PMCMCBase.C

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//* This file is part of the MOOSE framework
//* https://mooseframework.inl.gov
//*
//* All rights reserved, see COPYRIGHT for full restrictions
//* https://github.com/idaholab/moose/blob/master/COPYRIGHT
//*
//* Licensed under LGPL 2.1, please see LICENSE for details
//* https://www.gnu.org/licenses/lgpl-2.1.html
 
#include "PMCMCBase.h"
#include "AdaptiveMonteCarloUtils.h"
#include "Uniform.h"
#include "DelimitedFileReader.h"
 
registerMooseObject("StochasticToolsApp", PMCMCBase);
 
InputParameters
PMCMCBase::validParams()
{
  InputParameters params = Sampler::validParams();
  params.addClassDescription("Parallel Markov chain Monte Carlo base.");
  params.addRequiredParam<std::vector<DistributionName>>(
      "prior_distributions", "The prior distributions of the parameters to be calibrated.");
  params.addParam<DistributionName>(
      "prior_variance", "The prior distribution of the variance parameter to be calibrated.");
  params.addRequiredParam<unsigned int>(
      "num_parallel_proposals",
      "Number of proposals to make and corresponding subApps executed in "
      "parallel.");
  params.addRequiredParam<FileName>("file_name", "Name of the CSV file with configuration values.");
  params.addParam<std::string>(
      "file_column_name", "Name of column in CSV file to use, by default first column is used.");
  params.addParam<unsigned int>(
      "num_columns", "Number of columns to be used in the CSV file with the configuration values.");
  params.addParam<std::vector<Real>>("lower_bound", "Lower bounds for making the next proposal.");
  params.addParam<std::vector<Real>>("upper_bound", "Upper bounds for making the next proposal.");
  params.addParam<Real>("variance_bound",
                        std::numeric_limits<Real>::max(),
                        "Upper bound for variance for making the next proposal.");
  params.addRequiredParam<std::vector<Real>>("initial_values",
                                             "The starting values of the inputs to be calibrated.");
  params.addParam<unsigned int>(
      "num_random_seeds",
      100000,
      "Initialize a certain number of random seeds. Change from the default only if you have to.");
  return params;
}
 
PMCMCBase::PMCMCBase(const InputParameters & parameters)
  : Sampler(parameters),
    TransientInterface(this),
    _num_parallel_proposals(getParam<unsigned int>("num_parallel_proposals")),
    _lower_bound(isParamValid("lower_bound") ? &getParam<std::vector<Real>>("lower_bound")
                                             : nullptr),
    _upper_bound(isParamValid("upper_bound") ? &getParam<std::vector<Real>>("upper_bound")
                                             : nullptr),
    _variance_bound(getParam<Real>("variance_bound")),
    _initial_values(getParam<std::vector<Real>>("initial_values")),
    _new_var_samples(declareRecoverableData<std::vector<Real>>("new_var_samples")),
    _rnd_vec(declareRecoverableData<std::vector<Real>>("rnd_vec")),
    _num_random_seeds(getParam<unsigned int>("num_random_seeds")),
    _seed_index(0),
    _rand_index(0),
    _new_samples_confg(declareRecoverableData<std::vector<std::vector<Real>>>("new_samples_confg"))
{
  // Filling the `priors` vector with the user-provided distributions.
  for (const DistributionName & name :
       getParam<std::vector<DistributionName>>("prior_distributions"))
    _priors.push_back(&getDistributionByName(name));
 
  // Filling the `var_prior` object with the user-provided distribution for the variance.
  if (isParamValid("prior_variance"))
    _var_prior = &getDistributionByName(getParam<DistributionName>("prior_variance"));
  else
    _var_prior = nullptr;
 
  // Read the experimental configurations from a csv file
  MooseUtils::DelimitedFileReader reader(getParam<FileName>("file_name"));
  reader.read();
  _confg_values.resize(1);
  if (isParamValid("file_column_name"))
    _confg_values[0] = reader.getData(getParam<std::string>("file_column_name"));
  else if (isParamValid("num_columns"))
  {
    _confg_values.resize(getParam<unsigned int>("num_columns"));
    for (unsigned int i = 0; i < _confg_values.size(); ++i)
      _confg_values[i] = reader.getData(i);
  }
  else
    _confg_values[0] = reader.getData(0);
 
  // Setting the number of sampler rows to be equal to the number of parallel proposals
  setNumberOfRows(_num_parallel_proposals * _confg_values[0].size());
 
