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StandardTestStatDistributionDemo.C File Reference

Detailed Description

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This simple script plots the sampling distribution of the profile likelihood ratio test statistic based on the input Model File. To do this one needs to specify the value of the parameter of interest that will be used for evaluating the test statistic and the value of the parameters used for generating the toy data. In this case, it uses the upper-limit estimated from the ProfileLikleihoodCalculator, which assumes the asymptotic chi-square distribution for -2 log profile likelihood ratio. Thus, the script is handy for checking to see if the asymptotic approximations are valid. To aid, that comparison, the script overlays a chi-square distribution as well. The most common parameter of interest is a parameter proportional to the signal rate, and often that has a lower-limit of 0, which breaks the standard chi-square distribution. Thus the script allows the parameter to be negative so that the overlay chi-square is the correct asymptotic distribution.

=== Using the following for ModelConfig ===
Observables: RooArgSet:: = (obs_x_channel1,weightVar,channelCat)
Parameters of Interest: RooArgSet:: = (SigXsecOverSM)
Nuisance Parameters: RooArgSet:: = (alpha_syst2,alpha_syst3,gamma_stat_channel1_bin_0,gamma_stat_channel1_bin_1)
Global Observables: RooArgSet:: = (nom_alpha_syst2,nom_alpha_syst3,nom_gamma_stat_channel1_bin_0,nom_gamma_stat_channel1_bin_1)
PDF: RooSimultaneous::simPdf[ indexCat=channelCat channel1=model_channel1 ] = 0.190787
[#1] INFO:Minimization -- p.d.f. provides expected number of events, including extended term in likelihood.
[#1] INFO:Minimization -- createConstraintTerm: caching constraint set under name CACHE_CONSTR_OF_PDF_simPdf_FOR_OBS_channelCat:obs_x_channel1 with 4 entries
[#1] INFO:Minimization -- Including the following constraint terms in minimization: (alpha_syst2Constraint,alpha_syst3Constraint,gamma_stat_channel1_bin_0_constraint,gamma_stat_channel1_bin_1_constraint)
[#1] INFO:Minimization -- The following global observables have been defined and their values are taken from the model: (nom_alpha_syst2,nom_alpha_syst3,nom_gamma_stat_channel1_bin_0,nom_gamma_stat_channel1_bin_1)
[#0] PROGRESS:Minimization -- ProfileLikelihoodCalcultor::DoGLobalFit - find MLE
[#1] INFO:Fitting -- RooAddition::defaultErrorLevel(nll_simPdf_obsData_with_constr) Summation contains a RooNLLVar, using its error level
[#1] INFO:Minimization -- RooAbsMinimizerFcn::setOptimizeConst: activating const optimization
RooAbsTestStatistic::initSimMode: creating slave calculator #0 for state channel1 (2 dataset entries)
[#1] INFO:Fitting -- RooAbsTestStatistic::initSimMode: created 1 slave calculators.
[#1] INFO:Minimization -- The following expressions have been identified as constant and will be precalculated and cached: (signal_channel1_shapes,background1_channel1_Hist_alphanominal,channel1_model_binWidth,background2_channel1_Hist_alphanominal)
[#1] INFO:Minimization -- The following expressions will be evaluated in cache-and-track mode: (mc_stat_channel1)
[#0] PROGRESS:Minimization -- ProfileLikelihoodCalcultor::DoMinimizeNLL - using Minuit / with strategy 1
[#1] INFO:Minimization --
RooFitResult: minimized FCN value: 15.5775, estimated distance to minimum: 1.99576e-08
covariance matrix quality: Full, accurate covariance matrix
Status : MINIMIZE=0
Floating Parameter FinalValue +/- Error
-------------------- --------------------------
SigXsecOverSM 1.1153e+00 +/- 5.87e-01
alpha_syst2 -8.9073e-03 +/- 9.83e-01
alpha_syst3 1.7876e-02 +/- 9.48e-01
gamma_stat_channel1_bin_0 9.9955e-01 +/- 4.93e-02
gamma_stat_channel1_bin_1 1.0036e+00 +/- 8.01e-02
Warning: lower value for SigXsecOverSM is at limit 0
--------------------------------------
Will generate sampling distribution at SigXsecOverSM = 2.32744
1) 0x5629eef572b0 RooRealVar:: SigXsecOverSM = 2.32744 +/- 0.586576 L(-3 - 3) "SigXsecOverSM"
2) 0x5629edfa0360 RooRealVar:: alpha_syst2 = 0.710783 +/- 0.982506 L(-5 - 5) "alpha_syst2"
3) 0x5629eee33040 RooRealVar:: alpha_syst3 = 0.260224 +/- 0.947653 L(-5 - 5) "alpha_syst3"
4) 0x5629eb555890 RooRealVar:: gamma_stat_channel1_bin_0 = 1.03668 +/- 0.0493362 L(0 - 1.25) "gamma_stat_channel1_bin_0"
5) 0x5629eeda0440 RooRealVar:: gamma_stat_channel1_bin_1 = 1.05296 +/- 0.0800896 L(0 - 1.5) "gamma_stat_channel1_bin_1"
[#1] INFO:Fitting -- using CPU computation library compiled with -mavx2
[#0] PROGRESS:Generation -- generated toys: 500 / 1000
#include "TFile.h"
#include "TROOT.h"
#include "TH1F.h"
#include "TCanvas.h"
#include "TSystem.h"
#include "TF1.h"
#include "TSystem.h"
#include "RooWorkspace.h"
#include "RooAbsData.h"
using namespace RooFit;
using namespace RooStats;
bool useProof = false; // flag to control whether to use Proof
int nworkers = 0; // number of workers (default use all available cores)
// -------------------------------------------------------
// The actual macro
