import ROOT import os # structure defining the options class HypoTestInvOptions: plotHypoTestResult = True # plot test statistic result at each point writeResult = True # write HypoTestInverterResult in a file resultFileName = ( ROOT.TString() ) # file with results (by default is built automatically using the workspace input file name) optimize = True # optimize evaluation of test statistic useVectorStore = True # convert data to use roofit data store generateBinned = False # generate binned data sets noSystematics = False # force all systematics to be off (i.e. set all nuisance parameters as constat # to their nominal values) nToysRatio = 2 # ratio Ntoys S+b/ntoysB maxPOI = -1 # max value used of POI (in case of auto scan) enableDetailedOutput = ( False # enable detailed output with all fit information for each toys (output will be written in result file) ) rebuild = False # re-do extra toys for computing expected limits and rebuild test stat # distributions (N.B this requires much more CPU (factor is equivalent to nToyToRebuild) nToyToRebuild = 100 # number of toys used to rebuild rebuildParamValues = 0 # = 0 do a profile of all the parameters on the B (alt snapshot) before performing a # rebuild operation (default) # = 1 use initial workspace parameters with B snapshot values # = 2 use all initial workspace parameters with B # Otherwise the rebuild will be performed using initialFit = -1 # do a first fit to the model (-1 : default, 0 skip fit, 1 do always fit) randomSeed = -1 # random seed (if = -1: use default value, if = 0 always random ) nAsimovBins = 0 # number of bins in observables used for Asimov data sets (0 is the default and it is given by # workspace, typically is 100) reuseAltToys = False # reuse same toys for alternate hypothesis (if set one gets more stable bands) confLevel = 0.95 # confidence level value minimizerType = "" # minimizer type (default is what is in ROOT.Math.MinimizerOptions.DefaultMinimizerType() massValue = "" # extra string to tag output file of result printLevel = 0 # print level for debugging PL test statistics and calculators useNLLOffset = False # use NLL offset when fitting (this increase stability of fits) optHTInv = HypoTestInvOptions() # internal class to run the inverter and more large_declaration = """ \ namespace RooStats{ class HypoTestInvTool { public: HypoTestInvTool(); virtual ~HypoTestInvTool() {} HypoTestInverterResult *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 = false, const char *nuisPriorName = 0); void AnalyzeResult(HypoTestInverterResult *r, int calculatorType, int testStatType, bool useCLs, int npoints, const char *fileNameBase = 0); void SetParameter(const char *name, const char *value); void SetParameter(const char *name, bool value); void SetParameter(const char *name, int value); void SetParameter(const char *name, double value); //private: public: bool mPlotHypoTestResult; bool mWriteResult; bool mOptimize; bool mUseVectorStore; bool mGenerateBinned; bool mRebuild; bool mReuseAltToys; bool mEnableDetOutput; int mNToyToRebuild; int mRebuildParamValues; int mPrintLevel; int mInitialFit; int mRandomSeed; double mNToysRatio; double mMaxPoi; int mAsimovBins; std::string mMassValue; std::string mMinimizerType; // minimizer type (default is what is in ROOT::Math::MinimizerOptions::DefaultMinimizerType()) TString mResultFileName; }; } // end using RooStats namespace RooStats::HypoTestInvTool::HypoTestInvTool() : mPlotHypoTestResult(true), mWriteResult(false), mOptimize(true), mUseVectorStore(true), mGenerateBinned(false), mEnableDetOutput(false), mRebuild(false), mReuseAltToys(false), mNToyToRebuild(100), mRebuildParamValues(0), mPrintLevel(0), mInitialFit(-1), mRandomSeed(-1), mNToysRatio(2), mMaxPoi(-1), mAsimovBins(0), mMassValue(""), mMinimizerType(""), mResultFileName() { } """ ROOT.gInterpreter.Declare(large_declaration) # Expanding definitions of class: HypoTestInvTool into HypoTestInvTool_plus