ROOT   Reference Guide
StandardHypoTestInvDemo.C File Reference

## Detailed Description

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

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
int type
Definition: TGX11.cxx:120
float xmin
Definition: THbookFile.cxx:93
float xmax
Definition: THbookFile.cxx:93
point * points
Definition: X3DBuffer.c:22
RooCmdArg Asimov(Bool_t flag=kTRUE)
TString as(SEXP s)
Definition: RExports.h:71
Short_t Max(Short_t a, Short_t b)
Definition: TMathBase.h:212
Double_t Freq(Double_t x)
Computation of the normal frequency function freq(x).
Definition: TMath.cxx:265
Definition: test.py:1
␛[1mRooFit v3.60 -- Developed by Wouter Verkerke and David Kirkby␛[0m
Copyright (C) 2000-2013 NIKHEF, University of California & Stanford University
0x5594588d7f50 results/example_combined_GaussExample_model.root
Running HypoTestInverter on the workspace combined
RooWorkspace(combined) combined contents
variables
---------
(Lumi,SigXsecOverSM,alpha_syst1,alpha_syst2,alpha_syst3,binWidth_obs_x_channel1_0,binWidth_obs_x_channel1_1,binWidth_obs_x_channel1_2,channelCat,gamma_stat_channel1_bin_0,gamma_stat_channel1_bin_1,nom_alpha_syst1,nom_alpha_syst2,nom_alpha_syst3,nom_gamma_stat_channel1_bin_0,nom_gamma_stat_channel1_bin_1,nominalLumi,obs_x_channel1,weightVar)
p.d.f.s
-------
RooGaussian::alpha_syst1Constraint[ x=alpha_syst1 mean=nom_alpha_syst1 sigma=1 ] = 1
RooGaussian::alpha_syst2Constraint[ x=alpha_syst2 mean=nom_alpha_syst2 sigma=1 ] = 1
RooGaussian::alpha_syst3Constraint[ x=alpha_syst3 mean=nom_alpha_syst3 sigma=1 ] = 1
RooRealSumPdf::channel1_model[ binWidth_obs_x_channel1_0 * L_x_signal_channel1_overallSyst_x_Exp + binWidth_obs_x_channel1_1 * L_x_background1_channel1_overallSyst_x_StatUncert + binWidth_obs_x_channel1_2 * L_x_background2_channel1_overallSyst_x_StatUncert ] = 220
RooPoisson::gamma_stat_channel1_bin_0_constraint[ x=nom_gamma_stat_channel1_bin_0 mean=gamma_stat_channel1_bin_0_poisMean ] = 0.019943
RooPoisson::gamma_stat_channel1_bin_1_constraint[ x=nom_gamma_stat_channel1_bin_1 mean=gamma_stat_channel1_bin_1_poisMean ] = 0.039861
RooGaussian::lumiConstraint[ x=Lumi mean=nominalLumi sigma=0.1 ] = 1
RooProdPdf::model_channel1[ lumiConstraint * alpha_syst1Constraint * alpha_syst2Constraint * alpha_syst3Constraint * gamma_stat_channel1_bin_0_constraint * gamma_stat_channel1_bin_1_constraint * channel1_model(obs_x_channel1) ] = 0.174888
RooSimultaneous::simPdf[ indexCat=channelCat channel1=model_channel1 ] = 0.174888
functions
--------
RooProduct::L_x_background1_channel1_overallSyst_x_StatUncert[ Lumi * background1_channel1_overallSyst_x_StatUncert ] = 0
RooProduct::L_x_background2_channel1_overallSyst_x_StatUncert[ Lumi * background2_channel1_overallSyst_x_StatUncert ] = 100
RooProduct::L_x_signal_channel1_overallSyst_x_Exp[ Lumi * signal_channel1_overallSyst_x_Exp ] = 10
RooStats::HistFactory::FlexibleInterpVar::background1_channel1_epsilon[ paramList=(alpha_syst2) ] = 1
RooHistFunc::background1_channel1_nominal[ depList=(obs_x_channel1) depList=(obs_x_channel1) ] = 0
RooProduct::background1_channel1_overallSyst_x_Exp[ background1_channel1_nominal * background1_channel1_epsilon ] = 0
RooProduct::background1_channel1_overallSyst_x_StatUncert[ mc_stat_channel1 * background1_channel1_overallSyst_x_Exp ] = 0
RooStats::HistFactory::FlexibleInterpVar::background2_channel1_epsilon[ paramList=(alpha_syst3) ] = 1
RooHistFunc::background2_channel1_nominal[ depList=(obs_x_channel1) depList=(obs_x_channel1) ] = 100
