SALib.analyze.discrepancy module#

SALib.analyze.discrepancy.analyze(problem: Dict, X: ndarray, Y: ndarray, method: str = 'WD', print_to_console: bool = False, seed: int = None)[source]#

Discrepancy indices.

Parameters:

  • problem (dict) – The problem definition

  • X (numpy.ndarray) – An array of model inputs and outputs.

  • Y (numpy.ndarray) – An array of model inputs and outputs.

  • method ({"WD", "CD", "MD", "L2-star"}) – Type of discrepancy. Refer to scipy.stats.qmc.discrepancy for more details. Default is “WD”.

  • print_to_console (bool, optional) – Print results directly to console (default False)

  • seed (int, optional) – Seed value to ensure deterministic results Unused, but defined to maintain compatibility with other functions.

Notes

Compatible with:

all samplers

Based on 2D sub projections of [Xi,Y], the discrepancy of each sample is calculated which gives a value for all Xi. This information is used as a measure of sensitivity.

Discrepancy analysis is very fast and is visually explainable. Considering two variables X1 and X2, X1 is more influential than X2 when the scatterplot of X1 against Y displays a more discernible shape than the scatterplot of X2 against Y.

For the method to work properly, the input parameter space need to be uniformly covered as the quality of the measure depends on the value of the discrepancy. Taking a 2D sub projection, if the distribution of sample along Xi is not uniform, it will have an impact on the discrepancy, the value will increase, i.e. the importance of this parameter would be inflated.

References

1. A. Puy, P.T. Roy and A. Saltelli. 2023. Discrepancy measures for sensitivity analysis. https://arxiv.org/abs/2206.13470

2. A. Saltelli, M. Ratto, T. Andres, F. Campolongo, J. Cariboni, D. Gatelli, M. Saisana, and S. Tarantola. 2008. Global Sensitivity Analysis: The Primer. Wiley, West Sussex, U.K. https://dx.doi.org/10.1002/9780470725184 Accessible at: http://www.andreasaltelli.eu/file/repository/Primer_Corrected_2022.pdf

Examples

>>> import numpy as np
>>> from SALib.sample import latin
>>> from SALib.analyze import discrepancy
>>> from SALib.test_functions import Ishigami

>>> problem = {
...   'num_vars': 3,
...   'names': ['x1', 'x2', 'x3'],
...   'bounds': [[-np.pi, np.pi]]*3
... }
>>> X = latin.sample(problem, 1000)
>>> Y = Ishigami.evaluate(X)
>>> Si = discrepancy.analyze(problem, X, Y, print_to_console=True)
SALib.analyze.discrepancy.cli_action(args)[source]#
SALib.analyze.discrepancy.cli_parse(parser)[source]#