KrigingAnalysis¶

class persalys.KrigingAnalysis(*args)¶

Create a Kriging analysis.

Parameters

namestr: Name
designOfExperimentDesignOfExperiment: Design of experiments

Examples

Create the model:

>>> import openturns as ot
>>> import persalys
>>> from math import pi
>>> ot.RandomGenerator.SetSeed(0)

Create the model:

>>> 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.ProbabilisticDesignOfExperiment('aDesign', myPhysicalModel, 100, 'LHS')
>>> aDesign.run()

Create the Kriging Analysis:

>>> kriging = persalys.KrigingAnalysis('kriging', aDesign)
>>> kriging.setBasis(ot.LinearBasisFactory(3).build())
>>> kriging.setCovarianceModel(ot.GeneralizedExponential(3))
>>> kriging.setLeaveOneOutValidation(False)
>>> kriging.run()

Get the result:

>>> krigingResult = kriging.getResult()

Methods

`analyticalValidation`()	Whether an analytical validation is requested.
`getBasis`()	Trend basis accessor.
`getClassName`()	Accessor to the object's name.
`getCovarianceModel`()	Covariance model accessor.
`getDesignOfExperiment`()	Design of experiments accessor.
`getEffectiveInputSample`()	Effective input sample accessor.
`getEffectiveOutputSample`()	Effective output sample accessor.
`getErrorMessage`()	Error message accessor.
`getInterestVariables`()	Get the variables to analyse.
`getKFoldValidationNumberOfFolds`()	Number of folds accessor.
`getKFoldValidationSeed`()	Seed accessor.
`getName`()	Accessor to the object's name.
`getOptimizeParameters`()	Accessor to the covariance model parameters optimization flag.
`getPythonScript`()	Physical model accessor.
`getResult`()	Results accessor.
`getTestSampleValidationPercentageOfPoints`()	Percentage of points accessor.
`getTestSampleValidationSeed`()	Seed accessor.
`getWarningMessage`()	Warning message accessor.
`hasName`()	Test if the object is named.
`hasValidResult`()	Whether the analysis has been run.
`isReliabilityAnalysis`()	Whether the analysis involves reliability.
`isRunning`()	Whether the analysis is running.
`kFoldValidation`()	Whether a k-Fold cross-validation is requested.
`leaveOneOutValidation`()	Whether a validation by leave-one-out is requested.
`run`()	Launch the analysis.
`setAnalyticalValidation`(validation)	Whether an analytical validation is requested.
`setBasis`(basis)	Trend basis accessor.
`setCovarianceModel`(model)	Covariance model accessor.
`setInterestVariables`(variablesNames)	Set the variables to analyse.
`setKFoldValidation`(validation)	Whether a k-Fold cross-validation is requested.
`setKFoldValidationNumberOfFolds`(nbFolds)	Number of folds accessor.
`setKFoldValidationSeed`(seed)	Seed accessor.
`setLeaveOneOutValidation`(validation)	Whether it is sparse.
`setName`(name)	Accessor to the object's name.
`setOptimizeParameters`(optimize)	Accessor to the covariance model parameters optimization flag.
`setTestSampleValidation`(validation)	Whether a validation with a test sample is requested.
`setTestSampleValidationPercentageOfPoints`(...)	Percentage of points accessor.
`setTestSampleValidationSeed`(seed)	Seed accessor.
`testSampleValidation`()	Whether a validation with a test sample is requested.

canBeLaunched
getElapsedTime
getParentObserver

__init__(*args)¶

analyticalValidation()¶

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.

getBasis()¶

Trend basis accessor.

Returns

basisopenturns.Basis: Trend basis

getClassName()¶

Accessor to the object’s name.

Returns

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

getCovarianceModel()¶

Covariance model accessor.

Returns

modelopenturns.CovarianceModel: Covariance model

getDesignOfExperiment()¶

Design of experiments accessor.

Returns

modelDesignOfExperiment: Design of experiments

getEffectiveInputSample()¶

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()¶

Effective output sample accessor.

Returns

sampleopenturns.Sample: Sample of the interest output variables.

getErrorMessage()¶

Error message accessor.

Returns

messagestr: Error message if the analysis failed

getInterestVariables()¶

Get the variables to analyse.

Returns

variablesNamessequence of str: Names of the variables to analyse

getKFoldValidationNumberOfFolds()¶

Number of folds accessor.

Returns

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

getKFoldValidationSeed()¶

Seed accessor.

Returns

seedint: Seed value for k-Fold cross-validation

getName()¶

Accessor to the object’s name.

Returns

namestr: The name of the object.

getOptimizeParameters()¶

Accessor to the covariance model parameters optimization flag.

Returns

optimizebool: Whether to optimize the covariance model parameters

getPythonScript()¶

Physical model accessor.

Returns

scriptstr: Python script to replay the analysis

getResult()¶

Results accessor.

Returns

resultKrigingAnalysisResult: Results

getTestSampleValidationPercentageOfPoints()¶

Percentage of points accessor.

Returns

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

getTestSampleValidationSeed()¶

Seed accessor.

Returns

seedint: Seed value for the validation with a test sample

getWarningMessage()¶

Warning message accessor.

Returns

messagestr: Warning message which can appear during the analysis computation

hasName()¶

Test if the object is named.

Returns

hasNamebool: True if the name is not empty.

hasValidResult()¶

Whether the analysis has been run.

Returns

hasValidResultbool: Whether the analysis has already been run

isReliabilityAnalysis()¶

Whether the analysis involves reliability.

Returns

isReliabilityAnalysisbool: Whether the analysis involves a reliability analysis

isRunning()¶

Whether the analysis is running.

Returns

isRunningbool: Whether the analysis is running

kFoldValidation()¶

Whether a k-Fold cross-validation is requested.

Returns

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

leaveOneOutValidation()¶

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

Returns

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

run()¶: Launch the analysis.

setAnalyticalValidation(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.

setBasis(basis)¶

Trend basis accessor.

Parameters

basisopenturns.Basis: Trend basis

setCovarianceModel(model)¶

Covariance model accessor.

Parameters

modelopenturns.CovarianceModel: Covariance model

setInterestVariables(variablesNames)¶

Set the variables to analyse.

Parameters

variablesNamessequence of str: Names of the variables to analyse

setKFoldValidation(validation)¶

Whether a k-Fold cross-validation is requested.

Parameters

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

setKFoldValidationNumberOfFolds(nbFolds)¶

Number of folds accessor.

Parameters

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

setKFoldValidationSeed(seed)¶

Seed accessor.

Parameters

seedint: Seed value for k-Fold cross-validation

setLeaveOneOutValidation(validation)¶

Whether it is sparse.

Parameters

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

setName(name)¶

Accessor to the object’s name.

Parameters

namestr: The name of the object.

setOptimizeParameters(optimize)¶

Accessor to the covariance model parameters optimization flag.

Parameters

optimizebool: Whether to optimize the covariance model parameters

setTestSampleValidation(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(percentage)¶

Percentage of points accessor.

Parameters

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

setTestSampleValidationSeed(seed)¶

Seed accessor.

Parameters

seedint: Seed value for the validation with a test sample

testSampleValidation()¶

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).

KrigingAnalysis¶

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