Fast Bayesian inference with the parallel active learning (partly inspired from El Gammal et al. More...

#include <BayesianActiveLearningSampler.h>

Inheritance diagram for BayesianActiveLearningSampler:

Public Types
enum	SampleMode

typedef DataFileName	DataFileParameterType

Public Member Functions
	BayesianActiveLearningSampler (const InputParameters &parameters)

const std::vector< std::vector< Real > > &	getSampleTries () const
	Return the random samples for the GP to try in the reporter class.

const std::vector< Real > &	getVarSampleTries () const
	Return the random variance samples for the GP to try in the reporter class.

dof_id_type	getNumberOfConfigValues () const
	Return the number of configuration parameters.

dof_id_type	getNumberOfConfigParams () const
	Return the number of configuration parameters.

dof_id_type	getNumParallelProposals () const
	Return the number of parallel proposals.

const std::vector< Real > &	getRandomNumbers () const
	Return the random numbers to facilitate decision making in reporters.

const std::vector< Real > &	getVarSamples () const
	Return the proposed variance samples to facilitate decision making in reporters.

const std::vector< std::vector< Real > > &	getSamples () const
	Return the proposed samples to facilitate decision making in reporters.

const std::vector< const Distribution * >	getPriors () const
	Return the priors to facilitate decision making in reporters.

const Distribution *	getVarPrior () const
	Return the prior over variance to facilitate decision making in reporters.

virtual int	decisionStep () const
	Return the step after which decision making can begin.

std::vector< Real >	getSampleRow (dof_id_type row_index) const

Real	getSample (dof_id_type row_index, dof_id_type col_index) const

dof_id_type	getNumberOfRows () const

dof_id_type	getNumberOfCols () const

dof_id_type	getNumberOfLocalRows () const

const LocalRankConfig &	getRankConfig (bool batch_mode) const

virtual bool	isAdaptiveSamplingCompleted () const

virtual bool	enabled () const

std::shared_ptr< MooseObject >	getSharedPtr ()

std::shared_ptr< const MooseObject >	getSharedPtr () const

bool	isKokkosObject () const

MooseApp &	getMooseApp () const

const std::string &	type () const

const std::string &	name () const

std::string	typeAndName () const

MooseObjectParameterName	uniqueParameterName (const std::string &parameter_name) const

MooseObjectName	uniqueName () const

const InputParameters &	parameters () const

const hit::Node *	getHitNode () const

bool	hasBase () const

const std::string &	getBase () const

const T &	getParam (const std::string &name) const

std::vector< std::pair< T1, T2 > >	getParam (const std::string &param1, const std::string &param2) const

const T *	queryParam (const std::string &name) const

const T &	getRenamedParam (const std::string &old_name, const std::string &new_name) const

T	getCheckedPointerParam (const std::string &name, const std::string &error_string="") const

bool	isParamValid (const std::string &name) const

bool	isParamSetByUser (const std::string &name) const

void	connectControllableParams (const std::string &parameter, const std::string &object_type, const std::string &object_name, const std::string &object_parameter) const

void	paramError (const std::string &param, Args... args) const

void	paramWarning (const std::string &param, Args... args) const

void	paramWarning (const std::string &param, Args... args) const

void	paramInfo (const std::string &param, Args... args) const

std::string	messagePrefix (const bool hit_prefix=true) const

std::string	errorPrefix (const std::string &) const

void	mooseError (Args &&... args) const

void	mooseDocumentedError (const std::string &repo_name, const unsigned int issue_num, Args &&... args) const

void	mooseErrorNonPrefixed (Args &&... args) const

void	mooseWarning (Args &&... args) const

void	mooseWarning (Args &&... args) const

void	mooseWarningNonPrefixed (Args &&... args) const

void	mooseWarningNonPrefixed (Args &&... args) const

void	mooseDeprecated (Args &&... args) const

void	mooseDeprecated (Args &&... args) const

void	mooseDeprecatedNoTrace (Args &&... args) const

void	mooseInfo (Args &&... args) const

void	callMooseError (std::string msg, const bool with_prefix, const hit::Node *node=nullptr, const bool show_trace=true) const

std::string	getDataFileName (const std::string &param) const

std::string	getDataFileNameByName (const std::string &relative_path) const

std::string	getDataFilePath (const std::string &relative_path) const

virtual void	initialSetup ()

virtual void	timestepSetup ()

virtual void	jacobianSetup ()

virtual void	residualSetup ()

virtual void	subdomainSetup ()

virtual void	customSetup (const ExecFlagType &)

const ExecFlagEnum &	getExecuteOnEnum () const

PerfGraph &	perfGraph ()

T &	getSampler (const std::string &name)

