0.29.6

bestlhd accepts edges arg

Author: bartzbeielstein

pyproject.toml

@@ -7,7 +7,7 @@ build-backend = "setuptools.build_meta"
 
 [project]
 name = "spotpython"
-version = "0.29.5"
+version = "0.29.6"
 authors = [
   { name="T. Bartz-Beielstein", email="tbb@bartzundbartz.de" }
 ]

src/spotpython/utils/effects.py

@@ -229,35 +229,32 @@ def screening_print(X, fun, xi, p, labels, bounds=None) -> pd.DataFrame:
     return df
 
 
-def screening_plot(X, fun, xi, p, labels, bounds=None, show=True):
+def screening_plot(X, fun, xi, p, labels, bounds=None, show=True) -> None:
     """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.
 
     Args:
-        X (np.ndarray): The screening plan matrix, typically structured
-            within a [0,1]^k box.
-        fun (object): The objective function to evaluate at each
-            design point in the screening plan.
-        xi (float): The elementary effect step length factor.
-        p (int): Number of discrete levels along each dimension.
-        labels (list of str): A list of variable names corresponding to
-            the design variables.
-        bounds (np.ndarray): A 2xk matrix where the first row contains
-            lower bounds and the second row contains upper bounds for
-            each variable.
-        show: (bool): If True, the plot is displayed. Defaults to True.
+        X (np.ndarray):
+            The screening plan matrix, typically structured within a [0,1]^k box.
+        fun (object):
+            The objective function to evaluate at each design point in the screening plan.
+        xi (float):
+            The elementary effect step length factor.
+        p (int):
+            Number of discrete levels along each dimension.
+        labels (list of str):
+            A list of variable names corresponding to the design variables.
+        bounds (np.ndarray):
+            A 2xk matrix where the first row contains lower bounds and
+            the second row contains upper bounds for each variable.
+        show (bool):
+            If True, the plot is displayed. Defaults to True.
 
     Returns:
-        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.
-        or None: If print is set to False, a plot of the results is
-            generated instead of returning a DataFrame.
+        None: The function generates a plot of the results.
 
     Examples:
         >>> import numpy as np

src/spotpython/utils/sampling.py

@@ -620,7 +620,7 @@ def mmlhs(X_start: np.ndarray, population: int, iterations: int, q: Optional[flo
     return X_best
 
 
-def bestlh(n: int, k: int, population: int, iterations: int, p=1, plot=False, verbosity=0) -> np.ndarray:
+def bestlh(n: int, k: int, population: int, iterations: int, p=1, plot=False, verbosity=0, edges=0) -> np.ndarray:
     """
     Generates an optimized Latin hypercube by evolving the Morris-Mitchell
     criterion across multiple exponents (q values) and selecting the best plan.
@@ -642,6 +642,10 @@ def bestlh(n: int, k: int, population: int, iterations: int, p=1, plot=False, ve
             will be displayed. Only if k>=2.  Defaults to False.
         verbosity (int, optional):
             Verbosity level. 0 is silent, 1 prints the best q value found. Defaults to 0.
+        edges (int, optional):
+            If 1, places centers of the extreme bins at the domain edges ([0,1]).
+            Otherwise, bins are fully contained within the domain, i.e. midpoints.
+            Defaults to 0.
 
     Returns:
         np.ndarray:
@@ -675,7 +679,7 @@ def bestlh(n: int, k: int, population: int, iterations: int, p=1, plot=False, ve
     q_list = [1, 2, 5, 10, 20, 50, 100]
 
     # Start with a random Latin hypercube
-    X_start = rlh(n, k, edges=1)
+    X_start = rlh(n, k, edges=edges)
 
     # Allocate a 3D array to store the results for each q
     # (shape: (n, k, number_of_q_values))