MauntzKucherenkoSensitivityAlgorithm¶

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class MauntzKucherenkoSensitivityAlgorithm(*args)¶

Sensitivity analysis using MauntzKucherenko method.

Available constructors:

MauntzKucherenkoSensitivityAlgorithm(inputDesign, outputDesign, N)

MauntzKucherenkoSensitivityAlgorithm(distribution, N, model, computeSecondOrder)

MauntzKucherenkoSensitivityAlgorithm(experiment, model, computeSecondOrder)

Parameters

inputDesignSample: Design for the evaluation of sensitivity indices, obtained with the SobolIndicesExperiment.generate() method
outputDesignSample: Design for the evaluation of sensitivity indices, obtained as the evaluation of a Function (model) on the previous inputDesign
distributionDistribution: Input probabilistic model. Should have independent copula
experimentWeightedExperiment: Experiment for the generation of two independent samples.
Nint: Size of samples to generate
computeSecondOrderbool: If True, design that will be generated contains elements for the evaluation of second order indices.

See also

SobolIndicesAlgorithm

Notes

This class analyzes the influence of each component of a random vector $\vect{X} = \left( X^1, \ldots, X^{n_X} \right)$ on a random vector $\vect{Y} = \left( Y^1, \ldots, Y^{n_Y} \right)$ by computing Sobol’ indices (see also [sobol1993]).

The [sobol2007] method is used to estimate both first and total order indices.

The class constructor MauntzKucherenkoSensitivityAlgorithm(inputDesign, outputDesign, N) requires an outputDesign produced by SobolIndicesExperiment (see example below). Otherwise, results will be worthless.

Notations are defined in the documentation page of the SobolIndicesAlgorithm class.

The estimators of $V_i$ and $V_{-i}$ used by this class are respectively:

$\begin{array}{ccc} \hat{V}_i & = & \frac{1}{N-1} \sum_{k=1}^{N} \tilde{G}(\vect{B}_k) \left( \tilde{G}(\vect{E}_k) - \tilde{G}(\vect{A}_k) \right) \\ \hat{V}_{-i} & = & \frac{1}{N-1} \sum_{k=1}^{N} \tilde{G}(\vect{A}_k) \left( \tilde{G}(\vect{E}_k) - \tilde{G}(\vect{A}_k) \right) \end{array}$

Examples

Estimate first and total order Sobol’ indices:

>>> import openturns as ot
>>> ot.RandomGenerator.SetSeed(0)
>>> formula = ['sin(pi_*X1)+7*sin(pi_*X2)^2+0.1*(pi_*X3)^4*sin(pi_*X1)']
>>> model = ot.SymbolicFunction(['X1', 'X2', 'X3'], formula)
>>> distribution = ot.ComposedDistribution([ot.Uniform(-1.0, 1.0)] * 3)
>>> # Define designs to pre-compute
>>> size = 100000
>>> inputDesign = ot.SobolIndicesExperiment(distribution, size).generate()
>>> outputDesign = model(inputDesign)
>>> # sensitivity analysis algorithm
>>> sensitivityAnalysis = ot.MauntzKucherenkoSensitivityAlgorithm(inputDesign, outputDesign, size)
>>> print(sensitivityAnalysis.getFirstOrderIndices())
[0.311678,0.439075,0.00401876]
>>> print(sensitivityAnalysis.getTotalOrderIndices())
[0.556564,0.439185,0.24036]