class HypoTestInvTool_plus(ROOT.RooStats.HypoTestInvTool): # RooStats.HypoTestInvTool.SetParameter(name, value): def SetParameter(self, name, value): # SetParameter is polymorphic. if (type(name) is str) and (type(value) is str): # # set boolean parameters # s_name = ROOT.std.string(name) if s_name.find("PlotHypoTestResult") != ROOT.std.string.npos: self.mPlotHypoTestResult = value if s_name.find("WriteResult") != ROOT.std.string.npos: self.mWriteResult = value if s_name.find("Optimize") != ROOT.std.string.npos: self.mOptimize = value if s_name.find("UseVectorStore") != ROOT.std.string.npos: self.mUseVectorStore = value if s_name.find("GenerateBinned") != ROOT.std.string.npos: self.mGenerateBinned = value if s_name.find("EnableDetailedOutput") != ROOT.std.string.npos: self.mEnableDetOutput = value if s_name.find("Rebuild") != ROOT.std.string.npos: self.mRebuild = value if s_name.find("ReuseAltToys") != ROOT.std.string.npos: self.mReuseAltToys = value return # RooStats.HypoTestInvTool.SetParameter = SetParameter # RooStats.HypoTestInvTool.SetParameter(name, value): # def SetParameter(self, name, value): elif (type(name) is str) and (type(value) is bool): # # set integer parameters # s_name = ROOT.std.string(name) if s_name.find("NToyToRebuild") != ROOT.std.string.npos: self.mNToyToRebuild = value if s_name.find("RebuildParamValues") != ROOT.std.string.npos: self.mRebuildParamValues = value if s_name.find("PrintLevel") != ROOT.std.string.npos: self.mPrintLevel = value if s_name.find("InitialFit") != ROOT.std.string.npos: self.mInitialFit = value if s_name.find("RandomSeed") != ROOT.std.string.npos: self.mRandomSeed = value if s_name.find("AsimovBins") != ROOT.std.string.npos: self.mAsimovBins = value return # RooStats.HypoTestInvTool.SetParameter = SetParameter # RooStats.HypoTestInvTool.SetParameter(name, value): # def SetParameter(self, name, value): elif (type(name) is str) and (type(value) is int): # # set double precision parameters # s_name = ROOT.std.string(name) if s_name.find("NToysRatio") != ROOT.std.string.npos: self.mNToysRatio = value if s_name.find("MaxPOI") != ROOT.std.string.npos: self.mMaxPoi = value return # RooStats.HypoTestInvTool.SetParameter = SetParameter # RooStats.HypoTestInvTool.SetParameter(name, value): # def SetParameter(self, name, value): elif (type(name) is str) and (type(value) is (float or double)): # # set string parameters # s_name = ROOT.std.string(name) if s_name.find("MassValue") != ROOT.std.string.npos: global gselfmMassValue gselfmMassValue = self.mMassValue self.mMassValue = value # (self.mMassValue).assign(value) if s_name.find("MinimizerType") != ROOT.std.string.npos: self.mMassValue = value # self.mMinimizerType.assign(value) if s_name.find("ResultFileName") != ROOT.std.string.npos: self.mResultFileName = value return ROOT.RooStats.HypoTestInvTool.SetParameter = SetParameter # --piece of code moved forwards...- # --- # RooStats.HypoTestInvTool.AnalyzeResult def AnalyzeResult(self, r, calculatorType, testStatType, useCLs, npoints, fileNameBase): # type(r) is HypoTestInverterResult # analyze result produced by the inverter, optionally save it in a file lowerLimit = 0 llError = 0 # if defined ROOT_SVN_VERSION && ROOT_SVN_VERSION >= 44126 if r.IsTwoSided(): lowerLimit = r.LowerLimit() llError = r.LowerLimitEstimatedError() # else lowerLimit = r.LowerLimit() llError = r.LowerLimitEstimatedError() # endif upperLimit = r.UpperLimit() ulError = r.UpperLimitEstimatedError() # ROOT.std::cout << "DEBUG : [ " << lowerLimit << " , " << upperLimit << " ] " << ROOT.std::endl; if lowerLimit < upperLimit * (1.0 - 1.0e-4) and lowerLimit != 0: print(f"The computed lower limit is: ", lowerLimit, " +/- ", llError) print(f"The computed upper limit is: {upperLimit} +/- ", ulError) # compute expected limit print(f"Expected upper limits, using the B (alternate) model : ") print(f" expected limit (median) ", r.GetExpectedUpperLimit(0)) print(f" expected