Sampler &	getSampler (const std::string &name)

T &	getSamplerByName (const SamplerName &name)

Sampler &	getSamplerByName (const SamplerName &name)

const VectorPostprocessorValue &	getVectorPostprocessorValue (const std::string &param_name, const std::string &vector_name) const

const VectorPostprocessorValue &	getVectorPostprocessorValue (const std::string &param_name, const std::string &vector_name, bool needs_broadcast) const

const VectorPostprocessorValue &	getVectorPostprocessorValueByName (const VectorPostprocessorName &name, const std::string &vector_name) const

const VectorPostprocessorValue &	getVectorPostprocessorValueByName (const VectorPostprocessorName &name, const std::string &vector_name, bool needs_broadcast) const

const VectorPostprocessorValue &	getVectorPostprocessorValueOld (const std::string &param_name, const std::string &vector_name) const

const VectorPostprocessorValue &	getVectorPostprocessorValueOld (const std::string &param_name, const std::string &vector_name, bool needs_broadcast) const

const VectorPostprocessorValue &	getVectorPostprocessorValueOldByName (const VectorPostprocessorName &name, const std::string &vector_name) const

const VectorPostprocessorValue &	getVectorPostprocessorValueOldByName (const VectorPostprocessorName &name, const std::string &vector_name, bool needs_broadcast) const

const ScatterVectorPostprocessorValue &	getScatterVectorPostprocessorValue (const std::string &param_name, const std::string &vector_name) const

const ScatterVectorPostprocessorValue &	getScatterVectorPostprocessorValueByName (const VectorPostprocessorName &name, const std::string &vector_name) const

const ScatterVectorPostprocessorValue &	getScatterVectorPostprocessorValueOld (const std::string &param_name, const std::string &vector_name) const

const ScatterVectorPostprocessorValue &	getScatterVectorPostprocessorValueOldByName (const VectorPostprocessorName &name, const std::string &vector_name) const

bool	hasVectorPostprocessor (const std::string &param_name, const std::string &vector_name) const

bool	hasVectorPostprocessor (const std::string &param_name) const

bool	hasVectorPostprocessorByName (const VectorPostprocessorName &name, const std::string &vector_name) const

bool	hasVectorPostprocessorByName (const VectorPostprocessorName &name) const

const VectorPostprocessorName &	getVectorPostprocessorName (const std::string &param_name) const

DenseMatrix< Real >	getGlobalSamples ()

DenseMatrix< Real >	getGlobalSamples ()

DenseMatrix< Real >	getLocalSamples ()

DenseMatrix< Real >	getLocalSamples ()

dof_id_type	getLocalRowBegin () const

dof_id_type	getLocalRowBegin () const

dof_id_type	getLocalRowEnd () const

dof_id_type	getLocalRowEnd () const

const Distribution &	getDistribution (const std::string &name) const

const T &	getDistribution (const std::string &name) const

const Distribution &	getDistribution (const std::string &name) const

const T &	getDistribution (const std::string &name) const

const Distribution &	getDistributionByName (const DistributionName &name) const

const T &	getDistributionByName (const std::string &name) const

const Distribution &	getDistributionByName (const DistributionName &name) const

const T &	getDistributionByName (const std::string &name) const

bool	isVectorPostprocessorDistributed (const std::string &param_name) const

bool	isVectorPostprocessorDistributed (const std::string &param_name) const

bool	isVectorPostprocessorDistributedByName (const VectorPostprocessorName &name) const

bool	isVectorPostprocessorDistributedByName (const VectorPostprocessorName &name) const

const Parallel::Communicator &	comm () const

processor_id_type	n_processors () const

processor_id_type	processor_id () const

bool	isImplicit ()

Moose::StateArg	determineState () const

Static Public Member Functions
static InputParameters	validParams ()

static void	callMooseError (MooseApp const app, const InputParameters &params, std::string msg, const bool with_prefix, const hit::Node node, const bool show_trace=true)

Public Attributes
	usingCombinedWarningSolutionWarnings

const ConsoleStream	_console

Static Public Attributes
static const std::string	type_param

static const std::string	name_param

static const std::string	unique_name_param

static const std::string	app_param

static const std::string	moose_base_param

static const std::string	kokkos_object_param

Protected Member Functions
virtual void	proposeSamples () override
	Fill in the _new_samples vector of vectors (happens within sampleSetUp)

virtual void	executeSetUp () override

virtual Real	computeSample (dof_id_type row_index, dof_id_type col_index) const override

Real	random ()
	Sample a random number between 0 and 1.

unsigned int	randomIndex (const unsigned int &upper_bound, const unsigned int &exclude)
	Sample a random index excluding a specified index.

