/* -*- mode: c++ -*- */
// Standard tutorial macro for performing an inverted hypothesis test for computing an interval
//
// This macro will perform a scan of the p-values for computing the interval or limit
//
//Author: L. Moneta
//
// Usage:
//
// root>.L StandardHypoTestInvDemo.C
// root> StandardHypoTestInvDemo("fileName","workspace name","S+B modelconfig name","B model name","data set name",calculator type, test statistic type, use CLS,
// number of points, xmin, xmax, number of toys, use number counting)
//
//
// 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
// = 1 Tevatron
// = 2 Profile Likelihood two sided
// = 3 Profile Likelihood one sided (i.e. = 0 if mu < mu_hat)
// = 4 Profile Likelihood signed ( pll = -pll if mu < mu_hat)
// = 5 Max Likelihood Estimate as test statistic
// = 6 Number of observed event as test statistic
//
#include "TFile.h"
#include "RooWorkspace.h"
#include "RooAbsPdf.h"
#include "RooRealVar.h"
#include "RooDataSet.h"
#include "RooStats/ModelConfig.h"
#include "RooRandom.h"
#include "TGraphErrors.h"
#include "TGraphAsymmErrors.h"
#include "TCanvas.h"
#include "TLine.h"
#include "TROOT.h"
#include "RooStats/AsymptoticCalculator.h"
#include "RooStats/HybridCalculator.h"
#include "RooStats/FrequentistCalculator.h"
#include "RooStats/ToyMCSampler.h"
#include "RooStats/HypoTestPlot.h"
#include "RooStats/NumEventsTestStat.h"
#include "RooStats/ProfileLikelihoodTestStat.h"
#include "RooStats/SimpleLikelihoodRatioTestStat.h"
#include "RooStats/RatioOfProfiledLikelihoodsTestStat.h"
#include "RooStats/MaxLikelihoodEstimateTestStat.h"
#include "RooStats/NumEventsTestStat.h"
#include "RooStats/HypoTestInverter.h"
#include "RooStats/HypoTestInverterResult.h"
#include "RooStats/HypoTestInverterPlot.h"
using namespace RooFit;
using namespace RooStats;
bool plotHypoTestResult = true; // plot test statistic result at each point
bool writeResult = true; // write HypoTestInverterResult in a file
TString resultFileName; // file with results (by default is built automatically using the workspace input file name)
bool optimize = true; // optmize evaluation of test statistic
bool useVectorStore = true; // convert data to use new roofit data store
bool generateBinned = false; // generate binned data sets
bool noSystematics = false; // force all systematics to be off (i.e. set all nuisance parameters as constat
// to their nominal values)
double nToysRatio = 2; // ratio Ntoys S+b/ntoysB
double maxPOI = -1; // max value used of POI (in case of auto scan)
bool useProof = false; // use Proof Light when using toys (for freq or hybrid)
int nworkers = 4; // number of worker for Proof
bool 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)
int nToyToRebuild = 100; // number of toys used to rebuild
int initialFit = -1; // do a first fit to the model (-1 : default, 0 skip fit, 1 do always fit)
int randomSeed = -1; // random seed (if = -1: use default value, if = 0 always random )
// NOTE: Proof uses automatically a random seed
int 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)
bool reuseAltToys = false; // reuse same toys for alternate hypothesis (if set one gets more stable bands)
std::string massValue = ""; // extra string to tag output file of result
std::string minimizerType = ""; // minimizer type (default is what is in ROOT::Math::MinimizerOptions::DefaultMinimizerType()
int printLevel = 0; // print level for debugging PL test statistics and calculators
// internal class to run the inverter and more
namespace RooStats {
class HypoTestInvTool{
public:
HypoTestInvTool();
~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:
bool mPlotHypoTestResult;
bool mWriteResult;
bool mOptimize;
bool mUseVectorStore;
bool mGenerateBinned;
bool mUseProof;
bool mRebuild;
bool mReuseAltToys;
int mNWorkers;
int mNToyToRebuild;
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 namespace RooStats
RooStats::HypoTestInvTool::HypoTestInvTool() : mPlotHypoTestResult(true),
mWriteResult(false),
mOptimize(true),
mUseVectorStore(true),
mGenerateBinned(false),
mUseProof(false),
mRebuild(false),
mReuseAltToys(false),
mNWorkers(4),
mNToyToRebuild(100),
mPrintLevel(0),
mInitialFit(-1),
mRandomSeed(-1),
mNToysRatio(2),
mMaxPoi(-1),
mAsimovBins(0),
mMassValue(""),
mMinimizerType(""),
mResultFileName() {
}
void
RooStats::HypoTestInvTool::SetParameter(const char * name, bool value){
//
// set boolean parameters
//
std::string s_name(name);
