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ProfileLikelihoodCalculator.cxx
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1// @(#)root/roostats:$Id$
2// Author: Kyle Cranmer 28/07/2008
3
4/*************************************************************************
7 * *
8 * For the licensing terms see $ROOTSYS/LICENSE. * 9 * For the list of contributors see$ROOTSYS/README/CREDITS. *
10 *************************************************************************/
11
12/** \class RooStats::ProfileLikelihoodCalculator
13 \ingroup Roostats
14
15The ProfileLikelihoodCalculator is a concrete implementation of CombinedCalculator
16(the interface class for tools which can produce both a RooStats HypoTestResult
17and ConfInterval). The tool uses the profile likelihood ratio as a test statistic,
18and assumes that Wilks' theorem is valid. Wilks' theorem states that \f$-2 \cdot \ln(\lambda) \f$
19(profile likelihood ratio) is asymptotically distributed as a \f$\chi^2 \f$ distribution
20with \f$N \f$ degrees of freedom. Thus, \f$p\f$-values can be
21constructed, and the profile likelihood ratio can be used to construct a
22LikelihoodInterval. (In the future, this class could be extended to use toy
23Monte Carlo to calibrate the distribution of the test statistic).
24
25Usage: It uses the interface of the CombinedCalculator, so that it can be
26configured by specifying:
27
28 - A model common model (*e.g.* a family of specific models, which includes both
29 the null and alternate)
30 - A data set
31 - A set of parameters of interest. The nuisance parameters will be all other
32 parameters of the model.
33 - A set of parameters which specify the null hypothesis (including values
34 and const/non-const status).
35
36The interface allows one to pass the model, data, and parameters either directly
37or via a ModelConfig class. The alternate hypothesis leaves the parameter free
38to take any value other than those specified by the null hypothesis. There is
39therefore no need to specify the alternate parameters.
40
41After configuring the calculator, one only needs to call GetHypoTest() (which
42will return a HypoTestResult pointer) or GetInterval() (which will return a
43ConfInterval pointer).
44
45This calculator can work with both one-dimensional intervals or multi-
46dimensional ones (contours).
47
48Note that for hypothesis tests, it is often better to use the
49AsymptoticCalculator, which can compute in addition the expected
50\f$p\f$-value using an Asimov data set.
51
52*/
53
55
57
60
61#include "RooFitResult.h"
62#include "RooRealVar.h"
63#include "RooProfileLL.h"
64#include "RooNLLVar.h"
65#include "RooGlobalFunc.h"
66#include "RooMsgService.h"
67
69#include "RooMinimizer.h"
70//#include "RooProdPdf.h"
71
72using namespace std;
73
75
76using namespace RooFit;
77using namespace RooStats;
78
79
80////////////////////////////////////////////////////////////////////////////////
81/// default constructor
82
84 CombinedCalculator(), fFitResult(0), fGlobalFitDone(false)
85{
86}
87
89 Double_t size, const RooArgSet* nullParams ) :
90 CombinedCalculator(data,pdf, paramsOfInterest, size, nullParams ),
91 fFitResult(0), fGlobalFitDone(false)
92{
93 // constructor from pdf and parameters
94 // the pdf must contain eventually the nuisance parameters
95}
96
98 CombinedCalculator(data, model, size),
99 fFitResult(0), fGlobalFitDone(false)
100{
101 // construct from a ModelConfig. Assume data model.GetPdf() will provide full description of model including
102 // constraint term on the nuisances parameters
103 assert(model.GetPdf() );
