SALib.analyze.dgsm module

SALib.analyze.dgsm.analyze(problem, X, Y, num_resamples=100, conf_level=0.95, print_to_console=False, seed=None)

Calculates Derivative-based Global Sensitivity Measure on model outputs.

Returns a dictionary with keys ‘vi’, ‘vi_std’, ‘dgsm’, and ‘dgsm_conf’, where each entry is a list of size D (the number of parameters) containing the indices in the same order as the parameter file.

Notes

Compatible with:

finite_diff : SALib.sample.finite_diff.sample()

Examples

>>> X = finite_diff.sample(problem, 1000)
>>> Y = Ishigami.evaluate(X)
>>> Si = dgsm.analyze(problem, X, Y, print_to_console=False)

Parameters:

• problem (dict) – The problem definition

• X (numpy.matrix) – The NumPy matrix containing the model inputs

• Y (numpy.array) – The NumPy array containing the model outputs

• num_resamples (int) – The number of resamples used to compute the confidence intervals (default 1000)

• conf_level (float) – The confidence interval level (default 0.95)

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

• seed (int) – Seed to generate a random number

References

1. Sobol, I. M. and S. Kucherenko (2009). “Derivative based global

   sensitivity measures and their link with global sensitivity indices.” Mathematics and Computers in Simulation, 79(10):3009-3017, doi:10.1016/j.matcom.2009.01.023.

SALib.analyze.dgsm.calc_dgsm(base, perturbed, x_delta, bounds, num_resamples, conf_level)

v_i sensitivity measure following Sobol and Kucherenko (2009). For comparison, total order S_tot <= dgsm

SALib.analyze.dgsm.calc_vi_mean(base, perturbed, x_delta)

Calculate v_i mean.

Same as calc_vi_stats but only returns the mean.

SALib.analyze.dgsm.calc_vi_stats(base, perturbed, x_delta)

Calculate v_i mean and std.

v_i sensitivity measure following Sobol and Kucherenko (2009) For comparison, Morris mu* < sqrt(v_i)

Same as calc_vi_mean but returns standard deviation as well.

SALib.analyze.dgsm.cli_action(args)

SALib.analyze.dgsm.cli_parse(parser)