void HypoTestInvDemo(const char * fileName ="GausModel_b.root", const char * wsName = "w", const char * modelSBName = "model_sb", const char * modelBName = "model_b", const char * dataName = "data_obs", int type = 0, // calculator type int testStatType = 0, // test stat type int npoints = 10, int ntoys=1000, bool useCls = true ) { /* type = 0 Freq calculator type = 1 Hybrid testStatType = 0 LEP = 1 Tevatron = 2 PL */ if (fileName==0) { std::cout << "give input filename " << std::endl; return; } TFile * file = new TFile(fileName); RooWorkspace * w = dynamic_cast<RooWorkspace*>( file->Get(wsName) ); if (!w) { return; } w->Print(); RooAbsData * data = w->data(dataName); if (!data) { Error("HypoTestDemo","Not existing data %s",dataName); } // get models from WS // get the modelConfig out of the file ModelConfig* bModel = (ModelConfig*) w->obj(modelBName); ModelConfig* sbModel = (ModelConfig*) w->obj(modelSBName); SimpleLikelihoodRatioTestStat slrts(*bModel->GetPdf(),*sbModel->GetPdf()); slrts.SetNullParameters(*bModel->GetSnapshot()); slrts.SetAltParameters(*sbModel->GetSnapshot()); RatioOfProfiledLikelihoodsTestStat ropl(*bModel->GetPdf(), *sbModel->GetPdf(), sbModel->GetSnapshot()); ropl.SetSubtractMLE(false); ProfileLikelihoodTestStat profll(*sbModel->GetPdf()); profll.SetOneSided(0); TestStatistic * testStat = &slrts; if (testStatType == 1) testStat = &ropl; if (testStatType == 2) testStat = &profll; HypoTestCalculatorGeneric * hc = 0; if (type == 0) hc = new FrequentistCalculator(*data, *sbModel, *bModel); else new HybridCalculator(*data, *sbModel, *bModel); ToyMCSampler *toymcs = (ToyMCSampler*)hc->GetTestStatSampler(); //toymcs->SetNEventsPerToy(1); toymcs->SetTestStatistic(testStat); if (type == 1) { HybridCalculator *hhc = (HybridCalculator*) hc; hhc->SetToys(ntoys,ntoys); // hhc->ForcePriorNuisanceAlt(*pdfNuis); // hhc->ForcePriorNuisanceNull(*pdfNuis); } else ((FrequentistCalculator*) hc)->SetToys(ntoys,ntoys); // Get the result RooMsgService::instance().getStream(1).removeTopic(RooFit::NumIntegration); TStopwatch tw; tw.Start(); const RooArgSet * poiSet = sbModel->GetParametersOfInterest(); RooRealVar *poi = (RooRealVar*)poiSet->first(); // fit the data first sbModel->GetPdf()->fitTo(*data); double poihat = poi->getVal(); //poi->setVal(30); //poi->setError(10); HypoTestInverter calc(*hc); // GENA: for two-sided interval //calc.SetConfidenceLevel(0.95); // GENA: for 95% upper limit calc.SetConfidenceLevel(0.90); calc.UseCLs(useCls); calc.SetVerbose(true); // can spped up using proof ProofConfig pc(*w, 2, "workers=2", kFALSE); //ProofConfig pc(*w, 30, "localhost", kFALSE); //ToyMCSampler * toymcs = dynamic_cast<ToyMCSampler *> (calc.GetHypoTestCalculator()->GetTestStatSampler() ); // GENA: disable proof for now //toymcs->SetProofConfig(&pc); // enable proof if (npoints > 0) { // GENA double poimin = TMath::Max(poihat - 4 * poi->getError(), 0.0); //poimin = poihat; double poimax = poihat + 4 * poi->getError(); poimin = 0; poimax = 20; //double poimin = poi->getMin(); //double poimax = poi->getMax(); std::cout << "Doing a fixed scan in interval : " << poimin << " , " << poimax << std::endl; calc.SetFixedScan(npoints,poimin,poimax); } HypoTestInverterResult * r = calc.GetInterval(); // write to a file the results TString resultFileName = (useCls) ? "CLs_" : "Cls+b_"; resultFileName += fileName; // GENA //TFile * file = new TFile(resultFileName,"RECREATE"); file = new TFile(resultFileName,"RECREATE"); r->Write(); file->Close(); double ulError = r->UpperLimitEstimatedError(); double upperLimit = r->UpperLimit(); std::cout << "The computed upper limit is: " << upperLimit << std::endl; std::cout << "an estimated error on this upper limit is: " << ulError << std::endl; // check using interpolation // double interpLimit = r->FindInterpolatedLimit(1.