104}
105
106
107////////////////////////////////////////////////////////////////////////////////
108/// destructor
109/// cannot delete prod pdf because it will delete all the composing pdf's
110/// if (fOwnPdf) delete fPdf;
111/// fPdf = 0;
112
114 if (fFitResult) delete fFitResult;
115}
116
118 // reset and clear fit result
119 // to be called when a new model or data are set in the calculator
120 if (fFitResult) delete fFitResult;
121 fFitResult = 0;
122}
123
125 // perform a global fit of the likelihood letting with all parameter of interest and
126 // nuisance parameters
127 // keep the list of fitted parameters
128
129 DoReset();
130 RooAbsPdf * pdf = GetPdf();
131 RooAbsData* data = GetData();
132 if (!data || !pdf ) return 0;
133
134 // get all non-const parameters
135 RooArgSet* constrainedParams = pdf->getParameters(*data);
136 if (!constrainedParams) return 0;
137 RemoveConstantParameters(constrainedParams);
138
139 const auto& config = GetGlobalRooStatsConfig();
141 RooFit::Offset(config.useLikelihoodOffset) );
142
143 // check if global fit has been already done
144 if (fFitResult && fGlobalFitDone) {
145 delete constrainedParams;
146 return nll;
147 }
148
149 // calculate MLE
150 oocoutP((TObject*)0,Minimization) << "ProfileLikelihoodCalcultor::DoGLobalFit - find MLE " << std::endl;
151
152 if (fFitResult) delete fFitResult;
154
155 // print fit result
156 if (fFitResult) {
158
159 if (fFitResult->status() != 0)
160 oocoutW((TObject*)0,Minimization) << "ProfileLikelihoodCalcultor::DoGlobalFit - Global fit failed - status = " << fFitResult->status() << std::endl;
161 else
162 fGlobalFitDone = true;
163 }
164
165 delete constrainedParams;
166 return nll;
167}
168
170 // Minimizer the given NLL using the default options
171
172 const char * minimType = ROOT::Math::MinimizerOptions::DefaultMinimizerType().c_str();
173 const char * minimAlgo = ROOT::Math::MinimizerOptions::DefaultMinimizerAlgo().c_str();
175 int level = ROOT::Math::MinimizerOptions::DefaultPrintLevel() -1;// RooFit level starts from -1
177 oocoutP((TObject*)0,Minimization) << "ProfileLikelihoodCalcultor::DoMinimizeNLL - using " << minimType << " / " << minimAlgo << " with strategy " << strategy << std::endl;
178 // do global fit and store fit result for further use
179
180 const auto& config = GetGlobalRooStatsConfig();
181
182 RooMinimizer minim(*nll);
183 minim.setStrategy(strategy);
184 minim.setEps(tolerance);
185 minim.setPrintLevel(level);
186 minim.optimizeConst(2); // to optimize likelihood calculations
187 minim.setEvalErrorWall(config.useEvalErrorWall);
188
189 int status = -1;
190 for (int tries = 1, maxtries = 4; tries <= maxtries; ++tries) {
191 status = minim.minimize(minimType,minimAlgo);
192 if (status%1000 == 0) { // ignore erros from Improve
193 break;
194 } else if (tries < maxtries) {
195 cout << " ----> Doing a re-scan first" << endl;
196 minim.minimize(minimType,"Scan");
197 if (tries == 2) {
198 if (strategy == 0 ) {
199 cout << " ----> trying with strategy = 1" << endl;;
200 minim.setStrategy(1);
201 }
202 else
203 tries++; // skip this trial if strategy is already 1
204 }
205 if (tries == 3) {
206 cout << " ----> trying with improve" << endl;;
207 minimType = "Minuit";
209 }
210 }
211 }
212
213 RooFitResult * result = minim.save();
214
215
216 return result;
217}
218
219////////////////////////////////////////////////////////////////////////////////
220/// Main interface to get a RooStats::ConfInterval.
221/// It constructs a profile likelihood ratio, and uses that to construct a RooStats::LikelihoodInterval.
