MCMCCalculator.h

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// @(#)root/roostats:$Id$
// Authors: Kevin Belasco        17/06/2009
// Authors: Kyle Cranmer         17/06/2009
/*************************************************************************
 * Copyright (C) 1995-2008, Rene Brun and Fons Rademakers.               *
 * All rights reserved.                                                  *
 *                                                                       *
 * For the licensing terms see $ROOTSYS/LICENSE.                         *
 * For the list of contributors see $ROOTSYS/README/CREDITS.             *
 *************************************************************************/

#ifndef ROOSTATS_MCMCCalculator
#define ROOSTATS_MCMCCalculator

#include "Rtypes.h"

#include "TObject.h"
#include "RooAbsPdf.h"
#include "RooAbsData.h"
#include "RooArgSet.h"
#include "RooArgList.h"
#include "RooStats/ProposalFunction.h"
#include "RooStats/IntervalCalculator.h"
#include "RooStats/MCMCInterval.h"


namespace RooStats {

   class ModelConfig;

   class MCMCCalculator : public IntervalCalculator, public TNamed {

   public:
      /// default constructor
      MCMCCalculator();

      /// Constructor for automatic configuration with basic settings and a
      /// ModelConfig.  Uses a UniformProposal, 10,000 iterations, 40 burn in
      /// steps, 50 bins for each RooRealVar, determines interval by histogram,
      /// and finds a 95% confidence interval.  Any of these basic settings can
      /// be overridden by calling one of the Set...() methods.
      MCMCCalculator(RooAbsData& data, const ModelConfig& model);

      virtual ~MCMCCalculator() {}

      /// Main interface to get a ConfInterval
      virtual MCMCInterval* GetInterval() const;

      /// Get the size of the test (eg. rate of Type I error)
      virtual Double_t Size() const {return fSize;}
      /// Get the Confidence level for the test
      virtual Double_t ConfidenceLevel() const {return 1.-fSize;}

      virtual void SetModel(const ModelConfig & model);

      /// Set the DataSet if not already there
      virtual void SetData(RooAbsData& data) { fData = &data; }

      /// Set the Pdf if not already there
      virtual void SetPdf(RooAbsPdf& pdf) { fPdf = &pdf; }

      /// Set the Prior Pdf if not already there
      virtual void SetPriorPdf(RooAbsPdf& pdf) { fPriorPdf = &pdf; }

      /// specify the parameters of interest in the interval
      virtual void SetParameters(const RooArgSet& set) { fPOI.removeAll(); fPOI.add(set); }

      /// specify the parameters to store in the Markov chain
      /// By default all the parameters are stored
      virtual void SetChainParameters(const RooArgSet & set) { fChainParams.removeAll(); fChainParams.add(set); }

      /// specify the nuisance parameters (eg. the rest of the parameters)
      virtual void SetNuisanceParameters(const RooArgSet& set) {fNuisParams.removeAll(); fNuisParams.add(set);}

      /// set the conditional observables which will be used when creating the NLL
      /// so the pdf's will not be normalized on the conditional observables when computing the NLL
      virtual void SetConditionalObservables(const RooArgSet& set) {fConditionalObs.removeAll(); fConditionalObs.add(set);}

      /// set the size of the test (rate of Type I error) ( Eg. 0.05 for a 95% Confidence Interval)
      virtual void SetTestSize(Double_t size) {fSize = size;}

      /// set the confidence level for the interval (eg. 0.95 for a 95% Confidence Interval)
      virtual void SetConfidenceLevel(Double_t cl) {fSize = 1.-cl;}

      /// set the proposal function for suggesting new points for the MCMC
      virtual void SetProposalFunction(ProposalFunction& proposalFunction)
      { fPropFunc = &proposalFunction; }

      /// set the number of iterations to run the metropolis algorithm
      virtual void SetNumIters(Int_t numIters)
      { fNumIters = numIters; }

      /// set the number of steps in the chain to discard as burn-in,
      /// starting from the first
      virtual void SetNumBurnInSteps(Int_t numBurnInSteps)
      { fNumBurnInSteps = numBurnInSteps; }

      /// set the number of bins to create for each axis when constructing the interval
      virtual void SetNumBins(Int_t numBins) { fNumBins = numBins; }
      /// set which variables to put on each axis
      virtual void SetAxes(RooArgList& axes)
      { fAxes = &axes; }
      /// set whether to use kernel estimation to determine the interval
      virtual void SetUseKeys(Bool_t useKeys) { fUseKeys = useKeys; }
      /// set whether to use sparse histogram (if using histogram at all)
      virtual void SetUseSparseHist(Bool_t useSparseHist)
      { fUseSparseHist = useSparseHist; }