std::pair< unsigned int, unsigned int >	randomIndexPair (const unsigned int &upper_bound, const unsigned int &exclude)
	Sample two random indices without repitition excluding a specified index.

void	setNumberOfRandomSeeds (std::size_t number)

Real	getRand (std::size_t n, unsigned int index=0) const

unsigned int	getRandl (std::size_t n, unsigned int lower, unsigned int upper, unsigned int index=0) const

virtual void	computeSampleRow (dof_id_type i, std::vector< Real > &data) const

virtual void	advanceGenerators (const dof_id_type count)

virtual void	advanceGenerator (const unsigned int seed_index, const dof_id_type count)

void	setAutoAdvanceGenerators (const bool state)

virtual void	executeTearDown ()

virtual LocalRankConfig	constructRankConfig (bool batch_mode) const

void	flagInvalidSolutionInternal (const InvalidSolutionID invalid_solution_id) const

InvalidSolutionID	registerInvalidSolutionInternal (const std::string &message, const bool warning) const

PerfID	registerTimedSection (const std::string &section_name, const unsigned int level) const

PerfID	registerTimedSection (const std::string &section_name, const unsigned int level, const std::string &live_message, const bool print_dots=true) const

std::string	timedSectionName (const std::string &section_name) const

virtual void	addVectorPostprocessorDependencyHelper (const VectorPostprocessorName &) const

const ReporterContextBase &	getReporterContextBaseByName (const ReporterName &reporter_name) const

const ReporterName &	getReporterName (const std::string &param_name) const

virtual void	addReporterDependencyHelper (const ReporterName &)

T &	declareRestartableData (const std::string &data_name, Args &&... args)

ManagedValue< T >	declareManagedRestartableDataWithContext (const std::string &data_name, void *context, Args &&... args)

const T &	getRestartableData (const std::string &data_name) const

T &	declareRestartableDataWithContext (const std::string &data_name, void *context, Args &&... args)

T &	declareRecoverableData (const std::string &data_name, Args &&... args)

T &	declareRestartableDataWithObjectName (const std::string &data_name, const std::string &object_name, Args &&... args)

T &	declareRestartableDataWithObjectNameWithContext (const std::string &data_name, const std::string &object_name, void *context, Args &&... args)

std::string	restartableName (const std::string &data_name) const

void	setNumberOfRows (dof_id_type n_rows)

void	setNumberOfRows (dof_id_type n_rows)

void	setNumberOfCols (dof_id_type n_cols)

void	setNumberOfCols (dof_id_type n_cols)

virtual void	computeSampleMatrix (DenseMatrix< Real > &matrix)

virtual void	computeSampleMatrix (DenseMatrix< Real > &matrix)

virtual void	computeLocalSampleMatrix (DenseMatrix< Real > &matrix)

virtual void	computeLocalSampleMatrix (DenseMatrix< Real > &matrix)

const T &	getReporterValue (const std::string &param_name, const std::size_t time_index=0)

const T &	getReporterValue (const std::string &param_name, ReporterMode mode, const std::size_t time_index=0)

const T &	getReporterValue (const std::string &param_name, const std::size_t time_index=0)

const T &	getReporterValue (const std::string &param_name, ReporterMode mode, const std::size_t time_index=0)

const T &	getReporterValueByName (const ReporterName &reporter_name, const std::size_t time_index=0)

const T &	getReporterValueByName (const ReporterName &reporter_name, ReporterMode mode, const std::size_t time_index=0)

const T &	getReporterValueByName (const ReporterName &reporter_name, const std::size_t time_index=0)

const T &	getReporterValueByName (const ReporterName &reporter_name, ReporterMode mode, const std::size_t time_index=0)

bool	hasReporterValue (const std::string &param_name) const

bool	hasReporterValue (const std::string &param_name) const

bool	hasReporterValue (const std::string &param_name) const

bool	hasReporterValue (const std::string &param_name) const

bool	hasReporterValueByName (const ReporterName &reporter_name) const

bool	hasReporterValueByName (const ReporterName &reporter_name) const

bool	hasReporterValueByName (const ReporterName &reporter_name) const

bool	hasReporterValueByName (const ReporterName &reporter_name) const

Protected Attributes
const std::vector< unsigned int > &	_sorted_indices
	The selected sample indices to evaluate the subApp.

const unsigned int	_num_parallel_proposals
	Number of parallel proposals to be made and subApps to be executed.

std::vector< const Distribution * >	_priors
	Storage for prior distribution objects to be utilized.

const Distribution *	_var_prior
	Storage for prior distribution object of the variance to be utilized.

const std::vector< Real > *	_lower_bound
	Lower bounds for making the next proposal.

