sampling.effects.screening_print

sampling.effects.screening_print(X, fun, xi, p, labels, bounds=None)

Generates a DataFrame 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 returns the results as a Pandas DataFrame.

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

Returns

Name	Type	Description
	pd.DataFrame	pd.DataFrame: A DataFrame containing three columns: - ‘varname’: The name of each variable. - ‘mean’: The mean of the elementary effects for each variable. - ‘sd’: The standard deviation of the elementary effects for each variable.
	pd.DataFrame	or None: If print is set to False, a plot of the results is generated instead of returning a DataFrame.

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,
    )