#include <ProfileLikelihood.h>

Inheritance diagram for ProfileLikelihood:

Classes
class	MinimizerSentry
	Setup Minimizer configuration on creation, reset the previous one on destruction. More...

Public Member Functions
virtual void	applyOptions (const boost::program_options::variables_map &vm)

virtual const std::string &	name () const

	ProfileLikelihood ()

virtual bool	run (RooWorkspace w, RooStats::ModelConfig mc_s, RooStats::ModelConfig mc_b, RooAbsData &data, double &limit, double &limitErr, const double hint)

Public Member Functions inherited from LimitAlgo
virtual void	applyDefaultOptions ()

	LimitAlgo ()

	LimitAlgo (const char *desc)

const boost::program_options::options_description &	options () const

virtual void	setNToys (const int)

virtual void	setToyNumber (const int)

Protected Member Functions
bool	runLimit (RooWorkspace w, RooStats::ModelConfig mc, RooAbsData &data, double &limit, double &limitErr)

bool	runSignificance (RooWorkspace w, RooStats::ModelConfig mc, RooAbsData &data, double &limit, double &limitErr)

double	significanceBruteForce (RooAbsPdf &pdf, RooAbsData &data, RooRealVar &poi, const RooArgSet *nuisances, double tolerance) const

double	significanceFromScan (RooAbsPdf &pdf, RooAbsData &data, RooRealVar &poi, const RooArgSet *nuisances, double tolerance, int npoints) const

std::pair< double, double >	upperLimitBruteForce (RooAbsPdf &pdf, RooAbsData &data, RooRealVar &poi, const RooArgSet *nuisances, double tolerance, double cl) const

double	upperLimitWithMinos (RooAbsPdf &pdf, RooAbsData &data, RooRealVar &poi, const RooArgSet *nuisances, double tolerance, double cl) const

Static Protected Attributes
static std::string	bfAlgo_ = "scale"

static bool	bruteForce_ = false

static float	mass_

static float	maxOutlierFraction_ = 0.25
	Ignore up to this fraction of results if they're too far from the median. More...

static int	maxOutliers_ = 3
	Stop trying after finding N outliers. More...

static float	maxRelDeviation_ = 0.05
	maximum relative deviation of the different points from the median to accept More...

static int	maxTries_ = 1
	trying up to M times from different points More...

static std::string	minimizerAlgo_ = "Minuit2"

static std::string	minimizerAlgoForBF_ = "Minuit2,simplex"

static std::string static float	minimizerTolerance_ = 1e-2

static std::string static float	minimizerToleranceForBF_ = 1e-4

static std::string	plot_ = ""

static int	points_ = 20

static bool	preFit_ = false
	Try first a plain fit. More...

static bool	reportPVal_ = false
	Report p-value instead of significance. More...

static float	signalForSignificance_ = 0

static int	tries_ = 1
	compute the limit N times More...

static bool	useMinos_ = true

Additional Inherited Members
Protected Attributes inherited from LimitAlgo
boost::program_options::options_description	options_

Detailed Description

Class for computing limits and significances from naive Profile Likelihood asymptotics (i.e. no Asimov dataset)

Author: Giovanni Petrucciani (UCSD)

Definition at line 15 of file ProfileLikelihood.h.

Constructor & Destructor Documentation

ProfileLikelihood::ProfileLikelihood ( )

Definition at line 47 of file ProfileLikelihood.cc.

References bfAlgo_, maxOutlierFraction_, maxOutliers_, maxRelDeviation_, maxTries_, minimizerAlgo_, minimizerAlgoForBF_, minimizerTolerance_, minimizerToleranceForBF_, LimitAlgo::options_, plot_, points_, signalForSignificance_, and tries_.

