gemseo / uncertainty / sensitivity / sobol

# analysis module¶

Class for the estimation of Sobol’ indices.

Let us consider the model $$Y=f(X_1,\ldots,X_d)$$ where:

• $$X_1,\ldots,X_d$$ are independent random variables,

• $$E\left[f(X_1,\ldots,X_d)^2\right]<\infty$$.

Then, the following decomposition is unique:

$Y=f_0 + \sum_{i=1}^df_i(X_i) + \sum_{i,j=1\atop i\neq j}^d f_{i,j}(X_i,X_j) + \sum_{i,j,k=1\atop i\neq j\neq k}^d f_{i,j,k}(X_i,X_j,X_k) + \ldots + f_{1,\ldots,d}(X_1,\ldots,X_d)$

where:

• $$f_0=E[Y]$$,

• $$f_i(X_i)=E[Y|X_i]-f_0$$,

• $$f_{i,j}(X_i,X_j)=E[Y|X_i,X_j]-f_i(X_i)-f_j(X_j)-f_0$$

• and so on.

Then, the shift to variance leads to:

$V[Y]=\sum_{i=1}^dV\left[f_i(X_i)\right] + \sum_{i,j=1\atop j\neq i}^d V\left[f_{i,j}(X_i,X_j)\right] + \ldots + V\left[f_{1,\ldots,d}(X_1,\ldots,X_d)\right]$

and the Sobol’ indices are obtained by dividing by the variance and sum up to 1:

$1=\sum_{i=1}^dS_i + \sum_{i,j=1\atop j\neq i}^d S_{i,j} + \sum_{i,j,k=1\atop i\neq j\neq k}^d S_{i,j,k} + \ldots + S_{1,\ldots,d}$

A Sobol’ index represents the share of output variance explained by a parameter or a group of parameters. For the parameter $$X_i$$,

• $$S_i$$ is the first-order Sobol’ index measuring the individual effect of $$X_i$$,

• $$S_{i,j}$$ is the second-order Sobol’ index measuring the joint effect between $$X_i$$ and $$X_j$$,

• $$S_{i,j,k}$$ is the third-order Sobol’ index measuring the joint effect between $$X_i$$, $$X_j$$ and $$X_k$$,

• and so on.

In practice, we only consider the first-order Sobol’ index:

$S_i=\frac{V[E[Y|X_i]]}{V[Y]}$

and the total-order Sobol’ index:

$S_i^T=\sum_{u\subset\{1,\ldots,d\}\atop u \ni i}S_u$

The latter represents the sum of the individual effect of $$X_i$$ and the joint effects between $$X_i$$ and any parameter or group of parameters.

This methodology relies on the SobolAnalysis class. Precisely, SobolAnalysis.indices contains both SobolAnalysis.first_order_indices and SobolAnalysis.total_order_indices while SobolAnalysis.main_indices represents total-order Sobol’ indices. Lastly, the SobolAnalysis.plot() method represents the estimations of both first-order and total-order Sobol’ indices along with their 95% confidence interval.

The user can select the algorithm to estimate the Sobol’ indices. The computation relies on OpenTURNS capabilities.

class gemseo.uncertainty.sensitivity.sobol.analysis.SobolAnalysis(disciplines, parameter_space, n_samples, output_names=None, algo=None, algo_options=None, formulation='MDF', compute_second_order=True, use_asymptotic_distributions=True, **formulation_options)[source]

Sensitivity analysis based on the Sobol’ indices.

Examples

>>> from numpy import pi
>>> from gemseo.api import create_discipline, create_parameter_space
>>> from gemseo.uncertainty.sensitivity.sobol.analysis import SobolAnalysis
>>>
>>> expressions = {"y": "sin(x1)+7*sin(x2)**2+0.1*x3**4*sin(x1)"}
>>> discipline = create_discipline(
...     "AnalyticDiscipline", expressions=expressions
... )
>>>
>>> parameter_space = create_parameter_space()
...     "x1", "OTUniformDistribution", minimum=-pi, maximum=pi
... )
...     "x2", "OTUniformDistribution", minimum=-pi, maximum=pi
... )
...     "x3", "OTUniformDistribution", minimum=-pi, maximum=pi
... )
>>>
>>> analysis = SobolAnalysis([discipline], parameter_space, n_samples=10000)
>>> indices = analysis.compute_indices()

Parameters:
• disciplines (Collection[MDODiscipline]) – The discipline or disciplines to use for the analysis.

• parameter_space (ParameterSpace) – A parameter space.

• n_samples (int) – A number of samples. If None, the number of samples is computed by the algorithm.