const std::vector< Real > *	_upper_bound
	Upper bounds for making the next proposal.

const Real &	_variance_bound
	Upper bound for variance for making the next proposal.

const std::vector< Real > &	_initial_values
	Initial values of the input params to get the MCMC scheme started.

std::vector< std::vector< Real > >	_new_samples
	Vectors of new proposed samples.

std::vector< Real > &	_new_var_samples
	Vector of new proposed variance samples.

std::vector< Real > &	_rnd_vec
	Vector of random numbers for decision making.

const dof_id_type	_min_procs_per_row

const dof_id_type	_max_procs_per_row

const bool &	_enabled

MooseApp &	_app

Factory &	_factory

ActionFactory &	_action_factory

const std::string &	_type

const std::string &	_name

const InputParameters &	_pars

const ExecFlagEnum &	_execute_enum

const ExecFlagType &	_current_execute_flag

MooseApp &	_pg_moose_app

const std::string	_prefix

MooseApp &	_restartable_app

const std::string	_restartable_system_name

const THREAD_ID	_restartable_tid

const bool	_restartable_read_only

const Parallel::Communicator &	_communicator

const InputParameters &	_ti_params

FEProblemBase &	_ti_feproblem

bool	_is_implicit

Real &	_t

const Real &	_t_old

int &	_t_step

Real &	_dt

Real &	_dt_old

bool	_is_transient

Private Member Functions
void	combineWithExperimentalConfig ()
	Generates combinations of the new samples with the experimental configurations.

void	execute ()

void	checkReinitStatus () const

void	advanceGeneratorsInternal (const dof_id_type count)

const VectorPostprocessorValue &	getVectorPostprocessorByNameHelper (const VectorPostprocessorName &name, const std::string &vector_name, bool broadcast, std::size_t t_index) const

const VectorPostprocessorContext< VectorPostprocessorValue > &	getVectorPostprocessorContextByNameHelper (const VectorPostprocessorName &name, const std::string &vector_name) const

bool	vectorPostprocessorsAdded () const

bool	reportersAdded () const

void	possiblyCheckHasReporter (const ReporterName &reporter_name, const std::string &param_name="") const

RestartableDataValue &	registerRestartableDataOnApp (std::unique_ptr< RestartableDataValue > data, THREAD_ID tid) const

void	registerRestartableNameWithFilterOnApp (const std::string &name, Moose::RESTARTABLE_FILTER filter)

RestartableData< T > &	declareRestartableDataHelper (const std::string &data_name, void *context, Args &&... args) const

void	init ()

void	init ()

void	reinit ()

void	reinit ()

void	possiblyCheckHasVectorPostprocessor (const std::string &param_name, const std::string &vector_name) const

void	possiblyCheckHasVectorPostprocessor (const std::string &param_name, const std::string &vector_name) const

void	possiblyCheckHasVectorPostprocessorByName (const VectorPostprocessorName &name, const std::string &vector_name) const

void	possiblyCheckHasVectorPostprocessorByName (const VectorPostprocessorName &name, const std::string &vector_name) const

Static Private Member Functions
static const hit::Node *	getHitNode (const InputParameters &params)

static std::string	messagePrefix (const InputParameters &params, const bool hit_prefix)

Private Attributes
const unsigned int &	_num_tries
	Number of samples to propose in each iteration (not all are sent for subApp evals)

std::vector< std::vector< Real > > &	_inputs_test
	Storage for all the proposed samples.

std::vector< Real >	_var_test
	Storage for all the proposed variances.

const unsigned int	_num_random_seeds
	Initialize a certain number of random seeds. Change from the default only if you have to.

unsigned int	_seed_index
	Generator index when requesting random numbers.

std::size_t	_rand_index
	Running index for the random number generators.

std::vector< std::vector< Real > >	_confg_values
	Configuration values.

std::vector< std::vector< Real > > &	_new_samples_confg
	Vectors of new proposed samples combined with the experimental configuration values.