                                      :
     LimitAlgo("Profile Likelihood specific options")
 {
     options_.add_options()
         ("minimizerAlgo",      boost::program_options::value<std::string>(&minimizerAlgo_)->default_value(minimizerAlgo_), "Choice of minimizer (Minuit vs Minuit2)")
         ("minimizerTolerance", boost::program_options::value<float>(&minimizerTolerance_)->default_value(minimizerTolerance_),  "Tolerance for minimizer")
         ("tries",              boost::program_options::value<int>(&tries_)->default_value(tries_), "Compute PL limit N times, to check for numerical instabilities")
         ("maxTries",           boost::program_options::value<int>(&maxTries_)->default_value(maxTries_), "Stop trying after N attempts per point")
         ("maxRelDeviation",    boost::program_options::value<float>(&maxRelDeviation_)->default_value(maxOutlierFraction_), "Max absolute deviation of the results from the median")
         ("maxOutlierFraction", boost::program_options::value<float>(&maxOutlierFraction_)->default_value(maxOutlierFraction_), "Ignore up to this fraction of results if they're too far from the median")
         ("signalForSignificance", boost::program_options::value<float>(&signalForSignificance_)->default_value(signalForSignificance_), "Signal strength used when computing significances (default is zero, just background)")
         ("maxOutliers",        boost::program_options::value<int>(&maxOutliers_)->default_value(maxOutliers_),      "Stop trying after finding N outliers")
         ("plot",   boost::program_options::value<std::string>(&plot_)->default_value(plot_), "Save a plot of the negative log of the profiled likelihood into the specified file")
         ("pvalue", "Report p-value instead of significance (when running with --significance)")
         ("preFit", "Attept a fit before running the ProfileLikelihood calculator")
         ("usePLC",   "Compute PL limit using the ProfileLikelihoodCalculator (not default)")
         ("useMinos", "Compute PL limit using Minos directly, bypassing the ProfileLikelihoodCalculator (default)")
         ("bruteForce", "Compute PL limit by brute force, bypassing the ProfileLikelihoodCalculator and Minos")
         ("bfAlgo", boost::program_options::value<std::string>(&bfAlgo_)->default_value(bfAlgo_), "NLL scan algorithm used for --bruteForce. Supported values are 'scale' (default), 'stepUp[Twice]', 'stepDown[Twice]'")
         ("scanPoints", boost::program_options::value<int>(&points_)->default_value(points_), "Points for the scan")
         ("minimizerAlgoForBF",      boost::program_options::value<std::string>(&minimizerAlgoForBF_)->default_value(minimizerAlgoForBF_), "Choice of minimizer for brute-force search")
         ("minimizerToleranceForBF", boost::program_options::value<float>(&minimizerToleranceForBF_)->default_value(minimizerToleranceForBF_),  "Tolerance for minimizer when doing brute-force search")
     ;
 }

Member Function Documentation

void ProfileLikelihood::applyOptions ( const boost::program_options::variables_map & vm )

virtual

Reimplemented from LimitAlgo.

Definition at line 72 of file ProfileLikelihood.cc.

References bruteForce_, mass_, reportPVal_, and useMinos_.

 {
     if (vm.count("usePLC")) useMinos_ = false;
     else if (vm.count("useMinos")) useMinos_ = true;
     else useMinos_ = true;
     bruteForce_ = vm.count("bruteForce");
     reportPVal_ = vm.count("pvalue");
     mass_ = vm["mass"].as<float>();
 }

virtual const std::string& ProfileLikelihood::name ( void ) const

inlinevirtual

Implements LimitAlgo.

Definition at line 19 of file ProfileLikelihood.h.

Referenced by BeautifulSoup.Tag::_invert().

                                          {
     static const std::string name("ProfileLikelihood");
     return name;
   }

bool ProfileLikelihood::run	(	RooWorkspace *	w,
		RooStats::ModelConfig *	mc_s,
		RooStats::ModelConfig *	mc_b,
		RooAbsData &	data,
		double &	limit,
		double &	limitErr,
		const double *	hint
	)

virtual

Implements LimitAlgo.

Definition at line 105 of file ProfileLikelihood.cc.

References gather_cfg::cout, diffTreeTool::diff, doSignificance_, i, j, utils::makeNuisancePdf(), max(), maxOutlierFraction_, maxOutliers_, maxRelDeviation_, maxTries_, minimizerAlgo_, minimizerTolerance_, preFit_, alignCSCRings::r, runLimit(), runSignificance(), runtimedef::set(), python.multivaluedict::sort(), summarizeEdmComparisonLogfiles::success, tries_, and withSystematics.