RooProduct::background2_channel1_overallSyst_x_Exp[ background2_channel1_nominal * background2_channel1_epsilon ] = 100
RooProduct::background2_channel1_overallSyst_x_StatUncert[ mc_stat_channel1 * background2_channel1_overallSyst_x_Exp ] = 100
RooProduct::gamma_stat_channel1_bin_0_poisMean[ gamma_stat_channel1_bin_0 * gamma_stat_channel1_bin_0_tau ] = 400
RooProduct::gamma_stat_channel1_bin_1_poisMean[ gamma_stat_channel1_bin_1 * gamma_stat_channel1_bin_1_tau ] = 100
ParamHistFunc::mc_stat_channel1[ ] = 1
RooStats::HistFactory::FlexibleInterpVar::signal_channel1_epsilon[ paramList=(alpha_syst1) ] = 1
RooHistFunc::signal_channel1_nominal[ depList=(obs_x_channel1) depList=(obs_x_channel1) ] = 10
RooProduct::signal_channel1_overallNorm_x_sigma_epsilon[ SigXsecOverSM * signal_channel1_epsilon ] = 1
RooProduct::signal_channel1_overallSyst_x_Exp[ signal_channel1_nominal * signal_channel1_overallNorm_x_sigma_epsilon ] = 10
datasets
--------
RooDataSet::asimovData(obs_x_channel1,weightVar,channelCat)
RooDataSet::obsData(channelCat,obs_x_channel1)
embedded datasets (in pdfs and functions)
-----------------------------------------
RooDataHist::signal_channel1nominalDHist(obs_x_channel1)
RooDataHist::background1_channel1nominalDHist(obs_x_channel1)
RooDataHist::background2_channel1nominalDHist(obs_x_channel1)
parameter snapshots
-------------------
NominalParamValues = (nom_alpha_syst2=0[C],nom_alpha_syst3=0[C],nom_gamma_stat_channel1_bin_0=400[C],nom_gamma_stat_channel1_bin_1=100[C],weightVar=0,obs_x_channel1=1.75,Lumi=1[C],nominalLumi=1[C],alpha_syst1=0[C],nom_alpha_syst1=0[C],alpha_syst2=0,alpha_syst3=0,gamma_stat_channel1_bin_0=1 +/- 0.05,gamma_stat_channel1_bin_1=1 +/- 0.1,SigXsecOverSM=1,binWidth_obs_x_channel1_0=2[C],binWidth_obs_x_channel1_1=2[C],binWidth_obs_x_channel1_2=2[C])
named sets
----------
ModelConfig_GlobalObservables:(nom_alpha_syst2,nom_alpha_syst3,nom_gamma_stat_channel1_bin_0,nom_gamma_stat_channel1_bin_1)
ModelConfig_NuisParams:(alpha_syst2,alpha_syst3,gamma_stat_channel1_bin_0,gamma_stat_channel1_bin_1)
ModelConfig_Observables:(obs_x_channel1,weightVar,channelCat)
ModelConfig_POI:(SigXsecOverSM)
globalObservables:(nom_alpha_syst2,nom_alpha_syst3,nom_gamma_stat_channel1_bin_0,nom_gamma_stat_channel1_bin_1)
observables:(obs_x_channel1,weightVar,channelCat)
generic objects
---------------
RooStats::ModelConfig::ModelConfig
Using data set obsData
StandardHypoTestInvDemo : POI initial value: SigXsecOverSM = 1
[#1] INFO:InputArguments -- HypoTestInverter ---- Input models:
using as S+B (null) model : ModelConfig
using as B (alternate) model : ModelConfig_with_poi_0
Doing a fixed scan in interval : 0 , 5
[#1] INFO:Eval -- HypoTestInverter::GetInterval - run a fixed scan
[#0] WARNING:InputArguments -- HypoTestInverter::RunFixedScan - xMax > upper bound, using xmax = 3
[#1] INFO:ObjectHandling -- RooWorkspace::saveSnaphot(combined) replacing previous snapshot with name ModelConfig__snapshot
[#0] PROGRESS:Eval -- Running for SigXsecOverSM = 0
=== 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 ] = 0.158989
Snapshot:
1) 0x559459abab40 RooRealVar:: SigXsecOverSM = 0 L(0 - 3) "SigXsecOverSM"
=== Using the following for ModelConfig_with_poi_0 ===
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 ] = 0.158989
Snapshot:
1) 0x559459aa15a0 RooRealVar:: SigXsecOverSM = 0 L(0 - 3) "SigXsecOverSM"
[#0] PROGRESS:Generation -- Test Statistic on data: 0
[#1] INFO:InputArguments -- Profiling conditional MLEs for Null.
