SALib.analyze.fast module#
- SALib.analyze.fast.analyze(problem, Y, M=4, num_resamples=100, conf_level=0.95, print_to_console=False, seed=None)[source]#
Perform extended Fourier Amplitude Sensitivity Test on model outputs.
Returns a dictionary with keys ‘S1’ and ‘ST’, 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:
fast_sampler :
SALib.sample.fast_sampler.sample()
Examples
>>> X = fast_sampler.sample(problem, 1000) >>> Y = Ishigami.evaluate(X) >>> Si = fast.analyze(problem, Y, print_to_console=False)
- Parameters:
problem (dict) – The problem definition
Y (numpy.array) – A NumPy array containing the model outputs
M (int) – The interference parameter, i.e., the number of harmonics to sum in the Fourier series decomposition (default 4)
print_to_console (bool) – Print results directly to console (default False)
seed (int) – Seed to generate a random number
References
Cukier, R. I., C. M. Fortuin, K. E. Shuler, A. G. Petschek, and J. H. Schaibly (1973). Study of the sensitivity of coupled reaction systems to uncertainties in rate coefficients. J. Chem. Phys., 59(8):3873-3878 doi:10.1063/1.1680571
Saltelli, A., S. Tarantola, and K. P.-S. Chan (1999). A Quantitative Model-Independent Method for Global Sensitivity Analysis of Model Output. Technometrics, 41(1):39-56, doi:10.1080/00401706.1999.10485594.
Pujol, G. (2006) fast99 - R sensitivity package cran/sensitivity
Warning
The confidence intervals (
S1_conf/ST_conf) are estimated by bootstrap resampling of the search-curve output. This is known to be statistically unreliable for (e)FAST - see discussion at SALib/SALib#649 - and the CI estimates should be treated as indicative only. This feature may be reworked or removed in a future release.
- SALib.analyze.fast.bootstrap(Y: ndarray, M: int, resamples: int, conf_level: float)[source]#
Compute CIs.
Infers
Nfrom results of sub-sampleYand re-estimates omega (ω) for the aboveN.