This is the main trainer base class. More...

#include <SurrogateTrainer.h>

Inheritance diagram for SurrogateTrainer:

Public Types
typedef DataFileName	DataFileParameterType

Public Member Functions
	SurrogateTrainer (const InputParameters &parameters)

virtual void	initialize () final

virtual void	execute () final

virtual void	finalize () final

virtual void	threadJoin (const UserObject &) final

SubProblem &	getSubProblem () const

bool	shouldDuplicateInitialExecution () const

virtual Real	spatialValue (const Point &) const

virtual const std::vector< Point >	spatialPoints () const

void	gatherSum (T &value)

void	gatherMax (T &value)

void	gatherMin (T &value)

void	gatherProxyValueMax (T1 &proxy, T2 &value)

void	gatherProxyValueMin (T1 &proxy, T2 &value)

void	setPrimaryThreadCopy (UserObject *primary)

UserObject *	primaryThreadCopy ()

std::set< UserObjectName >	getDependObjects () const

virtual bool	needThreadedCopy () const

const std::set< std::string > &	getRequestedItems () override

const std::set< std::string > &	getSuppliedItems () override

unsigned int	systemNumber () const

virtual bool	enabled () const

std::shared_ptr< MooseObject >	getSharedPtr ()

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

MooseApp &	getMooseApp () const

const std::string &	type () const

virtual const std::string &	name () const

std::string	typeAndName () const

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

void	callMooseError (std::string msg, const bool with_prefix) const

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

const InputParameters &	parameters () const

MooseObjectName	uniqueName () 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 &nm) const

void	paramError (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

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

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

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

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

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

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

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

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

const ExecFlagEnum &	getExecuteOnEnum () const

UserObjectName	getUserObjectName (const std::string &param_name) const

const T &	getUserObject (const std::string &param_name, bool is_dependency=true) const

const T &	getUserObjectByName (const UserObjectName &object_name, bool is_dependency=true) const

const UserObject &	getUserObjectBase (const std::string &param_name, bool is_dependency=true) const

const UserObject &	getUserObjectBaseByName (const UserObjectName &object_name, bool is_dependency=true) const

const std::vector< MooseVariableScalar *> &	getCoupledMooseScalarVars ()

const std::set< TagID > &	getScalarVariableCoupleableVectorTags () const

const std::set< TagID > &	getScalarVariableCoupleableMatrixTags () const

const GenericMaterialProperty< T, is_ad > &	getGenericMaterialProperty (const std::string &name, MaterialData &material_data, const unsigned int state=0)

const GenericMaterialProperty< T, is_ad > &	getGenericMaterialProperty (const std::string &name, const unsigned int state=0)

const GenericMaterialProperty< T, is_ad > &	getGenericMaterialProperty (const std::string &name, const unsigned int state=0)

const MaterialProperty< T > &	getMaterialProperty (const std::string &name, MaterialData &material_data, const unsigned int state=0)

const MaterialProperty< T > &	getMaterialProperty (const std::string &name, const unsigned int state=0)

const MaterialProperty< T > &	getMaterialProperty (const std::string &name, const unsigned int state=0)

const ADMaterialProperty< T > &	getADMaterialProperty (const std::string &name, MaterialData &material_data)

const ADMaterialProperty< T > &	getADMaterialProperty (const std::string &name)

const ADMaterialProperty< T > &	getADMaterialProperty (const std::string &name)

const MaterialProperty< T > &	getMaterialPropertyOld (const std::string &name, MaterialData &material_data)

const MaterialProperty< T > &	getMaterialPropertyOld (const std::string &name)

const MaterialProperty< T > &	getMaterialPropertyOld (const std::string &name)

const MaterialProperty< T > &	getMaterialPropertyOlder (const std::string &name, MaterialData &material_data)

const MaterialProperty< T > &	getMaterialPropertyOlder (const std::string &name)

const MaterialProperty< T > &	getMaterialPropertyOlder (const std::string &name)

const GenericMaterialProperty< T, is_ad > &	getGenericMaterialPropertyByName (const MaterialPropertyName &name, MaterialData &material_data, const unsigned int state)

const GenericMaterialProperty< T, is_ad > &	getGenericMaterialPropertyByName (const MaterialPropertyName &name, const unsigned int state=0)

const GenericMaterialProperty< T, is_ad > &	getGenericMaterialPropertyByName (const MaterialPropertyName &name, const unsigned int state=0)

const MaterialProperty< T > &	getMaterialPropertyByName (const MaterialPropertyName &name, MaterialData &material_data, const unsigned int state=0)

const MaterialProperty< T > &	getMaterialPropertyByName (const MaterialPropertyName &name, const unsigned int state=0)

const MaterialProperty< T > &	getMaterialPropertyByName (const MaterialPropertyName &name, const unsigned int state=0)

const ADMaterialProperty< T > &	getADMaterialPropertyByName (const MaterialPropertyName &name, MaterialData &material_data)

const ADMaterialProperty< T > &	getADMaterialPropertyByName (const MaterialPropertyName &name)

const ADMaterialProperty< T > &	getADMaterialPropertyByName (const MaterialPropertyName &name)

const MaterialProperty< T > &	getMaterialPropertyOldByName (const MaterialPropertyName &name, MaterialData &material_data)

const MaterialProperty< T > &	getMaterialPropertyOldByName (const MaterialPropertyName &name)

const MaterialProperty< T > &	getMaterialPropertyOldByName (const MaterialPropertyName &name)

const MaterialProperty< T > &	getMaterialPropertyOlderByName (const MaterialPropertyName &name, MaterialData &material_data)

const MaterialProperty< T > &	getMaterialPropertyOlderByName (const MaterialPropertyName &name)

const MaterialProperty< T > &	getMaterialPropertyOlderByName (const MaterialPropertyName &name)

std::pair< const MaterialProperty< T > *, std::set< SubdomainID > >	getBlockMaterialProperty (const MaterialPropertyName &name)

const GenericMaterialProperty< T, is_ad > &	getGenericZeroMaterialProperty (const std::string &name)

const GenericMaterialProperty< T, is_ad > &	getGenericZeroMaterialProperty ()

const GenericMaterialProperty< T, is_ad > &	getGenericZeroMaterialPropertyByName (const std::string &prop_name)

const MaterialProperty< T > &	getZeroMaterialProperty (Ts... args)

std::set< SubdomainID >	getMaterialPropertyBlocks (const std::string &name)

std::vector< SubdomainName >	getMaterialPropertyBlockNames (const std::string &name)

std::set< BoundaryID >	getMaterialPropertyBoundaryIDs (const std::string &name)

std::vector< BoundaryName >	getMaterialPropertyBoundaryNames (const std::string &name)

void	checkBlockAndBoundaryCompatibility (std::shared_ptr< MaterialBase > discrete)

std::unordered_map< SubdomainID, std::vector< MaterialBase *> >	buildRequiredMaterials (bool allow_stateful=true)

void	statefulPropertiesAllowed (bool)

bool	getMaterialPropertyCalled () const

virtual const std::unordered_set< unsigned int > &	getMatPropDependencies () const

virtual void	resolveOptionalProperties ()

const GenericMaterialProperty< T, is_ad > &	getPossiblyConstantGenericMaterialPropertyByName (const MaterialPropertyName &prop_name, MaterialData &material_data, const unsigned int state)

bool	isImplicit ()