[#1] INFO:InputArguments -- Using a ToyMCSampler. Now configuring for Null.
[#0] PROGRESS:Generation -- generated toys: 500 / 1000
[#1] INFO:InputArguments -- Profiling conditional MLEs for Alt.
[#1] INFO:InputArguments -- Using a ToyMCSampler. Now configuring for Alt.
[#0] PROGRESS:Eval -- P values for SigXsecOverSM = 0
CLs = 1 +/- 0
CLb = 1 +/- 0
CLsplusb = 1 +/- 0
[#1] INFO:ObjectHandling -- RooWorkspace::saveSnaphot(combined) replacing previous snapshot with name ModelConfig__snapshot
[#0] PROGRESS:Eval -- Running for SigXsecOverSM = 0.6
=== 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 ] = 0.168529
Snapshot:
1) 0x559459c29ff0 RooRealVar:: SigXsecOverSM = 0.6 L(0 - 3) "SigXsecOverSM"
=== Using the following for ModelConfig_with_poi_0 ===
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 ] = 0.168529
Snapshot:
1) 0x559459abc3d0 RooRealVar:: SigXsecOverSM = 0 L(0 - 3) "SigXsecOverSM"
[#0] PROGRESS:Generation -- Test Statistic on data: -2.35222
[#1] INFO:InputArguments -- Profiling conditional MLEs for Null.
[#1] INFO:InputArguments -- Using a ToyMCSampler. Now configuring for Null.
[#0] PROGRESS:Generation -- generated toys: 500 / 1000
[#1] INFO:InputArguments -- Profiling conditional MLEs for Alt.
[#1] INFO:InputArguments -- Using a ToyMCSampler. Now configuring for Alt.
[#0] PROGRESS:Eval -- P values for SigXsecOverSM = 0.6
CLs = 0.819079 +/- 0.0188863
CLb = 0.912 +/- 0.0126693
CLsplusb = 0.747 +/- 0.0137474
[#1] INFO:ObjectHandling -- RooWorkspace::saveSnaphot(combined) replacing previous snapshot with name ModelConfig__snapshot
[#0] PROGRESS:Eval -- Running for SigXsecOverSM = 1.2
=== 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 ] = 0.178068
Snapshot:
1) 0x559459c6ac40 RooRealVar:: SigXsecOverSM = 1.2 L(0 - 3) "SigXsecOverSM"
=== Using the following for ModelConfig_with_poi_0 ===
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 ] = 0.178068
Snapshot:
1) 0x559458138580 RooRealVar:: SigXsecOverSM = 0 L(0 - 3) "SigXsecOverSM"
[#0] PROGRESS:Generation -- Test Statistic on data: -2.9364
[#1] INFO:InputArguments -- Profiling conditional MLEs for Null.
[#1] INFO:InputArguments -- Using a ToyMCSampler. Now configuring for Null.
[#0] PROGRESS:Generation -- generated toys: 500 / 1000
[#1] INFO:InputArguments -- Profiling conditional MLEs for Alt.
[#1] INFO:InputArguments -- Using a ToyMCSampler. Now configuring for Alt.