limit (-1 sig) ", r.GetExpectedUpperLimit(-1)) print(f" expected limit (+1 sig) ", r.GetExpectedUpperLimit(1)) print(f" expected limit (-2 sig) ", r.GetExpectedUpperLimit(-2)) print(f" expected limit (+2 sig) ", r.GetExpectedUpperLimit(2)) # detailed output if self.mEnableDetOutput: self.mWriteResult = True ROOT.Info("StandardHypoTestInvDemo", "detailed output will be written in output result file") # write result in a file if r != ROOT.kNone and self.mWriteResult: # write to a file the results calcType = "Freq" if (calculatorType == 0) else ("Hybr" if (calculatorType == 1) else "Asym") limitType = "CLs" if (useCLs) else "Cls+b" scanType = "auto" if (npoints < 0) else "grid" if self.mResultFileName.IsNull(): self.mResultFileName = ROOT.TString.Format( "%s_%s_%s_ts%d_", calcType, limitType, scanType, testStatType ) # strip the / from the filename if self.mMassValue.size() > 0: self.mResultFileName += self.mMassValue self.mResultFileName += "_" name = fileNameBase name.Replace(0, name.Last("/") + 1, "") self.mResultFileName += name # get (if existing) rebuilt UL distribution uldistFile = "RULDist.root" ulDist = 0 existULDist = not ROOT.gSystem.AccessPathName(uldistFile) if existULDist: fileULDist = TFile.Open(uldistFile) if fileULDist: ulDist = fileULDist.Get("RULDist") fileOut = TFile(self.mResultFileName, "RECREATE") r.Write() if ulDist: ulDist.Write() ROOT.Info( "StandardHypoTestInvDemo", "HypoTestInverterResult has been written in the file %s", self.mResultFileName.Data(), ) fileOut.Close() # plot the result ( p values vs scan points) typeName = "" if calculatorType == 0: typeName = "Frequentist" if calculatorType == 1: typeName = "Hybrid" elif calculatorType == 2 or calculatorType == 3: typeName = "Asymptotic" self.mPlotHypoTestResult = False resultName = r.GetName() plotTitle = "{} CL Scan for workspace {}".format(str(typeName), resultName) global gr gr = r plot = ROOT.RooStats.HypoTestInverterPlot("HTI_Result_Plot", plotTitle, r) # plot in a new canvas with style c1Name = "{}_Scan".format(typeName) c1 = ROOT.TCanvas(c1Name) c1.SetLogy(False) plot.Draw("CLb 2CL") # plot all and Clb # if (useCLs): # plot.Draw("CLb 2CL") # plot all and Clb ??? # else: # plot.Draw("") # plot all and Clb ??? c1.Update() c1.Draw() c1.SaveAs("StandardHypoTestInvDemo.c1.png") nEntries = r.ArraySize() # plot test statistics distributions for the two hypothesis if self.mPlotHypoTestResult: c2 = ROOT.TCanvas("c2") if nEntries > 1: ny = ROOT.TMath.CeilNint(ROOT.TMath.Sqrt(nEntries)) nx = ROOT.TMath.CeilNint(float(nEntries) / ny) c2.Divide(nx, ny) for i in range(nEntries): if nEntries > 1: c2.cd(i + 1) pl = plot.MakeTestStatPlot(i) pl.SetLogYaxis(True) pl.Draw() c2.Update() c2.Draw() c2.SaveAs("StandardHypoTestInvDemo.c2.png") ROOT.gPad = c1 ROOT.RooStats.HypoTestInvTool.AnalyzeResult = AnalyzeResult # internal routine to run the inverter # RooStats.HypoTestInvTool.RunInverter def RunInverter( self, w, modelSBName, modelBName, dataName, Type, testStatType, useCLs, npoints, poimin, poimax, ntoys, useNumberCounting, nuisPriorName, ): print(f"Running HypoTestInverter on the workspace ", w.GetName()) w.Print() data = w.data(dataName) if not data: Error("StandardHypoTestDemo", "Not existing data {}".format(dataName)) return 0 else: print(f"Using data set ", dataName) if self.mUseVectorStore: ROOT.RooAbsData.setDefaultStorageType(ROOT.RooAbsData.Vector) data.convertToVectorStore() # get models from WS # get the modelConfig out of the file bModel = w.obj(modelBName) sbModel = w.obj(modelSBName) if not sbModel: Error("StandardHypoTestDemo", "Not existing ModelConfig %s", modelSBName) return 0 # check the model if not sbModel.GetPdf(): Error("StandardHypoTestDemo", "Model %s has no pdf ", modelSBName) return 0 if not sbModel.GetParametersOfInterest(): Error("StandardHypoTestDemo", "Model %s has no poi ", modelSBName) return 0 if not sbModel.GetObservables(): Error("StandardHypoTestInvDemo", "Model %s has no observables ", modelSBName) return 0 if not sbModel.GetSnapshot(): ROOT.Info( "StandardHypoTestInvDemo", "Model {} has no snapshot - make one using model poi".format(modelSBName) ) sbModel.SetSnapshot(sbModel.GetParametersOfInterest()) # case of no systematics # remove nuisance parameters from model if optHTInv.noSystematics: nuisPar = sbModel.GetNuisanceParameters() if nuisPar and nuisPar.getSize() > 0: print( f"StandardHypoTestInvDemo", " - Switch off all systematics by setting them constant to their initial values", ) ROOT.RooStats.SetAllConstant(nuisPar) if bModel: bnuisPar = bModel.GetNuisanceParameters() if bnuisPar: ROOT.RooStats.SetAllConstant(bnuisPar) if (not bModel) or (bModel == sbModel): ROOT.Info("StandardHypoTestInvDemo", "The background model {} does not exist".format(modelBName)) ROOT.Info("StandardHypoTestInvDemo", "Copy it from ModelConfig {} and set POI to zero".format(modelSBName)) bModel = sbModel.Clone() bModel.SetName(modelSBName + "_with_poi_0") var = bModel.GetParametersOfInterest().first() if not var: return 0 oldval = var.getVal() var.setVal(0) bModel.SetSnapshot(ROOT.RooArgSet(var)) var.setVal(oldval) else: if not bModel.GetSnapshot(): ROOT.Info( "StandardHypoTestInvDemo", "Model %s has no snapshot - make one using model poi and 0 values ", modelBName, ) var = bModel.GetParametersOfInterest().first() if var: oldval = var.getVal() var.setVal(0) bModel.SetSnapshot(ROOT.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 globals are not needed if Type != 1: hasNuisParam = sbModel.GetNuisanceParameters() and sbModel.GetNuisanceParameters().getSize() > 0 hasGlobalObs = sbModel.GetGlobalObservables() and sbModel.GetGlobalObservables().getSize() > 0 if hasNuisParam and not hasGlobalObs: # try to see if model has nuisance parameters first 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 ", ) # save all initial parameters of the model including the global observables initialParameters = ROOT.RooArgSet() allParams = sbModel.GetPdf().getParameters(data) allParams.snapshot(initialParameters) # run first a data fit poiSet = sbModel.GetParametersOfInterest() poi = poiSet.first() print("StandardHypoTestInvDemo : POI initial value: ", poi.GetName(), " = ", poi.getVal()) # fit the data first (need to use constraint ) tw = ROOT.TStopwatch() doFit = self.mInitialFit if testStatType == 0 and self.mInitialFit == -1: doFit = False # case of LEP test statistic if type == 3 and self.mInitialFit == -1: doFit = False # case of Asymptoticcalculator with nominal Asimov poihat = 0 if len(self.mMinimizerType) == 0: self.mMinimizerType = ROOT.Math.MinimizerOptions.DefaultMinimizerType() else: ROOT.Math.MinimizerOptions.SetDefaultMinimizer(str(self.mMinimizerType)) ROOT.Info( "StandardHypoTestInvDemo", "Using {} as minimizer for computing the test statistic".format( str(ROOT.Math.MinimizerOptions.DefaultMinimizerType()) ), ) 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 ROOT.Info("StandardHypoTestInvDemo", " Doing a first fit to the observed data ") constrainParams = RooArgSet() if sbModel.GetNuisanceParameters(): constrainParams.add(sbModel.GetNuisanceParameters()) RooStats.RemoveConstantParameters(constrainParams) tw.Start() fitres = sbModel.GetPdf().fitTo( data, InitialHesse(False), Hesse(False), Minimizer(str(self.mMinimizerType), "Migrad"), Strategy(0), PrintLevel(self.mPrintLevel), Constrain(constrainParams), Save(True), Offset(RooStats.NLLOffsetMode()), ) 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(self.mMinimizerType, "Migrad"), Strategy(1), PrintLevel(self.mPrintLevel + 1), Constrain(constrainParams), Save(True), Offset(RooStats.NLLOffsetMode()), ) if fitres.status() != 0: Warning("StandardHypoTestInvDemo", " Fit still failed - continue anyway.....") print("StandardHypoTestInvDemo - Best Fit value : ", poi.GetName(), " = ", poihat, " +/- ", poi.getError()) print(f"Time for fitting : ") tw.Print() # save best fit value in the poi snapshot sbModel.SetSnapshot(sbModel.GetParametersOfInterest()) print(f"StandardHypoTestInvo: snapshot of S+B Model ", sbModel.GetName(), " is set to the best fit value") # print a message in case of LEP test statistics because it affects result by doing or not doing a fit if testStatType == 0: if not doFit: ROOT.Info( "StandardHypoTestInvDemo", "Using LEP test statistic - an initial fit is not done and the TS will use " + "the nuisances at the model value", ) else: ROOT.