                                                                                                                                                                         { 
   MinimizerSentry minimizerConfig(minimizerAlgo_, minimizerTolerance_);
   CloseCoutSentry sentry(verbose < 0);
 
   RooRealVar *r = dynamic_cast<RooRealVar *>(mc_s->GetParametersOfInterest()->first());
   bool success = false;
   std::vector<double> limits; double rMax = r->getMax();  
   std::auto_ptr<RooAbsPdf> nuisancePdf(0);
   for (int i = 0; i < maxTries_; ++i) {
       w->loadSnapshot("clean");
       if (i > 0) { // randomize starting point
         r->setMax(rMax*(0.5+RooRandom::uniform()));
         r->setVal((0.1+0.5*RooRandom::uniform())*r->getMax()); 
         if (withSystematics) { 
             if (nuisancePdf.get() == 0) nuisancePdf.reset(utils::makeNuisancePdf(*mc_s));
             RooArgSet set(*mc_s->GetNuisanceParameters()); 
             RooDataSet *randoms = nuisancePdf->generate(set, 1); 
             set = *randoms->get(0);
             if (verbose > 2) {
                 std::cout << "Starting minimization from point " << std::endl;
                 r->Print("V");
                 set.Print("V");
             }
             delete randoms;
         }
       }
       if (preFit_) {
         CloseCoutSentry sentry(verbose < 2);
         RooFitResult *res = mc_s->GetPdf()->fitTo(data, RooFit::Save(1), RooFit::Minimizer("Minuit2"));
         if (res == 0 || res->covQual() != 3 || res->edm() > minimizerTolerance_) {
             if (verbose > 1) std::cout << "Fit failed (covQual " << (res ? res->covQual() : -1) << ", edm " << (res ? res->edm() : 0) << ")" << std::endl;
             continue;
         }
         if (verbose > 1) {
             res->Print("V");
             std::cout << "Covariance quality: " << res->covQual() << ", Edm = " << res->edm() << std::endl;
         }
         delete res;
       }
       bool thisTry = (doSignificance_ ?  runSignificance(w,mc_s,data,limit,limitErr) : runLimit(w,mc_s,data,limit,limitErr));
       if (!thisTry) continue;
       if (tries_ == 1) { success = true; break; }
       limits.push_back(limit); 
       int nresults = limits.size();
       if (nresults < tries_) continue;
       std::sort(limits.begin(), limits.end());
       double median = (nresults % 2 ? limits[nresults/2] : 0.5*(limits[nresults/2] + limits[nresults/2+1]));
       int noutlier = 0; double spreadIn = 0, spreadOut = 0;
       for (int j = 0; j < nresults; ++j) {
         double diff = fabs(limits[j]-median)/median;
         if (diff < maxRelDeviation_) { 
           spreadIn = max(spreadIn, diff); 
         } else {
           noutlier++;
           spreadOut = max(spreadOut, diff); 
         }
       }
       if (verbose > 0) {
           std::cout << "Numer of tries: " << i << "   Number of successes: " << nresults 
                     << ", Outliers: " << noutlier << " (frac = " << noutlier/double(nresults) << ")"
                     << ", Spread of non-outliers: " << spreadIn <<" / of outliers: " << spreadOut << std::endl;
       }
       if (noutlier <= maxOutlierFraction_*nresults) {
         if (verbose > 0) std::cout << " \\--> success! " << std::endl;
         success = true;
         limit   = median;
         break;
       } else if (noutlier > maxOutliers_) {
         if (verbose > 0) std::cout << " \\--> failure! " << std::endl;
         break;
       }
   }
   return success;
 }

bool ProfileLikelihood::runLimit	(	RooWorkspace *	w,
		RooStats::ModelConfig *	mc,
		RooAbsData &	data,
		double &	limit,
		double &	limitErr
	)

protected

Definition at line 180 of file ProfileLikelihood.cc.

References bruteForce_, alignmentValidation::c1, dtNoiseDBValidation_cfg::cerr, cl, CloseCoutSentry::clear(), gather_cfg::cout, data, alignCSCRings::e, edm::detail::isnan(), asciidump::le, lowerLimit_, minimizerTolerance_, plotResiduals::plot(), plot_, alignCSCRings::r, summarizeEdmComparisonLogfiles::success, upperLimitBruteForce(), upperLimitWithMinos(), and useMinos_.

Referenced by run().

