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()
sensitivity_analysis.compute_samples([discipline], uncertain_space, 10000)
sensitivity_analysis.main_method = "total"
sensitivity_analysis.compute_indices()
 WARNING - 10:39:27: No coupling in MDA, switching chain_linearize to True.
    INFO - 10:39:27:
    INFO - 10:39:27: *** Start SobolAnalysisSamplingPhase execution ***
    INFO - 10:39:27: SobolAnalysisSamplingPhase
    INFO - 10:39:27:    Disciplines: IshigamiDiscipline
    INFO - 10:39:27:    MDO formulation: MDF
    INFO - 10:39:27: Running the algorithm OT_SOBOL_INDICES:
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    INFO - 10:39:37: *** End SobolAnalysisSamplingPhase execution (time: 0:00:10.255602) ***

SobolAnalysis.SensitivityIndices(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.]])}}]}, 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)
SobolAnalysis.SensitivityIndices(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.]])}}]}, total={'y': [{'x1': array([0.49769038]), 'x2': array([0.46903493]), 'x3': array([0.23786094])}]})

They can also be accessed separately:

pprint.pprint(sensitivity_analysis.indices.first)
pprint.pprint(sensitivity_analysis.indices.second)
pprint.pprint(sensitivity_analysis.indices.total)
{'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],
       [0.40231271]]),
        'x2': array([[0.42038752],
       [0.55909591]]),
        'x3': array([[-0.06311772],
       [ 0.09086516]])}]}
{'y': [{'x1': array([[0.42529255],
       [0.57008822]]),
        'x2': array([[0.42022722],
       [0.51784263]]),
        'x3': array([[0.19308657],
       [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_input_variables("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)
Sobol' indices for the output y Var=1.4e+01    StD=3.7e+00
<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 10.538 seconds)

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