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 slrts = ROOT.RooStats.SimpleLikelihoodRatioTestStat(sbModel.GetPdf(), bModel.GetPdf()) # null parameters must includes snapshot of poi plus the nuisance values nullParams = ROOT.RooArgSet(sbModel.GetSnapshot()) if sbModel.GetNuisanceParameters(): nullParams.add(sbModel.GetNuisanceParameters()) if sbModel.GetSnapshot(): slrts.SetNullParameters(nullParams) altParams = ROOT.RooArgSet(bModel.GetSnapshot()) if bModel.GetNuisanceParameters(): altParams.add(bModel.GetNuisanceParameters()) if bModel.GetSnapshot(): slrts.SetAltParameters(altParams) if self.mEnableDetOutput: slrts.EnableDetailedOutput() # ratio of profile likelihood - need to pass snapshot for the alt ropl = ROOT.RooStats.RatioOfProfiledLikelihoodsTestStat(sbModel.GetPdf(), bModel.GetPdf(), bModel.GetSnapshot()) ropl.SetSubtractMLE(False) if testStatType == 11: ropl.SetSubtractMLE(True) ropl.SetPrintLevel(self.mPrintLevel) ropl.SetMinimizer(self.mMinimizerType.c_str()) if self.mEnableDetOutput: ropl.EnableDetailedOutput() profll = ROOT.RooStats.ProfileLikelihoodTestStat(sbModel.GetPdf()) if testStatType == 3: profll.SetOneSided(True) if testStatType == 4: profll.SetSigned(True) profll.SetMinimizer(self.mMinimizerType.c_str()) profll.SetPrintLevel(self.mPrintLevel) if self.mEnableDetOutput: profll.EnableDetailedOutput() profll.SetReuseNLL(self.mOptimize) slrts.SetReuseNLL(self.mOptimize) ropl.SetReuseNLL(self.mOptimize) if self.mOptimize: profll.SetStrategy(0) ropl.SetStrategy(0) ROOT.Math.MinimizerOptions.SetDefaultStrategy(0) if self.mMaxPoi > 0: poi.setMax(self.mMaxPoi) # increase limit maxll = ROOT.RooStats.MaxLikelihoodEstimateTestStat(sbModel.GetPdf(), poi) nevtts = ROOT.RooStats.NumEventsTestStat() ROOT.RooStats.AsymptoticCalculator.SetPrintLevel(self.mPrintLevel) # create the HypoTest calculator class hc = ROOT.nullptr if Type == 0: hc = ROOT.RooStats.FrequentistCalculator(data, bModel, sbModel) elif Type == 1: hc = ROOT.RooStats.HybridCalculator(data, bModel, sbModel) # elif (Type == 2 ): # hc = AsymptoticCalculator(data, bModel, sbModel, false, self.mAsimovBins) # elif (Type == 3 ): # hc = AsymptoticCalculator(data, bModel, sbModel, True, self.mAsimovBins) # for using # Asimov data generated with nominal values elif Type == 2: hc = ROOT.RooStats.AsymptoticCalculator(data, bModel, sbModel, False) elif Type == 3: hc = ROOT.RooStats.AsymptoticCalculator( data, bModel, sbModel, True ) # for using Asimov data generated with nominal values else: Error( "StandardHypoTestInvDemo", "Invalid - calculator type = {Type}supported values are only :\n\t\t\t 0 " + "(Frequentist) , 1 (Hybrid) , 2 (Asymptotic) ", ) return 0 # set the test statistic testStat = 0 if testStatType == 0: testStat = slrts if testStatType == 1 or testStatType == 11: testStat = ropl if testStatType == 2 or testStatType == 3 or testStatType == 4: testStat = profll if testStatType == 5: testStat = maxll if testStatType == 6: testStat = nevtts if testStat == 0: Error( "StandardHypoTestInvDemo", "Invalid - test statistic type = {testStatType} supported values are only :\n\t\t\t 0 (SLR) " + ", 1 (Tevatron) , 2 (PLR), 3 (PLR1), 4(MLE)", ) return 0 toymcs = hc.GetTestStatSampler() if toymcs and (Type == 0 or 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 not useNumberCounting: nEvents = data.numEntries() ROOT.Info( "StandardHypoTestInvDemo", "Pdf is not extended: number of events to generate