MartinezSensitivityAlgorithm¶
(Source code, png, hires.png, pdf)

class
MartinezSensitivityAlgorithm
(*args)¶ Sensitivity analysis using Martinez method.
 Available constructors:
MartinezSensitivityAlgorithm(inputDesign, outputDesign, N)
MartinezSensitivityAlgorithm(distribution, N, model, computeSecondOrder)
MartinezSensitivityAlgorithm(experiment, model, computeSecondOrder)
 Parameters
 inputDesign
Sample
Design for the evaluation of sensitivity indices, obtained thanks to the SobolIndicesAlgorithmImplementation.Generate method
 outputDesign
Sample
Design for the evaluation of sensitivity indices, obtained as the evaluation of a Function (model) on the previous inputDesign
 distribution
Distribution
Input probabilistic model. Should have independent copula
 experiment
WeightedExperiment
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.
 inputDesign
See also
Notes
This class is concerned with analyzing the influence the random vector has on a random variable which is being studied for uncertainty, by using the [martinez2011] method for the evaluation of both first and total order indices.
These last ones are respectively given as follows:
where is the empirical correlation defined by:
The variance of the estimator is computed using:
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 precompute >>> size = 100 >>> inputDesign = ot.SobolIndicesExperiment(distribution, size).generate() >>> outputDesign = model(inputDesign) >>> # sensitivity analysis algorithm >>> sensitivityAnalysis = ot.MartinezSensitivityAlgorithm(inputDesign, outputDesign, size) >>> print(sensitivityAnalysis.getFirstOrderIndices()) [0.30449,0.448506,0.0711394] >>> print(sensitivityAnalysis.getTotalOrderIndices()) [0.543004,0.29501,0.304615]
Estimate first, total and second 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 precompute >>> size = 100 >>> computeSecondOrderIndices = True >>> inputDesign = ot.SobolIndicesExperiment(distribution, size, computeSecondOrderIndices).generate() >>> outputDesign = model(inputDesign) >>> # sensitivity analysis algorithm >>> sensitivityAnalysis = ot.MartinezSensitivityAlgorithm(inputDesign, outputDesign, size) >>> print(sensitivityAnalysis.getFirstOrderIndices()) [0.30449,0.448506,0.0711394] >>> print(sensitivityAnalysis.getTotalOrderIndices()) [0.543004,0.29501,0.304615] >>> print(sensitivityAnalysis.getSecondOrderIndices()) [[ 0 0.18008 0.169767 ] [ 0.18008 0 0.198839 ] [ 0.169767 0.198839 0 ]]
Methods
DrawCorrelationCoefficients
(\*args)Draw the correlation coefficients.
DrawImportanceFactors
(\*args)Draw the importance factors.
DrawSobolIndices
(inputDescription, …)Draw the Sobol’ indices.
draw
(self, \*args)Draw sensitivity indices.
Get the evaluation of aggregated first order Sobol indices.
Get the evaluation of aggregated total order Sobol indices.
getBootstrapSize
(self)Get the number of bootstrap sampling size.
getClassName
(self)Accessor to the object’s name.
getConfidenceLevel
(self)Get the confidence interval level for confidence intervals.
getFirstOrderIndices
(self[, marginalIndex])Get first order Sobol indices.
Get the distribution of the aggregated first order Sobol indices.
Get interval for the aggregated first order Sobol indices.
getId
(self)Accessor to the object’s id.
getName
(self)Accessor to the object’s name.
getSecondOrderIndices
(self[, marginalIndex])Get second order Sobol indices.
getShadowedId
(self)Accessor to the object’s shadowed id.
getTotalOrderIndices
(self[, marginalIndex])Get total order Sobol indices.
Get the distribution of the aggregated total order Sobol indices.
Get interval for the aggregated total order Sobol indices.
Select asymptotic or bootstrap confidence intervals.
getVisibility
(self)Accessor to the object’s visibility state.
hasName
(self)Test if the object is named.
hasVisibleName
(self)Test if the object has a distinguishable name.
setBootstrapSize
(self, bootstrapSize)Set the number of bootstrap sampling size.
setConfidenceLevel
(self, confidenceLevel)Set the confidence interval level for confidence intervals.
setDesign
(self, inputDesign, outputDesign, size)Sample accessor.
setName
(self, name)Accessor to the object’s name.
setShadowedId
(self, id)Accessor to the object’s shadowed id.
setUseAsymptoticDistribution
(self, …)Select asymptotic or bootstrap confidence intervals.
setVisibility
(self, visible)Accessor to the object’s visibility state.