if (s_name.find("PlotHypoTestResult") != std::string::npos) mPlotHypoTestResult = value;
if (s_name.find("WriteResult") != std::string::npos) mWriteResult = value;
if (s_name.find("Optimize") != std::string::npos) mOptimize = value;
if (s_name.find("UseVectorStore") != std::string::npos) mUseVectorStore = value;
if (s_name.find("GenerateBinned") != std::string::npos) mGenerateBinned = value;
if (s_name.find("UseProof") != std::string::npos) mUseProof = value;
if (s_name.find("Rebuild") != std::string::npos) mRebuild = value;
if (s_name.find("ReuseAltToys") != std::string::npos) mReuseAltToys = value;
return;
}
void
RooStats::HypoTestInvTool::SetParameter(const char * name, int value){
//
// set integer parameters
//
std::string s_name(name);
if (s_name.find("NWorkers") != std::string::npos) mNWorkers = value;
if (s_name.find("NToyToRebuild") != std::string::npos) mNToyToRebuild = value;
if (s_name.find("PrintLevel") != std::string::npos) mPrintLevel = value;
if (s_name.find("InitialFit") != std::string::npos) mInitialFit = value;
if (s_name.find("RandomSeed") != std::string::npos) mRandomSeed = value;
if (s_name.find("AsimovBins") != std::string::npos) mAsimovBins = value;
return;
}
void
RooStats::HypoTestInvTool::SetParameter(const char * name, double value){
//
// set double precision parameters
//
std::string s_name(name);
if (s_name.find("NToysRatio") != std::string::npos) mNToysRatio = value;
if (s_name.find("MaxPOI") != std::string::npos) mMaxPoi = value;
return;
}
void
RooStats::HypoTestInvTool::SetParameter(const char * name, const char * value){
//
// set string parameters
//
std::string s_name(name);
if (s_name.find("MassValue") != std::string::npos) mMassValue.assign(value);
if (s_name.find("MinimizerType") != std::string::npos) mMinimizerType.assign(value);
if (s_name.find("ResultFileName") != std::string::npos) mResultFileName = value;
return;
}
void
StandardHypoTestInvDemo(const char * infile = 0,
const char * wsName = "combined",
const char * modelSBName = "ModelConfig",
const char * modelBName = "",
const char * dataName = "obsData",
int calculatorType = 0,
int testStatType = 0,
bool useCLs = true ,
int npoints = 6,
double poimin = 0,
double poimax = 5,
int ntoys=1000,
bool useNumberCounting = 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
type = 3 Asymptotic calculator using nominal Asimov data sets (not using fitted parameter values but nominal ones)
testStatType = 0 LEP
= 1 Tevatron
= 2 Profile Likelihood
= 3 Profile Likelihood one sided (i.e. = 0 if mu < mu_hat)
= 4 Profiel Likelihood signed ( pll = -pll if mu < mu_hat)
= 5 Max Likelihood Estimate as test statistic
= 6 Number of observed event as test statistic
useCLs scan for CLs (otherwise for CLs+b)
npoints: number of points to scan , for autoscan set 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 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:
plotHypoTestResult plot result of tests at each point (TS distributions) (defauly is true)
useProof use Proof (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
a too large (>=3) number of observables
nToyRatio ratio of S+B/B toys (default is 2)
*/
TString fileName(infile);
if (fileName.IsNull()) {
fileName = "results/example_combined_GaussExample_model.root";
std::cout << "Use standard file generated with HistFactory : " << fileName << std::endl;
}
// open file and check if input file exists
TFile * file = TFile::Open(fileName);
// if input file was specified but not found, quit
if(!file && !TString(infile).IsNull()){
cout <<"file " << fileName << " 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;
}
HypoTestInvTool calc;
// set parameters
calc.SetParameter("PlotHypoTestResult", plotHypoTestResult);
calc.SetParameter("WriteResult", writeResult);
calc.SetParameter("Optimize", optimize);
calc.SetParameter("UseVectorStore", useVectorStore);
calc.SetParameter("GenerateBinned", generateBinned);
calc.SetParameter("NToysRatio", nToysRatio);
calc.SetParameter("MaxPOI", maxPOI);
calc.SetParameter("UseProof", useProof);
calc.SetParameter("NWorkers", nworkers);
calc.SetParameter("Rebuild", rebuild);
calc.SetParameter("ReuseAltToys", reuseAltToys);
calc.SetParameter("NToyToRebuild", nToyToRebuild);
calc.SetParameter("MassValue", massValue.c_str());
calc.SetParameter("MinimizerType", minimizerType.c_str());
calc.SetParameter("PrintLevel", printLevel);
calc.SetParameter("InitialFit",initialFit);
calc.SetParameter("ResultFileName",resultFileName);
calc.SetParameter("RandomSeed",randomSeed);
