MetaModelAlgorithm

class MetaModelAlgorithm(*args)

Base class to compute a metamodel.

Available constructor:

MetaModelAlgorithm(distribution, model)

Parameters

distributionDistribution

Joint probability density function of the physical input vector.

modelFunction

Physical model to be approximated by a metamodel.

Notes

A MetaModelAlgorithm object can be used only through its derived classes:

• KrigingAlgorithm

• FunctionalChaosAlgorithm

Methods

BuildDistribution(inputSample)	Recover the distribution, with metamodel performance in mind.
getClassName()	Accessor to the object’s name.
getDistribution()	Accessor to the joint probability density function of the physical input vector.
getId()	Accessor to the object’s id.
getInputSample()	Accessor to the input sample.
getName()	Accessor to the object’s name.
getOutputSample()	Accessor to the output sample.
getShadowedId()	Accessor to the object’s shadowed id.
getVisibility()	Accessor to the object’s visibility state.
hasName()	Test if the object is named.
hasVisibleName()	Test if the object has a distinguishable name.
run()	Compute the response surfaces.
setDistribution(distribution)	Accessor to the joint probability density function of the physical input vector.
setName(name)	Accessor to the object’s name.
setShadowedId(id)	Accessor to the object’s shadowed id.
setVisibility(visible)	Accessor to the object’s visibility state.

__init__(*args)

Initialize self. See help(type(self)) for accurate signature.

static BuildDistribution(inputSample)

Recover the distribution, with metamodel performance in mind.

For each marginal, find the best 1-d continuous parametric model else fallback to the use of a nonparametric one.

The selection is done as follow:

• We start with a list of all parametric models (all factories)

• For each model, we estimate its parameters if feasible.

• We check then if model is valid, ie if its Kolmogorov score exceeds a threshold fixed in the MetaModelAlgorithm-PValueThreshold ResourceMap key. Default value is 5%

• We sort all valid models and return the one with the optimal criterion.

For the last step, the criterion might be BIC, AIC or AICC. The specification of the criterion is done through the MetaModelAlgorithm-ModelSelectionCriterion ResourceMap key. Default value is fixed to BIC. Note that if there is no valid candidate, we estimate a non-parametric model (KernelSmoothing or Histogram). The MetaModelAlgorithm-NonParametricModel ResourceMap key allows selecting the preferred one. Default value is Histogram

One each marginal is estimated, we use the Spearman independence test on each component pair to decide whether an independent copula. In case of non independence, we rely on a NormalCopula.

Parameters

sampleSample

Input sample.

Returns

distributionDistribution

Input distribution.

getClassName()

Accessor to the object’s name.

Returns

class_namestr

The object class name (object.__class__.__name__).

getDistribution()

Accessor to the joint probability density function of the physical input vector.

Returns

distributionDistribution

Joint probability density function of the physical input vector.

getId()

Accessor to the object’s id.

Returns

idint

Internal unique identifier.

getInputSample()

Accessor to the input sample.

Returns

inputSampleSample

Input sample of a model evaluated apart.

getName()

Accessor to the object’s name.

Returns

namestr

The name of the object.

getOutputSample()

Accessor to the output sample.

Returns

outputSampleSample

Output sample of a model evaluated apart.

getShadowedId()

Accessor to the object’s shadowed id.

Returns

idint

Internal unique identifier.

getVisibility()

Accessor to the object’s visibility state.

Returns

visiblebool

Visibility flag.

hasName()

Test if the object is named.

Returns

hasNamebool

True if the name is not empty.

hasVisibleName()

Test if the object has a distinguishable name.

Returns

hasVisibleNamebool

True if the name is not empty and not the default one.

run()

Compute the response surfaces.

Notes

It computes the response surfaces and creates a MetaModelResult structure containing all the results.

setDistribution(distribution)

Accessor to the joint probability density function of the physical input vector.

Parameters

distributionDistribution

Joint probability density function of the physical input vector.

setName(name)

Accessor to the object’s name.

Parameters

namestr

The name of the object.

setShadowedId(id)

Accessor to the object’s shadowed id.

Parameters

idint

Internal unique identifier.

setVisibility(visible)

Accessor to the object’s visibility state.

Parameters

visiblebool

Visibility flag.