void StandardTestStatDistributionDemo(const char *infile = "", const char *workspaceName = "combined",
const char *modelConfigName = "ModelConfig", const char *dataName = "obsData")
{
// the number of toy MC used to generate the distribution
int nToyMC = 1000;
// The parameter below is needed for asymptotic distribution to be chi-square,
// but set to false if your model is not numerically stable if mu<0
bool allowNegativeMu = true;
// -------------------------------------------------------
// First part is just to access a user-defined file
// or create the standard example file if it doesn't exist
const char *filename = "";
if (!strcmp(infile, "")) {
filename = "results/example_combined_GaussExample_model.root";
bool fileExist = !gSystem->AccessPathName(filename); // note opposite return code
// if file does not exists generate with histfactory
if (!fileExist) {
#ifdef _WIN32
cout << "HistFactory file cannot be generated on Windows - exit" << endl;
return;
#endif
// 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;
}
} else
filename = infile;
// Try to open the file
// if input file was specified byt not found, quit
if (!file) {
cout << "StandardRooStatsDemoMacro: Input file " << filename << " is not found" << endl;
return;
}
// -------------------------------------------------------
// Now get the data and workspace
// get the workspace out of the file
RooWorkspace *w = (RooWorkspace *)file->Get(workspaceName);
if (!w) {
cout << "workspace not found" << endl;
return;
}
// get the modelConfig out of the file
ModelConfig *mc = (ModelConfig *)w->obj(modelConfigName);
// get the modelConfig out of the file
RooAbsData *data = w->data(dataName);
// make sure ingredients are found
if (!data || !mc) {
w->Print();
cout << "data or ModelConfig was not found" << endl;
return;
}
mc->Print();
// -------------------------------------------------------
// Now find the upper limit based on the asymptotic results
LikelihoodInterval *interval = plc.GetInterval();
double plcUpperLimit = interval->UpperLimit(*firstPOI);
delete interval;
cout << "\n\n--------------------------------------" << endl;
cout << "Will generate sampling distribution at " << firstPOI->GetName() << " = " << plcUpperLimit << endl;
int nPOI = mc->GetParametersOfInterest()->getSize();
if (nPOI > 1) {
cout << "not sure what to do with other parameters of interest, but here are their values" << endl;
}
// -------------------------------------------------------
// create the test stat sampler
// to avoid effects from boundary and simplify asymptotic comparison, set min=-max
if (allowNegativeMu)
firstPOI->setMin(-1 * firstPOI->getMax());
// temporary RooArgSet
RooArgSet poi;
// create and configure the ToyMCSampler
ToyMCSampler sampler(ts, nToyMC);
sampler.SetPdf(*mc->GetPdf());
sampler.SetObservables(*mc->GetObservables());
sampler.SetGlobalObservables(*mc->GetGlobalObservables());
if (!mc->GetPdf()->canBeExtended() && (data->numEntries() == 1)) {
cout << "tell it to use 1 event" << endl;
sampler.SetNEventsPerToy(1);
}
firstPOI->setVal(plcUpperLimit); // set POI value for generation
sampler.SetParametersForTestStat(*mc->GetParametersOfInterest()); // set POI value for evaluation
if (useProof) {
ProofConfig pc(*w, nworkers, "", false);
sampler.SetProofConfig(&pc); // enable proof
}
firstPOI->setVal(plcUpperLimit);
RooArgSet allParameters;
allParameters.add(*mc->GetParametersOfInterest());
allParameters.add(*mc->GetNuisanceParameters());
allParameters.Print("v");
SamplingDistribution *sampDist = sampler.GetSamplingDistribution(allParameters);
plot.AddSamplingDistribution(sampDist);
plot.GetTH1F(sampDist)->GetYaxis()->SetTitle(
Form("f(-log #lambda(#mu=%.2f) | #mu=%.2f)", plcUpperLimit, plcUpperLimit));
plot.SetAxisTitle(Form("-log #lambda(#mu=%.2f)", plcUpperLimit));
TCanvas *c1 = new TCanvas("c1");
c1->SetLogy();
double min = plot.GetTH1F(sampDist)->GetXaxis()->GetXmin();
double max = plot.GetTH1F(sampDist)->GetXaxis()->GetXmax();
TF1 *f = new TF1("f", Form("2*ROOT::Math::chisquared_pdf(2*x,%d,0)", nPOI), min, max);
f->Draw("same");
c1->SaveAs("standard_test_stat_distribution.pdf");
}
#define f(i)
Definition: RSha256.hxx:104
winID h TVirtualViewer3D TVirtualGLPainter char TVirtualGLPainter plot
Option_t Option_t TPoint TPoint const char GetTextMagnitude GetFillStyle GetLineColor GetLineWidth GetMarkerStyle GetTextAlign GetTextColor GetTextSize void data
Option_t Option_t TPoint TPoint const char GetTextMagnitude GetFillStyle GetLineColor GetLineWidth GetMarkerStyle GetTextAlign GetTextColor GetTextSize void char Point_t Rectangle_t WindowAttributes_t Float_t Float_t Float_t Int_t Int_t UInt_t UInt_t Rectangle_t Int_t Int_t Window_t TString Int_t GCValues_t GetPrimarySelectionOwner GetDisplay GetScreen GetColormap GetNativeEvent const char const char dpyName wid window const char font_name cursor keysym reg const char only_if_exist regb h Point_t winding char text const char depth char const char Int_t count const char ColorStruct_t color const char filename
#define gROOT
Definition: TROOT.h:404
char * Form(const char *fmt,...)
