dfi.h

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/* =====================================================================================
 
                      The UQ Toolkit (UQTk) version 3.1.5
                          Copyright (2024) NTESS
                        https://www.sandia.gov/UQToolkit/
                        https://github.com/sandialabs/UQTk
 
     Copyright 2024 National Technology & Engineering Solutions of Sandia, LLC (NTESS).
     Under the terms of Contract DE-NA0003525 with NTESS, the U.S. Government
     retains certain rights in this software.
 
     This file is part of The UQ Toolkit (UQTk)
 
     UQTk is open source software: you can redistribute it and/or modify
     it under the terms of BSD 3-Clause License
 
     UQTk is distributed in the hope that it will be useful,
     but WITHOUT ANY WARRANTY; without even the implied warranty of
     MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
     BSD 3 Clause License for more details.
 
     You should have received a copy of the BSD 3 Clause License
     along with UQTk. If not, see https://choosealicense.com/licenses/bsd-3-clause/.
 
     Questions? Contact the UQTk Developers at https://github.com/sandialabs/UQTk/discussions
     Sandia National Laboratories, Livermore, CA, USA
===================================================================================== */
#ifndef DFI_H_
#define DFI_H_
 
#include "Array1D.h"
#include "Array2D.h"
#include "Array3D.h"
#include "mcmc.h"
#include "amcmc.h"
#include "quad.h"
#include "math.h"
#include "arrayio.h"
 
#include "dsfmt_add.h"
#include <sys/time.h>
#include "arraytools.h"
#include <string>
#include <iostream>
//for setting stdout precision
#include <iomanip>
 
#include "sampling.hpp"
 
//for UQTk KDE functionality
#include "tools.h"
 
//for PCE surrogate construction
#ifndef PCSET_H_SEEN
#define __wsu
#include "PCSet.h"
#endif //PCSET_H_SEEN
 
#include <stdlib.h>
#include <sstream>
 
//data container for surrogate model
class DFIsurr{
 
        public:
 
        //model surrogate containers
 
        //PCE dimension
        int PCEdim;
 
        //surrogate limits
        Array1D<double> surrLo;
        Array1D<double> surrHi;
 
        //surrogate defined flag
        bool surrDefined;
 
        //PCset defines and initializes polynomial chaos basis function set
        PCSet* surrModel;
 
        //number of PCE terms
        int numPCETerms;
        //cotainer for coeefficients
        Array2D<double> PCEcoefficients;
        //container for evaluated PCE basis functions
        Array2D<double> psiPCE;
 
        // surrogate evaulation function
        void evaluateSurr(Array1D<double> & modelOutput, Array1D<double> & params);
};
 
 
 
 
//data container
class dataPosteriorInformation{
 
        public:
 
        int seed;
        //dimension of the data space
        int dataDim;
        //parameter dimension
        int paramDim;
        //the number of constraints to impose
        int numConstraints;
 
 
        //boolean flag indicating that the data likelihood function is running for noise optimzation
        bool errorOpt;
        //boolean flag indicating that the the data chain has achieved burn-in
        bool dataChainBurnedIn;
        //counter for entry in data chain
        int dataChain_count;
 
 
        //container for passing noisy data sample to 1D noise optimization chain
        Array1D<double> optErrorParameters;
 
        //containers for data signal, i.e. y=f(x)
        Array1D<double> trueDatax;
        Array1D<double> trueDatay;
        //container for error signal (e.g. noise + bias)
        Array1D<double> error;
        //container for nominal parameters, which define the nominal true data (trueDatay)
        Array1D<double> nominalParameters;
        //container for nominal error parameters
        Array1D<double> nominalErrorParameters;
        //container for additional parameters needed to run the model (but not inferred)
        Array1D<double> hyperparameters;
        //container for parameters for surrogate data model
        Array1D<double> surrParameters;
 
        //ABC data likelihood containers
        //container to store labels describing the statistics to enforce as constraints
        vector<string> statLabels;
        //container to store the values of the statistics to enforce as constraints
        vector<double> statValues;
        //container to store the values of the ABC deltas for approximately enforcing statistics as contraints
        vector<double> statDeltas;
 
 
        //parameter chain write options
        int paramWriteFlag;
        //burn in chain file
        string burninParamWriteFile;
        //parameter chainfile
        string mainParamWriteFile;
 
        //length of parameter chains
        int parameterBurnInNumSamples;
        int parameterChainNumSamples;
 
 
        //surrogate model
        DFIsurr surrModelObj;
        int numSurr;
        vector<DFIsurr> surrModels;
 
};
 
//data container
class parameterPosteriorInformation{
 
        public:
        Array1D<double> dataChainState;
        //container for passing optimal error parameters to inner likelihood during error optimation
        Array1D<double> optErrorParams;
        Array1D<double> hyperparameters;
        bool errorOpt;
        //the input 'x' possibly required to compute the model function y=f(x)
        Array1D<double> trueDatax;
 
        // pointer to surrogate model object
        DFIsurr * surrModelObj_;
 
        vector<DFIsurr> * surrModels_;
 
};
 
