MaCh3  2.5.0
Reference Guide
PredictiveThrower.h
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1 #pragma once
2 
3 #include "Fitters/FitterBase.h"
4 
6 
7 // ***************************
10 // ***************************
11  // Name of sample
12  std::string Name;
16  int LocalId;
19 };
20 
32 
38 class PredictiveThrower : public FitterBase {
39  public:
44  virtual ~PredictiveThrower();
45 
47  void ProduceToys();
48 
50  void RunPredictiveAnalysis();
51 
53  void RunMCMC() override {
54  MACH3LOG_ERROR("{} is not supported in {}", __func__, GetName());
55  throw MaCh3Exception(__FILE__ , __LINE__ );
56  };
57 
58  private:
60  void SetParamters(std::vector<std::string>& ParameterGroupsNotVaried,
61  std::unordered_set<int>& ParameterOnlyToVary);
62 
64  void SetupToyGeneration(std::vector<std::string>& ParameterGroupsNotVaried,
65  std::unordered_set<int>& ParameterOnlyToVary,
66  std::vector<const M3::float_t*>& BoundValuePointer,
67  std::vector<std::pair<double, double>>& ParamBounds);
68 
70  bool LoadToys();
72  void WriteToy(TDirectory* ToyDirectory, TDirectory* Toy_1DDirectory, TDirectory* Toy_2DDirectory, const int iToy);
75 
77  std::vector<std::string> GetStoredFancyName(ParameterHandlerBase* Systematics) const;
78 
80  std::vector<std::unique_ptr<TH1>> MakePredictive(const std::vector<std::vector<std::unique_ptr<TH1>>>& Toys,
81  const std::vector<TDirectory*>& Director,
82  const std::string& suffix,
83  const bool DebugHistograms,
84  const bool WriteHist);
85 
87  void Study1DProjections(const std::vector<TDirectory*>& SampleDirectories) const;
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) const;
92 
96  void MakeFluctuatedHistogram(TH1* FluctHist, TH1* PolyHist);
97 
99  void PredictiveLLH(const std::vector<std::unique_ptr<TH1>>& Data_histogram,
100  const std::vector<std::unique_ptr<TH1>>& PostPred_mc,
101  const std::vector<std::unique_ptr<TH1>>& PostPred_w,
102  const std::vector<TDirectory*>& SampleDir);
103 
104 
113  void PosteriorPredictivepValue(const std::vector<std::unique_ptr<TH1>>& PostPred_mc,
114  const std::vector<TDirectory*>& SampleDir);
120  void ExtractLLH(TH1* DatHist, TH1* MCHist, TH1* W2Hist, const SampleHandlerInterface* SampleHandler) const;
121 
127  double CalcLLH(const double data,
128  const double mc,
129  const double w2,
130  const SampleHandlerInterface* SampleHandler) const;
131 
137  double CalcLLH(const TH1* DatHist,
138  const TH1* MCHist,
139  const TH1* W2Hist,
140  const SampleHandlerInterface* SampleHandler) const;
141 
147  double GetLLH(const TH1D* DatHist,
148  const TH1D* MCHist,
149  const TH1D* W2Hist,
150  const SampleHandlerInterface* SampleHandler) const;
151 
157  double GetLLH(const TH2D* DatHist,
158  const TH2D* MCHist,
159  const TH2D* W2Hist,
160  const SampleHandlerInterface* SampleHandler) const;
161 
167  double GetLLH(const TH2Poly* DatHist,
168  const TH2Poly* MCHist,
169  const TH2Poly* W2Hist,
170  const SampleHandlerInterface* SampleHandler) const;
171 
173  void MakeChi2Plots(const std::vector<std::vector<double>>& Chi2_x,
174  const std::string& Chi2_x_title,
175  const std::vector<std::vector<double>>& Chi2_y,
176  const std::string& Chi2_y_title,
177  const std::vector<TDirectory*>& SampleDir,
178  const std::string Title);
179 
180 
183  const std::vector<std::unique_ptr<TH1>>& PostPred_mc,
184  const std::vector<std::unique_ptr<TH1>>& PostPred_w);
198  void StudyBIC(const std::vector<std::unique_ptr<TH1>>& PostPred_mc,
199  const std::vector<std::unique_ptr<TH1>>& PostPred_w);
220  void StudyDIC(const std::vector<std::unique_ptr<TH1>>& PostPred_mc,
221  const std::vector<std::unique_ptr<TH1>>& PostPred_w);
243  void StudyWAIC();
250  std::string GetBinName(TH1* hist,
251  const bool uniform,
252  const int Dim,
253  const std::vector<int>& bins) const;
269  std::vector<std::unique_ptr<TH1D>> PerBinHistogram(TH1* hist,
270  const int SampleId,
271  const int Dim,
272  const std::string& suffix) const;
273 
275  void StudyBetaParameters(TDirectory* PredictiveDir);
277  void MakeCutEventRate(TH1D *Histogram, const double DataRate) const;
279  void RateAnalysis(const std::vector<std::vector<std::unique_ptr<TH1>>>& Toys,
280  const std::vector<TDirectory*>& SampleDirectories) const;
281 
283  bool FullLLH;
288 
291 
293  std::vector<PredictiveSample> SampleInfo;
294 
296  int Ntoys;
297 
300 
302  std::vector<std::unique_ptr<TH1>> Data_Hist;
304  std::vector<std::unique_ptr<TH1>> MC_Nom_Hist;
306  std::vector<std::unique_ptr<TH1>> W2_Nom_Hist;
307 
310  std::vector<std::vector<std::unique_ptr<TH1>>> MC_Hist_Toy;
313  std::vector<std::vector<std::unique_ptr<TH1>>> W2_Hist_Toy;
314 
316  std::vector<double> ReweightWeight;
318  std::vector<double> PenaltyTerm;
319 
322 };
323 
#define MACH3LOG_ERROR
Definition: MaCh3Logger.h:37
Base class for implementing fitting algorithms.
Definition: FitterBase.h:26
std::string GetName() const
Get name of class.
Definition: FitterBase.h:71
Manager * fitMan
The manager for configuration handling.
Definition: FitterBase.h:109
Custom exception class used throughout MaCh3.
The manager class is responsible for managing configurations and settings.
Definition: Manager.h:16
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 RunPredictiveAnalysis()
Main routine responsible for producing posterior predictive distributions and $p$-value.
bool LoadToys()
Load existing toys.
void ProduceSpectra(const std::vector< std::vector< std::vector< std::unique_ptr< TH1D >>>> &Toys, const std::vector< TDirectory * > &Director, const std::string suffix) const
Produce Violin style spectra.
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 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 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.
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.