                                                                                                                               {
   RooArgSet  poi(*mc_s->GetParametersOfInterest());
   RooRealVar *r = dynamic_cast<RooRealVar *>(poi.first());
   double rMax = r->getMax();
   bool success = false;
   CloseCoutSentry coutSentry(verbose <= 1); // close standard output and error, so that we don't flood them with minuit messages
 
   while (!success) {
     ProfileLikelihoodCalculator plcB(data, *mc_s, 1.0-cl);
     std::auto_ptr<LikelihoodInterval> plInterval;
     if (useMinos_ || bruteForce_) {
         // try first with Minos, unless brute force requested
         if (!bruteForce_) { 
             limit = upperLimitWithMinos(*mc_s->GetPdf(), data, *r, mc_s->GetNuisanceParameters(), minimizerTolerance_, cl); 
         }
         // if brute force forced, or minos failed, go with the next one
         if (std::isnan(limit) || bruteForce_) {
             std::pair<double,double> le = upperLimitBruteForce(*mc_s->GetPdf(), data, *r, mc_s->GetNuisanceParameters(), 1e-3*minimizerTolerance_, cl); 
             limit = le.first; 
             limitErr = le.second;
         }
     } else {
         plInterval.reset(plcB.GetInterval());
         if (plInterval.get() == 0) break;
         limit = lowerLimit_ ? plInterval->LowerLimit(*r) : plInterval->UpperLimit(*r);
     }
     if (limit >= 0.75*r->getMax()) { 
       std::cout << "Limit " << r->GetName() << " < " << limit << "; " << r->GetName() << " max < " << r->getMax() << std::endl;
       if (r->getMax()/rMax > 20) break;
       r->setMax(r->getMax()*2); 
       continue;
     }
     if (limit == r->getMin()) {
       std::cerr << "ProfileLikelihoodCalculator failed (returned upper limit equal to the lower bound)" << std::endl;
       break;
     }
     success = true;
     if (!plot_.empty()) {
         TCanvas *c1 = new TCanvas("c1","c1");
         LikelihoodIntervalPlot plot(&*plInterval);
         plot.Draw();
         c1->Print(plot_.c_str());
         delete c1;
     }
   }
   coutSentry.clear();
   if (verbose >= 0) {
       if (success) {
         std::cout << "\n -- Profile Likelihood -- " << "\n";
         if (limitErr) { 
             std::cout << "Limit: " << r->GetName() << (lowerLimit_ ? " > " : " < ") << limit << " +/- " << limitErr << " @ " << cl * 100 << "% CL" << std::endl;
         } else {
             std::cout << "Limit: " << r->GetName() << (lowerLimit_ ? " > " : " < ") << limit << " @ " << cl * 100 << "% CL" << std::endl;
         }
       }
   }
   return success;
 }

bool ProfileLikelihood::runSignificance	(	RooWorkspace *	w,
		RooStats::ModelConfig *	mc,
		RooAbsData &	data,
		double &	limit,
		double &	limitErr
	)

protected

Definition at line 239 of file ProfileLikelihood.cc.

References bfAlgo_, bruteForce_, dtNoiseDBValidation_cfg::cerr, cl, CloseCoutSentry::clear(), gather_cfg::cout, data, ProfiledLikelihoodTestStatOpt::Evaluate(), minimizerTolerance_, points_, alignCSCRings::r, reportPVal_, query::result, signalForSignificance_, significanceBruteForce(), significanceFromScan(), mathSSE::sqrt(), and useMinos_.

Referenced by run().

                                                                                                                                      {
   RooArgSet  poi(*mc_s->GetParametersOfInterest());
   RooRealVar *r = dynamic_cast<RooRealVar *>(poi.first());
 
   ProfileLikelihoodCalculator plcS(data, *mc_s, 1.0-cl);
   RooArgSet nullParamValues; 
   r->setVal(signalForSignificance_);
   nullParamValues.addClone(*r); 
   plcS.SetNullParameters(nullParamValues);
 
   CloseCoutSentry coutSentry(verbose <= 1); // close standard output and error, so that we don't flood them with minuit messages
 
   if (bruteForce_) {
       double q0 = -1;
       if (bfAlgo_ == "scale") q0 = significanceBruteForce(*mc_s->GetPdf(), data, *r, mc_s->GetNuisanceParameters(), 0.1*minimizerTolerance_);
       else q0 = significanceFromScan(*mc_s->GetPdf(), data, *r, mc_s->GetNuisanceParameters(), 0.1*minimizerTolerance_, points_);
       if (q0 == -1) return false;
       limit = (q0 > 0 ? sqrt(2*q0) : 0);
   } else if (useMinos_) {
       ProfiledLikelihoodTestStatOpt testStat(*mc_s->GetObservables(), *mc_s->GetPdf(), mc_s->GetNuisanceParameters(), 
                                                    nullParamValues, *mc_s->GetParametersOfInterest(), RooArgList(), RooArgList(), verbose-1);
       Double_t q0 = testStat.Evaluate(data, nullParamValues);
       limit = q0 > 0 ? sqrt(2*q0) : 0;
   } else {
       std::auto_ptr<HypoTestResult> result(plcS.GetHypoTest());
       if (result.get() == 0) return false;
       limit = result->Significance();
   }
   coutSentry.clear();
   if (limit == 0 && signbit(limit)) {
       //..... This is not an error, it just means we have a deficit of events.....
       std::cerr << "The minimum of the likelihood is for r <= " << signalForSignificance_ << ", so the significance is zero" << std::endl;
       limit = 0;
   }
   if (reportPVal_) limit = RooStats::SignificanceToPValue(limit);
 