std::vector< std::unique_ptr< MooseRandomStateless > > &	_generators

dof_id_type &	_n_local_rows

dof_id_type &	_local_row_begin

dof_id_type &	_local_row_end

dof_id_type &	_n_rows

dof_id_type &	_n_cols

std::size_t	_n_seeds

bool	_initialized

bool	_needs_reinit

bool &	_has_executed

const dof_id_type	_limit_get_global_samples

const dof_id_type	_limit_get_local_samples

const dof_id_type	_limit_get_row

std::pair< LocalRankConfig, LocalRankConfig > &	_rank_config

bool	_auto_advance_generators

const ParallelParamObject &	_parent

const MooseBase &	_si_moose_base

const FEProblemBase *	_si_problem

ExecFlagEnum	_empty_execute_enum

const InputParameters &	_dni_params

FEProblemBase &	_dni_feproblem

const MooseObject *const	_dni_moose_object_ptr

const InputParameters &	_si_params

FEProblemBase &	_si_feproblem

THREAD_ID	_si_tid

const bool	_broadcast_by_default

const MooseObject &	_vpi_moose_object

const FEProblemBase &	_vpi_feproblem

const THREAD_ID	_vpi_tid

const InputParameters &	_ri_params

FEProblemBase &	_ri_fe_problem_base

const ReporterData &	_ri_reporter_data

const MooseObject &	_ri_moose_object

const RestartableDataMapName	_metaname

std::string	_restartable_name

const std::string	_ti_name

Detailed Description

Fast Bayesian inference with the parallel active learning (partly inspired from El Gammal et al.

2023)

Definition at line 19 of file BayesianActiveLearningSampler.h.

Constructor & Destructor Documentation

◆ BayesianActiveLearningSampler()

BayesianActiveLearningSampler::BayesianActiveLearningSampler ( const InputParameters & parameters )

Definition at line 31 of file BayesianActiveLearningSampler.C.

  : PMCMCBase(parameters),
    _sorted_indices(getReporterValue<std::vector<unsigned int>>("sorted_indices")),
    _num_tries(getParam<unsigned int>("num_tries")),
    _inputs_test(declareRecoverableData<std::vector<std::vector<Real>>>("inputs_test")),
    _var_test(_num_tries)
{
  _inputs_test.resize(_num_tries, std::vector<Real>(_priors.size()));
}

Member Function Documentation

◆ combineWithExperimentalConfig()

void PMCMCBase::combineWithExperimentalConfig ( )

privateinherited

Generates combinations of the new samples with the experimental configurations.

Definition at line 178 of file PMCMCBase.C.

{
  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;
  }
}

Referenced by PMCMCBase::executeSetUp().

◆ computeSample()

Real PMCMCBase::computeSample	(	dof_id_type	row_index,
		dof_id_type	col_index
	)		const

overrideprotectedvirtualinherited

Implements Sampler.

Definition at line 226 of file PMCMCBase.C.

{
  return _new_samples_confg[row_index][col_index];
}

◆ decisionStep()

virtual int PMCMCBase::decisionStep ( ) const

inlinevirtualinherited

Return the step after which decision making can begin.

Reimplemented in AffineInvariantDES, AffineInvariantStretchSampler, and IndependentGaussianMH.

Definition at line 73 of file PMCMCBase.h.

73{ return 1; }

Referenced by PMCMCDecision::execute().

◆ executeSetUp()

void PMCMCBase::executeSetUp ( )

overrideprotectedvirtualinherited

Reimplemented from Sampler.

Definition at line 131 of file PMCMCBase.C.

{
  _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();
}

◆ getNumberOfConfigParams()

dof_id_type PMCMCBase::getNumberOfConfigParams ( ) const

inlineinherited

Return the number of configuration parameters.

Definition at line 34 of file PMCMCBase.h.

34{ return _confg_values.size(); }

Referenced by BayesianActiveLearner::BayesianActiveLearner(), and PMCMCDecision::PMCMCDecision().

◆ getNumberOfConfigValues()

dof_id_type PMCMCBase::getNumberOfConfigValues ( ) const

inlineinherited

Return the number of configuration parameters.

Definition at line 29 of file PMCMCBase.h.

29{ return _confg_values[0].size(); }

Referenced by BayesianActiveLearner::BayesianActiveLearner(), and PMCMCDecision::PMCMCDecision().

◆ getNumParallelProposals()

dof_id_type PMCMCBase::getNumParallelProposals ( ) const

inlineinherited

Return the number of parallel proposals.

Definition at line 39 of file PMCMCBase.h.

39{ return _num_parallel_proposals; }

Referenced by PMCMCDecision::PMCMCDecision().

◆ getPriors()

const std::vector< const Distribution * > PMCMCBase::getPriors ( ) const

inherited

Return the priors to facilitate decision making in reporters.

Definition at line 214 of file PMCMCBase.C.

{
  return _priors;
}

◆ getRandomNumbers()

const std::vector< Real > & PMCMCBase::getRandomNumbers ( ) const

inherited

Return the random numbers to facilitate decision making in reporters.

Definition at line 196 of file PMCMCBase.C.

{
  return _rnd_vec;
}

◆ getSamples()

const std::vector< std::vector< Real > > & PMCMCBase::getSamples ( ) const

inherited

Return the proposed samples to facilitate decision making in reporters.

In MCMC schemes, there is a decision-making step after evaluating the computational model on whether or not to accept the proposed samples. To facilitate this decision-making, which happens in the Reporter, we have to provide it the proposed samples.