[#0] PROGRESS:Eval -- P values for SigXsecOverSM = 1.2
CLs = 0.48617 +/- 0.0176356
CLb = 0.94 +/- 0.0106207
CLsplusb = 0.457 +/- 0.0157528
[#1] INFO:ObjectHandling -- RooWorkspace::saveSnaphot(combined) replacing previous snapshot with name ModelConfig__snapshot
[#0] PROGRESS:Eval -- Running for SigXsecOverSM = 1.8
=== 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 ] = 0.187607
Snapshot:
1) 0x559459b64bd0 RooRealVar:: SigXsecOverSM = 1.8 L(0 - 3) "SigXsecOverSM"
=== Using the following for ModelConfig_with_poi_0 ===
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 ] = 0.187607
Snapshot:
1) 0x559458139040 RooRealVar:: SigXsecOverSM = 0 L(0 - 3) "SigXsecOverSM"
[#0] PROGRESS:Generation -- Test Statistic on data: -2.05075
[#1] INFO:InputArguments -- Profiling conditional MLEs for Null.
[#1] INFO:InputArguments -- Using a ToyMCSampler. Now configuring for Null.
[#0] PROGRESS:Generation -- generated toys: 500 / 1000
[#1] INFO:InputArguments -- Profiling conditional MLEs for Alt.
[#1] INFO:InputArguments -- Using a ToyMCSampler. Now configuring for Alt.
[#0] PROGRESS:Eval -- P values for SigXsecOverSM = 1.8
CLs = 0.190678 +/- 0.0130363
CLb = 0.944 +/- 0.0102824
CLsplusb = 0.18 +/- 0.0121491
[#1] INFO:ObjectHandling -- RooWorkspace::saveSnaphot(combined) replacing previous snapshot with name ModelConfig__snapshot
[#0] PROGRESS:Eval -- Running for SigXsecOverSM = 2.4
=== 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 ] = 0.197147
Snapshot:
1) 0x559459b5ceb0 RooRealVar:: SigXsecOverSM = 2.4 L(0 - 3) "SigXsecOverSM"
=== Using the following for ModelConfig_with_poi_0 ===
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 ] = 0.197147
Snapshot:
1) 0x559459b673d0 RooRealVar:: SigXsecOverSM = 0 L(0 - 3) "SigXsecOverSM"
[#0] PROGRESS:Generation -- Test Statistic on data: 0.0783908
[#1] INFO:InputArguments -- Profiling conditional MLEs for Null.
[#1] INFO:InputArguments -- Using a ToyMCSampler. Now configuring for Null.
[#0] PROGRESS:Generation -- generated toys: 500 / 1000
[#1] INFO:InputArguments -- Profiling conditional MLEs for Alt.
[#1] INFO:InputArguments -- Using a ToyMCSampler. Now configuring for Alt.
[#0] PROGRESS:Eval -- P values for SigXsecOverSM = 2.4
CLs = 0.0550847 +/- 0.00746178
CLb = 0.944 +/- 0.0102824
CLsplusb = 0.052 +/- 0.00702111
[#1] INFO:ObjectHandling -- RooWorkspace::saveSnaphot(combined) replacing previous snapshot with name ModelConfig__snapshot
[#0] PROGRESS:Eval -- Running for SigXsecOverSM = 3
=== 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 ] = 0.206686
Snapshot:
1) 0x559459e5dd70 RooRealVar:: SigXsecOverSM = 3 L(0 - 3) "SigXsecOverSM"
=== Using the following for ModelConfig_with_poi_0 ===
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 ] = 0.206686
Snapshot:
1) 0x559459bb25f0 RooRealVar:: SigXsecOverSM = 0 L(0 - 3) "SigXsecOverSM"
[#0] PROGRESS:Generation -- Test Statistic on data: 3.27476
[#1] INFO:InputArguments -- Profiling conditional MLEs for Null.
[#1] INFO:InputArguments -- Using a ToyMCSampler. Now configuring for Null.
[#0] PROGRESS:Generation -- generated toys: 500 / 1000
[#1] INFO:InputArguments -- Profiling conditional MLEs for Alt.
[#1] INFO:InputArguments -- Using a ToyMCSampler. Now configuring for Alt.
