0.14.10

Author: bartzbeielstein

makeSpot.sh

@@ -1,3 +1,3 @@
 #!/bin/sh
-rm -f dist/spotPython*; python -m build; python -m pip install dist/spotPython*.tar.gz
+rm -f dist/spotpython*; python -m build; python -m pip install dist/spotpython*.tar.gz
 python -m mkdocs build

pyproject.toml

@@ -6,12 +6,12 @@ requires = [
 build-backend = "setuptools.build_meta"
 
 [project]
-name = "spotPython"
-version = "0.14.9"
+name = "spotpython"
+version = "0.14.10"
 authors = [
   { name="T. Bartz-Beielstein", email="tbb@bartzundbartz.de" }
 ]
-description = "spotPython - Sequential Parameter Optimization in Python"
+description = "spotpython - Sequential Parameter Optimization in Python"
 readme = "README.md"
 license = { text="AGPL-3.0-or-later" }
 requires-python = ">=3.10"
@@ -57,8 +57,8 @@ dependencies = [
 
 [project.urls]
 "Homepage" = "https://www.spotseven.de"
-Issues = "https://github.com/sequential-parameter-optimization/spotPython/issues"
-Repository = "https://github.com/sequential-parameter-optimization/spotPython"
+Issues = "https://github.com/sequential-parameter-optimization/spotpython/issues"
+Repository = "https://github.com/sequential-parameter-optimization/spotpython"
 
 [tool.setuptools]
 include-package-data = true

src/spotPython/utils/convert.py

@@ -2,6 +2,7 @@
 import numpy as np
 import pandas as pd
 from itertools import combinations
+import copy
 
 
 def class_for_name(module_name, class_name) -> object:
@@ -208,3 +209,28 @@ def check_type(value):
         return "bool"
     else:
         return None
+
+
+def set_dataset_target_type(dataset, target="y"):
+    """Set the target column to 0 and 1.
+
+    Args:
+        dataset (pd.DataFrame): The input dataset.
+        target (str): The name of the target column. Default is "y".
+
+    Returns:
+        pd.DataFrame: The dataset with the target column set to 0 and 1.
+
+    Examples:
+    >>> from spotPython.utils.convert import set_dataset_target_type
+        dataset = pd.DataFrame({"a": [1, 2, 3], "b": [4, 5, 6], "c": [7, 8, 9], "y": [True, False, True]})
+        dataset = set_dataset_target_type(dataset)
+
+
+    """
+    val = copy.deepcopy(dataset.iloc[0, -1])
+    target_type = check_type(val)
+    if target_type == "bool" or target_type == "str":
+        # convert the target column to 0 and 1
+        dataset[target] = dataset[target].astype(int)
+    return dataset