FunctionalChaosAnalysis¶

class persalys.FunctionalChaosAnalysis(*args)¶

Create a Functional chaos analysis.

Parameters

namestr: Name
designOfExperimentDesignOfExperiment: Design of experiments

Examples

>>> import openturns as ot
>>> import persalys

Create the model:

>>> from math import pi
>>> ot.RandomGenerator.SetSeed(0)
>>> xi1 = persalys.Input('xi1', ot.Uniform(-pi, pi))
>>> xi2 = persalys.Input('xi2', ot.Uniform(-pi, pi))
>>> xi3 = persalys.Input('xi3', ot.Uniform(-pi, pi))
>>> y0 = persalys.Output('y0')
>>> myPhysicalModel = persalys.SymbolicPhysicalModel('myPhysicalModel', [xi1, xi2, xi3], [y0], ['sin(xi1) + 7. * (sin(xi2)) ^ 2 + 0.1 * xi3^4 * sin(xi1)'])

Create the design of experiments:

>>> aDesign = persalys.FixedDesignOfExperiment('aDesign', myPhysicalModel)
>>> inputSample = ot.LHSExperiment(myPhysicalModel.getDistribution(), 250).generate()
>>> aDesign.setOriginalInputSample(inputSample)
>>> aDesign.run()

Create the Functional Chaos Analysis:

>>> chaos = persalys.FunctionalChaosAnalysis('chaos', aDesign)
>>> chaos.setChaosDegree(6)
>>> chaos.setSparseChaos(False)
>>> chaos.setLeaveOneOutValidation(False)
>>> chaos.run()

Get the result:

>>> chaosResult = chaos.getResult()
>>> sobolResult = chaosResult.getSobolResult()

Methods

`analyticalValidation`(self)	Whether an analytical validation is requested.
`getChaosDegree`(self)	Chaos degree accessor.
`getClassName`(self)	Accessor to the object’s name.
`getDesignOfExperiment`(self)	Design of experiments accessor.
`getDistribution`(self)	Input distribution accessor.
`getEffectiveInputSample`(self)	Effective input sample accessor.
`getEffectiveOutputSample`(self)	Effective output sample accessor.
`getErrorMessage`(self)	Error message accessor.
`getId`(self)	Accessor to the object’s id.
`getInterestVariables`(self)	Get the variables to analyse.
`getKFoldValidationNumberOfFolds`(self)	Number of folds accessor.
`getKFoldValidationSeed`(self)	Seed accessor.
`getName`(self)	Accessor to the object’s name.
`getPythonScript`(self)	Physical model accessor.
`getResult`(self)	Results accessor.
`getShadowedId`(self)	Accessor to the object’s shadowed id.
`getSparseChaos`(self)	Whether it is sparse.
`getTestSampleValidationPercentageOfPoints`(self)	Percentage of points accessor.
`getTestSampleValidationSeed`(self)	Seed accessor.
`getVisibility`(self)	Accessor to the object’s visibility state.
`getWarningMessage`(self)	Warning message accessor.
`hasName`(self)	Test if the object is named.
`hasValidResult`(self)	Whether the analysis has been run.
`hasVisibleName`(self)	Test if the object has a distinguishable name.
`isReliabilityAnalysis`(self)	Whether the analysis involves reliability.
`isRunning`(self)	Whether the analysis is running.
`kFoldValidation`(self)	Whether a k-Fold cross-validation is requested.
`leaveOneOutValidation`(self)	Whether a validation by leave-one-out is requested.
`run`(self)	Launch the analysis.
`setAnalyticalValidation`(self, validation)	Whether an analytical validation is requested.
`setChaosDegree`(self, degree)	Chaos degree accessor.
`setInterestVariables`(self, variablesNames)	Set the variables to analyse.
`setKFoldValidation`(self, validation)	Whether a k-Fold cross-validation is requested.
`setKFoldValidationNumberOfFolds`(self, nbFolds)	Number of folds accessor.
`setKFoldValidationSeed`(self, seed)	Seed accessor.
`setLeaveOneOutValidation`(self, validation)	Whether it is sparse.
`setName`(self, name)	Accessor to the object’s name.
`setShadowedId`(self, id)	Accessor to the object’s shadowed id.
`setSparseChaos`(self, sparse)	Whether it is sparse.
`setTestSampleValidation`(self, validation)	Whether a validation with a test sample is requested.
`setTestSampleValidationPercentageOfPoints`(…)	Percentage of points accessor.
`setTestSampleValidationSeed`(self, seed)	Seed accessor.
`setVisibility`(self, visible)	Accessor to the object’s visibility state.
`testSampleValidation`(self)	Whether a validation with a test sample is requested.

canBeLaunched
getElapsedTime
getParentObserver

__init__(self, *args)¶: Initialize self. See help(type(self)) for accurate signature.

analyticalValidation(self)¶

Whether an analytical validation is requested.

Returns

validationbool: Whether an analytical validation is requested. This method corresponds to an approximation of the Leave-one-out method result.

getChaosDegree(self)¶

Chaos degree accessor.

Returns

degreeint: Chaos degree. It is set by default to 2

getClassName(self)¶

Accessor to the object’s name.

Returns

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

getDesignOfExperiment(self)¶

Design of experiments accessor.

Returns

modelDesignOfExperiment: Design of experiments

getDistribution(self)¶

Input distribution accessor.

Returns

distributionopenturns.ComposedDistribution: The distribution defined in the probabilistic model or a distribution composed of Uniform laws if there is no stochastic input variable.

getEffectiveInputSample(self)¶

Effective input sample accessor.

