SALib.analyze.delta module#
- SALib.analyze.delta.analyze(problem: Dict, X: ndarray, Y: ndarray, num_resamples: int = 100, conf_level: float = 0.95, print_to_console: bool = False, seed: int = None, y_resamples: int = None, method: str = 'all') Dict[source]#
Perform Delta Moment-Independent Analysis on model outputs.
Returns a dictionary with keys ‘delta’, ‘delta_conf’, ‘S1’, and ‘S1_conf’ (first-order sobol indices), 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:
all samplers
Examples
>>> X = latin.sample(problem, 1000) >>> Y = Ishigami.evaluate(X) >>> Si = delta.analyze(problem, X, Y, print_to_console=True)
- Parameters:
problem (dict) – The problem definition
X (numpy.matrix) – A NumPy matrix containing the model inputs
Y (numpy.array) – A NumPy array containing the model outputs
num_resamples (int) – The number of resamples when computing confidence intervals (default 100)
conf_level (float) – The confidence interval level (default 0.95)
print_to_console (bool) – Print results directly to console (default False)
y_resamples (int, optional) – Number of samples to use when resampling (bootstrap) (default None)
method ({"all", "delta", "sobol"}, optional) – Whether to compute “delta”, “sobol” or both (“all”) indices (default “all”)
References
- Borgonovo, E. (2007). “A new uncertainty importance measure.”
Reliability Engineering & System Safety, 92(6):771-784, doi:10.1016/j.ress.2006.04.015.
- Plischke, E., E. Borgonovo, and C. L. Smith (2013). “Global
sensitivity measures from given data.” European Journal of Operational Research, 226(3):536-550, doi:10.1016/j.ejor.2012.11.047.
- SALib.analyze.delta.bias_reduced_delta(Y, Ygrid, X, m, num_resamples, conf_level, y_resamples)[source]#
Plischke et al. 2013 bias reduction technique (eqn 30)