Methods

`DrawCorrelationCoefficients`(*args)	Draw the correlation coefficients.
`DrawImportanceFactors`(*args)	Draw the importance factors.
`DrawSobolIndices`(*args)	Draw the Sobol’ indices.
`draw`(*args)	Draw sensitivity indices.
`getAggregatedFirstOrderIndices`()	Get the evaluation of aggregated first order Sobol indices.
`getAggregatedTotalOrderIndices`()	Get the evaluation of aggregated total order Sobol indices.
`getBootstrapSize`()	Get the number of bootstrap sampling size.
`getClassName`()	Accessor to the object’s name.
`getConfidenceLevel`()	Get the confidence interval level for confidence intervals.
`getFirstOrderIndices`([marginalIndex])	Get first order Sobol indices.
`getFirstOrderIndicesDistribution`()	Get the distribution of the aggregated first order Sobol indices.
`getFirstOrderIndicesInterval`()	Get interval for the aggregated first order Sobol indices.
`getId`()	Accessor to the object’s id.
`getName`()	Accessor to the object’s name.
`getSecondOrderIndices`([marginalIndex])	Get second order Sobol indices.
`getShadowedId`()	Accessor to the object’s shadowed id.
`getTotalOrderIndices`([marginalIndex])	Get total order Sobol indices.
`getTotalOrderIndicesDistribution`()	Get the distribution of the aggregated total order Sobol indices.
`getTotalOrderIndicesInterval`()	Get interval for the aggregated total order Sobol indices.
`getUseAsymptoticDistribution`()	Select asymptotic or bootstrap confidence intervals.
`getVisibility`()	Accessor to the object’s visibility state.
`hasName`()	Test if the object is named.
`hasVisibleName`()	Test if the object has a distinguishable name.
`setBootstrapSize`(bootstrapSize)	Set the number of bootstrap sampling size.
`setConfidenceLevel`(confidenceLevel)	Set the confidence interval level for confidence intervals.
`setDesign`(inputDesign, outputDesign, size)	Sample accessor.
`setName`(name)	Accessor to the object’s name.
`setShadowedId`(id)	Accessor to the object’s shadowed id.
`setUseAsymptoticDistribution`(…)	Select asymptotic or bootstrap confidence intervals.
`setVisibility`(visible)	Accessor to the object’s visibility state.

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

static DrawCorrelationCoefficients(*args)¶

Draw the correlation coefficients.

As correlation coefficients are considered, values might be positive or negative.

Available usages:

DrawCorrelationCoefficients(correlationCoefficients, title=’Correlation coefficients’)

DrawCorrelationCoefficients(values, names, title=’Correlation coefficients’)

Parameters

correlationCoefficientsPointWithDescription: Sequence containing the correlation coefficients with a description for each component. The descriptions are used to build labels for the created graph. If they are not mentioned, default labels will be used.
valuessequence of float: Correlation coefficients.
namessequence of str: Variables’ names used to build labels for the created the graph.
titlestr: Title of the graph.

Returns

graphGraph: A graph containing a Cloud and a Text of the correlation coefficients.

static DrawImportanceFactors(*args)¶

Draw the importance factors.

Available usages:

DrawImportanceFactors(importanceFactors, title=’Importance Factors’)

DrawImportanceFactors(values, names, title=’Importance Factors’)

Parameters

importanceFactorsPointWithDescription: Sequence containing the importance factors with a description for each component. The descriptions are used to build labels for the created Pie. If they are not mentioned, default labels will be used.
valuessequence of float: Importance factors.
namessequence of str: Variables’ names used to build labels for the created Pie.
titlestr: Title of the graph.

Returns

graphGraph: A graph containing a Pie of the importance factors of the variables.

static DrawSobolIndices(*args)¶

Draw the Sobol’ indices.

Parameters

inputDescriptionsequence of str: Variable names
firstOrderIndicessequence of float: First order indices values
totalOrderIndicessequence of float: Total order indices values
fo_ciInterval, optional: First order indices confidence interval
to_ciInterval, optional: Total order indices confidence interval

Returns

graphGraph: For each variable, draws first and total indices

draw(*args)¶

Draw sensitivity indices.

Usage:

draw()

draw(marginalIndex)

With the first usage, draw the aggregated first and total order indices. With the second usage, draw the first and total order indices of a specific marginal in case of vectorial output

Parameters

marginalIndex: int: marginal of interest (case of second usage)

Returns

graphGraph: A graph containing the aggregated first and total order indices.

