Sobol’ analysis¶

from __future__ import annotations

import pprint

from gemseo import configure_logger
from gemseo.problems.uncertainty.ishigami.ishigami_discipline import IshigamiDiscipline
from gemseo.problems.uncertainty.ishigami.ishigami_space import IshigamiSpace
from gemseo.uncertainty.sensitivity.sobol.analysis import SobolAnalysis

configure_logger()

<RootLogger root (INFO)>


In this example, we consider the Ishigami function [IH90]

$f(x_1,x_2,x_3)=\sin(x_1)+7\sin(x_2)^2+0.1x_3^4\sin(x_1)$

implemented as an MDODiscipline by the IshigamiDiscipline. It is commonly used with the independent random variables $$X_1$$, $$X_2$$ and $$X_3$$ uniformly distributed between $$-\pi$$ and $$\pi$$ and defined in the IshigamiSpace.

discipline = IshigamiDiscipline()
uncertain_space = IshigamiSpace()


Then, we run sensitivity analysis of type SobolAnalysis:

sensitivity_analysis = SobolAnalysis([discipline], uncertain_space, 10000)
sensitivity_analysis.main_method = "total"
sensitivity_analysis.compute_indices()

 WARNING - 08:55:23: No coupling in MDA, switching chain_linearize to True.
INFO - 08:55:23:
INFO - 08:55:23: *** Start SobolAnalysisSamplingPhase execution ***
INFO - 08:55:23: SobolAnalysisSamplingPhase
INFO - 08:55:23:    Disciplines: IshigamiDiscipline
INFO - 08:55:23:    MDO formulation: MDF
INFO - 08:55:23: Running the algorithm OT_SOBOL_INDICES:
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INFO - 08:55:35: *** End SobolAnalysisSamplingPhase execution (time: 0:00:12.765860) ***

{<Method.FIRST: 'first'>: {'y': [{'x1': array([0.33668934]), 'x2': array([0.48974171]), 'x3': array([0.01387372])}]}, 'second': {'y': [{'x1': {'x1': array([[0.]]), 'x2': array([[-0.06556589]]), 'x3': array([[0.17784496]])}, 'x2': {'x1': array([[-0.06556589]]), 'x2': array([[0.]]), 'x3': array([[-0.05604046]])}, 'x3': {'x1': array([[0.17784496]]), 'x2': array([[-0.05604046]]), 'x3': array([[0.]])}}]}, <Method.TOTAL: 'total'>: {'y': [{'x1': array([0.49769038]), 'x2': array([0.46903493]), 'x3': array([0.23786094])}]}}


The resulting indices are the first-order and total Sobol’ indices:

pprint.pprint(sensitivity_analysis.indices)

{<Method.FIRST: 'first'>: {'y': [{'x1': array([0.33668934]),
'x2': array([0.48974171]),
'x3': array([0.01387372])}]},
'second': {'y': [{'x1': {'x1': array([[0.]]),
'x2': array([[-0.06556589]]),
'x3': array([[0.17784496]])},
'x2': {'x1': array([[-0.06556589]]),
'x2': array([[0.]]),
'x3': array([[-0.05604046]])},
'x3': {'x1': array([[0.17784496]]),
'x2': array([[-0.05604046]]),
'x3': array([[0.]])}}]},
<Method.TOTAL: 'total'>: {'y': [{'x1': array([0.49769038]),
'x2': array([0.46903493]),
'x3': array([0.23786094])}]}}


They can also be accessed separately:

pprint.pprint(sensitivity_analysis.first_order_indices)
pprint.pprint(sensitivity_analysis.second_order_indices)
pprint.pprint(sensitivity_analysis.total_order_indices)

{'y': [{'x1': array([0.33668934]),
'x2': array([0.48974171]),
'x3': array([0.01387372])}]}
{'y': [{'x1': {'x1': array([[0.]]),
'x2': array([[-0.06556589]]),
'x3': array([[0.17784496]])},
'x2': {'x1': array([[-0.06556589]]),
'x2': array([[0.]]),
'x3': array([[-0.05604046]])},
'x3': {'x1': array([[0.17784496]]),
'x2': array([[-0.05604046]]),
'x3': array([[0.]])}}]}
{'y': [{'x1': array([0.49769038]),
'x2': array([0.46903493]),
'x3': array([0.23786094])}]}


One can also obtain their confidence intervals:

pprint.pprint(sensitivity_analysis.get_intervals())
pprint.pprint(sensitivity_analysis.get_intervals(first_order=False))

{'y': [{'x1': (array([0.27106596]), array([0.40231271])),
'x2': (array([0.42038752]), array([0.55909591])),
'x3': (array([-0.06311772]), array([0.09086516]))}]}
{'y': [{'x1': (array([0.42529255]), array([0.57008822])),
'x2': (array([0.42022722]), array([0.51784263])),
'x3': (array([0.19308657]), array([0.2826353]))}]}


The main indices are the total Sobol’ indices (SobolAnalysis.main_method can also be set to "first" to use the first-order indices as main indices):

pprint.pprint(sensitivity_analysis.main_indices)

{'y': [{'x1': array([0.49769038]),
'x2': array([0.46903493]),
'x3': array([0.23786094])}]}


These main indices can be used to get the input parameters sorted by decreasing order of influence:

sensitivity_analysis.sort_parameters("y")

['x1', 'x2', 'x3']


We can use the method SobolAnalysis.plot() to visualize both first-order and total Sobol’ indices:

sensitivity_analysis.plot("y", save=False, show=True)

<Figure size 640x480 with 1 Axes>


Lastly, the sensitivity indices can be exported to a Dataset:

dataset = sensitivity_analysis.to_dataset()
dataset

GROUP first total
VARIABLE y y
COMPONENT 0 0
x1 0.336689 0.497690
x2 0.489742 0.469035
x3 0.013874 0.237861

Note that this view does not contain the second-order Sobol’ indices as the latter do not have a tabular structure. Use the attribute Dataset.misc to access it.

pprint.pprint(dataset.misc)

{'second': {'y': [{'x1': {'x1': array([[0.]]),
'x2': array([[-0.06556589]]),
'x3': array([[0.17784496]])},
'x2': {'x1': array([[-0.06556589]]),
'x2': array([[0.]]),
'x3': array([[-0.05604046]])},
'x3': {'x1': array([[0.17784496]]),
'x2': array([[-0.05604046]]),
'x3': array([[0.]])}}]}}


Total running time of the script: (0 minutes 14.938 seconds)

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