taken from observed data set is {nEvents}", ) toymcs.SetNEventsPerToy(nEvents) else: ROOT.Info("StandardHypoTestInvDemo", "using a number counting pdf") toymcs.SetNEventsPerToy(1) toymcs.SetTestStatistic(testStat) if data.isWeighted() and not self.mGenerateBinned: ROOT.Info( "StandardHypoTestInvDemo", ( "Data set is weighted, nentries = {} and sum of weights = {:8.1f} but toy" + "generation is unbinned - it would be faster to set self.mGenerateBinned to true\n" ).format(data.numEntries(), data.sumEntries()), ) toymcs.SetGenerateBinned(self.mGenerateBinned) toymcs.SetUseMultiGen(self.mOptimize) if self.mGenerateBinned and sbModel.GetObservables().getSize() > 2: Warning( "StandardHypoTestInvDemo", ( "generate binned is activated but the number of observable is {}. Too much " + "memory could be needed for allocating all the bins" ).format(sbModel.GetObservables().getSize()), ) # set the random seed if needed if self.mRandomSeed >= 0: RooRandom.randomGenerator().SetSeed(self.mRandomSeed) # specify if need to re-use same toys if self.mReuseAltToys: hc.UseSameAltToys() if Type == 1: hhc = HybridCalculator(hc) assert hhc hhc.SetToys(ntoys, ntoys / self.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() or sbModel.GetNuisanceParameters(): # fix for using multigen (does not work in this case) toymcs.SetUseMultiGen(False) ToyMCSampler.SetAlwaysUseMultiGen(False) nuisPdf = 0 if nuisPriorName: nuisPdf = w.pdf(nuisPriorName) # use prior defined first in bModel (then in SbModel) if not nuisPdf: ROOT.Info( "StandardHypoTestInvDemo", "No nuisance pdf given for the HybridCalculator - try to deduce pdf from the model", ) if bModel.GetPdf() and bModel.GetObservables(): nuisPdf = RooStats.MakeNuisancePdf(bModel, "nuisancePdf_bmodel") else: nuisPdf = RooStats.MakeNuisancePdf(sbModel, "nuisancePdf_sbmodel") if not nuisPdf: if bModel.GetPriorPdf(): nuisPdf = bModel.GetPriorPdf() ROOT.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", "Cannot run Hybrid calculator because no prior on the nuisance " "parameter is specified or can be derived", ) return 0 assert nuisPdf ROOT.Info("StandardHypoTestInvDemo", "Using as nuisance Pdf ... ") nuisPdf.Print() nuisParams = ( ibModel.GetNuisanceParameters() if (bModel.GetNuisanceParameters()) else (sbModel.GetNuisanceParameters()) ) npnuisPdf.getObservables(nuisParams) if np.getSize() == 0: Warning( "StandardHypoTestInvDemo", "Prior nuisance does not depend on nuisance parameters. They will be smeared in their full range", ) hhc.ForcePriorNuisanceAlt(nuisPdf) hhc.ForcePriorNuisanceNull(nuisPdf) elif type == 2 or type == 3: if testStatType == 3: hc.SetOneSided(True) if testStatType != 2 and testStatType != 3: Warning( "StandardHypoTestInvDemo", "Only the PL test statistic can be used with AsymptoticCalculator - use by default a two-sided PL", ) elif type == 0: hc.SetToys(ntoys, ntoys / self.mNToysRatio) # store also the fit information for each poi point used by calculator based on toys if self.mEnableDetOutput: hc.StoreFitInfo(True) elif type == 1: hc.SetToys(ntoys, ntoys / self.mNToysRatio) # store also the fit information for each poi point used by calculator based on toys # if (self.mEnableDetOutput): # hc.StoreFitInfo(True) # Get the result ROOT.RooMsgService.instance().getStream(1).removeTopic(ROOT.RooFit.NumericIntegration) calc = ROOT.RooStats.HypoTestInverter(hc) calc.SetConfidenceLevel(optHTInv.confLevel) calc.UseCLs(useCLs) calc.SetVerbose(True) 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()) print(f"Doing a fixed scan in interval : {poimin}, {poimax} ") calc.SetFixedScan(npoints, poimin, poimax) else: # poi.setMax(10*int( (poihat+ 10 *poi.getError() )/10 ) ) print(f"Doing an automatic scan in interval : {poi.getMin()} , ", poi.getMax()) tw.Start() r = calc.GetInterval() print(f"Time to perform limit scan \n") tw.Print() if self.mRebuild: print(f"\n\n") print( f"Rebuild the upper limit distribution by re-generating