Definition at line 208 of file PMCMCBase.C.

{
  return _new_samples;
}

◆ getSampleTries()

const std::vector< std::vector< Real > > & BayesianActiveLearningSampler::getSampleTries ( ) const

Return the random samples for the GP to try in the reporter class.

Definition at line 42 of file BayesianActiveLearningSampler.C.

{
  return _inputs_test;
}

◆ getVarPrior()

const Distribution * PMCMCBase::getVarPrior ( ) const

inherited

Return the prior over variance to facilitate decision making in reporters.

Definition at line 220 of file PMCMCBase.C.

{
  return _var_prior;
}

◆ getVarSamples()

const std::vector< Real > & PMCMCBase::getVarSamples ( ) const

inherited

Return the proposed variance samples to facilitate decision making in reporters.

Definition at line 202 of file PMCMCBase.C.

{
  return _new_var_samples;
}

◆ getVarSampleTries()

const std::vector< Real > & BayesianActiveLearningSampler::getVarSampleTries ( ) const

Return the random variance samples for the GP to try in the reporter class.

Definition at line 48 of file BayesianActiveLearningSampler.C.

{
  return _var_test;
}

◆ proposeSamples()

void BayesianActiveLearningSampler::proposeSamples ( )

overrideprotectedvirtual

Fill in the _new_samples vector of vectors (happens within sampleSetUp)

Parameters

seed_value The seed for the random number generator

Reimplemented from PMCMCBase.

Definition at line 54 of file BayesianActiveLearningSampler.C.

{
  auto fill_vector = [&](std::vector<Real> & vector)
  {
    for (unsigned int i = 0; i < _priors.size(); ++i)
      vector[i] = _priors[i]->quantile(random());
  };
 
  /* If step is 1, randomly generate the samples.
  Else, generate the samples informed by the GP from the reporter "sorted_indices" */
  for (dof_id_type i = 0; i < _num_parallel_proposals; ++i)
  {
    if (_t_step < 1)
    {
      fill_vector(_new_samples[i]);
      if (_var_prior)
        _new_var_samples[i] = _var_prior->quantile(random());
    }
    else
    {
      _new_samples[i] = _inputs_test[_sorted_indices[i]];
      if (_var_prior)
        _new_var_samples[i] = _var_test[_sorted_indices[i]];
    }
  }
 
  /* Finally, generate several new samples randomly for the GP to try and pass it to the
  reporter */
  for (dof_id_type i = 0; i < _num_tries; ++i)
  {
    fill_vector(_inputs_test[i]);
    if (_var_prior)
      _var_test[i] = _var_prior->quantile(random());
  }
}

◆ random()

Real PMCMCBase::random ( )

protectedinherited

Sample a random number between 0 and 1.

Parameters

upper_bound The upper bound provided

Returns: The required index

Definition at line 153 of file PMCMCBase.C.

{
  return getRand(_rand_index++, _seed_index);
}

Referenced by PMCMCBase::executeSetUp(), PMCMCBase::proposeSamples(), AffineInvariantDES::proposeSamples(), AffineInvariantStretchSampler::proposeSamples(), proposeSamples(), and IndependentGaussianMH::proposeSamples().

◆ randomIndex()

unsigned int PMCMCBase::randomIndex	(	const unsigned int &	upper_bound,
		const unsigned int &	exclude
	)

protectedinherited

Sample a random index excluding a specified index.

Parameters

upper_bound The upper bound provided

Returns: The required index

Definition at line 159 of file PMCMCBase.C.

{
  auto req_index = exclude;
  while (req_index == exclude)
    req_index = getRandl(_rand_index++, 0, upper_bound, _seed_index);
  return req_index;
}

Referenced by AffineInvariantStretchSampler::proposeSamples(), and PMCMCBase::randomIndexPair().

◆ randomIndexPair()

std::pair< unsigned int, unsigned int > PMCMCBase::randomIndexPair	(	const unsigned int &	upper_bound,
		const unsigned int &	exclude
	)

protectedinherited

Sample two random indices without repitition excluding a specified index.

Parameters

upper_bound	The upper bound provided
exclude	The index to be excluded from sampling

Returns: Pair of required indices

Definition at line 168 of file PMCMCBase.C.

{
  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};
}

Referenced by AffineInvariantDES::proposeSamples().

◆ validParams()

InputParameters BayesianActiveLearningSampler::validParams ( )

static

Definition at line 17 of file BayesianActiveLearningSampler.C.