Moose::StateArg	determineState () const

virtual void	subdomainSetup () override

virtual void	subdomainSetup () override

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

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

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

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

bool	hasUserObjectByName (const UserObjectName &object_name) const

bool	hasUserObjectByName (const UserObjectName &object_name) const

bool	hasUserObjectByName (const UserObjectName &object_name) const

bool	hasUserObjectByName (const UserObjectName &object_name) const

const GenericOptionalMaterialProperty< T, is_ad > &	getGenericOptionalMaterialProperty (const std::string &name, const unsigned int state=0)

const GenericOptionalMaterialProperty< T, is_ad > &	getGenericOptionalMaterialProperty (const std::string &name, const unsigned int state=0)

const OptionalMaterialProperty< T > &	getOptionalMaterialProperty (const std::string &name, const unsigned int state=0)

const OptionalMaterialProperty< T > &	getOptionalMaterialProperty (const std::string &name, const unsigned int state=0)

const OptionalADMaterialProperty< T > &	getOptionalADMaterialProperty (const std::string &name)

const OptionalADMaterialProperty< T > &	getOptionalADMaterialProperty (const std::string &name)

const OptionalMaterialProperty< T > &	getOptionalMaterialPropertyOld (const std::string &name)

const OptionalMaterialProperty< T > &	getOptionalMaterialPropertyOld (const std::string &name)

const OptionalMaterialProperty< T > &	getOptionalMaterialPropertyOlder (const std::string &name)

const OptionalMaterialProperty< T > &	getOptionalMaterialPropertyOlder (const std::string &name)

MaterialBase &	getMaterial (const std::string &name)

MaterialBase &	getMaterial (const std::string &name)

MaterialBase &	getMaterialByName (const std::string &name, bool no_warn=false)

MaterialBase &	getMaterialByName (const std::string &name, bool no_warn=false)

bool	hasMaterialProperty (const std::string &name)

bool	hasMaterialProperty (const std::string &name)

bool	hasMaterialPropertyByName (const std::string &name)

bool	hasMaterialPropertyByName (const std::string &name)

bool	hasADMaterialProperty (const std::string &name)

bool	hasADMaterialProperty (const std::string &name)

bool	hasADMaterialPropertyByName (const std::string &name)

bool	hasADMaterialPropertyByName (const std::string &name)

bool	hasGenericMaterialProperty (const std::string &name)

bool	hasGenericMaterialProperty (const std::string &name)

bool	hasGenericMaterialPropertyByName (const std::string &name)

bool	hasGenericMaterialPropertyByName (const std::string &name)

const Function &	getFunction (const std::string &name) const

const Function &	getFunctionByName (const FunctionName &name) const

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

bool	hasFunctionByName (const FunctionName &name) const

bool	isDefaultPostprocessorValue (const std::string &param_name, const unsigned int index=0) const

bool	hasPostprocessor (const std::string &param_name, const unsigned int index=0) const

bool	hasPostprocessorByName (const PostprocessorName &name) const

std::size_t	coupledPostprocessors (const std::string &param_name) const

const PostprocessorName &	getPostprocessorName (const std::string &param_name, const unsigned int index=0) const

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

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

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

T &	getSamplerByName (const SamplerName &name)

Sampler &	getSamplerByName (const SamplerName &name)

virtual void	meshChanged ()

virtual void	meshDisplaced ()

PerfGraph &	perfGraph ()

const PostprocessorValue &	getPostprocessorValue (const std::string &param_name, const unsigned int index=0) const

const PostprocessorValue &	getPostprocessorValue (const std::string &param_name, const unsigned int index=0) const

const PostprocessorValue &	getPostprocessorValueOld (const std::string &param_name, const unsigned int index=0) const

const PostprocessorValue &	getPostprocessorValueOld (const std::string &param_name, const unsigned int index=0) const

const PostprocessorValue &	getPostprocessorValueOlder (const std::string &param_name, const unsigned int index=0) const

const PostprocessorValue &	getPostprocessorValueOlder (const std::string &param_name, const unsigned int index=0) const

virtual const PostprocessorValue &	getPostprocessorValueByName (const PostprocessorName &name) const

virtual const PostprocessorValue &	getPostprocessorValueByName (const PostprocessorName &name) const

const PostprocessorValue &	getPostprocessorValueOldByName (const PostprocessorName &name) const

const PostprocessorValue &	getPostprocessorValueOldByName (const PostprocessorName &name) const

const PostprocessorValue &	getPostprocessorValueOlderByName (const PostprocessorName &name) const

const PostprocessorValue &	getPostprocessorValueOlderByName (const PostprocessorName &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 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

const Parallel::Communicator &	comm () const

processor_id_type	n_processors () const

processor_id_type	processor_id () const

const std::string &	modelMetaDataName () const
	Accessor for the name of the model meta data. More...

const FileName &	getModelDataFileName () const
	Get the associated filename. More...

bool	hasModelData () const
	Check if we need to load model data (if the filename parameter is used) More...

template<>
SurrogateModel &	getSurrogateModel (const std::string &name) const

template<>
SurrogateTrainerBase &	getSurrogateTrainer (const std::string &name) const

template<>
SurrogateModel &	getSurrogateModelByName (const UserObjectName &name) const

template<>
SurrogateTrainerBase &	getSurrogateTrainerByName (const UserObjectName &name) const


template<typename T , typename... Args>
T &	declareModelData (const std::string &data_name, Args &&... args)
	Declare model data for loading from file as well as restart. More...


template<typename T , typename... Args>
const T &	getModelData (const std::string &data_name, Args &&... args) const
	Retrieve model data from the interface. More...


template<typename T = SurrogateModel>
T &	getSurrogateModel (const std::string &name) const
	Get a SurrogateModel/Trainer with a given name. More...

template<typename T = SurrogateTrainerBase>
T &	getSurrogateTrainer (const std::string &name) const


template<typename T = SurrogateModel>
T &	getSurrogateModelByName (const UserObjectName &name) const
	Get a sampler with a given name. More...

template<typename T = SurrogateTrainerBase>
T &	getSurrogateTrainerByName (const UserObjectName &name) const

Static Public Member Functions
static InputParameters	validParams ()

static void	sort (typename std::vector< T > &vector)

static void	sortDFS (typename std::vector< T > &vector)

static void	cyclicDependencyError (CyclicDependencyException< T2 > &e, const std::string &header)