new set of pseudo-experiment and re-compute " + "for each of them a new upper limit\n\n" ) allParams = sbModel.GetPdf().getParameters(data) # define on which value of nuisance parameters to do the rebuild # default is best fit value for bmodel snapshot if self.mRebuildParamValues != 0: # set all parameters to their initial workspace values allParams.assign(initialParameters) if self.mRebuildParamValues == 0 or self.mRebuildParamValues == 1: constrainParams = RooArgSet() if sbModel.GetNuisanceParameters(): constrainParams.add(sbModel.GetNuisanceParameters()) RooStats.RemoveConstantParameters(constrainParams) poiModel = sbModel.GetParametersOfInterest() bModel.LoadSnapshot() # do a profile using the B model snapshot if self.mRebuildParamValues == 0: RooStats.SetAllConstant(poiModel, True) sbModel.GetPdf().fitTo( data, InitialHesse(False), Hesse(False), Minimizer(self.mMinimizerType, "Migrad"), Strategy(0), PrintLevel(self.mPrintLevel), Constrain(constrainParams), Offset(RooStats.IsNLLOffset()), ) print(f"rebuild using fitted parameter value for B-model snapshot") constrainParams.Print("v") RooStats.SetAllConstant(poiModel, False) print(f"StandardHypoTestInvDemo: Initial parameters used for rebuilding: ") RooStats.PrintListContent(allParams, ROOT.std.cout) tw.Start() limDist = calc.GetUpperLimitDistribution(True, self.mNToyToRebuild) print(f"Time to rebuild distributions :") tw.Print() if limDist: print(f"Expected limits after rebuild distribution ") print(f"expected upper limit (median of limit distribution) ", limDist.InverseCDF(0.5)) print( f"expected -1 sig limit (0.16% quantile of limit dist) ", limDist.InverseCDF(ROOT.Math.normal_cdf(-1)), ) print( f"expected +1 sig limit (0.84% quantile of limit dist) ", limDist.InverseCDF(ROOT.Math.normal_cdf(1)), ) print( f"expected -2 sig limit (.025% quantile of limit dist) ", limDist.InverseCDF(ROOT.Math.normal_cdf(-2)), ) print( f"expected +2 sig limit (.975% quantile of limit dist) ", limDist.InverseCDF(ROOT.Math.normal_cdf(2)), ) # Plot the upper limit distribution limPlot = SamplingDistPlot(50 if (self.mNToyToRebuild < 200) else 100) limPlot.AddSamplingDistribution(limDist) limPlot.GetTH1F().SetStats(True) # display statistics limPlot.SetLineColor(kBlue) c1 = TCanvas("limPlot", "Upper Limit Distribution") limPlot.Draw() c1.Update() c1.Draw() c1.SaveAs("StandardHypoTestInvDemo.1.png") # save result in a file limDist.SetName("RULDist") fileOut = TFile("RULDist.root", "RECREATE") limDist.Write() fileOut.Close() # update r to a new updated result object containing the rebuilt expected p-values distributions # (it will not recompute the expected limit) if r: del r # need to delete previous object since GetInterval will return a cloned copy r = calc.GetInterval() else: print(f"ERROR : failed to re-build distributions ") return r ROOT.RooStats.HypoTestInvTool.RunInverter = RunInverter # end class HypoTestInvTool # Loading new definitions of HypoTestInvTool_plus into HypoTestInvTool ... ROOT.RooStats.HypoTestInvTool = HypoTestInvTool_plus # Could seem redundant but it would help you if you want expand new-new definitions. # -------------------------------------------------- def StandardHypoTestInvDemo( infile=0, wsName="combined", modelSBName="ModelConfig", modelBName="", dataName="obsData", calculatorType=0, testStatType=0, useCLs=True, npoints=6, poimin=0, poimax=5, ntoys=1000, useNumberCounting=False, 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 type = 3 Asymptotic calculator using nominal Asimov data sets (not using fitted parameter values but nominal ones) testStatType = 0 LEP Tevatron Likelihood mu_hat) mu_hat) statistic statistic useCLs scan for CLs (otherwise for CLs+b) npoints = -1 poimin,poimax: min/max value to scan in case of fixed scans (if min > max, try to find automatically) ntoys: number of toys to use useNumberCounting: set to True when using number counting events nuisPriorName: name of prior