Example of sensitivity analyses on the wing weight model

This example is a brief overview of the use of the most usual sensitivity analysis techniques and how to call them:

  • PCC: Partial Correlation Coefficients

  • PRCC: Partial Rank Correlation Coefficients

  • SRC: Standard Regression Coefficients

  • SRRC: Standard Rank Regression Coefficients

  • Pearson coefficients

  • Spearman coefficients

  • Taylor expansion importance factors

  • Sobol’ indices

  • HSIC : Hilbert-Schmidt Independence Criterion

We present the methods on the WingWeight function and use the same notations.

Definition of the model

We load the model from the usecases module.

import openturns as ot
import openturns.viewer as otv
from openturns.usecases.wingweight_function import WingWeightModel
from matplotlib import pylab as plt
import numpy as np

m = WingWeightModel()

Cross cuts of the function

Let’s have a look on 2D cross cuts of the wing weight function. For each 2D cross cut, the other variables are fixed to the input distribution mean values. This graph allows to have a first idea of the variations of the function in pair of dimensions. The colors of each contour plot are comparable. The number of contour levels are related to the amount of variation of the function in the corresponding coordinates.

fig = plt.figure(figsize=(12, 12))
lowerBound = m.distributionX.getRange().getLowerBound()
upperBound = m.distributionX.getRange().getUpperBound()

# Definition of number of meshes in x and y axes for the 2D cross cut plots
nX = 20
nY = 20
for i in range(m.dim):
    for j in range(i):
        crossCutIndices = []
        crossCutReferencePoint = []
        for k in range(m.dim):
            if k != i and k != j:
                # Definition of the reference point
        # Definition of 2D cross cut function
        crossCutFunction = ot.ParametricFunction(
            m.model, crossCutIndices, crossCutReferencePoint
        crossCutLowerBound = [lowerBound[j], lowerBound[i]]
        crossCutUpperBound = [upperBound[j], upperBound[i]]
        # Definition of the mesh
        inputData = ot.Box([nX, nY]).generate()
        inputData *= ot.Point(crossCutUpperBound) - ot.Point(crossCutLowerBound)
        inputData += ot.Point(crossCutLowerBound)
        meshX = np.array(inputData)[:, 0].reshape(nX + 2, nY + 2)
        meshY = np.array(inputData)[:, 1].reshape(nX + 2, nY + 2)
        data = crossCutFunction(inputData)
        meshZ = np.array(data).reshape(nX + 2, nY + 2)
        levels = [(150 + 3 * i) for i in range(101)]

        # Creation of the contour
        index = 1 + i * m.dim + j

        ax = fig.add_subplot(m.dim, m.dim, index)
        ax.pcolormesh(meshX, meshY, meshZ, cmap="hsv", vmin=176., vmax=363., shading='auto')

        # Creation of axes title
        if j == 0:
        if i == 9:
plot sensitivity wingweight

We can see that the variables t_c, N_z, A, W_{dg} seem to be influent on the wing weight whereas \Lambda, \ell, q, W_p, W_{fw} have less influence on the function.

Data generation

We create the input and output data for the estimation of the different sensitivity coefficients and we get the input variables description:

inputNames = m.distributionX.getDescription()

size = 500
inputDesign = m.distributionX.getSample(size)
outputDesign = m.model(inputDesign)

Let’s estimate the PCC, PRCC, SRC, SRRC, Pearson and Spearman coefficients, display and analyze them. We create a CorrelationAnalysis model.

corr_analysis = ot.CorrelationAnalysis(inputDesign, outputDesign)

PCC coefficients

We compute here PCC coefficients using the CorrelationAnalysis.

pcc_indices = corr_analysis.computePCC()
graph = ot.SobolIndicesAlgorithm.DrawCorrelationCoefficients(
    pcc_indices, inputNames, "PCC coefficients - Wing weight"
view = otv.View(graph)
PCC coefficients - Wing weight

PRCC coefficients

We compute here PRCC coefficients using the CorrelationAnalysis.