   std::cout << "\n -- Profile Likelihood -- " << "\n";
   std::cout << (reportPVal_ ? "p-value of background: " : "Significance: ") << limit << std::endl;
   if (verbose > 0) {
         if (reportPVal_) std::cout << "       (Significance = " << RooStats::PValueToSignificance(limit) << ")" << std::endl;
         else             std::cout << "       (p-value = " << RooStats::SignificanceToPValue(limit) << ")" << std::endl;
   }
   return true;
 }

double ProfileLikelihood::significanceBruteForce	(	RooAbsPdf &	pdf,
		RooAbsData &	data,
		RooRealVar &	poi,
		const RooArgSet *	nuisances,
		double	tolerance
	)		const

protected

Definition at line 388 of file ProfileLikelihood.cc.

References dtNoiseDBValidation_cfg::cerr, CascadeMinimizer::Constrained, gather_cfg::cout, CascadeMinimizer::improve(), mass_, CascadeMinimizer::minimize(), minimizerAlgo_, minimizerAlgoForBF_, minimizerTolerance_, minimizerToleranceForBF_, download_sqlite_cfg::outputFile, CascadeMinimizer::save(), CascadeMinimizer::setStrategy(), errorMatrix2Lands_multiChannel::start, summarizeEdmComparisonLogfiles::success, and CascadeMinimizer::Unconstrained.

Referenced by runSignificance().

                                                                                                                                                       {
     poi.setConstant(false);
     //poi.setMin(0); 
     poi.setVal(0.05*poi.getMax());
     std::auto_ptr<RooAbsReal> nll(pdf.createNLL(data, RooFit::Constrain(*nuisances)));
     CascadeMinimizer minim0(*nll, CascadeMinimizer::Unconstrained, &poi);
     minim0.setStrategy(0);
     minim0.minimize(verbose-2);
     if (poi.getVal() < 0) {
         printf("Minimum found at %s = %8.5f < 0: significance will be zero\n", poi.GetName(), poi.getVal());
         return 0;
     }
     poi.setConstant(true);
     CascadeMinimizer minim(*nll, CascadeMinimizer::Constrained);
     if (!minim.minimize(verbose-2)) {
         std::cerr << "Initial minimization failed. Aborting." << std::endl;
         return -1;
     } else if (verbose > 0) {
         printf("Minimum found at %s = %8.5f\n", poi.GetName(), poi.getVal());
     }
     MinimizerSentry minimizerConfig(minimizerAlgoForBF_, minimizerToleranceForBF_);
     std::auto_ptr<RooFitResult> start(minim.save());
     double minnll = nll->getVal(), thisnll = minnll, lastnll = thisnll;
     double rbest = poi.getVal(), rval = rbest;
     TGraph *points = 0;
     if (verbose) {
         printf("  %-6s  delta(NLL)\n", poi.GetName());
         printf("%8.5f  %8.5f\n", rval, 0.);
         fflush(stdout);
         points = new TGraph(1); 
         points->SetName(Form("nll_scan_%g", mass_));
         points->SetPoint(0, rval, 0);
     }
     while (rval >= tolerance * poi.getMax()) {
         rval *= 0.8;
         poi.setVal(rval);
         minim.setStrategy(0);
         bool success = minim.improve(verbose-2, /*cascade=*/false);
         lastnll = thisnll;
         thisnll = nll->getVal();
         if (success == false) {
             std::cerr << "Minimization failed at " << poi.getVal() <<". exiting the loop" << std::endl;
             return -1;
         } 
         if (verbose) {  
             printf("%8.5f  %8.5f\n", rval, thisnll-minnll); fflush(stdout);  
             points->Set(points->GetN()+1);
             points->SetPoint(points->GetN()-1, rval, thisnll - minnll);
         }
         if (fabs(lastnll - thisnll) < 7*minimizerToleranceForBF_) {
             std::cout << "This is enough." << std::endl;
             if (thisnll < lastnll) {
                 std::cout << "Linear extrapolation from " << thisnll <<  " to " << ( thisnll - (lastnll-thisnll)*rval )<< std::endl;
                 thisnll -= (lastnll-thisnll)*rval;
             }
             break;
         }
 #if 0
         if (lastnll > thisnll) {
             std::cout << "Secondary minimum found, back to original minimization" << std::endl;
             {
                 MinimizerSentry minimizerConfig(minimizerAlgo_, minimizerTolerance_);
                 poi.setConstant(false);
                 poi.setVal(rbest);
                 minim.improve(verbose - 1);
                 printf("New global minimum at %8.5f (was %8.5f), the shift in nll is %8.5f\n", poi.getVal(), rbest, nll->getVal()-minnll);
                 minnll = nll->getVal(); 
                 rbest = poi.getVal(); 
                 rval = rbest;
                 poi.setConstant(true);
             }
         }
 #endif
    }
     if (points) outputFile->WriteTObject(points);
    return (thisnll - minnll);
 }