[#0] PROGRESS:Eval -- P values for SigXsecOverSM = 3
CLs = 0.00535332 +/- 0.00238893
CLb = 0.934 +/- 0.0111035
CLsplusb = 0.005 +/- 0.00223047
Time to perform limit scan
Real time 0:00:10, CP time 10.420
The computed upper limit is: 2.46135 +/- 0.0596845
Expected upper limits, using the B (alternate) model :
expected limit (median) 1.60988
expected limit (-1 sig) 1.34011
expected limit (+1 sig) 2.09019
expected limit (-2 sig) 1.14968
expected limit (+2 sig) 2.79445
[#0] WARNING:Plotting -- Could not determine xmin and xmax of sampling distribution that was given to plot.
[#0] WARNING:Plotting -- Could not determine xmin and xmax of sampling distribution that was given to plot.
#include "TFile.h"
#include "RooWorkspace.h"
#include "RooAbsPdf.h"
#include "RooRealVar.h"
#include "RooDataSet.h"
#include "RooRandom.h"
#include "TGraphErrors.h"
#include "TCanvas.h"
#include "TLine.h"
#include "TROOT.h"
#include "TSystem.h"
#include <cassert>
using namespace RooFit;
using namespace RooStats;
using namespace std;
// structure defining the options
struct HypoTestInvOptions {
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; // optimize 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 Lite when using toys (for freq or hybrid)
int nworkers = 0; // number of worker for ProofLite (default use all available cores)
bool enableDetailedOutput =
false; // enable detailed output with all fit information for each toys (output will be written in result file)
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 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
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)
double confLevel = 0.95; // confidence level value
std::string minimizerType =
""; // minimizer type (default is what is in ROOT::Math::MinimizerOptions::DefaultMinimizerType()
std::string massValue = ""; // extra string to tag output file of result
int printLevel = 0; // print level for debugging PL test statistics and calculators
bool useNLLOffset = false; // use NLL offset when fitting (this increase stability of fits)
};
HypoTestInvOptions optHTInv;
// 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;
bool mEnableDetOutput;
int mNWorkers;
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 namespace RooStats
RooStats::HypoTestInvTool::HypoTestInvTool()
: mPlotHypoTestResult(true), mWriteResult(false), mOptimize(true), mUseVectorStore(true), mGenerateBinned(false),
mUseProof(false), mEnableDetOutput(false), mRebuild(false), mReuseAltToys(false), mNWorkers(4),
mNToyToRebuild(100), mRebuildParamValues(0), 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("EnableDetailedOutput") != std::string::npos)
mEnableDetOutput = 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("RebuildParamValues") != std::string::npos)
mRebuildParamValues = 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 nuisance. 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 parameters of the macro. They major ones are:
plotHypoTestResult plot result of tests at each point (TS distributions) (default 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";
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
TFile *file = TFile::Open(filename);
if (!file) {
cout << "StandardRooStatsDemoMacro: Input file " << filename << " is not found" << endl;
return;
}
HypoTestInvTool calc;
// 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("UseProof", optHTInv.useProof);
calc.SetParameter("EnableDetailedOutput", optHTInv.enableDetailedOutput);
calc.SetParameter("NWorkers", optHTInv.nworkers);
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.c_str());
calc.SetParameter("MinimizerType", optHTInv.minimizerType.c_str());
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)
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;
// detailed output
if (mEnableDetOutput) {
mWriteResult = true;
Info("StandardHypoTestInvDemo", "detailed output will be written in output result file");
}
// 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;
}
// get (if existing) rebuilt UL distribution
TString uldistFile = "RULDist.root";
TObject *ulDist = 0;
bool existULDist = !gSystem->AccessPathName(uldistFile);
if (existULDist) {
TFile *fileULDist = TFile::Open(uldistFile);
if (fileULDist)
ulDist = fileULDist->Get("RULDist");
}