calc.SetParameter("AsimovBins",nAsimovBins);
RooWorkspace * w = dynamic_cast<RooWorkspace*>( file->Get(wsName) );
HypoTestInverterResult * r = 0;
std::cout << w << "\t" << fileName << std::endl;
if (w != NULL) {
r = calc.RunInverter(w, modelSBName, modelBName,
dataName, calculatorType, testStatType, useCLs,
npoints, poimin, poimax,
ntoys, useNumberCounting, nuisPriorName );
if (!r) {
std::cerr << "Error running the HypoTestInverter - Exit " << std::endl;
return;
}
}
else {
// case workspace is not present look for the inverter result
std::cout << "Reading an HypoTestInverterResult with name " << wsName << " from file " << fileName << std::endl;
r = dynamic_cast<HypoTestInverterResult*>( file->Get(wsName) ); //
if (!r) {
std::cerr << "File " << fileName << " does not contain a workspace or an HypoTestInverterResult - Exit "
<< std::endl;
file->ls();
return;
}
}
calc.AnalyzeResult( r, calculatorType, testStatType, useCLs, npoints, infile );
return;
}
void
RooStats::HypoTestInvTool::AnalyzeResult( HypoTestInverterResult * r,
int calculatorType,
int testStatType,
bool useCLs,
int npoints,
const char * fileNameBase ){
// analyze result produced by the inverter, optionally save it in a file
double lowerLimit = 0;
double 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
double upperLimit = r->UpperLimit();
double ulError = r->UpperLimitEstimatedError();
//std::cout << "DEBUG : [ " << lowerLimit << " , " << upperLimit << " ] " << std::endl;
if (lowerLimit < upperLimit*(1.- 1.E-4) && lowerLimit != 0)
std::cout << "The computed lower limit is: " << lowerLimit << " +/- " << llError << std::endl;
std::cout << "The computed upper limit is: " << upperLimit << " +/- " << ulError << std::endl;
// compute expected limit
std::cout << "Expected upper limits, using the B (alternate) model : " << std::endl;
std::cout << " expected limit (median) " << r->GetExpectedUpperLimit(0) << std::endl;
std::cout << " expected limit (-1 sig) " << r->GetExpectedUpperLimit(-1) << std::endl;
std::cout << " expected limit (+1 sig) " << r->GetExpectedUpperLimit(1) << std::endl;
std::cout << " expected limit (-2 sig) " << r->GetExpectedUpperLimit(-2) << std::endl;
std::cout << " expected limit (+2 sig) " << r->GetExpectedUpperLimit(2) << std::endl;
// write result in a file
if (r != NULL && mWriteResult) {
// write to a file the results
const char * calcType = (calculatorType == 0) ? "Freq" : (calculatorType == 1) ? "Hybr" : "Asym";
const char * limitType = (useCLs) ? "CLs" : "Cls+b";
const char * scanType = (npoints < 0) ? "auto" : "grid";
if (mResultFileName.IsNull()) {
mResultFileName = TString::Format("%s_%s_%s_ts%d_",calcType,limitType,scanType,testStatType);
//strip the / from the filename
if (mMassValue.size()>0) {
mResultFileName += mMassValue.c_str();
mResultFileName += "_";
}
TString name = fileNameBase;
name.Replace(0, name.Last('/')+1, "");
mResultFileName += name;
}
TFile * fileOut = new TFile(mResultFileName,"RECREATE");
r->Write();
fileOut->Close();
}
// plot the result ( p values vs scan points)
std::string typeName = "";
if (calculatorType == 0 )
typeName = "Frequentist";
if (calculatorType == 1 )
typeName = "Hybrid";
else if (calculatorType == 2 || calculatorType == 3) {
typeName = "Asymptotic";
mPlotHypoTestResult = false;
}
const char * resultName = r->GetName();
TString plotTitle = TString::Format("%s CL Scan for workspace %s",typeName.c_str(),resultName);
HypoTestInverterPlot *plot = new HypoTestInverterPlot("HTI_Result_Plot",plotTitle,r);
// plot in a new canvas with style
TString c1Name = TString::Format("%s_Scan",typeName.c_str());
TCanvas * c1 = new 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
const int nEntries = r->ArraySize();
// plot test statistics distributions for the two hypothesis
if (mPlotHypoTestResult) {
TCanvas * c2 = new TCanvas();
if (nEntries > 1) {
int ny = TMath::CeilNint(TMath::Sqrt(nEntries));
int nx = TMath::CeilNint(double(nEntries)/ny);
c2->Divide( nx,ny);
}
for (int i=0; i<nEntries; i++) {
if (nEntries > 1) c2->cd(i+1);
SamplingDistPlot * pl = plot->MakeTestStatPlot(i);
pl->SetLogYaxis(true);
pl->Draw();
}
}
}
// 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;
}
void ReadResult(const char * fileName, const char * resultName="", bool useCLs=true) {
// read a previous stored result from a file given the result name
StandardHypoTestInvDemo(fileName, resultName,"","","",0,0,useCLs);
}
#ifdef USE_AS_MAIN
int main() {
StandardHypoTestInvDemo();
}
#endif