• output_names (Iterable[str] | None) – The disciplines’ outputs to be considered for the analysis. If None, use all the outputs.

• algo (str | None) – The name of the DOE algorithm. If None, use the SensitivityAnalysis.DEFAULT_DRIVER.

• algo_options (Mapping[str, DOELibraryOptionType] | None) – The options of the DOE algorithm.

• formulation (str) –

The name of the MDOFormulation to sample the disciplines.

By default it is set to “MDF”.

• compute_second_order (bool) –

Whether to compute the second-order indices.

By default it is set to True.

• use_asymptotic_distributions (bool) –

Whether to estimate the confidence intervals of the first- and total-order Sobol’ indices with the asymptotic distributions.

By default it is set to True.

• **formulation_options (Any) – The options of the MDOFormulation.

Notes

The estimators of Sobol’ indices rely on the same DOE algorithm. This algorithm starts with two independent input datasets composed of $$N$$ independent samples and this number $$N$$ is the usual sampling size for Sobol’ analysis. When compute_second_order=False or when the input dimension $$d$$ is equal to 2, $$N=\frac{n_\text{samples}}{2+d}$$. Otherwise, $$N=\frac{n_\text{samples}}{2+2d}$$. The larger $$N$$, the more accurate the estimators of Sobol’ indices are. Therefore, for a small budget n_samples, the user can choose to set compute_second_order to False to ensure a better estimation of the first- and second-order indices.

class Algorithm(value)[source]

Bases: BaseEnum

The algorithms to estimate the Sobol’ indices.

Jansen(*args, **kwargs) = <class 'openturns.simulation.JansenSensitivityAlgorithm'>
Martinez(*args, **kwargs) = <class 'openturns.simulation.MartinezSensitivityAlgorithm'>
MauntzKucherenko(*args, **kwargs) = <class 'openturns.simulation.MauntzKucherenkoSensitivityAlgorithm'>
Saltelli(*args, **kwargs) = <class 'openturns.simulation.SaltelliSensitivityAlgorithm'>
class Method(value)[source]

Bases: BaseEnum

The names of the sensitivity methods.

first = 'Sobol(first)'
total = 'Sobol(total)'
compute_indices(outputs=None, algo=Algorithm.Saltelli)[source]

Compute the sensitivity indices.

Parameters:
• outputs (Sequence[str] | None) – The outputs for which to display sensitivity indices. If None, use the default outputs, that are all the discipline outputs.

• algo (Algorithm | str) –

The name of the algorithm to estimate the Sobol’ indices.

By default it is set to Saltelli.

Returns:

The sensitivity indices.

With the following structure:

{
"method_name": {
"output_name": [
{
"input_name": data_array,
}
]
}
}


Return type:

dict[str, IndicesType]

export_to_dataset()

Convert SensitivityAnalysis.indices into a Dataset.

Returns:

The sensitivity indices.

Return type:

Dataset

get_intervals(first_order=True)[source]

Get the confidence interval for Sobol’ indices.

Warning

You must first call compute_indices().

Parameters:

first_order (bool) –

If True, compute the intervals for the first-order indices. Otherwise, for the total-order indices.

By default it is set to True.

Returns:

The confidence intervals for the Sobol’ indices.

With the following structure:

{
"output_name": [
{
"input_name": data_array,
}
]
}


Return type:

Dict[str, List[Dict[str, ndarray]]]

Load a sensitivity analysis from the disk.

Parameters:

file_path (str | Path) – The path to the file.

Returns:

The sensitivity analysis.

Return type:

SensitivityAnalysis

plot(output, inputs=None, title=None, save=True, show=False, file_path=None, directory_path=None, file_name=None, file_format=None, sort=True, sort_by_total=True)[source]

Plot the first- and total-order Sobol’ indices.

For $$i\in\{1,\ldots,d\}$$, plot $$S_i^{1}$$ and $$S_T^{1}$$ with their confidence intervals.

Parameters:
• output (str | tuple[str, int]) – The output for which to display sensitivity indices, either a name or a tuple of the form (name, component). If name, its first component is considered.

• inputs (Iterable[str] | None) – The inputs to display. If None, display all.

• title (str | None) – The title of the plot. If None, no title.

• save (bool) –

If True, save the figure.

By default it is set to True.

• show (bool) –

If True, show the figure.

By default it is set to False.

• file_path (str | Path | None) – A file path. Either a complete file path, a directory name or a file name. If None, use a default file name and a default directory. The file extension is inferred from filepath extension, if any.

• directory_path (str | Path | None) – The description is missing.