Public Attributes
const ConsoleStream	_console

Static Public Attributes
static constexpr PropertyValue::id_type	default_property_id

static constexpr PropertyValue::id_type	zero_property_id

static constexpr auto	SYSTEM

static constexpr auto	NAME

Protected Member Functions
virtual void	preTrain ()

virtual void	train ()

virtual void	postTrain ()

template<typename T >
const T &	getTrainingData (const ReporterName &rname)

const std::vector< Real > &	getSamplerData () const

const std::vector< Real > &	getPredictorData () const

unsigned int	getCurrentSampleSize () const

unsigned int	getLocalSampleSize () const

virtual std::vector< Real >	evaluateModelError (const SurrogateModel &surr)

virtual void	addPostprocessorDependencyHelper (const PostprocessorName &name) const override

virtual void	addVectorPostprocessorDependencyHelper (const VectorPostprocessorName &name) const override

virtual void	addUserObjectDependencyHelper (const UserObject &uo) const override

void	addReporterDependencyHelper (const ReporterName &reporter_name) override

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

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

const T &	getMeshProperty (const std::string &data_name, const std::string &prefix)

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

bool	hasMeshProperty (const std::string &data_name, const std::string &prefix) const

bool	hasMeshProperty (const std::string &data_name, const std::string &prefix) const

bool	hasMeshProperty (const std::string &data_name) const

bool	hasMeshProperty (const std::string &data_name) const

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

bool	isCoupledScalar (const std::string &var_name, unsigned int i=0) const

unsigned int	coupledScalarComponents (const std::string &var_name) const

unsigned int	coupledScalar (const std::string &var_name, unsigned int comp=0) const

libMesh::Order	coupledScalarOrder (const std::string &var_name, unsigned int comp=0) const

const VariableValue &	coupledScalarValue (const std::string &var_name, unsigned int comp=0) const

const ADVariableValue &	adCoupledScalarValue (const std::string &var_name, unsigned int comp=0) const

const GenericVariableValue< is_ad > &	coupledGenericScalarValue (const std::string &var_name, unsigned int comp=0) const

const GenericVariableValue< false > &	coupledGenericScalarValue (const std::string &var_name, const unsigned int comp) const

const GenericVariableValue< true > &	coupledGenericScalarValue (const std::string &var_name, const unsigned int comp) const

const VariableValue &	coupledVectorTagScalarValue (const std::string &var_name, TagID tag, unsigned int comp=0) const

const VariableValue &	coupledMatrixTagScalarValue (const std::string &var_name, TagID tag, unsigned int comp=0) const

const VariableValue &	coupledScalarValueOld (const std::string &var_name, unsigned int comp=0) const

const VariableValue &	coupledScalarValueOlder (const std::string &var_name, unsigned int comp=0) const

const VariableValue &	coupledScalarDot (const std::string &var_name, unsigned int comp=0) const

const ADVariableValue &	adCoupledScalarDot (const std::string &var_name, unsigned int comp=0) const

const VariableValue &	coupledScalarDotDot (const std::string &var_name, unsigned int comp=0) const

const VariableValue &	coupledScalarDotOld (const std::string &var_name, unsigned int comp=0) const

const VariableValue &	coupledScalarDotDotOld (const std::string &var_name, unsigned int comp=0) const

const VariableValue &	coupledScalarDotDu (const std::string &var_name, unsigned int comp=0) const

const VariableValue &	coupledScalarDotDotDu (const std::string &var_name, unsigned int comp=0) const

const MooseVariableScalar *	getScalarVar (const std::string &var_name, unsigned int comp) const

virtual void	checkMaterialProperty (const std::string &name, const unsigned int state)

void	markMatPropRequested (const std::string &)

MaterialPropertyName	getMaterialPropertyName (const std::string &name) const

void	checkExecutionStage ()

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

const GenericMaterialProperty< T, is_ad > *	defaultGenericMaterialProperty (const std::string &name)

const GenericMaterialProperty< T, is_ad > *	defaultGenericMaterialProperty (const std::string &name)

const MaterialProperty< T > *	defaultMaterialProperty (const std::string &name)

const MaterialProperty< T > *	defaultMaterialProperty (const std::string &name)

const ADMaterialProperty< T > *	defaultADMaterialProperty (const std::string &name)

const ADMaterialProperty< T > *	defaultADMaterialProperty (const std::string &name)

Static Protected Member Functions
static std::string	meshPropertyName (const std::string &data_name, const std::string &prefix)

Protected Attributes
SubProblem &	_subproblem

FEProblemBase &	_fe_problem

SystemBase &	_sys

const THREAD_ID	_tid

Assembly &	_assembly

const Moose::CoordinateSystemType &	_coord_sys

const bool	_duplicate_initial_execution

std::set< std::string >	_depend_uo

const bool &	_enabled

MooseApp &	_app

const std::string	_type

const std::string	_name

const InputParameters &	_pars

Factory &	_factory

ActionFactory &	_action_factory

const ExecFlagEnum &	_execute_enum

const ExecFlagType &	_current_execute_flag

MooseApp &	_restartable_app

const std::string	_restartable_system_name

const THREAD_ID	_restartable_tid

const bool	_restartable_read_only

FEProblemBase &	_mci_feproblem

FEProblemBase &	_mdi_feproblem

MooseApp &	_pg_moose_app

const std::string	_prefix

FEProblemBase &	_sc_fe_problem

const THREAD_ID	_sc_tid

const Real &	_real_zero

const VariableValue &	_scalar_zero

const Point &	_point_zero

const InputParameters &	_mi_params

const std::string	_mi_name

const MooseObjectName	_mi_moose_object_name

FEProblemBase &	_mi_feproblem

SubProblem &	_mi_subproblem

const THREAD_ID	_mi_tid

const Moose::MaterialDataType	_material_data_type

MaterialData &	_material_data

bool	_stateful_allowed

bool	_get_material_property_called

std::vector< std::unique_ptr< PropertyValue > >	_default_properties

std::unordered_set< unsigned int >	_material_property_dependencies

const MaterialPropertyName	_get_suffix

const bool	_use_interpolated_state

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

const Parallel::Communicator &	_communicator


Sampler &	_sampler

dof_id_type	_row
	During training loop, this is the row index of the data. More...

dof_id_type	_local_row
	During training loop, this is the local row index of the data. More...

const Real *	_rval
	Response value. More...

const std::vector< Real > *	_rvecval
	Vector response value. More...

std::vector< const Real * >	_pvals
	Predictor values from reporters. More...

std::vector< unsigned int >	_pcols
	Columns from sampler for predictors. More...

unsigned int	_n_dims
	Dimension of predictor data - either _sampler.getNumberOfCols() or _pvals.size() + _pcols.size(). More...

unsigned int &	_n_outputs
	The number of outputs. More...

Static Protected Attributes
static const std::string	_interpolated_old

static const std::string	_interpolated_older

Private Member Functions
void	checkIntegrity () const

void	executeTraining ()

std::vector< Real >	crossValidate ()

void	updatePredictorRow ()

Private Attributes
std::vector< Real >	_row_data
	Sampler data for the current row. More...

std::vector< Real >	_predictor_data
	Predictor data for current row - can be combination of Sampler and Reporter values. More...

const bool	_skip_unconverged
	Whether or not we are skipping samples that have unconverged solutions. More...

const bool *	_converged
	Whether or not the current sample has a converged solution. More...

unsigned int	_current_sample_size
	Number of samples used to train the model. More...

unsigned int	_local_sample_size
	Number of samples (locally) used to train the model. More...

std::unordered_map< ReporterName, std::shared_ptr< TrainingDataBase > >	_training_data
	Vector of reporter names and their corresponding values (to be filled by getTrainingData) More...


std::vector< dof_id_type >	_skip_indices

const MooseEnum &	_cv_type
	Type of cross validation to perform - for now, just 'none' (no CV) or 'k_fold'. More...

const unsigned int &	_n_splits
	Number of splits (k) to split sampler data into. More...

const unsigned int &	_cv_n_trials
	Number of repeated trials of cross validation to perform. More...

const unsigned int &	_cv_seed
	Seed used for _cv_generator. More...