222
224// RooAbsPdf* pdf = fWS->pdf(fPdfName);
225// RooAbsData* data = fWS->data(fDataName);
226 RooAbsPdf * pdf = GetPdf();
227 RooAbsData* data = GetData();
228 if (!data || !pdf || fPOI.getSize() == 0) return 0;
229
230 RooArgSet* constrainedParams = pdf->getParameters(*data);
231 RemoveConstantParameters(constrainedParams);
232
233
234 /*
235 RooNLLVar* nll = new RooNLLVar("nll","",*pdf,*data, Extended(),Constrain(*constrainedParams));
236 RooProfileLL* profile = new RooProfileLL("pll","",*nll, *fPOI);
237 profile->addOwnedComponents(*nll) ; // to avoid memory leak
238 */
239
240 // do a global fit cloning the data
241 RooAbsReal * nll = DoGlobalFit();
242 if (!nll) return 0;
243
244 if (!fFitResult) {
245 delete nll;
246 return 0;
247 }
248
249 RooAbsReal* profile = nll->createProfile(fPOI);
250 profile->addOwnedComponents(*nll) ; // to avoid memory leak
251
252 // t.b.f. " RooProfileLL should keep and provide possibility to query on global minimum
253 // set POI to fit value (this will speed up profileLL calculation of global minimum)
254 const RooArgList & fitParams = fFitResult->floatParsFinal();
255 for (int i = 0; i < fitParams.getSize(); ++i) {
256 RooRealVar & fitPar = (RooRealVar &) fitParams[i];
257 RooRealVar * par = (RooRealVar*) fPOI.find( fitPar.GetName() );
258 if (par) {
259 par->setVal( fitPar.getVal() );
260 par->setError( fitPar.getError() );
261 }
262 }
263
264 // do this so profile will cache inside the absolute minimum and
265 // minimum values of nuisance parameters
266 // (no need to this here)
267 // profile->getVal();
268 //RooMsgService::instance().setGlobalKillBelow(RooFit::DEBUG) ;
269 // profile->Print();
270
271 TString name = TString("LikelihoodInterval_");// + TString(GetName() );
272
273 // make a list of fPOI with fit result values and pass to LikelihoodInterval class
274 // bestPOI is a cloned list of POI only with their best fit values
275 TIter iter = fPOI.createIterator();
276 RooArgSet fitParSet(fitParams);
277 RooArgSet * bestPOI = new RooArgSet();
278 while (RooAbsArg * arg = (RooAbsArg*) iter.Next() ) {
279 RooAbsArg * p = fitParSet.find( arg->GetName() );
282 }
283 // fPOI contains the parameter of interest of the PL object
284 // and bestPOI contains a snapshot with the best fit values
285 LikelihoodInterval* interval = new LikelihoodInterval(name, profile, &fPOI, bestPOI);
286 interval->SetConfidenceLevel(1.-fSize);
287 delete constrainedParams;
288 return interval;
289}
290
291////////////////////////////////////////////////////////////////////////////////
292/// Main interface to get a HypoTestResult.
293/// It does two fits:
294/// 1. The first lets the null parameters float, so it's a maximum likelihood estimate.
295/// 2. The second is to the null model (fixing null parameters to their specified values): *e.g.* a conditional maximum likelihood.
296/// Since not all parameters are floating, this likelihood will be lower than the unconditional model.
297///
298/// The ratio of the likelihood obtained from the conditional MLE to the MLE is the profile likelihood ratio.
299/// Wilks' theorem is used to get \f$p\f$-values.
300
302// RooAbsPdf* pdf = fWS->pdf(fPdfName);
303// RooAbsData* data = fWS->data(fDataName);
304 RooAbsPdf * pdf = GetPdf();
305 RooAbsData* data = GetData();
306
307
308 if (!data || !pdf) return 0;
309
310 if (fNullParams.getSize() == 0) return 0;
311
312 // make a clone and ordered list since a vector will be associated to keep parameter values
313 // clone the list since first fit will changes the fNullParams values
314 RooArgList poiList;
315 poiList.addClone(fNullParams); // make a clone list
316
317
318 // do a global fit
319 RooAbsReal * nll = DoGlobalFit();
320 if (!nll) return 0;
321
322 if (!fFitResult) {
323 delete nll;
324 return 0;
325 }
326
327 RooArgSet* constrainedParams = pdf->getParameters(*data);
328 RemoveConstantParameters(constrainedParams);
329
330 Double_t nLLatMLE = fFitResult->minNll();
331 // in case of using offset need to save offset value
332 Double_t nlloffset = (RooStats::IsNLLOffset() ) ? nll->getVal() - nLLatMLE : 0;
333
334 // set POI to given values, set constant, calculate conditional MLE
335 std::vector<double> oldValues(poiList.getSize() );
336 for (unsigned int i = 0; i < oldValues.size(); ++i) {
337 RooRealVar * mytarget = (RooRealVar*) constrainedParams->find(poiList[i].GetName());