double ProfileLikelihood::significanceFromScan	(	RooAbsPdf &	pdf,
		RooAbsData &	data,
		RooRealVar &	poi,
		const RooArgSet *	nuisances,
		double	tolerance,
		int	npoints
	)		const

protected

Definition at line 466 of file ProfileLikelihood.cc.

References bfAlgo_, dtNoiseDBValidation_cfg::cerr, CascadeMinimizer::Constrained, gather_cfg::cout, i, CascadeMinimizer::improve(), mass_, CascadeMinimizer::minimize(), minimizerAlgo_, minimizerAlgoForBF_, minimizerTolerance_, minimizerToleranceForBF_, download_sqlite_cfg::outputFile, run_regression::ret, CascadeMinimizer::save(), CascadeMinimizer::setStrategy(), errorMatrix2Lands_multiChannel::start, relval_steps::steps, summarizeEdmComparisonLogfiles::success, and CascadeMinimizer::Unconstrained.

Referenced by runSignificance().

                                                                                                                                                                {
     std::auto_ptr<RooAbsReal> nll(pdf.createNLL(data, RooFit::Constrain(*nuisances)));
     double maxScan = poi.getMax()*0.7;
     bool stepDown = (bfAlgo_.find("stepDown") != std::string::npos);
     bool twice    = (bfAlgo_.find("Twice")    != std::string::npos);
     poi.setConstant(false);
     poi.setVal(0.05*poi.getMax());
     CascadeMinimizer minim0(*nll, CascadeMinimizer::Unconstrained, &poi);
     minim0.setStrategy(0);
     minim0.minimize(verbose-2);
     if (!stepDown) {
         if (poi.getVal() < 0) {
             printf("Minimum found at %s = %8.5f < 0: significance will be zero\n", poi.GetName(), poi.getVal());
             return 0;
         }
         maxScan = poi.getVal()*1.4;
     } else {
         poi.setVal(0);
     }
     poi.setConstant(true);
     CascadeMinimizer minim(*nll, CascadeMinimizer::Constrained);
     if (!minim.minimize(verbose-2)) {
         std::cerr << "Initial minimization failed. Aborting." << std::endl;
         return -1;
     } else if (verbose > 0) {
         printf("Minimum found at %s = %8.5f\n", poi.GetName(), poi.getVal());
     }
     MinimizerSentry minimizerConfig(minimizerAlgoForBF_, minimizerToleranceForBF_);
     std::auto_ptr<RooFitResult> start(minim.save());
     double minnll = nll->getVal(), thisnll = minnll, refnll = thisnll, maxnll = thisnll;
     double rbest = poi.getVal(), rval = rbest;
     TGraph *points = new TGraph(steps+1); points->SetName(Form("nll_scan_%g", mass_));
     TF1    *fit    = new TF1("fit","[0]*pow(abs(x-[1]), [2])+[3]",0,poi.getMax());
     fit->SetParNames("norm","bestfit","power","offset");
     points->SetPoint(0, rval, 0);
     if (verbose) {
         printf("  %-6s  delta(NLL)\n", poi.GetName());
         printf("%8.5f  %8.5f\n", rval, 0.);
         fflush(stdout);
     }
     for (int i = 1; i < steps; ++i) {