MooseRandom	_cv_generator
	Random number generator used for shuffling sampler rows during splitting. More...

const SurrogateModel *	_cv_surrogate
	SurrogateModel used to evaluate model error relative to test points. More...

const bool	_doing_cv
	Set to true if cross validation is being performed, controls behavior in execute(). More...

std::vector< std::vector< Real > > &	_cv_trial_scores
	RMSE scores from each CV trial - can be grabbed by VPP or Reporter. More...

Detailed Description

This is the main trainer base class.

The main purpose is to avoid a lot of code duplication from performing sampler loops and dealing with distributed data. There three functions that derived trainer should override: preTrain, train, and postTrain. Derived class should also use the getTrainingData functionality, which provides a refernce to vector reporter data in its current state within the sampler loop.

The idea behind this is to emulate the element loop behaiviour in other MOOSE objects. For instance, in a kernel, the value of _u corresponds to the solution in an element. Here data referenced with getTrainingData will correspond to the the value of the data in a sampler row.

Definition at line 55 of file SurrogateTrainer.h.

Constructor & Destructor Documentation

◆ SurrogateTrainer()

SurrogateTrainer::SurrogateTrainer ( const InputParameters & parameters )

Definition at line 85 of file SurrogateTrainer.C.

   : SurrogateTrainerBase(parameters),
     SurrogateModelInterface(this),
     _sampler(getSampler("sampler")),
     _rval(nullptr),
     _rvecval(nullptr),
     _pvals(getParam<std::vector<ReporterName>>("predictors").size()),
     _pcols(getParam<std::vector<unsigned int>>("predictor_cols")),
     _n_outputs(declareModelData<unsigned int>("_n_outputs", 1)),
     _row_data(_sampler.getNumberOfCols()),
     _skip_unconverged(getParam<bool>("skip_unconverged_samples")),
     _converged(nullptr),
     _cv_type(getParam<MooseEnum>("cv_type")),
     _n_splits(getParam<unsigned int>("cv_splits")),
     _cv_n_trials(getParam<unsigned int>("cv_n_trials")),
     _cv_seed(getParam<unsigned int>("cv_seed")),
     _doing_cv(_cv_type != "none"),
     _cv_trial_scores(declareModelData<std::vector<std::vector<Real>>>("cv_scores"))
 {
   if (_skip_unconverged)
   {
     if (!isParamValid("converged_reporter"))
       paramError("skip_unconverged_samples",
                  "'converged_reporter' needs to be specified to skip unconverged sample.");
     _converged = &getTrainingData<bool>(getParam<ReporterName>("converged_reporter"));
   }
 
   if (_doing_cv)
   {
     if (!isParamValid("cv_surrogate"))
       paramError("cv_type",
                  "To perform cross-validation, the option cv_surrogate needs to be specified",
                  " to provide a Surrogate object for training and evaluation.");
 
     if (_n_splits > _sampler.getNumberOfRows())
       paramError("cv_splits",
                  "The specified number of splits (cv_splits = ",
                  _n_splits,
                  ")",
                  " exceeds the number of rows in Sampler '",
                  getParam<SamplerName>("sampler"),
                  "'");
 
     _cv_generator.seed(0, _cv_seed);
   }
 
   // Get TrainingData for responses and predictors
   if (getParam<MooseEnum>("response_type") == 0)
     _rval = &getTrainingData<Real>(getParam<ReporterName>("response"));
   else if (getParam<MooseEnum>("response_type") == 1)
     _rvecval = &getTrainingData<std::vector<Real>>(getParam<ReporterName>("response"));
 
   const auto & pnames = getParam<std::vector<ReporterName>>("predictors");
   for (unsigned int i = 0; i < pnames.size(); ++i)
     _pvals[i] = &getTrainingData<Real>(pnames[i]);
 
   // If predictors and predictor_cols are empty, use all sampler columns
   if (_pvals.empty() && _pcols.empty())
   {
     _pcols.resize(_sampler.getNumberOfCols());
     std::iota(_pcols.begin(), _pcols.end(), 0);
   }
   _n_dims = _pvals.size() + _pcols.size();
 
   _predictor_data.resize(_n_dims);
 }

Member Function Documentation

◆ checkIntegrity()

void SurrogateTrainer::checkIntegrity ( ) const

private

Definition at line 196 of file SurrogateTrainer.C.

Referenced by executeTraining().

 {
   // Check that the number of sampler columns hasn't changed
   if (_row_data.size() != _sampler.getNumberOfCols())
     mooseError("Number of sampler columns has changed.");
 
   // Check that training data is correctly sized
   for (auto & pair : _training_data)
   {
     dof_id_type rsize = pair.second->size();
     dof_id_type nrow =
         pair.second->isDistributed() ? _sampler.getNumberOfLocalRows() : _sampler.getNumberOfRows();
     if (rsize != nrow)
       mooseError("Reporter value ",
                  pair.first,
                  " of size ",
                  rsize,
                  " does not match sampler size (",
                  nrow,
                  ").");
   }
 }

◆ crossValidate()

std::vector< Real > SurrogateTrainer::crossValidate ( )

private

Definition at line 252 of file SurrogateTrainer.C.

Referenced by execute().

 {
   std::vector<Real> cv_score(1, 0.0);
 
   // Get skipped indices for each split
   dof_id_type n_rows = _sampler.getNumberOfRows();
   std::vector<std::vector<dof_id_type>> split_indices;
   if (processor_id() == 0)
   {
     std::vector<dof_id_type> indices_flat(n_rows);
     std::iota(indices_flat.begin(), indices_flat.end(), 0);
     MooseUtils::shuffle(indices_flat, _cv_generator, 0);
 
     split_indices.resize(_n_splits);
     for (const auto & k : make_range(_n_splits))
     {
       const dof_id_type num_ind = n_rows / _n_splits + (k < (n_rows % _n_splits) ? 1 : 0);
       split_indices[k].insert(split_indices[k].begin(),
                               std::make_move_iterator(indices_flat.begin()),
                               std::make_move_iterator(indices_flat.begin() + num_ind));
       std::sort(split_indices[k].begin(), split_indices[k].end());
       indices_flat.erase(indices_flat.begin(), indices_flat.begin() + num_ind);
     }
   }
 
   std::vector<dof_id_type> split_ids_buffer;
   for (const auto & k : make_range(_n_splits))
   {
     if (processor_id() == 0)
       split_ids_buffer = split_indices[k];
     _communicator.broadcast(split_ids_buffer, 0);
 