__init__
(self, *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
 correlationCoefficients
PointWithDescription
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.
 correlationCoefficients
 Returns

static
DrawImportanceFactors
(*args)¶ Draw the importance factors.
 Available usages:
DrawImportanceFactors(importanceFactors, title=’Importance Factors’)
DrawImportanceFactors(values, names, title=’Importance Factors’)
 Parameters
 importanceFactors
PointWithDescription
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.
 importanceFactors
 Returns

static
DrawSobolIndices
(inputDescription, firstOrderIndices, secondOrderIndices)¶ Draw the Sobol’ indices.
 Parameters
 inputDescriptionsequence of str
Variable names
 firstOrderIndicessequence of float
First order indices values
 totalOrderIndicessequence of float
Total order indices values
 Returns
 Graph
Graph
For each variable, draws first and total indices
 Graph

draw
(self, *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
 Graph
Graph
A graph containing the aggregated first and total order indices.
 Graph
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
(self)¶ Get the evaluation of aggregated first order Sobol indices.
 Returns
 indices
Point
Sequence containing aggregated first order Sobol indices.
 indices

getAggregatedTotalOrderIndices
(self)¶ Get the evaluation of aggregated total order Sobol indices.
 Returns
 indices
Point
Sequence containing aggregated total order Sobol indices.
 indices

getBootstrapSize
(self)¶ Get the number of bootstrap sampling size.
 Returns
 bootstrapSizeint
Number of bootsrap sampling

getClassName
(self)¶ Accessor to the object’s name.
 Returns
 class_namestr
The object class name (object.__class__.__name__).

getConfidenceLevel
(self)¶ Get the confidence interval level for confidence intervals.
 Returns
 confidenceLevelfloat
Confidence level for confidence intervals

getFirstOrderIndices
(self, marginalIndex=0)¶ Get first order Sobol indices.
 Parameters
 iint, optional
Index of the marginal of the function, equals to by default.
 Returns
 indices
Point
Sequence containing first order Sobol indices.
 indices

getFirstOrderIndicesDistribution
(self)¶ Get the distribution of the aggregated first order Sobol indices.
 Returns
 distribution
Distribution
Distribution for first order Sobol indices for each component.
 distribution

getFirstOrderIndicesInterval
(self)¶ Get interval for the aggregated first order Sobol indices.
 Returns
 interval
Interval
Interval for first order Sobol indices for each component. Computed marginal by marginal (not from the joint distribution).
 interval

getId
(self)¶ Accessor to the object’s id.
 Returns
 idint
Internal unique identifier.

getName
(self)¶ Accessor to the object’s name.
 Returns
 namestr
The name of the object.

getSecondOrderIndices
(self, marginalIndex=0)¶ Get second order Sobol indices.
 Parameters
 iint, optional
Index of the marginal of the function, equals to by default.
 Returns
 indices
SymmetricMatrix
Tensor containing second order Sobol indices.
 indices

getShadowedId
(self)¶ Accessor to the object’s shadowed id.
 Returns
 idint
Internal unique identifier.

getTotalOrderIndices
(self, marginalIndex=0)¶ Get total order Sobol indices.
 Parameters
 iint, optional
Index of the marginal of the function, equals to by default.
 Returns
 indices
Point
Sequence containing total order Sobol indices.
 indices

getTotalOrderIndicesDistribution
(self)¶ Get the distribution of the aggregated total order Sobol indices.
 Returns
 distribution
Distribution
Distribution for total order Sobol indices for each component.
 distribution

getTotalOrderIndicesInterval
(self)¶ Get interval for the aggregated total order Sobol indices.
 Returns
 interval
Interval
Interval for total order Sobol indices for each component. Computed marginal by marginal (not from the joint distribution).
 interval

getUseAsymptoticDistribution
(self)¶ Select asymptotic or bootstrap confidence intervals.
 Returns
 useAsymptoticDistributionbool
Whether to use bootstrap or asymptotic intervals

getVisibility
(self)¶ Accessor to the object’s visibility state.
 Returns
 visiblebool
Visibility flag.

hasName
(self)¶ Test if the object is named.
 Returns
 hasNamebool
True if the name is not empty.

hasVisibleName
(self)¶ Test if the object has a distinguishable name.
 Returns
 hasVisibleNamebool
True if the name is not empty and not the default one.

setBootstrapSize
(self, bootstrapSize)¶ Set the number of bootstrap sampling size.
Default value is 0.
 Parameters
 bootstrapSizeint
Number of bootsrap sampling

setConfidenceLevel
(self, confidenceLevel)¶ Set the confidence interval level for confidence intervals.
 Parameters
 confidenceLevelfloat
Confidence level for confidence intervals

setDesign
(self, inputDesign, outputDesign, size)¶ Sample accessor.
 Parameters
 inputDesign
Sample
Design for the evaluation of sensitivity indices, obtained thanks to the SobolIndicesAlgorithmImplementation.Generate method
 outputDesign
Sample
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
 inputDesign

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.

setUseAsymptoticDistribution
(self, useAsymptoticDistribution)¶ Select asymptotic or bootstrap confidence intervals.
Default value is set by the SobolIndicesAlgorithmDefaultUseAsymptoticDistribution key.
 Parameters
 useAsymptoticDistributionbool
Whether to use bootstrap or asymptotic intervals

setVisibility
(self, visible)¶ Accessor to the object’s visibility state.
 Parameters
 visiblebool
Visibility flag.