sampling.effects.screening_plot

sampling.effects.screening_plot(X, fun, xi, p, labels, bounds=None, show=True)

Generates a plot with elementary effect screening metrics.

This function calculates the mean and standard deviation of the elementary effects for a given set of design variables and plots the results.

Parameters

Name	Type	Description	Default
X	np.ndarray	The screening plan matrix, typically structured within a [0,1]^k box.	required
fun	object	The objective function to evaluate at each design point in the screening plan.	required
xi	float	The elementary effect step length factor.	required
p	int	Number of discrete levels along each dimension.	required
labels	list of str	A list of variable names corresponding to the design variables.	required
bounds	np.ndarray	A 2xk matrix where the first row contains lower bounds and the second row contains upper bounds for each variable.	None
show	bool	If True, the plot is displayed. Defaults to True.	True

Returns

Name	Type	Description
None	None	The function generates a plot of the results.

Examples

>>> import numpy as np
    from spotpython.utils.effects import screening, screeningplan
    from spotpython.fun.objectivefunctions import Analytical
    fun = Analytical()
    k = 10
    p = 10
    xi = 1
    r = 25
    X = screeningplan(k=k, p=p, xi=xi, r=r)  # shape (r x (k+1), k)
    # Provide real-world bounds from the wing weight docs (2 x 10).
    value_range = np.array([
        [150, 220,   6, -10, 16, 0.5, 0.08, 2.5, 1700, 0.025],
        [200, 300,  10,  10, 45, 1.0, 0.18, 6.0, 2500, 0.08 ],
    ])
    labels = [
        "S_W", "W_fw", "A", "Lambda",
        "q",   "lambda", "tc", "N_z",
        "W_dg", "W_p"
    ]
    screening(
        X=X,
        fun=fun.fun_wingwt,
        bounds=value_range,
        xi=xi,
        p=p,
        labels=labels,
        print=False,
    )