338 if (mytarget) {
339 oldValues[i] = mytarget->getVal();
340 mytarget->setVal( ( (RooRealVar&) poiList[i] ).getVal() );
341 mytarget->setConstant(kTRUE);
342 }
343 }
344
345
346
347 // perform the fit only if nuisance parameters are available
348 // get nuisance parameters
349 // nuisance parameters are the non const parameters from the likelihood parameters
350 RooArgSet nuisParams(*constrainedParams);
351
352 // need to remove the parameter of interest
353 RemoveConstantParameters(&nuisParams);
354
355 // check there are variable parameter in order to do a fit
356 bool existVarParams = false;
357 TIter it = nuisParams.createIterator();
358 RooRealVar * myarg = 0;
359 while ((myarg = (RooRealVar *)it.Next())) {
360 if ( !myarg->isConstant() ) {
361 existVarParams = true;
362 break;
363 }
364 }
365
366 Double_t nLLatCondMLE = nLLatMLE;
367 if (existVarParams) {
368 oocoutP((TObject*)0,Minimization) << "ProfileLikelihoodCalcultor::GetHypoTest - do conditional fit " << std::endl;
369
370 RooFitResult * fit2 = DoMinimizeNLL(nll);
371
372 // print fit result
373 if (fit2) {
374 nLLatCondMLE = fit2->minNll();
376
377 if (fit2->status() != 0)
378 oocoutW((TObject*)0,Minimization) << "ProfileLikelihoodCalcultor::GetHypotest - Conditional fit failed - status = " << fit2->status() << std::endl;
379 }
380
381 }
382 else {
383 // get just the likelihood value (no need to do a fit since the likelihood is a constant function)
384 nLLatCondMLE = nll->getVal();
385 // this value contains the offset
386 if (RooStats::IsNLLOffset() ) nLLatCondMLE -= nlloffset;
387 }
388
389 // Use Wilks' theorem to translate -2 log lambda into a significance/p-value
390 Double_t deltaNLL = std::max( nLLatCondMLE-nLLatMLE, 0.);
391
392 // get number of free parameter of interest
394 int ndf = poiList.getSize();
395
396 Double_t pvalue = ROOT::Math::chisquared_cdf_c( 2* deltaNLL, ndf);
397
398 // in case of one dimension (1 poi) do the one-sided p-value (need to divide by 2)
399 if (ndf == 1) pvalue = 0.5 * pvalue;
400
401 TString name = TString("ProfileLRHypoTestResult_");// + TString(GetName() );
402 HypoTestResult* htr = new HypoTestResult(name, pvalue, 0 );
403
404 // restore previous value of poi
405 for (unsigned int i = 0; i < oldValues.size(); ++i) {
406 RooRealVar * mytarget = (RooRealVar*) constrainedParams->find(poiList[i].GetName());
407 if (mytarget) {
408 mytarget->setVal(oldValues[i] );
409 mytarget->setConstant(false);
410 }
411 }
412
413 delete constrainedParams;
414 delete nll;
415 return htr;
416
417}
#define oocoutW(o, a)
#define oocoutI(o, a)
#define oocoutP(o, a)
const Bool_t kTRUE
Definition RtypesCore.h:91
#define ClassImp(name)
Definition Rtypes.h:364
char name[80]
Definition TGX11.cxx:110
static const std::string & DefaultMinimizerType()
static const std::string & DefaultMinimizerAlgo()
RooAbsArg is the common abstract base class for objects that represent a value and a "shape" in RooFi...
Definition RooAbsArg.h:72
Take ownership of the contents of 'comps'.
RooArgSet * getParameters(const RooAbsData *data, bool stripDisconnected=true) const
Create a list of leaf nodes in the arg tree starting with ourself as top node that don't match any of...
Bool_t isConstant() const
Check if the "Constant" attribute is set.
Definition RooAbsArg.h:380
Int_t getSize() const
virtual RooAbsArg * addClone(const RooAbsArg &var, Bool_t silent=kFALSE)
Add a clone of the specified argument to list.
const char * GetName() const
Returns name of object.
TIterator * createIterator(Bool_t dir=kIterForward) const
TIterator-style iteration over contained elements.
RooAbsArg * find(const char *name) const
Find object with given name in list.
RooAbsData is the common abstract base class for binned and unbinned datasets.
Definition RooAbsData.h:49
virtual RooAbsReal * createNLL(RooAbsData &data, const RooLinkedList &cmdList)
Construct representation of -log(L) of PDFwith given dataset.
void setConstant(Bool_t value=kTRUE)
RooAbsReal is the common abstract base class for objects that represent a real value and implements f...
Definition RooAbsReal.h:61
virtual RooAbsReal * createProfile(const RooArgSet &paramsOfInterest)
Create a RooProfileLL object that eliminates all nuisance parameters in the present function.
Double_t getVal(const RooArgSet *normalisationSet=nullptr) const
Evaluate object.
