was specified byt not found, quit
if(!file && strcmp(infile,"")){
cout <<"file not found" << endl;
return;
}
// if default file not found, try to create it
if(!file ){
// Normally this would be run on the command line
cout <<"will run standard hist2workspace example"<<endl;
gROOT->ProcessLine(".! prepareHistFactory .");
gROOT->ProcessLine(".! hist2workspace config/example.xml");
cout <<"\n\n---------------------"<<endl;
cout <<"Done creating example input"<<endl;
cout <<"---------------------\n\n"<<endl;
}
// now try to access the file again
file = TFile::Open(filename);
if(!file){
// if it is still not there, then we can't continue
cout << "Not able to run hist2workspace to create example input" <<endl;
return;
}
/////////////////////////////////////////////////////////////
// Tutorial starts here
////////////////////////////////////////////////////////////
// get the workspace out of the file
RooWorkspace* w = (RooWorkspace*) file->Get(workspaceName);
if(!w){
cout <<"workspace not found" << endl;
return;
}
w->Print();
// get the modelConfig out of the file
ModelConfig* sbModel = (ModelConfig*) w->obj(modelSBName);
// get the modelConfig out of the file
RooAbsData* data = w->data(dataName);
// make sure ingredients are found
if(!data || !sbModel){
w->Print();
cout << "data or ModelConfig was not found" <<endl;
return;
}
// make b model
ModelConfig* bModel = (ModelConfig*) w->obj(modelBName);
// case of no systematics
// remove nuisance parameters from model
if (noSystematics) {
const RooArgSet * nuisPar = sbModel->GetNuisanceParameters();
if (nuisPar && nuisPar->getSize() > 0) {
std::cout << "StandardHypoTestInvDemo" << " - Switch off all systematics by setting them constant to their initial values" << std::endl;
RooStats::SetAllConstant(*nuisPar);
}
if (bModel) {
const RooArgSet * bnuisPar = bModel->GetNuisanceParameters();
if (bnuisPar)
RooStats::SetAllConstant(*bnuisPar);
}
}
if (!bModel ) {
Info("StandardHypoTestInvDemo","The background model %s does not exist",modelBName);
Info("StandardHypoTestInvDemo","Copy it from ModelConfig %s and set POI to zero",modelSBName);
bModel = (ModelConfig*) sbModel->Clone();
bModel->SetName(TString(modelSBName)+TString("B_only"));
RooRealVar * var = dynamic_cast<RooRealVar*>(bModel->GetParametersOfInterest()->first());
if (!var) return;
double oldval = var->getVal();
var->setVal(0);
//bModel->SetSnapshot( RooArgSet(*var, *w->var("lumi")) );
bModel->SetSnapshot( RooArgSet(*var) );
var->setVal(oldval);
}
if (!sbModel->GetSnapshot() || poiValue > 0) {
Info("StandardHypoTestDemo","Model %s has no snapshot - make one using model poi",modelSBName);
RooRealVar * var = dynamic_cast<RooRealVar*>(sbModel->GetParametersOfInterest()->first());
if (!var) return;
double oldval = var->getVal();
if (poiValue > 0) var->setVal(poiValue);
//sbModel->SetSnapshot( RooArgSet(*var, *w->var("lumi") ) );
sbModel->SetSnapshot( RooArgSet(*var) );
if (poiValue > 0) var->setVal(oldval);
//sbModel->SetSnapshot( *sbModel->GetParametersOfInterest() );
}
// part 1, hypothesis testing
SimpleLikelihoodRatioTestStat * slrts = new SimpleLikelihoodRatioTestStat(*bModel->GetPdf(), *sbModel->GetPdf());
// null parameters must includes snapshot of poi plus the nuisance values
RooArgSet nullParams(*bModel->GetSnapshot());
if (bModel->GetNuisanceParameters()) nullParams.add(*bModel->GetNuisanceParameters());
slrts->SetNullParameters(nullParams);
RooArgSet altParams(*sbModel->GetSnapshot());
if (sbModel->GetNuisanceParameters()) altParams.add(*sbModel->GetNuisanceParameters());
slrts->SetAltParameters(altParams);
ProfileLikelihoodTestStat * profll = new ProfileLikelihoodTestStat(*bModel->GetPdf());
RatioOfProfiledLikelihoodsTestStat *
ropl = new RatioOfProfiledLikelihoodsTestStat(*bModel->GetPdf(), *sbModel->GetPdf(), sbModel->GetSnapshot());
ropl->SetSubtractMLE(false);
if (testStatType == 3) profll->SetOneSidedDiscovery(1);
profll->SetPrintLevel(printLevel);
// profll.SetReuseNLL(mOptimize);
// slrts.SetReuseNLL(mOptimize);
// ropl.SetReuseNLL(mOptimize);
AsymptoticCalculator::SetPrintLevel(printLevel);
HypoTestCalculatorGeneric * hypoCalc = 0;
// note here Null is B and Alt is S+B
if (calcType == 0) hypoCalc = new FrequentistCalculator(*data, *sbModel, *bModel);
else if (calcType == 1) hypoCalc= new HybridCalculator(*data, *sbModel, *bModel);
else if (calcType == 2) hypoCalc= new AsymptoticCalculator(*data, *sbModel, *bModel);
if (calcType == 0)
((FrequentistCalculator*)hypoCalc)->SetToys(ntoys, ntoys/nToysRatio);
if (calcType == 1)
((HybridCalculator*)hypoCalc)->SetToys(ntoys, ntoys/nToysRatio);
if (calcType == 2 ) {
if (testStatType == 3) ((AsymptoticCalculator*) hypoCalc)->SetOneSidedDiscovery(true);