Formats a string in a circular formatting buffer.
Definition: TString.cxx:2452
R__EXTERN TSystem * gSystem
Definition: TSystem.h:559
void Print(Option_t *options=0) const override
This method must be overridden when a class wants to print itself.
Int_t getSize() const
Return the number of elements in the collection.
virtual bool add(const RooAbsArg &var, bool silent=false)
Add the specified argument to list.
RooAbsArg * first() const
RooAbsData is the common abstract base class for binned and unbinned datasets.
Definition: RooAbsData.h:62
bool canBeExtended() const
If true, PDF can provide extended likelihood term.
Definition: RooAbsPdf.h:263
virtual double getMax(const char *name=nullptr) const
Get maximum of currently defined range.
RooArgSet is a container object that can hold multiple RooAbsArg objects.
Definition: RooArgSet.h:56
RooRealVar represents a variable that can be changed from the outside.
Definition: RooRealVar.h:40
void setVal(double value) override
Set value of variable to 'value'.
Definition: RooRealVar.cxx:254
void setMin(const char *name, double value)
Set minimum of name range to given value.
Definition: RooRealVar.cxx:460
LikelihoodInterval is a concrete implementation of the RooStats::ConfInterval interface.
double UpperLimit(const RooRealVar &param)
return the upper bound of the interval on a given parameter
ModelConfig is a simple class that holds configuration information specifying how a model should be u...
Definition: ModelConfig.h:30
const RooArgSet * GetGlobalObservables() const
get RooArgSet for global observables (return nullptr if not existing)
Definition: ModelConfig.h:252
const RooArgSet * GetParametersOfInterest() const
get RooArgSet containing the parameter of interest (return nullptr if not existing)
Definition: ModelConfig.h:234
const RooArgSet * GetNuisanceParameters() const
get RooArgSet containing the nuisance parameters (return nullptr if not existing)
Definition: ModelConfig.h:237
void Print(Option_t *option="") const override
overload the print method
const RooArgSet * GetObservables() const
get RooArgSet for observables (return nullptr if not existing)
Definition: ModelConfig.h:246
RooAbsPdf * GetPdf() const
get model PDF (return nullptr if pdf has not been specified or does not exist)
Definition: ModelConfig.h:231
The ProfileLikelihoodCalculator is a concrete implementation of CombinedCalculator (the interface cla...
ProfileLikelihoodTestStat is an implementation of the TestStatistic interface that calculates the pro...
Holds configuration options for proof and proof-lite.
Definition: ProofConfig.h:46
This class provides simple and straightforward utilities to plot SamplingDistribution objects.
This class simply holds a sampling distribution of some test statistic.
const std::vector< double > & GetSamplingDistribution() const
Get test statistics values.
ToyMCSampler is an implementation of the TestStatSampler interface.
Definition: ToyMCSampler.h:67
The RooWorkspace is a persistable container for RooFit projects.
Definition: RooWorkspace.h:43
The Canvas class.
Definition: TCanvas.h:23
1-Dim function class
Definition: TF1.h:213
A ROOT file is a suite of consecutive data records (TKey instances) with a well defined format.
Definition: TFile.h:54
static TFile * Open(const char *name, Option_t *option="", const char *ftitle="", Int_t compress=ROOT::RCompressionSetting::EDefaults::kUseCompiledDefault, Int_t netopt=0)
Create / open a file.
Definition: TFile.cxx:4019
const char * GetName() const override
Returns name of object.
Definition: TNamed.h:47
virtual void Draw(Option_t *option="")
Default Draw method for all objects.
Definition: TObject.cxx:197
virtual Bool_t AccessPathName(const char *path, EAccessMode mode=kFileExists)
Returns FALSE if one can access a file using the specified access mode.
Definition: TSystem.cxx:1296
return c1
Definition: legend1.C:41
The namespace RooFit contains mostly switches that change the behaviour of functions of PDFs (or othe...
Definition: Common.h:18
Namespace for the RooStats classes.
Definition: Asimov.h:19
static constexpr double pc
Definition: file.py:1
Author
Kyle Cranmer

Definition in file StandardTestStatDistributionDemo.C.