         rval = (maxScan * (stepDown ? i : steps-i-1))/steps;
         poi.setVal(rval);
         bool success = minim.improve(verbose-2, /*cascade=*/false);
         thisnll = nll->getVal();
         if (success == false) std::cerr << "Minimization failed at " << poi.getVal() <<"." << std::endl;
         if (verbose) {  printf("%8.5f  %8.5f\n", rval, thisnll-refnll); fflush(stdout);  }
         points->SetPoint(i, rval, thisnll-refnll);
         if (thisnll < minnll) { minnll = thisnll; rbest = rval; }
         if (rval <= rbest && thisnll > maxnll) { maxnll = thisnll;  }
     }
     if (twice) {
         if (verbose) {
             printf("\nPlay it again, sam.\n");
             printf("  %-6s  delta(NLL)\n", poi.GetName());
             fflush(stdout);
         }
         for (int i = steps-1; i >= 0; --i) {
             rval = (maxScan * (stepDown ? i : steps-i-1))/steps;
             if (i == 0 && !stepDown) rval = rbest;
             poi.setVal(rval);
             bool success = minim.improve(verbose-2, /*cascade=*/false);
             thisnll = nll->getVal();
             if (success == false) std::cerr << "Minimization failed at " << poi.getVal() <<"." << std::endl;
             if (verbose) {  printf("%8.5f  %8.5f\n", rval, thisnll-refnll); fflush(stdout);  }
             points->SetPoint(i, rval, thisnll-refnll);
             if (thisnll < minnll) { minnll = thisnll; rbest = rval; }
             if (rval <= rbest && thisnll > maxnll) { maxnll = thisnll;  }
         }
     }
     fit->SetParameters(1, rbest, 2.0, minnll-refnll);
     points->Sort();
     double ret = (maxnll - minnll);
     TFitResultPtr res;
     {
         MinimizerSentry minimizerConfig(minimizerAlgo_, minimizerTolerance_);
         res = points->Fit(fit,"S0");
     }
     outputFile->WriteTObject(points);
     outputFile->WriteTObject(fit);
     if (res.Get()->Status() == 0) {
         std::cout << "Using first and last value, the result would be " << ret << std::endl;
         ret = fit->Eval(0) - fit->Eval(fit->GetParameter(1)) ;
         std::cout << "Using output of fit, the result is " << ret << std::endl;
     } else {
         std::cout << "Using first and last value" << std::endl;
     }
     return ret;
 }

std::pair< double, double > ProfileLikelihood::upperLimitBruteForce	(	RooAbsPdf &	pdf,
		RooAbsData &	data,
		RooRealVar &	poi,
		const RooArgSet *	nuisances,
		double	tolerance,
		double	cl
	)		const

protected

Definition at line 308 of file ProfileLikelihood.cc.

References dtNoiseDBValidation_cfg::cerr, cmsPerfPublish::fail(), lowerLimit_, minimizerAlgoForBF_, minimizerToleranceForBF_, nllutils::robustMinimize(), errorMatrix2Lands_multiChannel::start, summarizeEdmComparisonLogfiles::success, and filterCSVwithJSON::target.

Referenced by runLimit().