TFile *fileOut = new TFile(mResultFileName, "RECREATE");
r->Write();
if (ulDist)
ulDist->Write();
Info("StandardHypoTestInvDemo", "HypoTestInverterResult has been written in the file %s", mResultFileName.Data());
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("c2");
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) {
}
// 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 (optHTInv.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;
}
if (bModel) {
const RooArgSet *bnuisPar = bModel->GetNuisanceParameters();
if (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 globals 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 ");
}
}
}
// save all initial parameters of the model including the global observables
RooArgSet initialParameters;
RooArgSet *allParams = sbModel->GetPdf()->getParameters(*data);
allParams->snapshot(initialParameters);
delete allParams;
// 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 )
bool doFit = mInitialFit;
if (testStatType == 0 && mInitialFit == -1)
doFit = false; // case of LEP test statistic
if (type == 3 && mInitialFit == -1)
doFit = false; // case of Asymptoticcalculator with nominal Asimov
double poihat = 0;
if (mMinimizerType.size() == 0)
else
Info("StandardHypoTestInvDemo", "Using %s as minimizer for computing the test statistic",
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())
tw.Start();
RooFitResult *fitres = sbModel->GetPdf()->fitTo(
*data, InitialHesse(false), Hesse(false), Minimizer(mMinimizerType.c_str(), "Migrad"), Strategy(0),
PrintLevel(mPrintLevel), Constrain(constrainParams), Save(true), Offset(RooStats::IsNLLOffset()));
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(mMinimizerType.c_str(), "Migrad"), Strategy(1),
PrintLevel(mPrintLevel + 1), Constrain(constrainParams), Save(true), Offset(RooStats::IsNLLOffset()));
}
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())
if (sbModel->GetSnapshot())
slrts.SetNullParameters(nullParams);
RooArgSet altParams(*bModel->GetSnapshot());
if (bModel->GetNuisanceParameters())
if (bModel->GetSnapshot())
slrts.SetAltParameters(altParams);
if (mEnableDetOutput)
slrts.EnableDetailedOutput();
// 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(mMinimizerType.c_str());
if (mEnableDetOutput)
ropl.EnableDetailedOutput();
ProfileLikelihoodTestStat profll(*sbModel->GetPdf());
if (testStatType == 3)
profll.SetOneSided(true);
if (testStatType == 4)
profll.SetSigned(true);
profll.SetMinimizer(mMinimizerType.c_str());
profll.SetPrintLevel(mPrintLevel);
if (mEnableDetOutput)
profll.EnableDetailedOutput();
profll.SetReuseNLL(mOptimize);
slrts.SetReuseNLL(mOptimize);
ropl.SetReuseNLL(mOptimize);
if (mOptimize) {
profll.SetStrategy(0);
ropl.SetStrategy(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 observable 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)
}
// specify if need to re-use same toys
if (mReuseAltToys) {
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", "Cannot 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) {
((FrequentistCalculator *)hc)->SetToys(ntoys, ntoys / mNToysRatio);
// store also the fit information for each poi point used by calculator based on toys
if (mEnableDetOutput)
((FrequentistCalculator *)hc)->StoreFitInfo(true);
} else if (type == 1) {
((HybridCalculator *)hc)->SetToys(ntoys, ntoys / mNToysRatio);
// store also the fit information for each poi point used by calculator based on toys
// if (mEnableDetOutput) ((HybridCalculator*) hc)->StoreFitInfo(true);
}
// Get the result
HypoTestInverter calc(*hc);
calc.SetConfidenceLevel(optHTInv.confLevel);
calc.UseCLs(useCLs);
calc.SetVerbose(true);
// can speed up using proof-lite
if (mUseProof) {
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) {
std::cout << "\n***************************************************************\n";
std::cout << "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 (mRebuildParamValues != 0) {
// set all parameters to their initial workspace values
*allParams = initialParameters;
}
if (mRebuildParamValues == 0 || mRebuildParamValues == 1) {
RooArgSet constrainParams;
if (sbModel->GetNuisanceParameters())
const RooArgSet *poiModel = sbModel->GetParametersOfInterest();
// do a profile using the B model snapshot
if (mRebuildParamValues == 0) {
