InferenceAnalysis¶

class persalys.InferenceAnalysis(*args)¶

Perform a Kolmogorov goodness-of-fit test for 1-D continuous distributions.

Parameters:

name : str

Name

design : DesignOfExperiment

Design of experiments

Examples

>>> import openturns as ot
>>> import persalys
>>> ot.RandomGenerator_SetSeed(0)

Create the model:

>>> filename = 'data.csv'
>>> sample = ot.Normal(3).getSample(100)
>>> sample.exportToCSVFile(filename)
>>> model = persalys.DataModel('myDataModel', 'data.csv', [0, 1, 2])

Create the Inference Analysis:

>>> analysis = persalys.InferenceAnalysis('analysis', model)
>>> analysis.run()

Get the result:

>>> result = analysis.getResult()
>>> resultX0 = result.getFittingTestResultForVariable('X0')

Methods

`getClassName`()	Accessor to the object’s name.
`getDesignOfExperiment`()	Design of experiments accessor.
`getDistributionsFactories`(variableName)	Get the sequence of distributions to test for a variable.
`getErrorMessage`()	Error message accessor.
`getId`()	Accessor to the object’s id.
`getInterestVariables`()	Get the variables to analyse.
`getLevel`()	Level accessor.
`getName`()	Accessor to the object’s name.
`getPythonScript`()	Physical model accessor.
`getResult`()	Result accessor.
`getShadowedId`()	Accessor to the object’s shadowed id.
`getVisibility`()	Accessor to the object’s visibility state.
`getWarningMessage`()	Warning message accessor.
`hasName`()	Test if the object is named.
`hasValidResult`()	Whether the analysis has been run.
`hasVisibleName`()	Test if the object has a distinguishable name.
`isReliabilityAnalysis`()	Whether the analysis involves reliability.
`isRunning`()	Whether the analysis is running.
`run`()	Launch the analysis.
`setDistributionsFactories`(variableName, …)	Set the sequence of distributions to test for a variable.
`setInterestVariables`(variablesNames)	Set the variables to analyse.
`setLevel`(level)	Level accessor.
`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)¶

getClassName()¶

Accessor to the object’s name.

Returns:

class_name : str

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

getDesignOfExperiment()¶

Design of experiments accessor.

Returns:

model : DesignOfExperiment

Design of experiments

getDistributionsFactories(variableName)¶

Get the sequence of distributions to test for a variable.

Parameters:

variable : str

Name of the variable

Returns:

factories : sequence of openturns.DistributionFactory

Distributions to test

getErrorMessage()¶

Error message accessor.

Returns:

message : str

Error message if the analysis failed

getId()¶

Accessor to the object’s id.

Returns:

id : int

Internal unique identifier.

getInterestVariables()¶

Get the variables to analyse.

Returns:

variablesNames : sequence of str

Names of the variables to analyse

getLevel()¶

Level accessor.

Returns:

level : float, $0 < {\rm level} < 1$ , optional

The risk of committing a Type I error, that is an incorrect rejection of a true null hypothesis

getName()¶

Accessor to the object’s name.

Returns:

name : str

The name of the object.

getPythonScript()¶

Physical model accessor.

Returns:

script : str

Python script to replay the analysis

getResult()¶

Result accessor.

Returns:

result : InferenceResult

Result.

getShadowedId()¶

Accessor to the object’s shadowed id.

Returns:

id : int

Internal unique identifier.

getVisibility()¶

Accessor to the object’s visibility state.

Returns:

visible : bool

Visibility flag.

getWarningMessage()¶

Warning message accessor.

Returns:

message : str

Warning message which can appear during the analysis computation

hasName()¶

Test if the object is named.

Returns:

hasName : bool

True if the name is not empty.

hasValidResult()¶

Whether the analysis has been run.

Returns:

hasValidResult : bool

Whether the analysis has already been run

hasVisibleName()¶

Test if the object has a distinguishable name.

Returns:

hasVisibleName : bool

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

isReliabilityAnalysis()¶

Whether the analysis involves reliability.

Returns:

isReliabilityAnalysis : bool

Whether the analysis involves a reliability analysis

isRunning()¶

Whether the analysis is running.

Returns:

isRunning : bool

Whether the analysis is running

run()¶: Launch the analysis.

setDistributionsFactories(variableName, distributionsFactories)¶

Set the sequence of distributions to test for a variable.

Parameters:

variable : str

Name of the variable

factories : sequence of openturns.DistributionFactory

Distributions to test

setInterestVariables(variablesNames)¶

Set the variables to analyse.

Parameters:

variablesNames : sequence of str

Names of the variables to analyse

setLevel(level)¶

Level accessor.

Parameters:

level : float, $0 < {\rm level} < 1$ , optional

The risk of committing a Type I error, that is an incorrect rejection of a true null hypothesis

setName(name)¶

Accessor to the object’s name.

Parameters:

name : str

The name of the object.

setShadowedId(id)¶

Accessor to the object’s shadowed id.

Parameters:

id : int

Internal unique identifier.

setVisibility(visible)¶

Accessor to the object’s visibility state.

Parameters:

visible : bool

Visibility flag.

InferenceAnalysis¶

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