gemseo.post.variable_influence module#

Plot the partial sensitivity of the functions.

class VariableInfluence(opt_problem)[source]#

Bases: BasePost[VariableInfluence_Settings]

First order variable influence analysis.

This post-processing computes \(\frac{\partial f(x)}{\partial x_i}\left(x_i^* - x_i^{(0)}\right)\) where \(x_i^{(0)}\) is the initial value of the variable and \(x_i^*\) is the optimal value of the variable.

Options of the plot method are:

  • proportion of the total sensitivity to use as a threshold to filter the variables,

  • the use of a logarithmic scale,

  • the possibility to save the indices of the influential variables indices in a NumPy file.

Initialize self. See help(type(self)) for accurate signature.

Parameters:

opt_problem (OptimizationProblem) -- The optimization problem to be post-processed.

Settings#

alias of VariableInfluence_Settings