                                                                                                                                                                                {
     poi.setConstant(false);
     std::auto_ptr<RooAbsReal> nll(pdf.createNLL(data, RooFit::Constrain(*nuisances)));
     RooMinimizerOpt minim0(*nll);
     minim0.setStrategy(0);
     minim0.setPrintLevel(-1);
     nllutils::robustMinimize(*nll, minim0, verbose-2);
     poi.setConstant(true);
     RooMinimizerOpt minim(*nll);
     minim.setPrintLevel(-1);
     if (!nllutils::robustMinimize(*nll, minim, verbose-2)) {
         std::cerr << "Initial minimization failed. Aborting." << std::endl;
         return std::pair<double,double>(0, -1);
     }
     std::auto_ptr<RooFitResult> start(minim.save());
     double minnll = nll->getVal();
     double rval = poi.getVal() + (lowerLimit_ ? -3 : +3)*poi.getError(), rlow = poi.getVal(), rhigh = lowerLimit_ ? poi.getMin() : poi.getMax();
     if (rval >= rhigh || rval <= rlow) rval = 0.5*(rlow + rhigh);
     double target = minnll + 0.5*TMath::ChisquareQuantile(cl,1);
     //minim.setPrintLevel(verbose-2);
     MinimizerSentry minimizerConfig(minimizerAlgoForBF_, minimizerToleranceForBF_);
     bool fail = false;
     if (verbose) {
         printf("  %-6s  delta(NLL)\n",poi.GetName());
         printf("%8.5f  %8.5f\n", rval, 0.);
         fflush(stdout);
     }
     do {
         poi.setVal(rval);
         minim.setStrategy(0);
         bool success = nllutils::robustMinimize(*nll, minim, verbose-2);
         if (success == false) {
             std::cerr << "Minimization failed at " << poi.getVal() <<". exiting the bisection loop" << std::endl;
             fail = true; 
             break;
         }
         double nllthis = nll->getVal();
         if (verbose) {  printf("%8.5f  %8.5f\n", rval, nllthis-minnll); fflush(stdout);  }
         if (fabs(nllthis - target) < tolerance) {
             return std::pair<double,double>(rval, (rhigh - rlow)*0.5);
         } else if (nllthis < target) {
             (lowerLimit_ ? rhigh : rlow) = rval;
             rval = 0.5*(rval + rhigh); 
         } else {
             (lowerLimit_ ? rlow : rhigh) = rval;
             rval = 0.5*(rval + rlow); 
         }
     } while (fabs(rhigh - rlow) > tolerance);
     if (fail) {
         // try do do it in small steps instead
         std::auto_ptr<RooArgSet> pars(nll->getParameters((const RooArgSet *)0));
         double dx = (lowerLimit_ ? -0.05 : +0.05)*poi.getError();
         *pars = start->floatParsFinal();
         rval = poi.getVal() + dx;
         do {
             poi.setVal(rval);
             minim.setStrategy(0);
             bool success = nllutils::robustMinimize(*nll, minim, verbose-2);
             if (success == false) {
                 std::cerr << "Minimization failed at " << poi.getVal() <<". exiting the stepping loop" << std::endl;
                 return std::pair<double,double>(poi.getVal(), fabs(rhigh - rlow)*0.5);
             }
             double nllthis = nll->getVal();
             if (verbose) {  printf("%8.5f  %8.5f\n", rval, nllthis-minnll); fflush(stdout);  }
             if (fabs(nllthis - target) < tolerance) {
                 return std::pair<double,double>(rval, fabs(dx));
             } else if (nllthis < target) {
                 rval += dx;
             } else {
                 dx *= 0.5;
                 rval -= dx;
             }
         } while (rval < poi.getMax() && rval > poi.getMin());
         return std::pair<double,double>(poi.getMax(), 0);
     } else {
         return std::pair<double,double>(poi.getVal(), fabs(rhigh - rlow)*0.5);
     }
 }

double ProfileLikelihood::upperLimitWithMinos	(	RooAbsPdf &	pdf,
		RooAbsData &	data,
		RooRealVar &	poi,
		const RooArgSet *	nuisances,
		double	tolerance,
		double	cl
	)		const

protected

Definition at line 285 of file ProfileLikelihood.cc.

References dtNoiseDBValidation_cfg::cerr, MessageLogger_cff::limit, lowerLimit_, and nllutils::robustMinimize().

Referenced by runLimit().

                                                                                                                                                               {
     std::auto_ptr<RooAbsReal> nll(pdf.createNLL(data, RooFit::Constrain(*nuisances)));
     RooMinimizerOpt minim(*nll);
     minim.setStrategy(0);
     minim.setPrintLevel(verbose-1);
     minim.setErrorLevel(0.5*TMath::ChisquareQuantile(cl,1));
     nllutils::robustMinimize(*nll, minim, verbose-1);
     int minosStat = minim.minos(RooArgSet(poi));
     if (minosStat == -1) return std::numeric_limits<double>::quiet_NaN();
     std::auto_ptr<RooFitResult> res(minim.save());
     double muhat = poi.getVal(), limit = poi.getVal() + (lowerLimit_ ? poi.getAsymErrorLo() : poi.getAsymErrorHi());
     double nll0 = nll->getVal();
     poi.setVal(limit);
     double nll2 = nll->getVal();
     if (nll2 < nll0 + 0.75 * 0.5*TMath::ChisquareQuantile(cl,1)) {
         std::cerr << "ERROR: unprofiled likelihood gives better result than profiled one. deltaNLL = " << (nll2-nll0) << ". will try brute force." << std::endl;
         poi.setVal(muhat);
         return std::numeric_limits<double>::quiet_NaN();
     }
     if (verbose > 1) res->Print("V");
     return limit;
 }