     _current_sample_size = _sampler.getNumberOfRows() - split_ids_buffer.size();
 
     auto first = std::lower_bound(
         split_ids_buffer.begin(), split_ids_buffer.end(), _sampler.getLocalRowBegin());
     auto last = std::upper_bound(
         split_ids_buffer.begin(), split_ids_buffer.end(), _sampler.getLocalRowEnd());
     _skip_indices.insert(_skip_indices.begin(), first, last);
 
     _local_sample_size = _sampler.getNumberOfLocalRows() - _skip_indices.size();
 
     // Train the model
     executeTraining();
 
     // Evaluate the model
     std::vector<Real> split_mse(1, 0.0);
     std::vector<Real> row_mse(1, 0.0);
 
     auto skipped_row = _skip_indices.begin();
 
     for (dof_id_type p = _sampler.getLocalRowBegin(); p < _sampler.getLocalRowEnd(); ++p)
     {
       const std::vector<Real> row = _sampler.getNextLocalRow();
       if (skipped_row != _skip_indices.end() && p == *skipped_row)
       {
         for (unsigned int i = 0; i < _row_data.size(); ++i)
           _row_data[i] = row[i];
 
         for (auto & pair : _training_data)
           pair.second->setCurrentIndex(
               (pair.second->isDistributed() ? p - _sampler.getLocalRowBegin() : p));
 
         updatePredictorRow();
 
         row_mse = evaluateModelError(*_cv_surrogate);
 
         // Expand split_mse if needed.
         split_mse.resize(row_mse.size(), 0.0);
 
         // Increment errors
         for (unsigned int r = 0; r < split_mse.size(); ++r)
           split_mse[r] += row_mse[r];
 
         skipped_row++;
       }
     }
     gatherSum(split_mse);
 
     // Expand cv_score if necessary.
     cv_score.resize(split_mse.size(), 0.0);
 
     for (auto r : make_range(split_mse.size()))
       cv_score[r] += split_mse[r] / n_rows;
 
     _skip_indices.clear();
   }
 
   for (auto r : make_range(cv_score.size()))
     cv_score[r] = std::sqrt(cv_score[r]);
 
   return cv_score;
 }

◆ declareModelData()

template<typename T , typename... Args>

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

inherited

Declare model data for loading from file as well as restart.

Definition at line 78 of file RestartableModelInterface.h.

 {
   return _model_restartable.declareRestartableData<T>(data_name, std::forward<Args>(args)...);
 }

◆ evaluateModelError()

std::vector< Real > SurrogateTrainer::evaluateModelError ( const SurrogateModel & surr )

protectedvirtual

Definition at line 347 of file SurrogateTrainer.C.

Referenced by crossValidate().

 {
   std::vector<Real> error(1, 0.0);
 
   if (_rval)
   {
     Real model_eval = surr.evaluate(_predictor_data);
     error[0] = MathUtils::pow(model_eval - (*_rval), 2);
   }
   else if (_rvecval)
   {
     error.resize(_rvecval->size());
 
     // Evaluate for vector response.
     std::vector<Real> model_eval(error.size());
     surr.evaluate(_predictor_data, model_eval);
     for (auto r : make_range(_rvecval->size()))
       error[r] = MathUtils::pow(model_eval[r] - (*_rvecval)[r], 2);
   }
 
   return error;
 }

◆ execute()

void SurrogateTrainer::execute ( )

finalvirtual

Implements GeneralUserObject.

Definition at line 176 of file SurrogateTrainer.C.

 {
   if (_doing_cv)
     for (const auto & trial : make_range(_cv_n_trials))
     {
       std::vector<Real> trial_score = crossValidate();
 
       // Expand _cv_trial_scores with more columns if necessary, then insert values.
       for (unsigned int r = _cv_trial_scores.size(); r < trial_score.size(); ++r)
         _cv_trial_scores.push_back(std::vector<Real>(_cv_n_trials, 0.0));
       for (auto r : make_range(trial_score.size()))
         _cv_trial_scores[r][trial] = trial_score[r];
     }
 
   _current_sample_size = _sampler.getNumberOfRows();
   _local_sample_size = _sampler.getNumberOfLocalRows();
   executeTraining();
 }

◆ executeTraining()

void SurrogateTrainer::executeTraining ( )

private

Definition at line 220 of file SurrogateTrainer.C.

Referenced by crossValidate(), and execute().

 {
   checkIntegrity();
   _row = _sampler.getLocalRowBegin();
   _local_row = 0;
 
   preTrain();
 
   for (_row = _sampler.getLocalRowBegin(); _row < _sampler.getLocalRowEnd(); ++_row)
   {
     // Need to do this manually in order to keep the iterators valid
     const std::vector<Real> data = _sampler.getNextLocalRow();
     for (unsigned int i = 0; i < _row_data.size(); ++i)
       _row_data[i] = data[i];
 
     // Set training data
     for (auto & pair : _training_data)
       pair.second->setCurrentIndex((pair.second->isDistributed() ? _local_row : _row));
 
     updatePredictorRow();
 
     if ((!_skip_unconverged || *_converged) &&
         std::find(_skip_indices.begin(), _skip_indices.end(), _row) == _skip_indices.end())
       train();
 
     _local_row++;
   }
 
   postTrain();
 }

◆ finalize()

virtual void SurrogateTrainer::finalize ( )

inlinefinalvirtual

Reimplemented from SurrogateTrainerBase.

Definition at line 63 of file SurrogateTrainer.h.

63 {}

◆ getCurrentSampleSize()

unsigned int SurrogateTrainer::getCurrentSampleSize ( ) const

inlineprotected

Definition at line 102 of file SurrogateTrainer.h.

Referenced by PolynomialChaosTrainer::postTrain(), and PolynomialChaosTrainer::preTrain().

102 { return _current_sample_size; };

SurrogateTrainer::_current_sample_size

unsigned int _current_sample_size

Number of samples used to train the model.

Definition: SurrogateTrainer.h:173

◆ getLocalSampleSize()

unsigned int SurrogateTrainer::getLocalSampleSize ( ) const

inlineprotected

Definition at line 107 of file SurrogateTrainer.h.

Referenced by NearestPointTrainer::preTrain(), GaussianProcessTrainer::preTrain(), and LibtorchANNTrainer::preTrain().

107 { return _local_sample_size; };

SurrogateTrainer::_local_sample_size

unsigned int _local_sample_size

Number of samples (locally) used to train the model.

Definition: SurrogateTrainer.h:176

◆ getModelData()

template<typename T , typename... Args>

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

inherited

Retrieve model data from the interface.

Definition at line 85 of file RestartableModelInterface.h.

 {
   return _model_restartable.getRestartableData<T>(data_name, std::forward<Args>(args)...);
 }

◆ getModelDataFileName()

const FileName & RestartableModelInterface::getModelDataFileName ( ) const

inherited

Get the associated filename.

Definition at line 33 of file RestartableModelInterface.C.