prcc_indices = corr_analysis.computePRCC()
graph = ot.SobolIndicesAlgorithm.DrawCorrelationCoefficients(
    prcc_indices, inputNames, "PRCC coefficients - Wing weight"
view = otv.View(graph)
PRCC coefficients - Wing weight

SRC coefficients

We compute here SRC coefficients using the CorrelationAnalysis.

src_indices = corr_analysis.computeSRC()
graph = ot.SobolIndicesAlgorithm.DrawCorrelationCoefficients(
    src_indices, inputNames, "SRC coefficients - Wing weight"
view = otv.View(graph)
SRC coefficients - Wing weight

Normalized squared SRC coefficients (coefficients are made to sum to 1) :

squared_src_indices = corr_analysis.computeSquaredSRC(True)

And their associated graph:

graph = ot.SobolIndicesAlgorithm.DrawCorrelationCoefficients(
    squared_src_indices, inputNames, "Squared SRC coefficients - Wing weight"
view = otv.View(graph)
Squared SRC coefficients - Wing weight

SRRC coefficients

We compute here SRRC coefficients using the CorrelationAnalysis.

srrc_indices = corr_analysis.computeSRRC()
graph = ot.SobolIndicesAlgorithm.DrawCorrelationCoefficients(
    srrc_indices, inputNames, "SRRC coefficients - Wing weight"
view = otv.View(graph)
SRRC coefficients - Wing weight

Pearson coefficients

We compute here the Pearson \rho coefficients using the CorrelationAnalysis.

pearson_correlation = corr_analysis.computePearsonCorrelation()
title_pearson_graph = "Pearson correlation coefficients - Wing weight"
graph = ot.SobolIndicesAlgorithm.DrawCorrelationCoefficients(
    pearson_correlation, inputNames, title_pearson_graph
view = otv.View(graph)
Pearson correlation coefficients - Wing weight

Spearman coefficients

We compute here the Spearman \rho_s coefficients using the CorrelationAnalysis.

spearman_correlation = corr_analysis.computeSpearmanCorrelation()
title_spearman_graph = "Spearman correlation coefficients - Wing weight"
graph = ot.SobolIndicesAlgorithm.DrawCorrelationCoefficients(
    spearman_correlation, inputNames, title_spearman_graph
view = otv.View(graph)
Spearman correlation coefficients - Wing weight

The different computed correlation estimators show that the variables S_w, A, N_z, t_c seem to be the most correlated with the wing weight in absolute value. Pearson and Spearman coefficients do not reveal any linear nor monotonic correlation as no coefficients are equal to +/- 1. Coefficients about t_c are negative revealing a negative correlation with the wing weight, that is consistent with the model expression.

Taylor expansion importance factors

We compute here the Taylor expansion importance factors using TaylorExpansionMoments.

We create a distribution-based RandomVector.

X = ot.RandomVector(m.distributionX)

We create a composite RandomVector Y from X and m.model.

Y = ot.CompositeRandomVector(m.model, X)

We create a Taylor expansion method to approximate moments.

taylor = ot.TaylorExpansionMoments(Y)

We get the importance factors.

[Sw : 0.130315, Wfw : 2.94004e-06, A : 0.228153, Lambda : 0, q : 8.25053e-05, l : 0.00180269, tc : 0.135002, Nz : 0.412794, Wdg : 0.0883317, Wp : 0.00351621]

We draw the importance factors

graph = taylor.drawImportanceFactors()
graph.setTitle("Taylor expansion imporfance factors - Wing weight")
view = otv.View(graph)
Taylor expansion imporfance factors - Wing weight

The Taylor expansion importance factors is consistent with the previous estimators as S_w, A, N_z, t_c seem to be the most influent variables. To analyze the relevance of the previous indices, a Sobol’ analysis is now carried out.

Sobol’ indices

We compute the Sobol’ indices from both sampling approach and Polynomial Chaos Expansion.

sizeSobol = 1000
sie = ot.SobolIndicesExperiment(m.distributionX, sizeSobol)
inputDesignSobol = sie.generate()
inputNames = m.distributionX.getDescription()

We see that 12000 function evaluations are required to estimate the first order and total Sobol’ indices.