      /// set what type of interval to have the MCMCInterval represent
      virtual void SetIntervalType(enum MCMCInterval::IntervalType intervalType)
      { fIntervalType = intervalType; }

      /// Set the left side tail fraction. This will automatically configure the
      /// MCMCInterval to find a tail-fraction interval.
      /// Note: that `a' must be in the range 0 <= a <= 1
      /// or the user will be notified of the error
      virtual void SetLeftSideTailFraction(Double_t a);

      /// Set the desired level of confidence-level accuracy  for Keys interval
      /// determination.
      //
      /// When determining the cutoff PDF height that gives the
      /// desired confidence level (C_d), the algorithm will consider acceptable
      /// any found confidence level c such that Abs(c - C_d) < epsilon.
      ///
      /// Any value of this "epsilon" > 0 is considered acceptable, though it is
      /// advisable to not use a value too small, because the integration of the
      /// Keys PDF sometimes does not have extremely high accuracy.
      virtual void SetKeysConfidenceAccuracy(Double_t epsilon)
      {
         if (epsilon < 0)
            coutE(InputArguments) << "MCMCInterval::SetEpsilon will not allow "
                                  << "negative epsilon value" << std::endl;
         else
            fEpsilon = epsilon;
      }

      /// When the shortest interval using Keys PDF could not be found to have
      /// the desired confidence level +/- the accuracy (see
      /// SetKeysConfidenceAccuracy()), the interval determination algorithm
      /// will have to terminate with an unsatisfactory confidence level when
      /// the bottom and top of the cutoff search range are very close to being
      /// equal.  This scenario comes into play when there seems to be an error
      /// in the accuracy of the Keys PDF integration, so the search range
      /// continues to shrink without converging to a cutoff value that will
      /// give an acceptable confidence level.  To choose how small to allow the
      /// search range to be before terminating, set the fraction delta such
      /// that the search will terminate when topCutoff (a) and bottomCutoff (b)
      /// satisfy this condition:
      ///
      /// TMath::Abs(a - b) < TMath::Abs(delta * (a + b)/2)
      virtual void SetKeysTerminationThreshold(Double_t delta)
      {
         if (delta < 0.)
            coutE(InputArguments) << "MCMCInterval::SetDelta will not allow "
                                  << "negative delta value" << std::endl;
         else
            fDelta = delta;
      }

   protected:

      Double_t fSize;   // size of the test (eg. specified rate of Type I error)
      RooArgSet   fPOI;        // parameters of interest for interval
      RooArgSet   fNuisParams; // nuisance parameters for interval (not really used)
      RooArgSet   fChainParams; // parameters to store in the chain (if not specified they are all of them )
      RooArgSet   fConditionalObs; // conditional observables
      mutable ProposalFunction* fPropFunc; // Proposal function for MCMC integration
      RooAbsPdf * fPdf;        // pointer to common PDF (owned by the workspace)
      RooAbsPdf * fPriorPdf;   // pointer to prior  PDF (owned by the workspace)
      RooAbsData * fData;     // pointer to the data (owned by the workspace)
      Int_t fNumIters; // number of iterations to run metropolis algorithm
      Int_t fNumBurnInSteps; // number of iterations to discard as burn-in, starting from the first
      Int_t fNumBins; // set the number of bins to create for each
                      // axis when constructing the interval
      RooArgList * fAxes; // which variables to put on each axis
      Bool_t fUseKeys; // whether to use kernel estimation to determine interval
      Bool_t fUseSparseHist; // whether to use sparse histogram (if using hist at all)
      Double_t fLeftSideTF; // left side tail-fraction for interval
      Double_t fEpsilon; // acceptable error for Keys interval determination

      Double_t fDelta; // acceptable error for Keys cutoffs being equal
                       // topCutoff (a) considered == bottomCutoff (b) iff
                       // (TMath::Abs(a - b) < TMath::Abs(fDelta * (a + b)/2));
                       // Theoretically, the Abs is not needed here, but
                       // floating-point arithmetic does not always work
                       // perfectly, and the Abs doesn't hurt
      enum MCMCInterval::IntervalType fIntervalType; // type of interval to find

      void SetupBasicUsage();
      void SetBins(const RooAbsCollection& coll, Int_t numBins) const
      {
         TIterator* it = coll.createIterator();
         RooAbsArg* r;
         while ((r = (RooAbsArg*)it->Next()) != NULL)
            if (dynamic_cast<RooRealVar*>(r))
               ((RooRealVar*)r)->setBins(numBins);
         delete it;
      }

      ClassDef(MCMCCalculator,3) // Markov Chain Monte Carlo calculator for Bayesian credible intervals
   };
}


#endif