forked from wiso/StatisticsLectures
-
Notifications
You must be signed in to change notification settings - Fork 0
/
StandardHypoTestInvDemo.C
1070 lines (851 loc) · 42.3 KB
/
StandardHypoTestInvDemo.C
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
468
469
470
471
472
473
474
475
476
477
478
479
480
481
482
483
484
485
486
487
488
489
490
491
492
493
494
495
496
497
498
499
500
501
502
503
504
505
506
507
508
509
510
511
512
513
514
515
516
517
518
519
520
521
522
523
524
525
526
527
528
529
530
531
532
533
534
535
536
537
538
539
540
541
542
543
544
545
546
547
548
549
550
551
552
553
554
555
556
557
558
559
560
561
562
563
564
565
566
567
568
569
570
571
572
573
574
575
576
577
578
579
580
581
582
583
584
585
586
587
588
589
590
591
592
593
594
595
596
597
598
599
600
601
602
603
604
605
606
607
608
609
610
611
612
613
614
615
616
617
618
619
620
621
622
623
624
625
626
627
628
629
630
631
632
633
634
635
636
637
638
639
640
641
642
643
644
645
646
647
648
649
650
651
652
653
654
655
656
657
658
659
660
661
662
663
664
665
666
667
668
669
670
671
672
673
674
675
676
677
678
679
680
681
682
683
684
685
686
687
688
689
690
691
692
693
694
695
696
697
698
699
700
701
702
703
704
705
706
707
708
709
710
711
712
713
714
715
716
717
718
719
720
721
722
723
724
725
726
727
728
729
730
731
732
733
734
735
736
737
738
739
740
741
742
743
744
745
746
747
748
749
750
751
752
753
754
755
756
757
758
759
760
761
762
763
764
765
766
767
768
769
770
771
772
773
774
775
776
777
778
779
780
781
782
783
784
785
786
787
788
789
790
791
792
793
794
795
796
797
798
799
800
801
802
803
804
805
806
807
808
809
810
811
812
813
814
815
816
817
818
819
820
821
822
823
824
825
826
827
828
829
830
831
832
833
834
835
836
837
838
839
840
841
842
843
844
845
846
847
848
849
850
851
852
853
854
855
856
857
858
859
860
861
862
863
864
865
866
867
868
869
870
871
872
873
874
875
876
877
878
879
880
881
882
883
884
885
886
887
888
889
890
891
892
893
894
895
896
897
898
899
900
901
902
903
904
905
906
907
908
909
910
911
912
913
914
915
916
917
918
919
920
921
922
923
924
925
926
927
928
929
930
931
932
933
934
935
936
937
938
939
940
941
942
943
944
945
946
947
948
949
950
951
952
953
954
955
956
957
958
959
960
961
962
963
964
965
966
967
968
969
970
971
972
973
974
975
976
977
978
979
980
981
982
983
984
985
986
987
988
989
990
991
992
993
994
995
996
997
998
999
1000
/* -*- mode: c++ -*- */
// Standard tutorial macro for performing an inverted hypothesis test for computing an interval
//
// This macro will perform a scan of the p-values for computing the interval or limit
//
//Author: L. Moneta
//
// Usage:
//
// root>.L StandardHypoTestInvDemo.C
// root> StandardHypoTestInvDemo("fileName","workspace name","S+B modelconfig name","B model name","data set name",calculator type, test statistic type, use CLS,
// number of points, xmin, xmax, number of toys, use number counting)
//
//
// type = 0 Freq calculator
// type = 1 Hybrid calculator
// type = 2 Asymptotic calculator
// type = 3 Asymptotic calculator using nominal Asimov data sets (not using fitted parameter values but nominal ones)
//
// testStatType = 0 LEP
// = 1 Tevatron
// = 2 Profile Likelihood two sided
// = 3 Profile Likelihood one sided (i.e. = 0 if mu < mu_hat)
// = 4 Profile Likelihood signed ( pll = -pll if mu < mu_hat)
// = 5 Max Likelihood Estimate as test statistic
// = 6 Number of observed event as test statistic
//
#include "TFile.h"
#include "RooWorkspace.h"
#include "RooAbsPdf.h"
#include "RooRealVar.h"
#include "RooDataSet.h"
#include "RooStats/ModelConfig.h"
#include "RooRandom.h"
#include "TGraphErrors.h"
#include "TGraphAsymmErrors.h"
#include "TCanvas.h"
#include "TLine.h"
#include "TROOT.h"
#include "TSystem.h"
#include "RooStats/AsymptoticCalculator.h"
#include "RooStats/HybridCalculator.h"
#include "RooStats/FrequentistCalculator.h"
#include "RooStats/ToyMCSampler.h"
#include "RooStats/HypoTestPlot.h"
#include "RooStats/NumEventsTestStat.h"
#include "RooStats/ProfileLikelihoodTestStat.h"
#include "RooStats/SimpleLikelihoodRatioTestStat.h"
#include "RooStats/RatioOfProfiledLikelihoodsTestStat.h"
#include "RooStats/MaxLikelihoodEstimateTestStat.h"
#include "RooStats/NumEventsTestStat.h"