• file_name (str | None) – The description is missing.

• file_format (str | None) – A file format, e.g. ‘png’, ‘pdf’, ‘svg’, … Used when file_path does not have any extension. If None, use a default file extension.

• sort (bool) –

The sorting option. If True, sort variables before display.

By default it is set to True.

• sort_by_total (bool) –

The type of sorting. If True, sort variables according to total-order Sobol’ indices. Otherwise, use first-order Sobol’ indices.

By default it is set to True.

plot_bar(outputs, inputs=None, standardize=False, title=None, save=True, show=False, file_path=None, directory_path=None, file_name=None, file_format=None, **options)

Plot the sensitivity indices on a bar chart.

This method may consider one or more outputs, as well as all inputs (default behavior) or a subset.

Parameters:
• outputs (OutputsType) – The outputs for which to display sensitivity indices, either a name, a list of names, a (name, component) tuple, a list of such tuples or a list mixing such tuples and names. When a name is specified, all its components are considered. If None, use the default outputs.

• inputs (Iterable[str] | None) – The inputs to display. If None, display all.

• standardize (bool) –

If True, standardize the indices between 0 and 1 for each output.

By default it is set to False.

• title (str | None) – The title of the plot. If None, no title.

• save (bool) –

If True, save the figure.

By default it is set to True.

• show (bool) –

If True, show the figure.

By default it is set to False.

• file_path (str | Path | None) – The path of the file to save the figures. If the extension is missing, use file_extension. If None, create a file path from directory_path, file_name and file_extension.

• directory_path (str | Path | None) – The path of the directory to save the figures. If None, use the current working directory.

• file_name (str | None) – The name of the file to save the figures. If None, use a default one generated by the post-processing.

• file_format (str | None) – A file extension, e.g. ‘png’, ‘pdf’, ‘svg’, … If None, use a default file extension.

• options (int) –

Returns:

A bar chart representing the sensitivity indices.

Return type:

BarPlot

plot_comparison(indices, output, inputs=None, title=None, use_bar_plot=True, save=True, show=False, file_path=None, directory_path=None, file_name=None, file_format=None, **options)

Plot a comparison between the current sensitivity indices and other ones.

This method allows to use either a bar chart (default option) or a radar one.

Parameters:
• indices (list[SensitivityAnalysis]) – The sensitivity indices.

• output (str | tuple[str, int]) – The output for which to display sensitivity indices, either a name or a tuple of the form (name, component). If name, its first component is considered.

• inputs (Iterable[str] | None) – The inputs to display. If None, display all.

• title (str | None) – The title of the plot. If None, no title.

• use_bar_plot (bool) –

The type of graph. If True, use a bar plot. Otherwise, use a radar chart.

By default it is set to True.

• save (bool) –

If True, save the figure.

By default it is set to True.

• show (bool) –

If True, show the figure.

By default it is set to False.

• file_path (str | Path | None) – The path of the file to save the figures. If None, create a file path from directory_path, file_name and file_format.

• directory_path (str | Path | None) – The path of the directory to save the figures. If None, use the current working directory.

• file_name (str | None) – The name of the file to save the figures. If None, use a default one generated by the post-processing.

• file_format (str | None) – A file format, e.g. ‘png’, ‘pdf’, ‘svg’, … If None, use a default file extension.

• **options (bool) – The options passed to the underlying DatasetPlot.

Returns:

A graph comparing sensitivity indices.

Return type:
plot_field(output, mesh=None, inputs=None, standardize=False, title=None, save=True, show=False, file_path=None, directory_path=None, file_name=None, file_format=None, properties=None)

Plot the sensitivity indices related to a 1D or 2D functional output.

The output is considered as a 1D or 2D functional variable, according to the shape of the mesh on which it is represented.

Parameters:
• output (str | tuple[str, int]) – The output for which to display sensitivity indices, either a name or a tuple of the form (name, component) where (name, component) is used to sort the inputs. If it is a name, its first component is considered.

• mesh (ndarray | None) – The mesh on which the p-length output is represented. Either a p-length array for a 1D functional output or a (p, 2) array for a 2D one. If None, assume a 1D functional output.

• inputs (Iterable[str] | None) – The inputs to display. If None, display all inputs.

• standardize (bool) –

If True, standardize the indices between 0 and 1 for each output.

By default it is set to False.

• title (str | None) – The title of the plot. If None, no title is displayed.

• save (bool) –

If True, save the figure.

By default it is set to True.

• show (bool) –

If True, show the figure.

By default it is set to False.

