salib_samplers
Samplers for working with SALib.
- class ema_workbench.em_framework.salib_samplers.FASTSampler
Sampler generating a Fourier Amplitude Sensitivity Test (FAST).
- sample(problem: dict, size: int, rng: Generator | None, **kwargs) ndarray
Call the underlying salib sampling method and return the samples.
Any additional keyword arguments will be passed to the underlying salib sampling method
- Parameters:
problem (a dictionary with the problem specification)
size (the number of samples to generate)
rng (a np.random.Generator, or something that can seed a rgn.)
kwargs (any additional keyword arguments)
are (Additional valid keyword arguments)
M (int (default: 4)) – The interference parameter, i.e., the number of harmonics to sum in the Fourier series decomposition (default 4)
decomposition (Fourier series)
- class ema_workbench.em_framework.salib_samplers.MorrisSampler
Sampler generating a morris design using SALib.
- sample(problem: dict, size: int, rng: Generator | None, **kwargs) ndarray
Call the underlying salib sampling method and return the samples.
Any additional keyword arguments will be passed to the underlying salib sampling method
- Parameters:
problem (a dictionary with the problem specification)
size (the number of samples to generate)
rng (a np.random.Generator, or something that can seed a rgn.)
kwargs (any additional keyword arguments)
are (Additional valid keyword arguments)
num_levels (int) – The number of grid levels
grid_jump (int) – The grid jump size
optimal_trajectories (int, optional) – The number of optimal trajectories to sample (between 2 and N)
local_optimization (bool, optional) – Flag whether to use local optimization according to Ruano et al. (2012) Speeds up the process tremendously for bigger N and num_levels. Stating this variable to be true causes the function to ignore gurobi.
- class ema_workbench.em_framework.salib_samplers.SobolSampler
Sampler generating a Sobol design using SALib.
- sample(problem: dict, size: int, rng: Generator | None, **kwargs) ndarray
Call the underlying salib sampling method and return the samples.
Any additional keyword arguments will be passed to the underlying salib sampling method
- Parameters:
problem (a dictionary with the problem specification)
size (the number of samples to generate)
rng (a np.random.Generator, or something that can seed a rgn.)
kwargs (any additional keyword arguments)
are (Additional valid keyword arguments)
calc_second_order (bool, optional) – Calculate second-order sensitivities. Default is True.
scramble (bool, optional) – If True, use LMS+shift scrambling. Otherwise, no scrambling is done. Default is True.
skip_values (int, optional) – Number of points in Sobol’ sequence to skip, ideally a value of base 2. It’s recommended not to change this value and use scramble instead. scramble and skip_values can be used together. Default is 0.