#include "RooStats/HypoTestInverter.h"
#include "RooStats/HypoTestInverterResult.h"
#include "RooStats/HypoTestInverterPlot.h"
using namespace RooFit;
using namespace RooStats;
using namespace std;
bool plotHypoTestResult = true; // plot test statistic result at each point
bool writeResult = true; // write HypoTestInverterResult in a file
TString resultFileName; // file with results (by default is built automatically using the workspace input file name)
bool optimize = true; // optmize evaluation of test statistic
bool useVectorStore = true; // convert data to use new roofit data store
bool generateBinned = false; // generate binned data sets
bool noSystematics = false; // force all systematics to be off (i.e. set all nuisance parameters as constat
// to their nominal values)
double nToysRatio = 2; // ratio Ntoys S+b/ntoysB
double maxPOI = -1; // max value used of POI (in case of auto scan)
bool useProof = false; // use Proof 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 confidenceLevel = 0.95; // confidence level value
std::string massValue = ""; // extra string to tag output file of result
std::string minimizerType = ""; // minimizer type (default is what is in ROOT::Math::MinimizerOptions::DefaultMinimizerType()
int printLevel = 0; // print level for debugging PL test statistics and calculators
bool useNLLOffset = false; // use NLL offset when fitting (this increase stability of fits)
// 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 nnuisance. This is often expressed as constraint term in the global model
It is needed only when using the HybridCalculator (type=1)
If not given by default the prior pdf from ModelConfig is used.
extra options are available as global paramwters of the macro. They major ones are:
plotHypoTestResult plot result of tests at each point (TS distributions) (defauly is true)
useProof use Proof (default is true)
writeResult write result of scan (default is true)
rebuild rebuild scan for expected limits (require extra toys) (default is false)
generateBinned generate binned data sets for toys (default is false) - be careful not to activate with
a too large (>=3) number of observables
nToyRatio ratio of S+B/B toys (default is 2)
*/
TString filename(infile);
if (filename.IsNull()) {
filename = "results/example_combined_GaussExample_model.root";
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", plotHypoTestResult);
calc.SetParameter("WriteResult", writeResult);
calc.SetParameter("Optimize", optimize);
calc.SetParameter("UseVectorStore", useVectorStore);
calc.SetParameter("GenerateBinned", generateBinned);
calc.SetParameter("NToysRatio", nToysRatio);
calc.SetParameter("MaxPOI", maxPOI);
calc.SetParameter("UseProof", useProof);
calc.SetParameter("EnableDetailedOutput", enableDetailedOutput);
calc.SetParameter("NWorkers", nworkers);
calc.SetParameter("Rebuild", rebuild);
calc.SetParameter("ReuseAltToys", reuseAltToys);
calc.SetParameter("NToyToRebuild", nToyToRebuild);
calc.SetParameter("RebuildParamValues", rebuildParamValues);
calc.SetParameter("MassValue", massValue.c_str());
calc.SetParameter("MinimizerType", minimizerType.c_str());
calc.SetParameter("PrintLevel", printLevel);
calc.SetParameter("InitialFit",initialFit);
calc.SetParameter("ResultFileName",resultFileName);
calc.SetParameter("RandomSeed",randomSeed);
calc.SetParameter("AsimovBins",nAsimovBins);
// enable offset for all roostats
if (useNLLOffset) RooStats::UseNLLOffset(true);
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();
if (nEntries > 1) {
int ny = TMath::CeilNint(TMath::Sqrt(nEntries));
int nx = TMath::CeilNint(double(nEntries)/ny);
c2->Divide( nx,ny);
}
for (int i=0; i<nEntries; i++) {
if (nEntries > 1) c2->cd(i+1);
SamplingDistPlot * pl = plot->MakeTestStatPlot(i);
pl->SetLogYaxis(true);
pl->Draw();
}
}
}
// internal routine to run the inverter
HypoTestInverterResult *
RooStats::HypoTestInvTool::RunInverter(RooWorkspace * w,
const char * modelSBName, const char * modelBName,
const char * dataName, int type, int testStatType,
bool useCLs, int npoints, double poimin, double poimax,
int ntoys,
bool useNumberCounting,
const char * nuisPriorName ){
std::cout << "Running HypoTestInverter on the workspace " << w->GetName() << std::endl;
w->Print();