 {
   return _model_object.getParam<FileName>("filename");
 }

◆ getPredictorData()

const std::vector<Real>& SurrogateTrainer::getPredictorData ( ) const

inlineprotected

Definition at line 97 of file SurrogateTrainer.h.

97 { return _predictor_data; };

SurrogateTrainer::_predictor_data

std::vector< Real > _predictor_data

Predictor data for current row - can be combination of Sampler and Reporter values.

Definition: SurrogateTrainer.h:164

◆ getSamplerData()

const std::vector<Real>& SurrogateTrainer::getSamplerData ( ) const

inlineprotected

Definition at line 92 of file SurrogateTrainer.h.

92 { return _row_data; };

SurrogateTrainer::_row_data

std::vector< Real > _row_data

Sampler data for the current row.

Definition: SurrogateTrainer.h:161

◆ getSurrogateModel() [1/2]

template<>

SurrogateModel& SurrogateModelInterface::getSurrogateModel ( const std::string & name ) const

inherited

Definition at line 46 of file SurrogateModelInterface.C.

 {
   return getSurrogateModelByName<SurrogateModel>(_smi_params.get<UserObjectName>(name));
 }

◆ getSurrogateModel() [2/2]

template<typename T >

T & SurrogateModelInterface::getSurrogateModel ( const std::string & name ) const

inherited

Get a SurrogateModel/Trainer with a given name.

Parameters

name	The name of the parameter key of the sampler to retrieve

Returns: The sampler with name associated with the parameter 'name'

Definition at line 81 of file SurrogateModelInterface.h.

Referenced by initialize().

 {
   return getSurrogateModelByName<T>(_smi_params.get<UserObjectName>(name));
 }

◆ getSurrogateModelByName() [1/2]

template<>

SurrogateModel& SurrogateModelInterface::getSurrogateModelByName ( const UserObjectName & name ) const

inherited

Definition at line 31 of file SurrogateModelInterface.C.

 {
   std::vector<SurrogateModel *> models;
   _smi_feproblem.theWarehouse()
       .query()
       .condition<AttribName>(name)
       .condition<AttribSystem>("SurrogateModel")
       .queryInto(models);
   if (models.empty())
     mooseError("Unable to find a SurrogateModel object with the name '" + name + "'");
   return *(models[0]);
 }

◆ getSurrogateModelByName() [2/2]

template<typename T >

T & SurrogateModelInterface::getSurrogateModelByName ( const UserObjectName & name ) const

inherited

Get a sampler with a given name.

Parameters

name	The name of the sampler to retrieve

Returns: The sampler with name 'name'

Definition at line 88 of file SurrogateModelInterface.h.

Referenced by CrossValidationScores::CrossValidationScores(), EvaluateSurrogate::EvaluateSurrogate(), and InverseMapping::initialSetup().

 {
   std::vector<T *> models;
   _smi_feproblem.theWarehouse()
       .query()
       .condition<AttribName>(name)
       .condition<AttribSystem>("SurrogateModel")
       .queryInto(models);
   if (models.empty())
     mooseError("Unable to find a SurrogateModel object of type " + std::string(typeid(T).name()) +
                " with the name '" + name + "'");
   return *(models[0]);
 }

◆ getSurrogateTrainer() [1/2]

template<typename T >

T & SurrogateModelInterface::getSurrogateTrainer ( const std::string & name ) const

inherited

Definition at line 104 of file SurrogateModelInterface.h.

 {
   return getSurrogateTrainerByName<T>(_smi_params.get<UserObjectName>(name));
 }

◆ getSurrogateTrainer() [2/2]

template<>

SurrogateTrainerBase& SurrogateModelInterface::getSurrogateTrainer ( const std::string & name ) const

inherited

Definition at line 60 of file SurrogateModelInterface.C.

 {
   return getSurrogateTrainerByName<SurrogateTrainerBase>(_smi_params.get<UserObjectName>(name));
 }

◆ getSurrogateTrainerByName() [1/2]

template<>

SurrogateTrainerBase& SurrogateModelInterface::getSurrogateTrainerByName ( const UserObjectName & name ) const

inherited

Definition at line 53 of file SurrogateModelInterface.C.

 {
   return _smi_feproblem.getUserObject<SurrogateTrainerBase>(name);
 }

◆ getSurrogateTrainerByName() [2/2]

template<typename T >

T & SurrogateModelInterface::getSurrogateTrainerByName ( const UserObjectName & name ) const

inherited

Definition at line 111 of file SurrogateModelInterface.h.

Referenced by SurrogateTrainerOutput::output().

 {
   SurrogateTrainerBase * base_ptr =
       &_smi_feproblem.getUserObject<SurrogateTrainerBase>(name, _smi_tid);
   T * obj_ptr = dynamic_cast<T *>(base_ptr);
   if (!obj_ptr)
     mooseError("Failed to find a SurrogateTrainer object of type " + std::string(typeid(T).name()) +
                    " with the name '",
                name,
                "' for the desired type.");
   return *obj_ptr;
 }

◆ getTrainingData()

template<typename T >

const T & SurrogateTrainer::getTrainingData ( const ReporterName & rname )

protected

Definition at line 208 of file SurrogateTrainer.h.

 {
   auto it = _training_data.find(rname);
   if (it != _training_data.end())
   {
     auto data = std::dynamic_pointer_cast<TrainingData<T>>(it->second);
     if (!data)
       mooseError("Reporter value ", rname, " already exists but is of different type.");
     return data->get();
   }
   else
   {
     const std::vector<T> & rval = getReporterValueByName<std::vector<T>>(rname);
     _training_data[rname] = std::make_shared<TrainingData<T>>(rval);
     return std::dynamic_pointer_cast<TrainingData<T>>(_training_data[rname])->get();
   }
 }

◆ hasModelData()

bool RestartableModelInterface::hasModelData ( ) const

inherited

Check if we need to load model data (if the filename parameter is used)

Definition at line 39 of file RestartableModelInterface.C.

 {
   return _model_object.isParamValid("filename");
 }

◆ initialize()

void SurrogateTrainer::initialize ( )

finalvirtual

Reimplemented from SurrogateTrainerBase.

Definition at line 153 of file SurrogateTrainer.C.

 {
   // Figure out if data is distributed
   for (auto & pair : _training_data)
   {
     const ReporterName & name = pair.first;
     TrainingDataBase & data = *pair.second;
 
     const auto & mode = _fe_problem.getReporterData().getReporterMode(name);
     if (mode == REPORTER_MODE_DISTRIBUTED || (mode == REPORTER_MODE_ROOT && processor_id() != 0))
       data.isDistributed() = true;
     else if (mode == REPORTER_MODE_REPLICATED ||
              (mode == REPORTER_MODE_ROOT && processor_id() == 0))
       data.isDistributed() = false;
     else
       mooseError("Predictor reporter value ", name, " is not of supported mode.");
   }
 
   if (_doing_cv)
     _cv_surrogate = &getSurrogateModel("cv_surrogate");
 }

◆ modelMetaDataName()

const std::string& RestartableModelInterface::modelMetaDataName ( ) const

inlineinherited

Accessor for the name of the model meta data.