Definition RooAbsReal.h:91
RooArgList is a container object that can hold multiple RooAbsArg objects.
Definition RooArgList.h:21
RooArgSet is a container object that can hold multiple RooAbsArg objects.
Definition RooArgSet.h:29
RooAbsArg * addClone(const RooAbsArg &var, Bool_t silent=kFALSE) override
Add clone of specified element to an owning set.
RooFitResult is a container class to hold the input and output of a PDF fit to a dataset.
virtual Int_t defaultPrintContents(Option_t *opt) const
Configure default contents to be printed.
virtual StyleOption defaultPrintStyle(Option_t *opt) const
Configure mapping of Print() arguments to RooPrintable print styles.
Double_t minNll() const
Return minimized -log(L) value.
const RooArgList & floatParsFinal() const
Return list of floarting parameters after fit.
Int_t status() const
Return MINUIT status code.
RooMinimizer is a wrapper class around ROOT::Fit:Fitter that provides a seamless interface between th...
void setEvalErrorWall(Bool_t flag)
RooFitResult * save(const char *name=0, const char *title=0)
Save and return a RooFitResult snapshot of current minimizer status.
Int_t minimize(const char *type, const char *alg=0)
Minimise the function passed in the constructor.
void setEps(Double_t eps)
Change MINUIT epsilon.
Int_t setPrintLevel(Int_t newLevel)
Change the MINUIT internal printing level.
void optimizeConst(Int_t flag)
If flag is true, perform constant term optimization on function being minimized.
void setStrategy(Int_t strat)
Change MINUIT strategy to istrat.
virtual void printStream(std::ostream &os, Int_t contents, StyleOption style, TString indent="") const
Print description of object on ostream, printing contents set by contents integer,...
RooRealVar represents a variable that can be changed from the outside.
Definition RooRealVar.h:39
void setError(Double_t value)
Definition RooRealVar.h:62
Double_t getError() const
Definition RooRealVar.h:60
virtual void setVal(Double_t value)
Set value of variable to 'value'.
CombinedCalculator is an interface class for a tools which can produce both RooStats HypoTestResults ...
HypoTestResult is a base class for results from hypothesis tests.
LikelihoodInterval is a concrete implementation of the RooStats::ConfInterval interface.
virtual void SetConfidenceLevel(Double_t cl)
set the confidence level for the interval (e.g 0.682 for a 1-sigma interval)
ModelConfig is a simple class that holds configuration information specifying how a model should be u...
Definition ModelConfig.h:30
RooAbsPdf * GetPdf() const
get model PDF (return NULL if pdf has not been specified or does not exist)
The ProfileLikelihoodCalculator is a concrete implementation of CombinedCalculator (the interface cla...
virtual HypoTestResult * GetHypoTest() const
Return the hypothesis test result obtained from the likelihood ratio of the maximum likelihood value ...
static RooFitResult * DoMinimizeNLL(RooAbsReal *nll)
virtual LikelihoodInterval * GetInterval() const
Return a likelihood interval.
ProfileLikelihoodCalculator()
Default constructor (needed for I/O)
virtual ~ProfileLikelihoodCalculator()
destructor cannot delete prod pdf because it will delete all the composing pdf's if (fOwnPdf) delete ...
TObject * Next()
virtual const char * GetName() const
Returns name of object.
Definition TNamed.h:47
Mother of all ROOT objects.
Definition TObject.h:37
Basic string class.
Definition TString.h:136
RooCmdArg Constrain(const RooArgSet &params)
RooCmdArg GlobalObservables(const RooArgSet &globs)
RooCmdArg CloneData(Bool_t flag)
RooCmdArg ConditionalObservables(const RooArgSet &set)
RooCmdArg Offset(Bool_t flag=kTRUE)
double chisquared_cdf_c(double x, double r, double x0=0)
Complement of the cumulative distribution function of the distribution with degrees of freedom (upp...
The namespace RooFit contains mostly switches that change the behaviour of functions of PDFs (or othe...
Namespace for the RooStats classes.
Definition Asimov.h:19
void RemoveConstantParameters(RooArgSet *set)
RooStatsConfig & GetGlobalRooStatsConfig()
Retrieve the config object which can be used to set flags for things like offsetting the likelihood o...
bool IsNLLOffset()
Test of RooStats should by default offset NLL calculations.