RooAbsData * data = w->data(dataName);
if (!data) {
Error("StandardHypoTestDemo","Not existing data %s",dataName);
return 0;
}
else
std::cout << "Using data set " << dataName << std::endl;
if (mUseVectorStore) {
RooAbsData::setDefaultStorageType(RooAbsData::Vector);
data->convertToVectorStore() ;
}
// get models from WS
// get the modelConfig out of the file
ModelConfig* bModel = (ModelConfig*) w->obj(modelBName);
ModelConfig* sbModel = (ModelConfig*) w->obj(modelSBName);
if (!sbModel) {
Error("StandardHypoTestDemo","Not existing ModelConfig %s",modelSBName);
return 0;
}
// check the model
if (!sbModel->GetPdf()) {
Error("StandardHypoTestDemo","Model %s has no pdf ",modelSBName);
return 0;
}
if (!sbModel->GetParametersOfInterest()) {
Error("StandardHypoTestDemo","Model %s has no poi ",modelSBName);
return 0;
}
if (!sbModel->GetObservables()) {
Error("StandardHypoTestInvDemo","Model %s has no observables ",modelSBName);
return 0;
}
if (!sbModel->GetSnapshot() ) {
Info("StandardHypoTestInvDemo","Model %s has no snapshot - make one using model poi",modelSBName);
sbModel->SetSnapshot( *sbModel->GetParametersOfInterest() );
}
// case of no systematics
// remove nuisance parameters from model
if (noSystematics) {
const RooArgSet * nuisPar = sbModel->GetNuisanceParameters();
if (nuisPar && nuisPar->getSize() > 0) {
std::cout << "StandardHypoTestInvDemo" << " - Switch off all systematics by setting them constant to their initial values" << std::endl;
RooStats::SetAllConstant(*nuisPar);
}
if (bModel) {
const RooArgSet * bnuisPar = bModel->GetNuisanceParameters();
if (bnuisPar)
RooStats::SetAllConstant(*bnuisPar);
}
}
if (!bModel || bModel == sbModel) {
Info("StandardHypoTestInvDemo","The background model %s does not exist",modelBName);
Info("StandardHypoTestInvDemo","Copy it from ModelConfig %s and set POI to zero",modelSBName);
bModel = (ModelConfig*) sbModel->Clone();
bModel->SetName(TString(modelSBName)+TString("_with_poi_0"));
RooRealVar * var = dynamic_cast<RooRealVar*>(bModel->GetParametersOfInterest()->first());
if (!var) return 0;
double oldval = var->getVal();
var->setVal(0);
bModel->SetSnapshot( RooArgSet(*var) );
var->setVal(oldval);
}
else {
if (!bModel->GetSnapshot() ) {
Info("StandardHypoTestInvDemo","Model %s has no snapshot - make one using model poi and 0 values ",modelBName);
RooRealVar * var = dynamic_cast<RooRealVar*>(bModel->GetParametersOfInterest()->first());
if (var) {
double oldval = var->getVal();
var->setVal(0);
bModel->SetSnapshot( RooArgSet(*var) );
var->setVal(oldval);
}
else {
Error("StandardHypoTestInvDemo","Model %s has no valid poi",modelBName);
return 0;
}
}
}
// check model has global observables when there are nuisance pdf
// for the hybrid case the globobs are not needed
if (type != 1 ) {
bool hasNuisParam = (sbModel->GetNuisanceParameters() && sbModel->GetNuisanceParameters()->getSize() > 0);
bool hasGlobalObs = (sbModel->GetGlobalObservables() && sbModel->GetGlobalObservables()->getSize() > 0);
if (hasNuisParam && !hasGlobalObs ) {
// try to see if model has nuisance parameters first
RooAbsPdf * constrPdf = RooStats::MakeNuisancePdf(*sbModel,"nuisanceConstraintPdf_sbmodel");
if (constrPdf) {
Warning("StandardHypoTestInvDemo","Model %s has nuisance parameters but no global observables associated",sbModel->GetName());
Warning("StandardHypoTestInvDemo","\tThe effect of the nuisance parameters will not be treated correctly ");
}
}
}
// 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 )
TStopwatch tw;
bool doFit = initialFit;
if (testStatType == 0 && initialFit == -1) doFit = false; // case of LEP test statistic
if (type == 3 && initialFit == -1) doFit = false; // case of Asymptoticcalculator with nominal Asimov
double poihat = 0;
if (minimizerType.size()==0) minimizerType = ROOT::Math::MinimizerOptions::DefaultMinimizerType();
else
ROOT::Math::MinimizerOptions::SetDefaultMinimizer(minimizerType.c_str());
Info("StandardHypoTestInvDemo","Using %s as minimizer for computing the test statistic",
ROOT::Math::MinimizerOptions::DefaultMinimizerType().c_str() );
if (doFit) {
// do the fit : By doing a fit the POI snapshot (for S+B) is set to the fit value
// and the nuisance parameters nominal values will be set to the fit value.