• file_path (str | Path | None) – The path of the file to save the figures. If None, create a file path from directory_path, file_name and file_extension.

• directory_path (str | Path | None) – The path of the directory to save the figures. If None, use the current working directory.

• file_name (str | None) – The name of the file to save the figures. If None, use a default one generated by the post-processing.

• file_format (str | None) – A file extension, e.g. ‘png’, ‘pdf’, ‘svg’, … If None, use a default file extension.

• properties (Mapping[str, DatasetPlotPropertyType]) – The general properties of a DatasetPlot.

Returns:

A bar plot representing the sensitivity indices.

Raises:

NotImplementedError – If the dimension of the mesh is greater than 2.

Return type:

Plot the sensitivity indices on a radar chart.

This method may consider one or more outputs, as well as all inputs (default behavior) or a subset.

For visualization purposes, it is also possible to change the minimum and maximum radius values.

Parameters:
• outputs (OutputsType) – The outputs for which to display sensitivity indices, either a name, a list of names, a (name, component) tuple, a list of such tuples or a list mixing such tuples and names. When a name is specified, all its components are considered. If None, use the default outputs.

• inputs (Iterable[str] | None) – The inputs to display. If None, display all.

• standardize (bool) –

If True, standardize the indices between 0 and 1 for each output.

By default it is set to False.

• title (str | None) – The title of the plot. If None, no title.

• save (bool) –

If True, save the figure.

By default it is set to True.

• show (bool) –

If True, show the figure.

By default it is set to False.

• file_path (str | Path | None) – The path of the file to save the figures. If the extension is missing, use file_extension. If None, create a file path from directory_path, file_name and file_extension.

• directory_path (str | Path | None) – The path of the directory to save the figures. If None, use the current working directory.

• file_name (str | None) – The name of the file to save the figures. If None, use a default one generated by the post-processing.

• file_format (str | None) – A file extension, e.g. ‘png’, ‘pdf’, ‘svg’, … If None, use a default file extension.

• min_radius (float | None) – The minimal radial value. If None, from data.

• max_radius (float | None) – The maximal radial value. If None, from data.

• options (bool) –

Returns:

A radar chart representing the sensitivity indices.

Return type:

save(file_path)

Save the current sensitivity analysis on the disk.

Parameters:

file_path (str | Path) – The path to the file.

Return type:

None

sort_parameters(output)

Return the parameters sorted in descending order.

Parameters:

output (str | tuple[str, int]) – An output of the form (name, component), where name is the output name and component is the output component. If a string is passed, the tuple (name, 0) will be considered corresponding to the first component of the output name.

Returns:

The input parameters sorted in descending order.

Return type:

list[str]

static standardize_indices(indices)

Standardize the sensitivity indices for each output component.

Each index is replaced by its absolute value divided by the largest index. Thus, the standardized indices belong to the interval $$[0,1]$$.

Parameters:

indices (Dict[str, List[Dict[str, ndarray]]]) – The indices to be standardized.

Returns:

The standardized indices.

Return type:

Dict[str, List[Dict[str, ndarray]]]

AVAILABLE_ALGOS: ClassVar[list[str]] = ['Jansen', 'Martinez', 'MauntzKucherenko', 'Saltelli']

The names of the available algorithms to estimate the Sobol’ indices.

DEFAULT_DRIVER: ClassVar[str] = 'OT_SOBOL_INDICES'
dataset: Dataset

The dataset containing the discipline evaluations.

default_output: list[str]

The default outputs of interest.

property first_order_indices: Dict[str, List[Dict[str, ndarray]]]

The first-order Sobol’ indices.

With the following structure:

{
"output_name": [
{
"input_name": data_array,
}
]
}

property indices: dict[str, Dict[str, List[Dict[str, numpy.ndarray]]]]

The sensitivity indices.

With the following structure:

{
"method_name": {
"output_name": [
{
"input_name": data_array,
}
]
}
}

property inputs_names: list[str]

The names of the inputs.

property main_indices: Dict[str, List[Dict[str, ndarray]]]

The main sensitivity indices.

With the following structure:

{
"output_name": [
{
"input_name": data_array,
}
]
}

property main_method: str

The name of the main method.

property second_order_indices: Dict[str, List[Dict[str, ndarray]]]

The second-order Sobol’ indices.

With the following structure:

{
"output_name": [
{
"input_name": data_array,
}
]
}

property total_order_indices: Dict[str, List[Dict[str, ndarray]]]

The total-order Sobol’ indices.

With the following structure:

{
"output_name": [
{
"input_name": data_array,
}
]
}


Sobol’ analysis

Sobol' analysis