Returns

sampleopenturns.Sample: Sample of all the input variables if all of them are deterministic. Otherwise, sample of the stochastic input variables.

getEffectiveOutputSample(self)¶

Effective output sample accessor.

Returns

sampleopenturns.Sample: Sample of the interest output variables.

getErrorMessage(self)¶

Error message accessor.

Returns

messagestr: Error message if the analysis failed

getId(self)¶

Accessor to the object’s id.

Returns

idint: Internal unique identifier.

getInterestVariables(self)¶

Get the variables to analyse.

Returns

variablesNamessequence of str: Names of the variables to analyse

getKFoldValidationNumberOfFolds(self)¶

Number of folds accessor.

Returns

foldsint: Number of folds. By default it is 3.

getKFoldValidationSeed(self)¶

Seed accessor.

Returns

seedint: Seed value for k-Fold cross-validation

getName(self)¶

Accessor to the object’s name.

Returns

namestr: The name of the object.

getPythonScript(self)¶

Physical model accessor.

Returns

scriptstr: Python script to replay the analysis

getResult(self)¶

Results accessor.

Returns

resultFunctionalChaosAnalysisResult: Results

getShadowedId(self)¶

Accessor to the object’s shadowed id.

Returns

idint: Internal unique identifier.

getSparseChaos(self)¶

Whether it is sparse.

Returns

isSparsebool: Whether it is sparse. By default, the chaos is not sparse

getTestSampleValidationPercentageOfPoints(self)¶

Percentage of points accessor.

Returns

percentageint: Percentage of points used to validate the metamodel. By default it is 20%.

getTestSampleValidationSeed(self)¶

Seed accessor.

Returns

seedint: Seed value for the validation with a test sample

getVisibility(self)¶

Accessor to the object’s visibility state.

Returns

visiblebool: Visibility flag.

getWarningMessage(self)¶

Warning message accessor.

Returns

messagestr: Warning message which can appear during the analysis computation

hasName(self)¶

Test if the object is named.

Returns

hasNamebool: True if the name is not empty.

hasValidResult(self)¶

Whether the analysis has been run.

Returns

hasValidResultbool: Whether the analysis has already been run

hasVisibleName(self)¶

Test if the object has a distinguishable name.

Returns

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

isReliabilityAnalysis(self)¶

Whether the analysis involves reliability.

Returns

isReliabilityAnalysisbool: Whether the analysis involves a reliability analysis

isRunning(self)¶

Whether the analysis is running.

Returns

isRunningbool: Whether the analysis is running

kFoldValidation(self)¶

Whether a k-Fold cross-validation is requested.

Returns

validationbool: Whether a k-Fold cross-validation is requested

leaveOneOutValidation(self)¶

Whether a validation by leave-one-out is requested.

Returns

validationbool: Whether a validation by leave-one-out is requested

run(self)¶: Launch the analysis.

setAnalyticalValidation(self, validation)¶

Whether an analytical validation is requested.

Parameters

validationbool: Whether an analytical validation is requested. This method corresponds to an approximation of the Leave-one-out method result.

setChaosDegree(self, degree)¶

Chaos degree accessor.

Parameters

degreeint: Chaos degree

setInterestVariables(self, variablesNames)¶

Set the variables to analyse.

Parameters

variablesNamessequence of str: Names of the variables to analyse

setKFoldValidation(self, validation)¶

Whether a k-Fold cross-validation is requested.

Parameters

validationbool: Whether a k-Fold cross-validation is requested

setKFoldValidationNumberOfFolds(self, nbFolds)¶

Number of folds accessor.

Parameters

foldsint: Number of folds. By default it is 3.

setKFoldValidationSeed(self, seed)¶

Seed accessor.

Parameters

seedint: Seed value for k-Fold cross-validation

setLeaveOneOutValidation(self, validation)¶

Whether it is sparse.

Parameters

validationbool: Whether a validation by leave-one-out is requested

setName(self, name)¶

Accessor to the object’s name.

Parameters

namestr: The name of the object.

setShadowedId(self, id)¶

Accessor to the object’s shadowed id.

Parameters

idint: Internal unique identifier.

setSparseChaos(self, sparse)¶

Whether it is sparse.

Parameters

isSparsebool: Whether it is sparse

setTestSampleValidation(self, validation)¶

Whether a validation with a test sample is requested.

Parameters

validationbool: Whether a validation with a test sample is requested. The data sample is dividing into two sub-samples: a training sample (default: 80% of the sample points) and a test sample (default: 20% of the sample points). A new metamodel is built with the training sample and is validated with the test sample. The points are randomly picked in the data sample (by default the seed is 1).

setTestSampleValidationPercentageOfPoints(self, percentage)¶

Percentage of points accessor.

Parameters

percentageint: Percentage of points used to validate the metamodel. By default it is 20%.

setTestSampleValidationSeed(self, seed)¶

Seed accessor.

Parameters

seedint: Seed value for the validation with a test sample

setVisibility(self, visible)¶

Accessor to the object’s visibility state.

Parameters

visiblebool: Visibility flag.

testSampleValidation(self)¶

Whether a validation with a test sample is requested.

Returns

validationbool: Whether a validation with a test sample is requested. The data sample is dividing into two sub-samples: a training sample (default: 80% of the sample points) and a test sample (default: 20% of the sample points). A new metamodel is built with the training sample and is validated with the test sample. The points are randomly picked in the data sample (by default the seed is 1).

FunctionalChaosAnalysis¶

Previous topic

Next topic

This Page