  // Setting the number of columns in the sampler matrix (equal to the number of distributions).
  setNumberOfCols(_priors.size() + _confg_values.size());
 
  // Resizing the vectors and vector of vectors
  _new_samples.resize(_num_parallel_proposals, _initial_values);
  _new_samples_confg.resize(_num_parallel_proposals * _confg_values[0].size(),
                            std::vector<Real>(_priors.size() + _confg_values.size(), 0.0));
  _rnd_vec.resize(_num_parallel_proposals);
  _new_var_samples.assign(_num_parallel_proposals, 0.0);
 
  setNumberOfRandomSeeds(_num_random_seeds);
  setAutoAdvanceGenerators(false);
 
  // Check whether both the lower and the upper bounds are specified and of same size
  bool bound_check1 = _lower_bound && !_upper_bound;
  bool bound_check2 = !_lower_bound && _upper_bound;
  if (bound_check1 || bound_check2)
    mooseError("Both lower and upper bounds should be specified.");
  bool size_check = _lower_bound ? ((*_lower_bound).size() != (*_upper_bound).size()) : 0;
  if (size_check)
    mooseError("Lower and upper bounds should be of the same size.");
 
  // Check whether the priors, bounds, and initial values are all of the same size
  if (_priors.size() != _initial_values.size())
    mooseError("The priors and initial values should be of the same size.");
}
 
void
PMCMCBase::proposeSamples()
{
  for (unsigned int j = 0; j < _num_parallel_proposals; ++j)
    for (unsigned int i = 0; i < _priors.size(); ++i)
      _new_samples[j][i] = _priors[i]->quantile(random());
}
 
void
PMCMCBase::executeSetUp()
{
  _seed_index = _t_step;
  _rand_index = 0;
 
  // Filling the new_samples vector of vectors with new proposal samples
  proposeSamples();
 
  // At the first step, override with user-provided initial values
  if (_t_step < 1)
    for (unsigned int i = 0; i < _num_parallel_proposals; ++i)
      _new_samples[i] = _initial_values;
 
  // Draw random numbers to facilitate decision making later on
  for (unsigned int j = 0; j < _num_parallel_proposals; ++j)
    _rnd_vec[j] = random();
 
  // Combine the proposed samples with experimental configurations
  combineWithExperimentalConfig();
}
 
Real
PMCMCBase::random()
{
  return getRand(_rand_index++, _seed_index);
}
 
unsigned int
PMCMCBase::randomIndex(const unsigned int & upper_bound, const unsigned int & exclude)
{
  auto req_index = exclude;
  while (req_index == exclude)
    req_index = getRandl(_rand_index++, 0, upper_bound, _seed_index);
  return req_index;
}
 
std::pair<unsigned int, unsigned int>
PMCMCBase::randomIndexPair(const unsigned int & upper_bound, const unsigned int & exclude)
{
  auto req_index1 = randomIndex(upper_bound, exclude);
  auto req_index2 = req_index1;
  while (req_index1 == req_index2)
    req_index2 = randomIndex(upper_bound, exclude);
  return {req_index1, req_index2};
}
 
void
PMCMCBase::combineWithExperimentalConfig()
{
  unsigned int index1;
  int index2 = -1;
  std::vector<Real> tmp;
  for (unsigned int i = 0; i < _num_parallel_proposals * _confg_values[0].size(); ++i)
  {
    index1 = i % _num_parallel_proposals;
    if (index1 == 0)
      ++index2;
    tmp = _new_samples[index1];
    for (unsigned int j = 0; j < _confg_values.size(); ++j)
      tmp.push_back(_confg_values[j][index2]);
    _new_samples_confg[i] = tmp;
  }
}
 
const std::vector<Real> &
PMCMCBase::getRandomNumbers() const
{
  return _rnd_vec;
}
 
const std::vector<Real> &
PMCMCBase::getVarSamples() const
{
  return _new_var_samples;
}
 
const std::vector<std::vector<Real>> &
PMCMCBase::getSamples() const
{
  return _new_samples;
}
 
const std::vector<const Distribution *>
PMCMCBase::getPriors() const
{
  return _priors;
}
 
const Distribution *
PMCMCBase::getVarPrior() const
{
  return _var_prior;
}
 
Real
PMCMCBase::computeSample(dof_id_type row_index, dof_id_type col_index) const
{
  return _new_samples_confg[row_index][col_index];
}