Definition at line 47 of file RestartableModelInterface.h.

Referenced by SurrogateTrainerOutput::output(), and MappingOutput::output().

47 { return _model_meta_data_name; }

RestartableModelInterface::_model_meta_data_name

const std::string _model_meta_data_name

The model meta data name.

Definition: RestartableModelInterface.h:61

◆ postTrain()

virtual void SurrogateTrainer::postTrain ( )

inlineprotectedvirtual

Reimplemented in LibtorchANNTrainer, GaussianProcessTrainer, PolynomialRegressionTrainer, PolynomialChaosTrainer, and NearestPointTrainer.

Definition at line 79 of file SurrogateTrainer.h.

Referenced by executeTraining().

79 {}

◆ preTrain()

virtual void SurrogateTrainer::preTrain ( )

inlineprotectedvirtual

Reimplemented in LibtorchANNTrainer, GaussianProcessTrainer, PolynomialChaosTrainer, PolynomialRegressionTrainer, and NearestPointTrainer.

Definition at line 69 of file SurrogateTrainer.h.

Referenced by executeTraining().

69 {}

◆ threadJoin()

virtual void SurrogateTrainerBase::threadJoin ( const UserObject & )

inlinefinalvirtualinherited

Reimplemented from GeneralUserObject.

Definition at line 40 of file SurrogateTrainer.h.

40 {} // GeneralUserObjects are not threaded

◆ train()

virtual void SurrogateTrainer::train ( )

inlineprotectedvirtual

Reimplemented in LibtorchANNTrainer, GaussianProcessTrainer, PolynomialChaosTrainer, PolynomialRegressionTrainer, and NearestPointTrainer.

Definition at line 74 of file SurrogateTrainer.h.

Referenced by executeTraining().

74 {}

◆ updatePredictorRow()

void SurrogateTrainer::updatePredictorRow ( )

private

Definition at line 371 of file SurrogateTrainer.C.

Referenced by crossValidate(), and executeTraining().

 {
   unsigned int d = 0;
   for (const auto & val : _pvals)
     _predictor_data[d++] = *val;
   for (const auto & col : _pcols)
     _predictor_data[d++] = _row_data[col];
 }

◆ validParams()

InputParameters SurrogateTrainer::validParams ( )

static

Definition at line 34 of file SurrogateTrainer.C.

Referenced by NearestPointTrainer::validParams(), PolynomialRegressionTrainer::validParams(), PolynomialChaosTrainer::validParams(), LibtorchANNTrainer::validParams(), and GaussianProcessTrainer::validParams().

 {
   InputParameters params = SurrogateTrainerBase::validParams();
   params.addRequiredParam<SamplerName>("sampler",
                                        "Sampler used to create predictor and response data.");
   params.addParam<ReporterName>(
       "converged_reporter",
       "Reporter value used to determine if a sample's multiapp solve converged.");
   params.addParam<bool>("skip_unconverged_samples",
                         false,
                         "True to skip samples where the multiapp did not converge, "
                         "'stochastic_reporter' is required to do this.");
 
   // Common Training Data
   MooseEnum data_type("real=0 vector_real=1", "real");
   params.addRequiredParam<ReporterName>(
       "response",
       "Reporter value of response results, can be vpp with <vpp_name>/<vector_name> or sampler "
       "column with 'sampler/col_<index>'.");
   params.addParam<MooseEnum>("response_type", data_type, "Response data type.");
   params.addParam<std::vector<ReporterName>>(
       "predictors",
       std::vector<ReporterName>(),
       "Reporter values used as the independent random variables, If 'predictors' and "
       "'predictor_cols' are both empty, all sampler columns are used.");
   params.addParam<std::vector<unsigned int>>(
       "predictor_cols",
       std::vector<unsigned int>(),
       "Sampler columns used as the independent random variables, If 'predictors' and "
       "'predictor_cols' are both empty, all sampler columns are used.");
   // End Common Training Data
 
   MooseEnum cv_type("none=0 k_fold=1", "none");
   params.addParam<MooseEnum>(
       "cv_type",
       cv_type,
       "Cross-validation method to use for dataset. Options are 'none' or 'k_fold'.");
   params.addRangeCheckedParam<unsigned int>(
       "cv_splits", 10, "cv_splits > 1", "Number of splits (k) to use in k-fold cross-validation.");
   params.addParam<UserObjectName>("cv_surrogate",
                                   "Name of Surrogate object used for model cross-validation.");
   params.addParam<unsigned int>(
       "cv_n_trials", 1, "Number of repeated trials of cross-validation to perform.");
   params.addParam<unsigned int>("cv_seed",
                                 std::numeric_limits<unsigned int>::max(),
                                 "Seed used to initialize random number generator for data "
                                 "splitting during cross validation.");
 
   return params;
 }

Member Data Documentation

◆ _converged

const bool* SurrogateTrainer::_converged

private

Whether or not the current sample has a converged solution.

Definition at line 170 of file SurrogateTrainer.h.

Referenced by executeTraining(), and SurrogateTrainer().

◆ _current_sample_size

unsigned int SurrogateTrainer::_current_sample_size

private

Number of samples used to train the model.

Definition at line 173 of file SurrogateTrainer.h.

Referenced by crossValidate(), execute(), and getCurrentSampleSize().

◆ _cv_generator

MooseRandom SurrogateTrainer::_cv_generator

private

Random number generator used for shuffling sampler rows during splitting.

Definition at line 196 of file SurrogateTrainer.h.

Referenced by crossValidate(), and SurrogateTrainer().

◆ _cv_n_trials

const unsigned int& SurrogateTrainer::_cv_n_trials

private

Number of repeated trials of cross validation to perform.

Definition at line 192 of file SurrogateTrainer.h.

Referenced by execute().

◆ _cv_seed

const unsigned int& SurrogateTrainer::_cv_seed

private

Seed used for _cv_generator.

Definition at line 194 of file SurrogateTrainer.h.

Referenced by SurrogateTrainer().

◆ _cv_surrogate

const SurrogateModel* SurrogateTrainer::_cv_surrogate

private

SurrogateModel used to evaluate model error relative to test points.

Definition at line 198 of file SurrogateTrainer.h.

Referenced by crossValidate(), and initialize().

◆ _cv_trial_scores

std::vector<std::vector<Real> >& SurrogateTrainer::_cv_trial_scores

private

RMSE scores from each CV trial - can be grabbed by VPP or Reporter.

Definition at line 202 of file SurrogateTrainer.h.

Referenced by execute().

◆ _cv_type

const MooseEnum& SurrogateTrainer::_cv_type

private

Type of cross validation to perform - for now, just 'none' (no CV) or 'k_fold'.

Definition at line 188 of file SurrogateTrainer.h.

◆ _doing_cv

const bool SurrogateTrainer::_doing_cv

private

Set to true if cross validation is being performed, controls behavior in execute().

Definition at line 200 of file SurrogateTrainer.h.

Referenced by execute(), initialize(), and SurrogateTrainer().

◆ _local_row

dof_id_type SurrogateTrainer::_local_row

protected

During training loop, this is the local row index of the data.