Then, we evaluate the outputs corresponding to this design of experiments.

outputDesignSobol = m.model(inputDesignSobol)

We estimate the Sobol’ indices with the SaltelliSensitivityAlgorithm.

sensitivityAnalysis = ot.SaltelliSensitivityAlgorithm(
    inputDesignSobol, outputDesignSobol, sizeSobol

The getFirstOrderIndices and getTotalOrderIndices methods respectively return estimates of all first order and total Sobol’ indices.

print("First order indices:", sensitivityAnalysis.getFirstOrderIndices())
First order indices: [0.0895403,-0.0324985,0.224239,-0.0324775,-0.0326605,-0.0297425,0.111533,0.459428,0.0692415,-0.0257065]#10
print("Total order indices:", sensitivityAnalysis.getTotalOrderIndices())
Total order indices: [0.132254,1.75663e-05,0.25098,0.000159035,0.000417434,0.000214447,0.144213,0.410061,0.101327,0.00225025]#10

The draw method produces the following graph. The vertical bars represent the 95% confidence intervals of the estimates.

graph = sensitivityAnalysis.draw()
graph.setTitle("Sobol indices with Saltelli - wing weight")
view = otv.View(graph)
Sobol indices with Saltelli - wing weight

We see that several Sobol’ indices are negative, that is inconsistent with the theory. Therefore, a larger number of samples is required to get consistent indices

sizeSobol = 10000
sie = ot.SobolIndicesExperiment(m.distributionX, sizeSobol)
inputDesignSobol = sie.generate()
inputNames = m.distributionX.getDescription()
outputDesignSobol = m.model(inputDesignSobol)

sensitivityAnalysis = ot.SaltelliSensitivityAlgorithm(
    inputDesignSobol, outputDesignSobol, sizeSobol


graph = sensitivityAnalysis.draw()
graph.setTitle("Sobol indices with Saltelli - wing weight")
view = otv.View(graph)
Sobol indices with Saltelli - wing weight

It improves the accuracy of the estimation but, for very low indices, Saltelli scheme is not accurate since several confidence intervals provide negative lower bounds.

Now, we estimate the Sobol’ indices using Polynomial Chaos Expansion. We create a Functional Chaos Expansion.

sizePCE = 800
inputDesignPCE = m.distributionX.getSample(sizePCE)
outputDesignPCE = m.model(inputDesignPCE)

algo = ot.FunctionalChaosAlgorithm(inputDesignPCE, outputDesignPCE, m.distributionX)

result = algo.getResult()

The relative errors are low : this indicates that the PCE model has good accuracy. Then, we exploit the surrogate model to compute the Sobol’ indices.

sensitivityAnalysis = ot.FunctionalChaosSobolIndices(result)
firstOrder = [sensitivityAnalysis.getSobolIndex(i) for i in range(m.dim)]
totalOrder = [sensitivityAnalysis.getSobolTotalIndex(i) for i in range(m.dim)]
graph = ot.SobolIndicesAlgorithm.DrawSobolIndices(inputNames, firstOrder, totalOrder)
graph.setTitle("Sobol indices by Polynomial Chaos Expansion - wing weight")
view = otv.View(graph)
Sobol indices by Polynomial Chaos Expansion - wing weight
 input dimension: 10
 output dimension: 1
 basis size: 299
 mean: [268.074]
 std-dev: [48.0827]
Index   | Multi-indice                  | Part of variance
      7 | [0,0,0,0,0,0,0,1,0,0]         | 0.410327
      3 | [0,0,1,0,0,0,0,0,0,0]         | 0.220054
      6 | [0,0,0,0,0,0,1,0,0,0]         | 0.13849
      1 | [1,0,0,0,0,0,0,0,0,0]         | 0.124432
      8 | [0,0,0,0,0,0,0,0,1,0]         | 0.0849403

