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Reference Guide
StandardHypoTestInvDemo.C File Reference

Detailed Description

View in nbviewer Open in SWAN 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
pict1_StandardHypoTestInvDemo.C.png
pict2_StandardHypoTestInvDemo.C.png
RooFit v3.60 -- Developed by Wouter Verkerke and David Kirkby
Copyright (C) 2000-2013 NIKHEF, University of California & Stanford University
All rights reserved, please read http://roofit.sourceforge.net/license.txt
0x44827f0 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) 0x4fae9b0 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) 0x4f6a160 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) 0x4fa5730 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) 0x4fb03c0 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) 0x5063e70 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) 0x5147b20 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) 0x505a630 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) 0x5064260 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) 0x4fab5d0 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) 0x50a28d0 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) 0x50284a0 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) 0x5061920 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 0:00:13, CP time 12.870
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 input file was specified byt not found, quit
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));
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();
}
}
gPad = c1;
}
// 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())
constrainParams.add(*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())
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);
if (mEnableDetOutput)
// 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.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);
AsymptoticCalculator::SetPrintLevel(mPrintLevel);
// create the HypoTest calculator class
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;
}
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) {
}
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)
// 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 =
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())
constrainParams.add(*sbModel->GetNuisanceParameters());
const RooArgSet *poiModel = sbModel->GetParametersOfInterest();
bModel->LoadSnapshot();
// do a profile using the B model snapshot
if (mRebuildParamValues == 0) {
RooStats::SetAllConstant(*poiModel, true);
sbModel->GetPdf()->fitTo(*data, InitialHesse(false), Hesse(false),
Minimizer(mMinimizerType.c_str(), "Migrad"), Strategy(0), PrintLevel(mPrintLevel),
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.AddSamplingDistribution(limDist);
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
Author
Lorenzo Moneta

Definition in file StandardHypoTestInvDemo.C.