DataModel

class persalys.DataModel(*args)

Create a data model from an imported dataset.

The interface allows the user to load data samples and analyse them. They are considered as data models.

DataModel(name, fileName, inputColumns, outputColumns, inputNames, outputNames)

DataModel(name, inputSample, outputSample)

Parameters:

namestr

Name

fileNamestr

Name of a data file (.txt ot .csv) to load

inputColumnssequence of int

Indices of columns of the input variables in file to consider

outputColumnssequence of int

Indices of columns of the output variables in file to consider (optional)

inputNamessequence of str

Names of the input variables (optional)

outputNamessequence of str

Names of the output variables (optional)

inputSampleopenturns.Sample

Input sample

outputSampleopenturns.Sample

Output sample (optional)

Methods

getClassName()	Accessor to the object's name.
getEffectiveInputIndices()	Effective indices accessor.
getFileName()	File name accessor.
getInputColumns()	Columns of the input variables accessor.
getInputNames()	Names of the input variables accessor.
getInputSample()	Input sample accessor.
getListXMax()	List of input values.
getListXMin()	List of input values.
getMarginalWithoutNaN(index)	Returns a marginal sample with NaN values removed.
getName()	Accessor to the object's name.
getOutputColumns()	Columns of the output variables accessor.
getOutputNames()	Names of the output variables accessor.
getOutputSample()	Output sample accessor.
getPhysicalModel()	Physical model accessor.
getPythonScript()	Python script accessor.
getSample()	Sample accessor.
getSampleFromFile()	Sample from the file accessor.
hasName()	Test if the object is named.
hasPhysicalModel()	Whether it contains a physical model.
initialize()	Empty the input and output samples.
isValid()	Whether the model is valid.
setColumns(*args)	Columns and names of variables accessor.
setFileName(*args)	File name accessor.
setInputSample(sample)	Input sample accessor.
setName(name)	Accessor to the object's name.
setOutputSample(sample)	Output sample accessor.

setSample

Examples

>>> import openturns as ot
>>> import persalys
>>> ot.RandomGenerator.SetSeed(0)
>>> fileName = 'sample.csv'
>>> sample = ot.Normal(3).getSample(30)
>>> sample.exportToCSVFile(fileName)
>>> model = persalys.DataModel('dataModel', fileName, [0, 2], [1], ['var1', 'var2'], ['var3'])

__init__(*args)

getClassName()

Accessor to the object’s name.

Returns:

class_namestr

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

getEffectiveInputIndices()

Effective indices accessor.

Indices of non-const variables in the design.

Returns:

indicesopenturns.Indices

Input sample and output sample

getFileName()

File name accessor.

Returns:

fileNamestr

Name of the file containing data

getInputColumns()

Columns of the input variables accessor.

Returns:

columnsopenturns.Indices

Columns of the input variables

getInputNames()

Names of the input variables accessor.

Returns:

namesopenturns.Description

Names of the input variables

getInputSample()

Input sample accessor.

Returns:

sampleopenturns.Sample

Input sample

getListXMax()

List of input values.

Returns:

listSampleCollection

List of input values

getListXMin()

List of input values.

Returns:

listSampleCollection

List of input values

getMarginalWithoutNaN(index)

Returns a marginal sample with NaN values removed.

Parameters:

indexint

Index of the wanted marginal

Returns:

sampleopenturns.Sample

A subsample of the present sample with the requested marginal with NaN values removed.

getName()

Accessor to the object’s name.

Returns:

namestr

The name of the object.

getOutputColumns()

Columns of the output variables accessor.

Returns:

columnsopenturns.Indices

Columns of the output variables

getOutputNames()

Names of the output variables accessor.

Returns:

namesopenturns.Description

Names of the output variables

getOutputSample()

Output sample accessor.

Returns:

sampleopenturns.Sample

Output sample

getPhysicalModel()

Physical model accessor.

Returns:

modelPhysicalModel

Physical model

getPythonScript()

Python script accessor.

Returns:

scriptstr

Python script to rebuild the design of experiments

getSample()

Sample accessor.

Returns:

sampleopenturns.Sample

Input sample and output sample

getSampleFromFile()

Sample from the file accessor.

Returns:

sampleopenturns.Sample

Sample from the file

hasName()

Test if the object is named.

Returns:

hasNamebool

True if the name is not empty.

hasPhysicalModel()

Whether it contains a physical model.

Returns:

hasPhysicalModelbool

Whether it contains a physical model

initialize()

Empty the input and output samples.

isValid()

Whether the model is valid.

Returns:

isValidbool

Whether the model is valid

setColumns(*args)

Columns and names of variables accessor.

Parameters:

inputColumnssequence of int

Columns of input variables

inNamessequence of str

Names of input variables

outputColumnssequence of int, optional

Columns of output variables

outNamessequence of str

Names of output variables

setFileName(*args)

File name accessor.

Parameters:

fileNamestr

Name of the file containing data

setInputSample(sample)

Input sample accessor.

Parameters:

sampleopenturns.Sample

Input sample

setName(name)

Accessor to the object’s name.

Parameters:

namestr

The name of the object.

setOutputSample(sample)

Output sample accessor.

Parameters:

sampleopenturns.Sample

Output sample