{
  InputParameters params = PMCMCBase::validParams();
  params.addClassDescription("Fast Bayesian inference with the parallel active learning (partly "
                             "inspired from El Gammal et al. 2023).");
  params.addRequiredParam<ReporterName>(
      "sorted_indices", "The sorted sample indices in order of importance to evaluate the subApp.");
  params.addRequiredRangeCheckedParam<unsigned int>(
      "num_tries",
      "num_tries>0",
      "Number of samples to propose in each iteration (not all are sent for subApp evals).");
  return params;
}

Member Data Documentation

◆ _confg_values

std::vector<std::vector<Real> > PMCMCBase::_confg_values

privateinherited

Configuration values.

Definition at line 157 of file PMCMCBase.h.

Referenced by PMCMCBase::combineWithExperimentalConfig(), PMCMCBase::getNumberOfConfigParams(), PMCMCBase::getNumberOfConfigValues(), and PMCMCBase::PMCMCBase().

◆ _initial_values

const std::vector<Real>& PMCMCBase::_initial_values

protectedinherited

Initial values of the input params to get the MCMC scheme started.

Definition at line 130 of file PMCMCBase.h.

Referenced by PMCMCBase::executeSetUp(), PMCMCBase::PMCMCBase(), and IndependentGaussianMH::proposeSamples().

◆ _inputs_test

std::vector<std::vector<Real> >& BayesianActiveLearningSampler::_inputs_test

private

Storage for all the proposed samples.

Definition at line 47 of file BayesianActiveLearningSampler.h.

Referenced by BayesianActiveLearningSampler(), getSampleTries(), and proposeSamples().

◆ _lower_bound

const std::vector<Real>* PMCMCBase::_lower_bound

protectedinherited

Lower bounds for making the next proposal.

Definition at line 121 of file PMCMCBase.h.

Referenced by PMCMCBase::PMCMCBase(), AffineInvariantDES::proposeSamples(), AffineInvariantStretchSampler::proposeSamples(), and IndependentGaussianMH::proposeSamples().

◆ _new_samples

std::vector<std::vector<Real> > PMCMCBase::_new_samples

protectedinherited

Vectors of new proposed samples.

Definition at line 133 of file PMCMCBase.h.

Referenced by PMCMCBase::combineWithExperimentalConfig(), PMCMCBase::executeSetUp(), PMCMCBase::getSamples(), PMCMCBase::PMCMCBase(), PMCMCBase::proposeSamples(), AffineInvariantDES::proposeSamples(), AffineInvariantStretchSampler::proposeSamples(), proposeSamples(), and IndependentGaussianMH::proposeSamples().

◆ _new_samples_confg

std::vector<std::vector<Real> >& PMCMCBase::_new_samples_confg

privateinherited

Vectors of new proposed samples combined with the experimental configuration values.

Definition at line 160 of file PMCMCBase.h.

Referenced by PMCMCBase::combineWithExperimentalConfig(), PMCMCBase::computeSample(), and PMCMCBase::PMCMCBase().

◆ _new_var_samples

std::vector<Real>& PMCMCBase::_new_var_samples

protectedinherited

Vector of new proposed variance samples.

Definition at line 136 of file PMCMCBase.h.

Referenced by PMCMCBase::getVarSamples(), PMCMCBase::PMCMCBase(), AffineInvariantDES::proposeSamples(), AffineInvariantStretchSampler::proposeSamples(), and proposeSamples().

◆ _num_parallel_proposals

const unsigned int PMCMCBase::_num_parallel_proposals

protectedinherited

Number of parallel proposals to be made and subApps to be executed.

Definition at line 112 of file PMCMCBase.h.

Referenced by AffineInvariantDES::AffineInvariantDES(), AffineInvariantStretchSampler::AffineInvariantStretchSampler(), PMCMCBase::combineWithExperimentalConfig(), PMCMCBase::executeSetUp(), PMCMCBase::getNumParallelProposals(), PMCMCBase::PMCMCBase(), PMCMCBase::proposeSamples(), AffineInvariantDES::proposeSamples(), AffineInvariantStretchSampler::proposeSamples(), proposeSamples(), and IndependentGaussianMH::proposeSamples().

◆ _num_random_seeds

const unsigned int PMCMCBase::_num_random_seeds

privateinherited

Initialize a certain number of random seeds. Change from the default only if you have to.

Definition at line 148 of file PMCMCBase.h.

Referenced by PMCMCBase::PMCMCBase().

◆ _num_tries

const unsigned int& BayesianActiveLearningSampler::_num_tries

private

Number of samples to propose in each iteration (not all are sent for subApp evals)

Definition at line 44 of file BayesianActiveLearningSampler.h.

Referenced by BayesianActiveLearningSampler(), and proposeSamples().

◆ _priors

std::vector<const Distribution *> PMCMCBase::_priors

protectedinherited

Storage for prior distribution objects to be utilized.