if (testStatType != 2 && testStatType != 3)
Warning("StandardHypoTestDemo","Only the PL test statistic can be used with AsymptoticCalculator - use by default a two-sided PL");
}
// check for nuisance prior pdf in case of nuisance parameters
if (calcType == 1 && (bModel->GetNuisanceParameters() || sbModel->GetNuisanceParameters() )) {
RooAbsPdf * nuisPdf = 0;
if (nuisPriorName) nuisPdf = w->pdf(nuisPriorName);
// use prior defined first in bModel (then in SbModel)
if (!nuisPdf) {
Info("StandardHypoTestDemo","No nuisance pdf given for the HybridCalculator - try to deduce pdf from the model");
if (bModel->GetPdf() && bModel->GetObservables() )
nuisPdf = RooStats::MakeNuisancePdf(*bModel,"nuisancePdf_bmodel");
else
nuisPdf = RooStats::MakeNuisancePdf(*sbModel,"nuisancePdf_sbmodel");
}
if (!nuisPdf ) {
if (bModel->GetPriorPdf()) {
nuisPdf = bModel->GetPriorPdf();
Info("StandardHypoTestDemo","No nuisance pdf given - try to use %s that is defined as a prior pdf in the B model",nuisPdf->GetName());
}
else {
Error("StandardHypoTestDemo","Cannnot run Hybrid calculator because no prior on the nuisance parameter is specified or can be derived");
return;
}
}
assert(nuisPdf);
Info("StandardHypoTestDemo","Using as nuisance Pdf ... " );
nuisPdf->Print();
const RooArgSet * nuisParams = (bModel->GetNuisanceParameters() ) ? bModel->GetNuisanceParameters() : sbModel->GetNuisanceParameters();
RooArgSet * np = nuisPdf->getObservables(*nuisParams);
if (np->getSize() == 0) {
Warning("StandardHypoTestDemo","Prior nuisance does not depend on nuisance parameters. They will be smeared in their full range");
}
delete np;
((HybridCalculator*)hypoCalc)->ForcePriorNuisanceAlt(*nuisPdf);
((HybridCalculator*)hypoCalc)->ForcePriorNuisanceNull(*nuisPdf);
}
// hypoCalc->ForcePriorNuisanceAlt(*sbModel->GetPriorPdf());
// hypoCalc->ForcePriorNuisanceNull(*bModel->GetPriorPdf());
ToyMCSampler * sampler = (ToyMCSampler *)hypoCalc->GetTestStatSampler();
if (sampler && (calcType == 0 || calcType == 1) ) {
// look if pdf is number counting or extended
if (sbModel->GetPdf()->canBeExtended() ) {
if (useNC) Warning("StandardHypoTestDemo","Pdf is extended: but number counting flag is set: ignore it ");
}
else {
// for not extended pdf
if (!useNC) {
int nEvents = data->numEntries();
Info("StandardHypoTestDemo","Pdf is not extended: number of events to generate taken from observed data set is %d",nEvents);
sampler->SetNEventsPerToy(nEvents);
}
else {
Info("StandardHypoTestDemo","using a number counting pdf");
sampler->SetNEventsPerToy(1);
}
}
if (data->isWeighted() && !generateBinned) {
Info("StandardHypoTestDemo","Data set is weighted, nentries = %d and sum of weights = %8.1f but toy generation is unbinned - it would be faster to set generateBinned to true\n",data->numEntries(), data->sumEntries());
}
if (generateBinned) sampler->SetGenerateBinned(generateBinned);
// set the test statistic
if (testStatType == 0) sampler->SetTestStatistic(slrts);
if (testStatType == 1) sampler->SetTestStatistic(ropl);
if (testStatType == 2 || testStatType == 3) sampler->SetTestStatistic(profll);
}
HypoTestResult * htr = hypoCalc->GetHypoTest();
htr->SetPValueIsRightTail(true);
htr->SetBackgroundAsAlt(false);
htr->Print(); // how to get meaningfull CLs at this point?
delete sampler;
delete slrts;
delete ropl;
delete profll;
if (calcType != 2) {
HypoTestPlot * plot = new HypoTestPlot(*htr,100);
plot->SetLogYaxis(true);
plot->Draw();
}
else {
std::cout << "Asymptotic results " << std::endl;
}
// look at expected significances
// found median of S+B distribution
if (calcType != 2) {
SamplingDistribution * altDist = htr->GetAltDistribution();
HypoTestResult htExp("Expected Result");
htExp.Append(htr);
// find quantiles in alt (S+B) distribution
double p[5];
double q[5];
for (int i = 0; i < 5; ++i) {
double sig = -2 + i;
p[i] = ROOT::Math::normal_cdf(sig,1);
}
std::vector<double> values = altDist->GetSamplingDistribution();
TMath::Quantiles( values.size(), 5, &values[0], q, p, false);
for (int i = 0; i < 5; ++i) {
htExp.SetTestStatisticData( q[i] );
double sig = -2 + i;
std::cout << " Expected p -value and significance at " << sig << " sigma = "
<< htExp.NullPValue() << " significance " << htExp.Significance() << " sigma " << std::endl;
}
}
else {
// case of asymptotic calculator
for (int i = 0; i < 5; ++i) {
double sig = -2 + i;
// sigma is inverted here
double pval = AsymptoticCalculator::GetExpectedPValues( htr->NullPValue(), htr->AlternatePValue(), -sig, false);
std::cout << " Expected p -value and significance at " << sig << " sigma = "
<< pval << " significance " << ROOT::Math::normal_quantile_c(pval,1) << " sigma " << std::endl;
}
}
}