54 void SetParamters(std::vector<std::string>& ParameterGroupsNotVaried,
55 std::unordered_set<int>& ParameterOnlyToVary);
59 std::unordered_set<int>& ParameterOnlyToVary,
60 std::vector<const M3::float_t*>& BoundValuePointer,
61 std::vector<std::pair<double, double>>& ParamBounds);
66 void WriteToy(TDirectory* ToyDirectory, TDirectory* Toy_1DDirectory, TDirectory* Toy_2DDirectory,
const int iToy);
77 std::vector<std::unique_ptr<TH1>>
MakePredictive(
const std::vector<std::vector<std::unique_ptr<TH1>>>& Toys,
78 const std::vector<TDirectory*>& Director,
79 const std::string& suffix,
80 const bool DebugHistograms,
81 const bool WriteHist);
89 void ProduceSpectra(
const std::vector<std::vector<std::vector<std::unique_ptr<TH1D>>>>& Toys,
90 const std::vector<TDirectory*>& Director,
91 const std::string suffix,
92 const bool DoSummary =
true)
const;
100 void PredictiveLLH(
const std::vector<std::unique_ptr<TH1>>& Data_histogram,
101 const std::vector<std::unique_ptr<TH1>>& PostPred_mc,
102 const std::vector<std::unique_ptr<TH1>>& PostPred_w,
103 const std::vector<TDirectory*>& SampleDir);
115 const std::vector<TDirectory*>& SampleDir);
128 double CalcLLH(
const double data,
138 double CalcLLH(
const TH1* DatHist,
148 double GetLLH(
const TH1D* DatHist,
158 double GetLLH(
const TH2D* DatHist,
168 double GetLLH(
const TH2Poly* DatHist,
169 const TH2Poly* MCHist,
170 const TH2Poly* W2Hist,
174 void MakeChi2Plots(
const std::vector<std::vector<double>>& Chi2_x,
175 const std::string& Chi2_x_title,
176 const std::vector<std::vector<double>>& Chi2_y,
177 const std::string& Chi2_y_title,
178 const std::vector<TDirectory*>& SampleDir,
179 const std::string Title);
183 const std::vector<std::vector<std::unique_ptr<TH1>>>& Toys,
184 const bool DebugHistograms)
const;
188 const std::vector<std::unique_ptr<TH1>>& PostPred_mc,
189 const std::vector<std::unique_ptr<TH1>>& PostPred_w);
203 void StudyBIC(
const std::vector<std::unique_ptr<TH1>>& PostPred_mc,
204 const std::vector<std::unique_ptr<TH1>>& PostPred_w);
225 void StudyDIC(
const std::vector<std::unique_ptr<TH1>>& PostPred_mc,
226 const std::vector<std::unique_ptr<TH1>>& PostPred_w);
258 const std::vector<int>& bins)
const;
277 const std::string& suffix)
const;
284 void RateAnalysis(
const std::vector<std::vector<std::unique_ptr<TH1>>>& Toys,
285 const std::vector<TDirectory*>& SampleDirectories)
const;
Base class for implementing fitting algorithms.
std::string GetName() const
Get name of class.
Manager * fitMan
The manager for configuration handling.
Custom exception class used throughout MaCh3.
The manager class is responsible for managing configurations and settings.
Base class responsible for handling of systematic error parameters. Capable of using PCA or using ada...
Class responsible for handling of systematic error parameters with different types defined in the con...
Implementation of Prior/Posterior Predictive and Bayesian p-Value calculations following the approach...
std::vector< double > PenaltyTerm
Penalty term values for each toy by default 0.
virtual ~PredictiveThrower()
Destructor.
void ExtractLLH(TH1 *DatHist, TH1 *MCHist, TH1 *W2Hist, const SampleHandlerInterface *SampleHandler) const
Calculate the LLH for TH1, set the LLH to title of MCHist.
bool FullLLH
KS: Use Full LLH or only sample contribution based on discussion with Asher we almost always only wan...
void WriteToy(TDirectory *ToyDirectory, TDirectory *Toy_1DDirectory, TDirectory *Toy_2DDirectory, const int iToy)
Save histograms for a single MCMC Throw/Toy.
void StudyCorrelations(TDirectory *PredictiveDir, const std::vector< std::vector< std::unique_ptr< TH1 >>> &Toys, const bool DebugHistograms) const
Study Prior/Posterior correlations between samples etc.
void RunPredictiveAnalysis()
Main routine responsible for producing posterior predictive distributions and $p$-value.
bool LoadToys()
Load existing toys.
void PosteriorPredictivepValue(const std::vector< std::unique_ptr< TH1 >> &PostPred_mc, const std::vector< TDirectory * > &SampleDir)
Calculate Posterior Predictive $p$-value Compares observed data to toy datasets generated from:
void WriteByModeToys(TDirectory *ByModeDirectory, const int iToy)
Save mode histograms for a single MCMC Throw/Toy.
void StudyBIC(const std::vector< std::unique_ptr< TH1 >> &PostPred_mc, const std::vector< std::unique_ptr< TH1 >> &PostPred_w)
Study Bayesian Information Criterion (BIC) The BIC is defined as:
void SetupToyGeneration(std::vector< std::string > &ParameterGroupsNotVaried, std::unordered_set< int > &ParameterOnlyToVary, std::vector< const M3::float_t * > &BoundValuePointer, std::vector< std::pair< double, double >> &ParamBounds)
Setup useful variables etc before stating toy generation.