Notes

If number of bootstrap sampling is not 0, and confidence level associated > 0, the graph includes confidence interval plots in the first usage.

getAggregatedFirstOrderIndices()¶

Get the evaluation of aggregated first order Sobol indices.

Returns

indicesPoint: Sequence containing aggregated first order Sobol indices.

getAggregatedTotalOrderIndices()¶

Get the evaluation of aggregated total order Sobol indices.

Returns

indicesPoint: Sequence containing aggregated total order Sobol indices.

getBootstrapSize()¶

Get the number of bootstrap sampling size.

Returns

bootstrapSizeint: Number of bootsrap sampling

getClassName()¶

Accessor to the object’s name.

Returns

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

getConfidenceLevel()¶

Get the confidence interval level for confidence intervals.

Returns

confidenceLevelfloat: Confidence level for confidence intervals

getFirstOrderIndices(marginalIndex=0)¶

Get first order Sobol indices.

Parameters

iint, optional: Index of the marginal of the function, equals to $0$ by default.

Returns

indicesPoint: Sequence containing first order Sobol indices.

getFirstOrderIndicesDistribution()¶

Get the distribution of the aggregated first order Sobol indices.

Returns

distributionDistribution: Distribution for first order Sobol indices for each component.

getFirstOrderIndicesInterval()¶

Get interval for the aggregated first order Sobol indices.

Returns

intervalInterval: Interval for first order Sobol indices for each component. Computed marginal by marginal (not from the joint distribution).

getId()¶

Accessor to the object’s id.

Returns

idint: Internal unique identifier.

getName()¶

Accessor to the object’s name.

Returns

namestr: The name of the object.

getSecondOrderIndices(marginalIndex=0)¶

Get second order Sobol indices.

Parameters

iint, optional: Index of the marginal of the function, equals to $0$ by default.

Returns

indicesSymmetricMatrix: Tensor containing second order Sobol indices.

getShadowedId()¶

Accessor to the object’s shadowed id.

Returns

idint: Internal unique identifier.

getTotalOrderIndices(marginalIndex=0)¶

Get total order Sobol indices.

Parameters

iint, optional: Index of the marginal of the function, equals to $0$ by default.

Returns

indicesPoint: Sequence containing total order Sobol indices.

getTotalOrderIndicesDistribution()¶

Get the distribution of the aggregated total order Sobol indices.

Returns

distributionDistribution: Distribution for total order Sobol indices for each component.

getTotalOrderIndicesInterval()¶

Get interval for the aggregated total order Sobol indices.

Returns

intervalInterval: Interval for total order Sobol indices for each component. Computed marginal by marginal (not from the joint distribution).

getUseAsymptoticDistribution()¶

Select asymptotic or bootstrap confidence intervals.

Returns

useAsymptoticDistributionbool: Whether to use bootstrap or asymptotic intervals

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.

setBootstrapSize(bootstrapSize)¶

Set the number of bootstrap sampling size.

Default value is 0.

Parameters

bootstrapSizeint: Number of bootsrap sampling

setConfidenceLevel(confidenceLevel)¶

Set the confidence interval level for confidence intervals.

Parameters

confidenceLevelfloat: Confidence level for confidence intervals

setDesign(inputDesign, outputDesign, size)¶

Sample accessor.

Parameters

inputDesignSample: Design for the evaluation of sensitivity indices, obtained thanks to the SobolIndicesAlgorithmImplementation.Generate method
outputDesignSample: Design for the evaluation of sensitivity indices, obtained as the evaluation of a Function (model) on the previous inputDesign
Nint: Size of samples to generate

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.

setUseAsymptoticDistribution(useAsymptoticDistribution)¶

Select asymptotic or bootstrap confidence intervals.

Default value is set by the SobolIndicesAlgorithm-DefaultUseAsymptoticDistribution key.

Parameters

useAsymptoticDistributionbool: Whether to use bootstrap or asymptotic intervals

setVisibility(visible)¶

Accessor to the object’s visibility state.

Parameters

visiblebool: Visibility flag.

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