RooStats::SetAllConstant(*poiModel, true);
sbModel->GetPdf()->fitTo(*data, InitialHesse(false), Hesse(false),
Constrain(constrainParams), Offset(RooStats::IsNLLOffset()));
std::cout << "rebuild using fitted parameter value for B-model snapshot" << std::endl;
constrainParams.Print("v");
RooStats::SetAllConstant(*poiModel, false);
}
}
std::cout << "StandardHypoTestInvDemo: Initial parameters used for rebuilding: ";
RooStats::PrintListContent(*allParams, std::cout);
delete allParams;
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 limits after rebuild distribution " << std::endl;
std::cout << "expected upper limit (median of limit distribution) " << limDist->InverseCDF(0.5) << std::endl;
std::cout << "expected -1 sig limit (0.16% quantile of limit dist) "
<< limDist->InverseCDF(ROOT::Math::normal_cdf(-1)) << std::endl;
std::cout << "expected +1 sig limit (0.84% quantile of limit dist) "
<< limDist->InverseCDF(ROOT::Math::normal_cdf(1)) << std::endl;
std::cout << "expected -2 sig limit (.025% quantile of limit dist) "
<< limDist->InverseCDF(ROOT::Math::normal_cdf(-2)) << std::endl;
std::cout << "expected +2 sig limit (.975% quantile of limit dist) "
<< limDist->InverseCDF(ROOT::Math::normal_cdf(2)) << std::endl;
// Plot the upper limit distribution
SamplingDistPlot limPlot((mNToyToRebuild < 200) ? 50 : 100);
limPlot.GetTH1F()->SetStats(true); // display statistics
limPlot.SetLineColor(kBlue);
new TCanvas("limPlot", "Upper Limit Distribution");
limPlot.Draw();
/// save result in a file
limDist->SetName("RULDist");
TFile *fileOut = new 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)
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
ROOT::R::TRInterface & r
Definition: Object.C:4
const Bool_t kFALSE
Definition: RtypesCore.h:90
@ kBlue
Definition: Rtypes.h:64
void Info(const char *location, const char *msgfmt,...)
void Error(const char *location, const char *msgfmt,...)
void Warning(const char *location, const char *msgfmt,...)
char name[80]
Definition: TGX11.cxx:109
#define gROOT
Definition: TROOT.h:406
R__EXTERN TSystem * gSystem
Definition: TSystem.h:556
static void SetDefaultMinimizer(const char *type, const char *algo=0)
static void SetDefaultStrategy(int strat)
static const std::string & DefaultMinimizerType()
RooArgSet * getObservables(const RooArgSet &set, Bool_t valueOnly=kTRUE) const
Return the observables of this pdf given a set of observables.
Definition: RooAbsArg.h:276
virtual void Print(Option_t *options=0) const
Print the object to the defaultPrintStream().
Definition: RooAbsArg.h:302
Int_t getSize() const
RooAbsArg * first() const
virtual void Print(Option_t *options=0) const
This method must be overridden when a class wants to print itself.
RooAbsData is the common abstract base class for binned and unbinned datasets.
Definition: RooAbsData.h:44
static void setDefaultStorageType(StorageType s)
Definition: RooAbsData.cxx:77
virtual Double_t sumEntries() const =0
virtual Bool_t isWeighted() const
Definition: RooAbsData.h:108
void convertToVectorStore()
Convert tree-based storage to vector-based storage.
Definition: RooAbsData.cxx:274
virtual Int_t numEntries() const
Definition: RooAbsData.cxx:306
virtual Double_t getMax(const char *name=0) const
Get maximum of currently defined range.
virtual Double_t getMin(const char *name=0) const
Get miniminum of currently defined range.
Double_t getVal(const RooArgSet *normalisationSet=nullptr) const
Evaluate object.
Definition: RooAbsReal.h:90
RooArgSet is a container object that can hold multiple RooAbsArg objects.
Definition: RooArgSet.h:28
RooArgSet * snapshot(bool deepCopy=true) const
Use RooAbsCollection::snapshot(), but return as RooArgSet.
Definition: RooArgSet.h:126
virtual Bool_t add(const RooAbsCollection &col, Bool_t silent=kFALSE)
Add a collection of arguments to this collection by calling add() for each element in the source coll...
Definition: RooArgSet.h:88
RooFitResult is a container class to hold the input and output of a PDF fit to a dataset.
Definition: RooFitResult.h:40
Int_t status() const
Definition: RooFitResult.h:77
static RooMsgService & instance()
Return reference to singleton instance.
StreamConfig & getStream(Int_t id)
static TRandom * randomGenerator()
Return a pointer to a singleton random-number generator implementation.