Definition at line 123 of file SurrogateTrainer.h.

Referenced by executeTraining().

◆ _local_sample_size

unsigned int SurrogateTrainer::_local_sample_size

private

Number of samples (locally) used to train the model.

Definition at line 176 of file SurrogateTrainer.h.

Referenced by crossValidate(), execute(), and getLocalSampleSize().

◆ _n_dims

unsigned int SurrogateTrainer::_n_dims

protected

Dimension of predictor data - either _sampler.getNumberOfCols() or _pvals.size() + _pcols.size().

Definition at line 133 of file SurrogateTrainer.h.

Referenced by NearestPointTrainer::NearestPointTrainer(), GaussianProcessTrainer::postTrain(), LibtorchANNTrainer::postTrain(), LibtorchANNTrainer::preTrain(), SurrogateTrainer(), NearestPointTrainer::train(), and PolynomialRegressionTrainer::train().

◆ _n_outputs

unsigned int& SurrogateTrainer::_n_outputs

protected

The number of outputs.

Definition at line 135 of file SurrogateTrainer.h.

Referenced by GaussianProcessTrainer::GaussianProcessTrainer(), GaussianProcessTrainer::postTrain(), and GaussianProcessTrainer::train().

◆ _n_splits

const unsigned int& SurrogateTrainer::_n_splits

private

Number of splits (k) to split sampler data into.

Definition at line 190 of file SurrogateTrainer.h.

Referenced by crossValidate(), and SurrogateTrainer().

◆ _pcols

std::vector<unsigned int> SurrogateTrainer::_pcols

protected

Columns from sampler for predictors.

Definition at line 131 of file SurrogateTrainer.h.

Referenced by PolynomialChaosTrainer::PolynomialChaosTrainer(), SurrogateTrainer(), and updatePredictorRow().

◆ _predictor_data

std::vector<Real> SurrogateTrainer::_predictor_data

private

Predictor data for current row - can be combination of Sampler and Reporter values.

Definition at line 164 of file SurrogateTrainer.h.

Referenced by evaluateModelError(), getPredictorData(), SurrogateTrainer(), and updatePredictorRow().

◆ _pvals

std::vector<const Real *> SurrogateTrainer::_pvals

protected

Predictor values from reporters.

Definition at line 129 of file SurrogateTrainer.h.

Referenced by PolynomialChaosTrainer::PolynomialChaosTrainer(), SurrogateTrainer(), and updatePredictorRow().

◆ _row

dof_id_type SurrogateTrainer::_row

protected

During training loop, this is the row index of the data.

Definition at line 121 of file SurrogateTrainer.h.

Referenced by executeTraining(), and PolynomialChaosTrainer::train().

◆ _row_data

std::vector<Real> SurrogateTrainer::_row_data

private

Sampler data for the current row.

Definition at line 161 of file SurrogateTrainer.h.

Referenced by checkIntegrity(), crossValidate(), executeTraining(), getSamplerData(), and updatePredictorRow().

◆ _rval

const Real* SurrogateTrainer::_rval

protected

Response value.

Definition at line 125 of file SurrogateTrainer.h.

Referenced by evaluateModelError(), SurrogateTrainer(), NearestPointTrainer::train(), PolynomialRegressionTrainer::train(), GaussianProcessTrainer::train(), and LibtorchANNTrainer::train().

◆ _rvecval

const std::vector<Real>* SurrogateTrainer::_rvecval

protected

Vector response value.

Definition at line 127 of file SurrogateTrainer.h.

Referenced by evaluateModelError(), SurrogateTrainer(), NearestPointTrainer::train(), PolynomialRegressionTrainer::train(), and GaussianProcessTrainer::train().

◆ _sampler

Sampler& SurrogateTrainer::_sampler

protected

Sampler being used for training

Definition at line 119 of file SurrogateTrainer.h.

Referenced by checkIntegrity(), crossValidate(), execute(), executeTraining(), GaussianProcessTrainer::GaussianProcessTrainer(), PolynomialChaosTrainer::PolynomialChaosTrainer(), PolynomialRegressionTrainer::PolynomialRegressionTrainer(), and SurrogateTrainer().

◆ _skip_indices

std::vector<dof_id_type> SurrogateTrainer::_skip_indices

private

Vector of indices to skip during executeTraining()

Definition at line 186 of file SurrogateTrainer.h.

Referenced by crossValidate(), and executeTraining().

◆ _skip_unconverged

const bool SurrogateTrainer::_skip_unconverged

private

Whether or not we are skipping samples that have unconverged solutions.

Definition at line 167 of file SurrogateTrainer.h.

Referenced by executeTraining(), and SurrogateTrainer().

◆ _training_data

std::unordered_map<ReporterName, std::shared_ptr<TrainingDataBase> > SurrogateTrainer::_training_data

private

Vector of reporter names and their corresponding values (to be filled by getTrainingData)

Definition at line 179 of file SurrogateTrainer.h.

Referenced by checkIntegrity(), crossValidate(), executeTraining(), getTrainingData(), and initialize().

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

stochastic_tools/include/trainers/SurrogateTrainer.h
stochastic_tools/src/trainers/SurrogateTrainer.C

Public Types

Public Member Functions

Static Public Member Functions

Public Attributes

Static Public Attributes

Protected Member Functions

Static Protected Member Functions

Protected Attributes

Static Protected Attributes

Private Member Functions

Private Attributes

Detailed Description

Constructor & Destructor Documentation

◆ SurrogateTrainer()

Member Function Documentation

◆ checkIntegrity()

◆ crossValidate()

◆ declareModelData()

◆ evaluateModelError()

◆ execute()

◆ executeTraining()

◆ finalize()

◆ getCurrentSampleSize()

◆ getLocalSampleSize()

◆ getModelData()

◆ getModelDataFileName()

◆ getPredictorData()

◆ getSamplerData()

◆ getSurrogateModel() [1/2]

◆ getSurrogateModel() [2/2]

◆ getSurrogateModelByName() [1/2]

◆ getSurrogateModelByName() [2/2]

◆ getSurrogateTrainer() [1/2]

◆ getSurrogateTrainer() [2/2]

◆ getSurrogateTrainerByName() [1/2]

◆ getSurrogateTrainerByName() [2/2]

◆ getTrainingData()

◆ hasModelData()

◆ initialize()

◆ modelMetaDataName()

◆ postTrain()

◆ preTrain()

◆ threadJoin()

◆ train()

◆ updatePredictorRow()

◆ validParams()

Member Data Documentation

◆ _converged

◆ _current_sample_size

◆ _cv_generator

◆ _cv_n_trials

◆ _cv_seed

◆ _cv_surrogate

◆ _cv_trial_scores

◆ _cv_type

◆ _doing_cv

◆ _local_row

◆ _local_sample_size

◆ _n_dims

◆ _n_outputs

◆ _n_splits

◆ _pcols

◆ _predictor_data

◆ _pvals

◆ _row

◆ _row_data

◆ _rval

◆ _rvecval

◆ _sampler

◆ _skip_indices

◆ _skip_unconverged

◆ _training_data