Parallel coordinates graph as sensitivity tool

The parallel coordinates graph enables to visualize all the combinations of the input variables which lead to a specific range of the output variable.

Let us consider a model f: \mathbb{R}^n \longrightarrow \mathbb{R}, where f(\underline{X}) = Y.

The graph requires to have an input sample X_s and an output sample Y_s.

The first figure draws such a graph: each column represents one component X_i of the input vector \underline{X}. The last column represents the scalar output variable Y. For each point \underline{X}^j, each component X_i^j is noted on its respective axe and the last mark is the one which corresponds to the associated Y^j. A line joins all the marks. Thus, each point of the sample corresponds to a particular line on the graph.

The scale of the axes are quantile based: each axe runs between 0 and 1 and each value is represented by its quantile with respect to its marginal empirical distribution.

It is interesting to select, among those lines, the ones which correspond to a specific range of the output variable. These particular lines are colored differently. This specific range is defined in the quantile based scale of Y or in its specific scale. In that second case, the range is automatically converted into a quantile based scale range.

from __future__ import print_function
import openturns as ot
import openturns.viewer as viewer
from matplotlib import pylab as plt
ot.Log.Show(ot.Log.NONE)

Create data to visualize

# Create the model Y = x1^2 + x2
model = ot.SymbolicFunction(["x1", "x2"], ["x1^2+x2"])

# Create the input distribution and random vector X
myCorMat = ot.CorrelationMatrix(2)
myCorMat[0, 1] = -0.6
inputDist = ot.Normal([0., 0.], myCorMat)
inputDist.setDescription(['X1', 'X2'])

inputVector = ot.RandomVector(inputDist)

# Create the output random vector Y=model(X)
output = ot.CompositeRandomVector(model, inputVector)

# Generate the input sample
N = 500
X = inputVector.getSample(N)

# Evaluate the associated output sample
Y = model(X)
Y.setDescription("Y")

print(Y.getMin(), Y.getMax(), Y.computeQuantilePerComponent(0.9))

Out:

[-2.33438] [8.9382] [3.07013]

Example 1: value based scale to describe the Y range

minValue = 3.35
maxValue = 20.0
quantileScale = False
graphCobweb = ot.VisualTest.DrawParallelCoordinates(
    X, Y, minValue, maxValue, 'red', quantileScale)
graphCobweb.setLegendPosition('bottomright')
view = viewer.View(graphCobweb)
Cobweb graph - [Y] vs [X1,X2]

Example 2: rank based scale to describe the Y range

minValue = 0.9
maxValue = 1.0
quantileScale = True
graphCobweb = ot.VisualTest.DrawParallelCoordinates(
    X, Y, minValue, maxValue, 'red', quantileScale)
graphCobweb.setLegendPosition('bottomright')
view = viewer.View(graphCobweb)
plt.show()
Cobweb graph - [Y] vs [X1,X2]

Total running time of the script: ( 0 minutes 1.098 seconds)

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