Component | Sobol index            | Sobol total index
        0 | 0.124441               | 0.127859
        1 | 2.77423e-06            | 2.93343e-06
        2 | 0.220203               | 0.225955
        3 | 0.000487693            | 0.000504128
        4 | 8.81411e-05            | 9.11378e-05
        5 | 0.00179913             | 0.00185859
        6 | 0.141038               | 0.145127
        7 | 0.411636               | 0.419666
        8 | 0.0849948              | 0.0876214
        9 | 0.00333265             | 0.00335527

The Sobol’ indices confirm the previous analyses, in terms of ranking of the most influent variables. We also see that five variables have a quasi null total Sobol’ indices, that indicates almost no influence on the wing weight. There is no discrepancy between first order and total Sobol’ indices, that indicates no or very low interaction between the variables in the variance of the output. As the most important variables act only through decoupled first degree contributions, the hypothesis of a linear dependence between the input variables and the weight is legitimate. This explains why both squared SRC and Taylor give the exact same results even if the first one is based on a \mathcal{L}^2 linear approximation and the second one is based on a linear expansion around the mean value of the input variables.

HSIC indices

We then estimate the HSIC indices using a data-driven approach.

sizeHSIC = 250
inputDesignHSIC = m.distributionX.getSample(sizeHSIC)
outputDesignHSIC = m.model(inputDesignHSIC)

covarianceModelCollection = []
for i in range(m.dim):
    Xi = inputDesignHSIC.getMarginal(i)
    inputCovariance = ot.SquaredExponential(1)

We define a covariance kernel associated to the output variable.

outputCovariance = ot.SquaredExponential(1)

In this paragraph, we perform the analysis on the raw data: that is the global HSIC estimator.

estimatorType = ot.HSICUStat()

We now build the HSIC estimator:

globHSIC = ot.HSICEstimatorGlobalSensitivity(
    covarianceModelCollection, inputDesignHSIC, outputDesignHSIC, estimatorType

We get the R2-HSIC indices:

R2HSICIndices = globHSIC.getR2HSICIndices()
print("\n Global HSIC analysis")
print("R2-HSIC Indices: ", R2HSICIndices)
 Global HSIC analysis
R2-HSIC Indices:  [0.102709,-0.00336782,0.148905,0.000299579,-0.00488949,0.00479322,0.0845911,0.34874,0.0601762,-0.00321501]#10

and the HSIC indices:

HSICIndices = globHSIC.getHSICIndices()
print("HSIC Indices: ", HSICIndices)
HSIC Indices:  [0.00906902,-0.000293314,0.013635,2.72569e-05,-0.000444497,0.000436191,0.00777676,0.0309526,0.00545591,-0.000300297]#10

The p-value by permutation.

pvperm = globHSIC.getPValuesPermutation()
print("p-value (permutation): ", pvperm)
p-value (permutation):  [0,0.712871,0,0.376238,0.841584,0.128713,0,0,0,0.712871]#10

We have an asymptotic estimate of the value for this estimator.

pvas = globHSIC.getPValuesAsymptotic()
print("p-value (asymptotic): ", pvas)
p-value (asymptotic):  [3.02901e-12,0.680845,1.28369e-16,0.392815,0.82049,0.169798,1.63072e-09,6.32092e-35,4.2536e-07,0.675604]#10

We vizualise the results.

graph1 = globHSIC.drawHSICIndices()
view1 = otv.View(graph1)

graph2 = globHSIC.drawPValuesAsymptotic()
view2 = otv.View(graph2)

graph3 = globHSIC.drawR2HSICIndices()
view3 = otv.View(graph3)

graph4 = globHSIC.drawPValuesPermutation()
view4 = otv.View(graph4)
  • HSIC indices
  • Asymptotic p-values
  • R2-HSIC indices
  • p-values by permutation

The HSIC indices go in the same way as the other estimators in terms the most influent variables. The variables W_{fw}, q, l, W_p seem to be independent to the output as the corresponding p-values are high. We can also see that the asymptotic p-values and p-values estimated by permutation are quite similar.

Total running time of the script: ( 2 minutes 43.578 seconds)