// This is relevant when using LEP test statistics
Info( "StandardHypoTestInvDemo"," Doing a first fit to the observed data ");
RooArgSet constrainParams;
if (sbModel->GetNuisanceParameters() ) constrainParams.add(*sbModel->GetNuisanceParameters());
RooStats::RemoveConstantParameters(&constrainParams);
tw.Start();
RooFitResult * fitres = sbModel->GetPdf()->fitTo(*data,InitialHesse(false), Hesse(false),
Minimizer(minimizerType.c_str(),"Migrad"), Strategy(0), PrintLevel(mPrintLevel), Constrain(constrainParams), Save(true), 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(minimizerType.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) 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(minimizerType.c_str());
if (mEnableDetOutput) ropl.EnableDetailedOutput();
ProfileLikelihoodTestStat profll(*sbModel->GetPdf());
if (testStatType == 3) profll.SetOneSided(true);
if (testStatType == 4) profll.SetSigned(true);
profll.SetMinimizer(minimizerType.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);
ROOT::Math::MinimizerOptions::SetDefaultStrategy(0);
}
if (mMaxPoi > 0) poi->setMax(mMaxPoi); // increase limit
MaxLikelihoodEstimateTestStat maxll(*sbModel->GetPdf(),*poi);
NumEventsTestStat nevtts;
AsymptoticCalculator::SetPrintLevel(mPrintLevel);
// create the HypoTest calculator class
HypoTestCalculatorGeneric * hc = 0;
if (type == 0) hc = new FrequentistCalculator(*data, *bModel, *sbModel);
else if (type == 1) hc = new HybridCalculator(*data, *bModel, *sbModel);
// else if (type == 2 ) hc = new AsymptoticCalculator(*data, *bModel, *sbModel, false, mAsimovBins);
// else if (type == 3 ) hc = new AsymptoticCalculator(*data, *bModel, *sbModel, true, mAsimovBins); // for using Asimov data generated with nominal values
else if (type == 2 ) hc = new AsymptoticCalculator(*data, *bModel, *sbModel, false );
else if (type == 3 ) hc = new AsymptoticCalculator(*data, *bModel, *sbModel, true ); // for using Asimov data generated with nominal values
else {
Error("StandardHypoTestInvDemo","Invalid - calculator type = %d supported values are only :\n\t\t\t 0 (Frequentist) , 1 (Hybrid) , 2 (Asymptotic) ",type);
return 0;
}
// set the test statistic
TestStatistic * testStat = 0;
if (testStatType == 0) testStat = &slrts;
if (testStatType == 1 || testStatType == 11) testStat = &ropl;
if (testStatType == 2 || testStatType == 3 || testStatType == 4) testStat = &profll;
if (testStatType == 5) testStat = &maxll;
if (testStatType == 6) testStat = &nevtts;
if (testStat == 0) {
Error("StandardHypoTestInvDemo","Invalid - test statistic type = %d supported values are only :\n\t\t\t 0 (SLR) , 1 (Tevatron) , 2 (PLR), 3 (PLR1), 4(MLE)",testStatType);
return 0;
}
ToyMCSampler *toymcs = (ToyMCSampler*)hc->GetTestStatSampler();
if (toymcs && (type == 0 || type == 1) ) {
// look if pdf is number counting or extended
if (sbModel->GetPdf()->canBeExtended() ) {
if (useNumberCounting) Warning("StandardHypoTestInvDemo","Pdf is extended: but number counting flag is set: ignore it ");
}
else {
// for not extended pdf
if (!useNumberCounting ) {
int nEvents = data->numEntries();
Info("StandardHypoTestInvDemo","Pdf is not extended: number of events to generate taken from observed data set is %d",nEvents);
toymcs->SetNEventsPerToy(nEvents);
}
else {
Info("StandardHypoTestInvDemo","using a number counting pdf");
toymcs->SetNEventsPerToy(1);
}
}
toymcs->SetTestStatistic(testStat);
if (data->isWeighted() && !mGenerateBinned) {
Info("StandardHypoTestInvDemo","Data set is weighted, nentries = %d and sum of weights = %8.1f but toy generation is unbinned - it would be faster to set mGenerateBinned to true\n",data->numEntries(), data->sumEntries());