Definition at line 115 of file PMCMCBase.h.

Referenced by AffineInvariantDES::AffineInvariantDES(), AffineInvariantStretchSampler::AffineInvariantStretchSampler(), BayesianActiveLearningSampler(), PMCMCBase::getPriors(), IndependentGaussianMH::IndependentGaussianMH(), PMCMCBase::PMCMCBase(), PMCMCBase::proposeSamples(), AffineInvariantDES::proposeSamples(), AffineInvariantStretchSampler::proposeSamples(), proposeSamples(), IndependentGaussianMH::proposeSamples(), and AffineInvariantDES::tuneParams().

◆ _rand_index

std::size_t PMCMCBase::_rand_index

privateinherited

Running index for the random number generators.

Definition at line 154 of file PMCMCBase.h.

Referenced by PMCMCBase::executeSetUp(), PMCMCBase::random(), and PMCMCBase::randomIndex().

◆ _rnd_vec

std::vector<Real>& PMCMCBase::_rnd_vec

protectedinherited

Vector of random numbers for decision making.

Definition at line 139 of file PMCMCBase.h.

Referenced by PMCMCBase::executeSetUp(), PMCMCBase::getRandomNumbers(), and PMCMCBase::PMCMCBase().

◆ _seed_index

unsigned int PMCMCBase::_seed_index

privateinherited

Generator index when requesting random numbers.

Definition at line 151 of file PMCMCBase.h.

Referenced by PMCMCBase::executeSetUp(), PMCMCBase::random(), and PMCMCBase::randomIndex().

◆ _sorted_indices

const std::vector<unsigned int>& BayesianActiveLearningSampler::_sorted_indices

protected

The selected sample indices to evaluate the subApp.

Definition at line 40 of file BayesianActiveLearningSampler.h.

Referenced by proposeSamples().

◆ _upper_bound

const std::vector<Real>* PMCMCBase::_upper_bound

protectedinherited

Upper bounds for making the next proposal.

Definition at line 124 of file PMCMCBase.h.

Referenced by PMCMCBase::PMCMCBase(), and IndependentGaussianMH::proposeSamples().

◆ _var_prior

const Distribution* PMCMCBase::_var_prior

protectedinherited

Storage for prior distribution object of the variance to be utilized.

Definition at line 118 of file PMCMCBase.h.

Referenced by PMCMCBase::getVarPrior(), PMCMCBase::PMCMCBase(), AffineInvariantDES::proposeSamples(), AffineInvariantStretchSampler::proposeSamples(), and proposeSamples().

◆ _var_test

std::vector<Real> BayesianActiveLearningSampler::_var_test

private

Storage for all the proposed variances.

Definition at line 50 of file BayesianActiveLearningSampler.h.

Referenced by getVarSampleTries(), and proposeSamples().

◆ _variance_bound

const Real& PMCMCBase::_variance_bound

protectedinherited

Upper bound for variance for making the next proposal.

Definition at line 127 of file PMCMCBase.h.

Referenced by AffineInvariantDES::proposeSamples(), and AffineInvariantStretchSampler::proposeSamples().

The documentation for this class was generated from the following files:

stochastic_tools/include/samplers/BayesianActiveLearningSampler.h
stochastic_tools/src/samplers/BayesianActiveLearningSampler.C

Public Types

Public Member Functions

Static Public Member Functions

Public Attributes

Static Public Attributes

Protected Member Functions

Protected Attributes

Private Member Functions

Static Private Member Functions

Private Attributes

Detailed Description

Constructor & Destructor Documentation

◆ BayesianActiveLearningSampler()

Member Function Documentation

◆ combineWithExperimentalConfig()

◆ computeSample()

◆ decisionStep()

◆ executeSetUp()

◆ getNumberOfConfigParams()

◆ getNumberOfConfigValues()

◆ getNumParallelProposals()

◆ getPriors()

◆ getRandomNumbers()

◆ getSamples()

◆ getSampleTries()

◆ getVarPrior()

◆ getVarSamples()

◆ getVarSampleTries()

◆ proposeSamples()

◆ random()

◆ randomIndex()

◆ randomIndexPair()

◆ validParams()

Member Data Documentation

◆ _confg_values

◆ _initial_values

◆ _inputs_test

◆ _lower_bound

◆ _new_samples

◆ _new_samples_confg

◆ _new_var_samples

◆ _num_parallel_proposals

◆ _num_random_seeds

◆ _num_tries

◆ _priors

◆ _rand_index

◆ _rnd_vec

◆ _seed_index

◆ _sorted_indices

◆ _upper_bound

◆ _var_prior

◆ _var_test

◆ _variance_bound