std::string GetBinName(TH1 *hist, const bool uniform, const int Dim, const std::vector< int > &bins) const
Construct a human-readable label describing a specific analysis bin.
std::vector< std::string > GetStoredFancyName(ParameterHandlerBase *Systematics) const
Get Fancy parameters stored in mcmc chains for passed ParameterHandler.
std::vector< std::unique_ptr< TH1 > > W2_Nom_Hist
Vector of W2 histograms.
std::vector< std::vector< std::unique_ptr< TH1 > > > W2_Hist_Toy
bool Is_PriorPredictive
Whether it is Prior or Posterior predictive.
int NModelParams
KS: Count total number of model parameters which can be used for stuff like BIC.
void RunMCMC() override
This is not used in this class.
void MakeCutEventRate(TH1D *Histogram, const double DataRate) const
Make the 1D Event Rate Hist.
void StudyInformationCriterion(M3::kInfCrit Criterion, const std::vector< std::unique_ptr< TH1 >> &PostPred_mc, const std::vector< std::unique_ptr< TH1 >> &PostPred_w)
Information Criterion.
int Ntoys
Number of toys we are generating analysing.
void StudyByMode1DProjections(const std::vector< TDirectory * > &SampleDirectories) const
Load 1D projections by mode and produce post pred for each.
void PredictiveLLH(const std::vector< std::unique_ptr< TH1 >> &Data_histogram, const std::vector< std::unique_ptr< TH1 >> &PostPred_mc, const std::vector< std::unique_ptr< TH1 >> &PostPred_w, const std::vector< TDirectory * > &SampleDir)
Calculate Posterior Predictive LLH.
void RateAnalysis(const std::vector< std::vector< std::unique_ptr< TH1 >>> &Toys, const std::vector< TDirectory * > &SampleDirectories) const
Produce distribution of number of events for each sample.
void MakeChi2Plots(const std::vector< std::vector< double >> &Chi2_x, const std::string &Chi2_x_title, const std::vector< std::vector< double >> &Chi2_y, const std::string &Chi2_y_title, const std::vector< TDirectory * > &SampleDir, const std::string Title)
Produce Chi2 plot for a single sample based on which $p$-value is calculated.
void SetParamters(std::vector< std::string > &ParameterGroupsNotVaried, std::unordered_set< int > &ParameterOnlyToVary)
This set some params to prior value this way you can evaluate errors from subset of errors.
void StudyWAIC()
KS: Get the Watanabe-Akaike information criterion (WAIC)
void Study1DProjections(const std::vector< TDirectory * > &SampleDirectories) const
Load 1D projections and later produce violin plots for each.
void SetupSampleInformation()
Setup sample information.
std::vector< std::unique_ptr< TH1 > > MC_Nom_Hist
Vector of MC histograms.
int TotalNumberOfSamples
Number of toys we are generating analysing.
void ProduceToys()
Produce toys by throwing from MCMC.
void StudyBetaParameters(TDirectory *PredictiveDir)
Evaluate prior/post predictive distribution for beta parameters (used for evaluating impact MC statis...
double CalcLLH(const double data, const double mc, const double w2, const SampleHandlerInterface *SampleHandler) const
Calculates the -2LLH (likelihood) for a single sample.
std::vector< std::unique_ptr< TH1 > > Data_Hist
Vector of Data histograms.
bool StandardFluctuation
KS: We have two methods for Poissonian fluctuation.
ParameterHandlerGeneric * ModelSystematic
Pointer to El Generico.
std::vector< double > ReweightWeight
Reweighting factors applied for each toy, by default 1.
std::vector< std::unique_ptr< TH1 > > MakePredictive(const std::vector< std::vector< std::unique_ptr< TH1 >>> &Toys, const std::vector< TDirectory * > &Director, const std::string &suffix, const bool DebugHistograms, const bool WriteHist)
Produce posterior predictive distribution.
PredictiveThrower(Manager *const fitMan)
Constructor.
void MakeFluctuatedHistogram(TH1 *FluctHist, TH1 *PolyHist)
Make Poisson fluctuation of TH1D hist.
std::vector< std::vector< std::unique_ptr< TH1 > > > MC_Hist_Toy
std::vector< PredictiveSample > SampleInfo
Handy struct for all sample info.
void StudyDIC(const std::vector< std::unique_ptr< TH1 >> &PostPred_mc, const std::vector< std::unique_ptr< TH1 >> &PostPred_w)
KS: Get the Deviance Information Criterion (DIC) The deviance is defined as:
double GetLLH(const TH1D *DatHist, const TH1D *MCHist, const TH1D *W2Hist, const SampleHandlerInterface *SampleHandler) const
Helper functions to calculate likelihoods using TH1D.
void ProduceSpectra(const std::vector< std::vector< std::vector< std::unique_ptr< TH1D >>>> &Toys, const std::vector< TDirectory * > &Director, const std::string suffix, const bool DoSummary=true) const
Produce Violin style spectra.
std::vector< std::unique_ptr< TH1D > > PerBinHistogram(TH1 *hist, const int SampleId, const int Dim, const std::string &suffix) const
Create per-bin posterior histograms for a given sample.
Class responsible for handling implementation of samples used in analysis, reweighting and returning ...
kInfCrit
KS: Different Information Criterion tests mostly based Gelman paper.
KS: Summary of sample info to be used by.
int LocalId
Local SampleId in SampleHandler.
const SampleHandlerInterface * SamHandler
Pointer to SampleHandler.
int Dimenstion
Sample Dimension.