Definition: RooRandom.cxx:53
RooRealVar represents a variable that can be changed from the outside.
Definition: RooRealVar.h:35
void setMax(const char *name, Double_t value)
Set maximum of name range to given value.
Definition: RooRealVar.cxx:496
Double_t getError() const
Definition: RooRealVar.h:62
virtual void setVal(Double_t value)
Set value of variable to 'value'.
Definition: RooRealVar.cxx:261
The RooWorkspace is a persistable container for RooFit projects.
Definition: RooWorkspace.h:43
RooAbsData * data(const char *name) const
Retrieve dataset (binned or unbinned) with given name. A null pointer is returned if not found.
void Print(Option_t *opts=0) const
Print contents of the workspace.
TObject * obj(const char *name) const
Return any type of object (RooAbsArg, RooAbsData or generic object) with given name)
RooAbsPdf * pdf(const char *name) const
Retrieve p.d.f (RooAbsPdf) with given name. A null pointer is returned if not found.
The Canvas class.
Definition: TCanvas.h:27
TObject * Get(const char *namecycle) override
Return pointer to object identified by namecycle.
A ROOT file is a suite of consecutive data records (TKey instances) with a well defined format.
Definition: TFile.h:53
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:3942
void Close(Option_t *option="") override
Close a file.
Definition: TFile.cxx:873
virtual const char * GetName() const
Returns name of object.
Definition: TNamed.h:47
Mother of all ROOT objects.
Definition: TObject.h:37
virtual Int_t Write(const char *name=0, Int_t option=0, Int_t bufsize=0)
Write this object to the current directory.
Definition: TObject.cxx:796
virtual const char * GetName() const
Returns name of object.
Definition: TObject.cxx:357
virtual void SetSeed(ULong_t seed=0)
Set the random generator seed.
Definition: TRandom.cxx:597
Stopwatch class.
Definition: TStopwatch.h:28
void Start(Bool_t reset=kTRUE)
Start the stopwatch.
Definition: TStopwatch.cxx:58
void Print(Option_t *option="") const
Print the real and cpu time passed between the start and stop events.
Definition: TStopwatch.cxx:219
Basic string class.
Definition: TString.h:131
static TString Format(const char *fmt,...)
Static method which formats a string using a printf style format descriptor and return a TString.
Definition: TString.cxx:2311
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:1291
RooCmdArg Constrain(const RooArgSet &params)
RooCmdArg Strategy(Int_t code)
RooCmdArg Hesse(Bool_t flag=kTRUE)
RooCmdArg InitialHesse(Bool_t flag=kTRUE)
RooCmdArg Save(Bool_t flag=kTRUE)
RooCmdArg PrintLevel(Int_t code)
RooCmdArg Offset(Bool_t flag=kTRUE)
RooCmdArg Minimizer(const char *type, const char *alg=0)
double normal_cdf(double x, double sigma=1, double x0=0)
Cumulative distribution function of the normal (Gaussian) distribution (lower tail).
int main(int argc, char **argv)
return c1
Definition: legend1.C:41
return c2
Definition: legend2.C:14
The namespace RooFit contains mostly switches that change the behaviour of functions of PDFs (or othe...
@ NumIntegration
Definition: RooGlobalFunc.h:69
Namespace for the RooStats classes.
Definition: Asimov.h:19
bool SetAllConstant(const RooAbsCollection &coll, bool constant=true)
Definition: RooStatsUtils.h:89
void RemoveConstantParameters(RooArgSet *set)
Definition: RooStatsUtils.h:69
RooAbsPdf * MakeNuisancePdf(RooAbsPdf &pdf, const RooArgSet &observables, const char *name)
void UseNLLOffset(bool on)
Use an offset in NLL calculations.
bool IsNLLOffset()
Test of RooStats should by default offset NLL calculations.
void PrintListContent(const RooArgList &l, std::ostream &os=std::cout)
static constexpr double pc
Double_t Sqrt(Double_t x)
Definition: TMath.h:681
Int_t CeilNint(Double_t x)
Definition: TMath.h:689
Definition: file.py:1
void removeTopic(RooFit::MsgTopic oldTopic)

Definition in file StandardHypoTestInvDemo.C.