-r->ConfidenceLevel() ); // cout << "The computer interpolated limits is " << interpLimit << endl; const int nEntries = r->ArraySize(); std::vector<Double_t> xArray(nEntries); std::vector<Double_t> yArray(nEntries); std::vector<Double_t> yErrArray(nEntries); for (int i=0; i<nEntries; i++) { xArray[i] = r->GetXValue(i); yArray[i] = r->GetYValue(i); yErrArray[i] = r->GetYError(i); std::cout << xArray[i] << " , " << yArray[i] << " err = " << yErrArray[i] << std::endl; } // see expected result (bands) TGraph * g0 = new TGraph(nEntries); TGraphAsymmErrors * g1 = new TGraphAsymmErrors(nEntries); TGraphAsymmErrors * g2l = new TGraphAsymmErrors(nEntries); TGraphAsymmErrors * g2u = new TGraphAsymmErrors(nEntries); double p[7]; double q[7]; p[0] = ROOT::Math::normal_cdf(-2); p[1] = ROOT::Math::normal_cdf(-1.5); p[2] = ROOT::Math::normal_cdf(-1); p[3] = 0.5; p[4] = ROOT::Math::normal_cdf(1); p[5] = ROOT::Math::normal_cdf(1.5); p[6] = ROOT::Math::normal_cdf(2); for (int i=0; i<nEntries; i++) { SamplingDistribution * s = r->GetExpectedDistribution(i); // GENA //const std::vector<double> & values = s->GetSamplingDistribution(); const std::vector<Double_t> & cValues = s->GetSamplingDistribution(); std::vector<Double_t> values; for (std::vector<Double_t>::const_iterator val = cValues.begin(); val != cValues.end(); ++val) values.push_back(*val); TMath::Quantiles(values.size(), 7, &values[0],q,p,false); double p0 = q[3]; double p2l = q[1]; double p2u = q[5]; g0->SetPoint(i, r->GetXValue(i), p0 ) ; g1->SetPoint(i, r->GetXValue(i), p0); g2l->SetPoint(i, r->GetXValue(i), p2l); g2u->SetPoint(i, r->GetXValue(i), p2u); //g2->SetPoint(i, r->GetXValue(i), s->InverseCDF(0.50)); g1->SetPointEYlow(i, q[3] - q[2]); // -1 sigma errorr g1->SetPointEYhigh(i, q[4] - q[3]);//+1 sigma error g2l->SetPointEYlow(i, q[1]-q[0]); // -2 -- -1 sigma error g2l->SetPointEYhigh(i, q[2]-q[1]); g2u->SetPointEYlow(i, q[5]-q[4]); g2u->SetPointEYhigh(i, q[6]-q[5]); if (plotHypoTestResult) { HypoTestResult * hr = new HypoTestResult(); hr->SetNullDistribution( r->GetBackgroundDistribution() ); hr->SetAltDistribution( r->GetSignalAndBackgroundDistribution(i) ); new TCanvas(); HypoTestPlot * pl = new HypoTestPlot(*hr); pl->Draw(); } } HypoTestInverterPlot *plot = new HypoTestInverterPlot("result","",r); TGraphErrors * g = plot->MakePlot(); g->Draw("APL"); g2l->SetFillColor(kYellow); g2l->Draw("3"); g2u->SetFillColor(kYellow); g2u->Draw("3"); g1->SetFillColor(kGreen); g1->Draw("3"); g0->SetLineColor(kBlue); g0->SetLineStyle(2); g0->SetLineWidth(2); g0->Draw("L"); //g1->Draw("P"); //g2->Draw("P"); g->SetLineWidth(2); g->Draw("PL"); // GENA: two-sided interval //double alpha = 1.-r->ConfidenceLevel(); // GENA: upper limit double alpha = (1.-r->ConfidenceLevel())/2.0; double x1 = g->GetXaxis()->GetXmin(); double x2 = g->GetXaxis()->GetXmax(); TLine * line = new TLine(x1, alpha, x2,alpha); line->SetLineColor(kRed); line->Draw(); // see the expected limit and -1 +1 sigma bands // SamplingDistribution * limits = r->GetUpperLimitDistribution(); // std::cout << " expected limit (median) " << limits->InverseCDF(0.50) << std::endl; // std::cout << " expected limit (-1 sig) " << limits->InverseCDF((ROOT::Math::normal_cdf(-1))) << std::endl; // std::cout << " expected limit (+1 sig) " << limits->InverseCDF((ROOT::Math::normal_cdf(+1))) << std::endl; tw.Print(); }