Note
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Sobol' analysis#
from __future__ import annotations
import pprint
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
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 Discipline 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()
INFO - 16:16:38: *** Start SobolAnalysisSamplingPhase execution ***
INFO - 16:16:38: SobolAnalysisSamplingPhase
INFO - 16:16:38: Disciplines: IshigamiDiscipline
INFO - 16:16:38: MDO formulation: MDF
INFO - 16:16:38: Running the algorithm OT_SOBOL_INDICES:
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INFO - 16:16:46: *** End SobolAnalysisSamplingPhase execution ***
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, second-order and total-order Sobol' indices:
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)
<Figure size 640x480 with 1 Axes>
Lastly,
the sensitivity indices can be exported to a Dataset:
dataset = sensitivity_analysis.to_dataset()
dataset
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 35.878 seconds)