}
toymcs->SetGenerateBinned(mGenerateBinned);
toymcs->SetUseMultiGen(mOptimize);
if (mGenerateBinned && sbModel->GetObservables()->getSize() > 2) {
Warning("StandardHypoTestInvDemo","generate binned is activated but the number of ovservable is %d. Too much memory could be needed for allocating all the bins",sbModel->GetObservables()->getSize() );
}
// set the random seed if needed
if (mRandomSeed >= 0) RooRandom::randomGenerator()->SetSeed(mRandomSeed);
}
// specify if need to re-use same toys
if (reuseAltToys) {
hc->UseSameAltToys();
}
if (type == 1) {
HybridCalculator *hhc = dynamic_cast<HybridCalculator*> (hc);
assert(hhc);
hhc->SetToys(ntoys,ntoys/mNToysRatio); // can use less ntoys for b hypothesis
// remove global observables from ModelConfig (this is probably not needed anymore in 5.32)
bModel->SetGlobalObservables(RooArgSet() );
sbModel->SetGlobalObservables(RooArgSet() );
// check for nuisance prior pdf in case of nuisance parameters
if (bModel->GetNuisanceParameters() || sbModel->GetNuisanceParameters() ) {
// fix for using multigen (does not work in this case)
toymcs->SetUseMultiGen(false);
ToyMCSampler::SetAlwaysUseMultiGen(false);
RooAbsPdf * nuisPdf = 0;
if (nuisPriorName) nuisPdf = w->pdf(nuisPriorName);
// use prior defined first in bModel (then in SbModel)
if (!nuisPdf) {
Info("StandardHypoTestInvDemo","No nuisance pdf given for the HybridCalculator - try to deduce pdf from the model");
if (bModel->GetPdf() && bModel->GetObservables() )
nuisPdf = RooStats::MakeNuisancePdf(*bModel,"nuisancePdf_bmodel");
else
nuisPdf = RooStats::MakeNuisancePdf(*sbModel,"nuisancePdf_sbmodel");
}
if (!nuisPdf ) {
if (bModel->GetPriorPdf()) {
nuisPdf = bModel->GetPriorPdf();
Info("StandardHypoTestInvDemo","No nuisance pdf given - try to use %s that is defined as a prior pdf in the B model",nuisPdf->GetName());
}
else {
Error("StandardHypoTestInvDemo","Cannnot run Hybrid calculator because no prior on the nuisance parameter is specified or can be derived");
return 0;
}
}
assert(nuisPdf);
Info("StandardHypoTestInvDemo","Using as nuisance Pdf ... " );
nuisPdf->Print();
const RooArgSet * nuisParams = (bModel->GetNuisanceParameters() ) ? bModel->GetNuisanceParameters() : sbModel->GetNuisanceParameters();
RooArgSet * np = nuisPdf->getObservables(*nuisParams);
if (np->getSize() == 0) {
Warning("StandardHypoTestInvDemo","Prior nuisance does not depend on nuisance parameters. They will be smeared in their full range");
}
delete np;
hhc->ForcePriorNuisanceAlt(*nuisPdf);
hhc->ForcePriorNuisanceNull(*nuisPdf);
}
}
else if (type == 2 || type == 3) {
if (testStatType == 3) ((AsymptoticCalculator*) hc)->SetOneSided(true);
if (testStatType != 2 && testStatType != 3)
Warning("StandardHypoTestInvDemo","Only the PL test statistic can be used with AsymptoticCalculator - use by default a two-sided PL");
}
else if (type == 0 || type == 1) {
((FrequentistCalculator*) hc)->SetToys(ntoys,ntoys/mNToysRatio);
// store also the fit information for each poi point used by calculator based on toys
if (mEnableDetOutput) ((FrequentistCalculator*) hc)->StoreFitInfo(true);
}
// Get the result
RooMsgService::instance().getStream(1).removeTopic(RooFit::NumIntegration);
HypoTestInverter calc(*hc);
calc.SetConfidenceLevel(confidenceLevel);
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());
RooStats::RemoveConstantParameters(&constrainParams);
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(minimizerType.c_str(),"Migrad"), Strategy(0), PrintLevel(mPrintLevel), Constrain(constrainParams), Offset(RooStats::IsNLLOffset()) );