for the nuisance. This is often expressed as constraint term in the global model HybridCalculator (type=1) If not given by default the prior pdf from ModelConfig is used. extra options are available as global parameters of the macro. They major ones are: plotHypoTestResult plot result of tests at each point (TS distributions) (default is True) writeResult write result of scan (default is True) rebuild rebuild scan for expected limits (require extra toys) (default is False) generateBinned generate binned data sets for toys (default is False) - be careful not to activate with large (>=3) number of observables nToyRatio ratio of S+B/B toys (default is 2) """ filename = ROOT.TString(infile) if filename.IsNull(): filename = "results/example_combined_GaussExample_model.root" fileExist = not ROOT.gSystem.AccessPathName(filename) # note opposite return code # if file does not exists generate with histfactory if not fileExist: if os.name == "nt": print("HistFactory file cannto be generated on Windows - exit") return # Normally this would be run on the command line print(f"will run standard hist2workspace example") ROOT.gROOT.ProcessLine(".! prepareHistFactory .") ROOT.gROOT.ProcessLine(".! hist2workspace config/example.xml") print(f"\n\n---------------------") print(f"Done creating example input") print(f"---------------------\n\n") else: filename = infile # Try to open the file file = ROOT.TFile.Open(filename) # if input file was specified but not found, quit if not file: print(f"StandardRooStatsDemoMacro: Input file {filename} is not found") return calc = ROOT.RooStats.HypoTestInvTool() # instance of HypoTestInvTool # calc = HypoTestInvTool_plus() # instance of HypoTestInvTool # set parameters calc.SetParameter("PlotHypoTestResult", optHTInv.plotHypoTestResult) calc.SetParameter("WriteResult", optHTInv.writeResult) calc.SetParameter("Optimize", optHTInv.optimize) calc.SetParameter("UseVectorStore", optHTInv.useVectorStore) calc.SetParameter("GenerateBinned", optHTInv.generateBinned) calc.SetParameter("NToysRatio", optHTInv.nToysRatio) calc.SetParameter("MaxPOI", optHTInv.maxPOI) calc.SetParameter("EnableDetailedOutput", optHTInv.enableDetailedOutput) calc.SetParameter("Rebuild", optHTInv.rebuild) calc.SetParameter("ReuseAltToys", optHTInv.reuseAltToys) calc.SetParameter("NToyToRebuild", optHTInv.nToyToRebuild) calc.SetParameter("RebuildParamValues", optHTInv.rebuildParamValues) calc.SetParameter("MassValue", optHTInv.massValue) calc.SetParameter("MinimizerType", optHTInv.minimizerType) calc.SetParameter("PrintLevel", optHTInv.printLevel) calc.SetParameter("InitialFit", optHTInv.initialFit) calc.SetParameter("ResultFileName", optHTInv.resultFileName) calc.SetParameter("RandomSeed", optHTInv.randomSeed) calc.SetParameter("AsimovBins", optHTInv.nAsimovBins) # enable offset for all roostats if optHTInv.useNLLOffset: RooStats.SetNLLOffsetMode("initial") w = file.Get(wsName) r = 0 print(w, "\t", filename) if w != ROOT.kNone: r = calc.RunInverter( w, modelSBName, modelBName, dataName, calculatorType, testStatType, useCLs, npoints, poimin, poimax, ntoys, useNumberCounting, nuisPriorName, ) if not r: raise RuntimeError("Error running the HypoTestInverter - Exit ") return else: # case workspace is not present look for the inverter result print(f"Reading an HypoTestInverterResult with name {wsName} from file " << filename) r = file.Get(wsName) # dynamic_cast if not r: raise RuntimeError("File {filename} does not contain a workspace or an HypoTestInverterResult - Exit ") file.ls() return calc.AnalyzeResult(r, calculatorType, testStatType, useCLs, npoints, infile) return # -------------------------------------------------- def ReadResult(fileName, resultName="", useCLs=True): # read a previous stored result from a file given the result name StandardHypoTestInvDemo(fileName, resultName, "", "", "", 0, 0, useCLs) # ifdef USE_AS_MAIN def main(): StandardHypoTestInvDemo() main() # endif