0.14.22

Author: bartzbeielstein

pyproject.toml

@@ -7,7 +7,7 @@ build-backend = "setuptools.build_meta"
 
 [project]
 name = "spotpython"
-version = "0.14.21"
+version = "0.14.22"
 authors = [
   { name="T. Bartz-Beielstein", email="tbb@bartzundbartz.de" }
 ]

src/spotPython/data/friedman.py

@@ -5,7 +5,7 @@
 class FriedmanDriftDataset:
     """Friedman Drift Dataset."""
 
-    def __init__(self, n_samples=100, change_point1=50, change_point2=75, seed=None, constant=False):
+    def __init__(self, n_samples=100, change_point1=50, change_point2=75, seed=None, constant=False) -> None:
         """Constructor for the Friedman Drift Dataset.
 
         Args:
@@ -16,7 +16,7 @@ def __init__(self, n_samples=100, change_point1=50, change_point2=75, seed=None,
             constant (bool): If True, only the first feature is set to 1 and all others are set to 0.
 
         Returns:
-            None
+            None (None): None
 
         Examples:
             >>> from spotPython.data.friedman import FriedmanDriftDataset

src/spotPython/hyperparameters/values.py

@@ -1466,31 +1466,31 @@ def update_fun_control_with_hyper_num_cat_dicts(fun_control, num_dict, cat_dict,
             fun_control["core_model_hyper_dict"][key].update({"upper": len(fle) - 1})
 
 
-def get_core_model_from_name(core_model_name):
+def get_core_model_from_name(core_model_name) -> object:
     """
-    Returns the core model name and instance from a core model name.
+    Returns the river core model name and instance from a core model name.
 
     Args:
         core_model_name (str): The name of the core model.
 
     Returns:
-        Tuple: The core model name and instance.
+        (str, object): The core model name and instance.
     """
     core_model_module = core_model_name.split(".")[0]
     coremodel = core_model_name.split(".")[1]
     core_model_instance = getattr(getattr(river, core_model_module), coremodel)
     return coremodel, core_model_instance
 
 
-def get_prep_model(prepmodel_name):
+def get_prep_model(prepmodel_name) -> object:
     """
-    Get the preprocessing model from the name.
+    Get the river preprocessing model from the name.
 
     Args:
         prepmodel_name (str): The name of the preprocessing model.
 
     Returns:
-        river.preprocessing: The preprocessing model.
+        river.preprocessing (object): The river preprocessing model.
 
     """
     if prepmodel_name == "None":
@@ -1500,15 +1500,15 @@ def get_prep_model(prepmodel_name):
     return prepmodel
 
 
-def get_metric_sklearn(metric_name):
+def get_metric_sklearn(metric_name) -> object:
     """
-    Returns the metric from the metric name.
+    Returns the sklearn metric from the metric name.
 
     Args:
         metric_name (str): The name of the metric.
 
     Returns:
-        sklearn.metrics: The metric from the metric name.
+        sklearn.metrics (object): The sklearn metric.
     """
     metric_sklearn = getattr(sklearn.metrics, metric_name)
     return metric_sklearn

src/spotPython/plot/xai.py

@@ -585,7 +585,7 @@ def get_attributions(
     baseline=None,
     abs_attr=True,
     n_rel=5,
-):
+) -> pd.DataFrame:
     """Get the attributions of a neural network.
 
     Args:
@@ -603,7 +603,7 @@ def get_attributions(
             The number of relevant features. Defaults to 5.
 
     Returns:
-        pd.DataFrame: A DataFrame with the attributions.
+        pd.DataFrame (object): A DataFrame with the attributions.
     """
     try:
         fun_control["data_set"].names
@@ -673,7 +673,7 @@ def get_attributions(
     return df
 
 
-def plot_attributions(df, attr_method="IntegratedGradients"):
+def plot_attributions(df, attr_method="IntegratedGradients") -> None:
     """
     Plot the attributions of a neural network.
 
@@ -696,7 +696,7 @@ def plot_attributions(df, attr_method="IntegratedGradients"):
     plt.show()
 
 
-def is_square(n):
+def is_square(n) -> bool:
     """Check if a number is a square number.
 
     Args:
@@ -714,7 +714,7 @@ def is_square(n):
     return n == int(math.sqrt(n)) ** 2
 
 
-def get_layer_conductance(spot_tuner, fun_control, layer_idx):
+def get_layer_conductance(spot_tuner, fun_control, layer_idx) -> np.ndarray:
     """
     Compute the average layer conductance attributions for a specified layer in the model.
 

src/spotPython/utils/convert.py

@@ -182,16 +182,17 @@ def sort_by_kth_and_return_indices(array, k) -> list:
     return sorted_indices
 
 
-def check_type(value):
+def check_type(value) -> str:
     """Check the type of the input value and return the type as a string.
 
     Args:
         value (object): The input value.
 
     Returns:
-        str: The type of the input value as a string.
-        Possible values are "int", "float", "str", "bool", or None.
-        Checks for numpy types as well, i.e., np.integer, np.floating, np.str_, np.bool_.
+        str:
+            The type of the input value as a string.
+            Possible values are "int", "float", "str", "bool", or None.
+            Checks for numpy types as well, i.e., np.integer, np.floating, np.str_, np.bool_.
 
     Examples:
         >>> from spotPython.utils.convert import check_type
@@ -211,16 +212,16 @@ def check_type(value):
         return None
 
 
-def set_dataset_target_type(dataset, target="y"):
+def set_dataset_target_type(dataset, target="y") -> pd.DataFrame:
     """Set the target column to 0 and 1 for boolean and string values.
 
     Args:
         dataset (pd.DataFrame): The input dataset.
         target (str): The name of the target column. Default is "y".
 
     Returns:
-        pd.DataFrame: The dataset with boolean and string target column
-        values set to 0 and 1.
+        pd.DataFrame:
+            The dataset with boolean and string target column values set to 0 and 1.
 
     Examples:
     >>> from spotPython.utils.convert import set_dataset_target_type