//============================================
//user defined functions
 
//run the true data model
void userRunModel(Array1D<double> &modelDataY, Array1D<double> &modelDataX, Array1D<double> & parameters, Array1D<double> &hyperparameters);
//compute parameter (truth model and error model) parameter posterior
double userComputeParamLogPosterior(parameterPosteriorInformation * paramPostInfo, Array1D<double> parameters);
//compute parameter (truth model and error model) likelihood
double userComputeParamLogLikelihood(parameterPosteriorInformation * paramPostInfo, Array1D<double> modelDataOut, Array1D<double> parameters, Array1D<double> hyperparameters);
//compute statistics from inner parameter chain
void userComputeStatistics(Array1D<double> &parameterStatistics, Array1D<MCMC::chainstate> & parameterChain);
//define the target data signal
void userDefineData(dataPosteriorInformation & dataPostInfo);
//define the constraints
void userDefineConstraints(dataPosteriorInformation & dataPostInfo);
//specify the nominal values of the parameters
void userSpecifyNominalParams(dataPosteriorInformation & dataPostInfo);
//=============================================
 
//data log postrior function to be passed to UQTk MCMC object for data chain
double dataInferenceLogPosterior(Array1D<double>& m, void *info);
//parameter log posterior function to be passed to UQTk MCMC object for parameter chains
double parameterInferenceLogPosterior(Array1D<double>& beta, void *info);
 
//define inner parameter inference
void parameterInference(dataPosteriorInformation *dataPostInfo, Array1D<double> &m, Array1D<MCMC::chainstate>& parameterChainEntries);
 
//compute parameter (truth model and error model) parameter posterior
double computeParamLogPosterior(parameterPosteriorInformation * paramPostInfo, Array1D<double> parameters);
//compute parameter (truth model and error model) likelihood
double computeParamLogLikelihood(parameterPosteriorInformation * paramPostInfo, Array1D<double> modelDataOut, Array1D<double> parameters, Array1D<double> hyperparameters);
//compute statistics from inner parameter chain
void computeStatistics(Array1D<double> &parameterStatistics, Array1D<MCMC::chainstate> & parameterChain);
 
 
 
class DFI{
 
        private:
        //seed
        int seed;
 
 
        //metadata container to pass by argument to data MCMC chain
        dataPosteriorInformation dataPostInfo;
        //metadata container to pass by argument to parameter MCMC chain
        parameterPosteriorInformation paramPostInfo;
 
        //I/O ===============
        //log file name
        stringstream logFileName;
        //log file
        ofstream logFile;
        //===================
 
        //container for noisy data
        Array1D<double> noisyData;
        //data scale (defines an approximate scale for the noise to be added)
        double dataScale;
 
 
        //length of main data chain after burn-in
        int dataChainNumSamples;
        //length of burn-in chainlets
        int dataChainNumSamples_burnin;
        //length of error model parameter optimization chain
        double errorOptChainNumSamples;
        //target data chain acceptance ratio (a burn-in parameter)
        double targetDataChainAcceptanceRatio;
        //data chain acceptance ratio
        double dataChainAcceptanceRatio;
        //data chain mode ratio
        double dataPosteriorMode;
 
        //intial data chain (Gaussian) proposal covariance
        double dataChainPropCov_init;
        //scale for setting proposal covariance
        double dataChainPropCov_fac;
 
 
        //data chain proposal covariance matrix
        Array2D<double> dataChainPropCovMatrix;
 
        //=====wrappers for user specified functions=========
        //define the target data signal
        void defineData(dataPosteriorInformation & dataPostInfo){
                userDefineData(dataPostInfo);
        };
        //define the constraints
        void defineConstraints(dataPosteriorInformation & dataPostInfo){
                userDefineConstraints(dataPostInfo);
        };
        //specify the nominal values of the parameters
        void specifyNominalParams(dataPosteriorInformation & dataPostInfo){
                userSpecifyNominalParams(dataPostInfo);
        };
        //run the true data model
        void runModel(Array1D<double> &modelDataY, Array1D<double> &modelDataX, Array1D<double> & parameters, Array1D<double> &hyperparameters){
 
        //if a surrogate is defined, use it
        if (dataPostInfo.surrModelObj.surrDefined){
 
                /* map parameters of interest to surrogate parameters */
                //userSurrMap(dataPostInfo.surrParameters, parameters, hyperparameters);
 
                /* check if parameters are within surrogate bounds */
                bool inBounds=true;
                for (int i=0; i<parameters.XSize();i++){
                        if (  (parameters(i) < dataPostInfo.surrModelObj.surrLo(i)) || (parameters(i) >dataPostInfo.surrModelObj.surrHi(i)) ){
                                inBounds=false;
                                std::cout<<"Out of bounds: param("<<i+1<<"<<) = "<<parameters(i)<<std::endl;
                        }
                }
 
                if (inBounds){
                        dataPostInfo.surrModelObj.evaluateSurr(modelDataY, parameters);
                }else{
                        userRunModel(modelDataY, modelDataX, parameters, hyperparameters);
                }
 
        }else{
                //run user specified detailed model
                userRunModel(modelDataY, modelDataX, parameters, hyperparameters);
        }
 
        };
        //===================================================
 
 
        public:
        //constructor
        DFI();
        //constructor
        DFI(string inputfile);
        //destructor
        ~DFI();
        //perform data inference
        void dataInference();
        //perform data refitting
        void dataRefit();
        //build KDE densities
        void buildKDE(Array1D<int> KDEdim);
        //generate samples from a pdf
        void genSamples(Array2D<double> &pdf);
        //build surrogate model
        void buildSurrogateModel();
        //load surrogate model
        void loadSurrogateModel();
        //test surrogate model
        void testSurrogateModel();
 
 
};
 
#endif  //DFI_H_