0.14.28

New hyperparameter handling: set_int_hyperparameter_values, set_float_hyperparameter_values, set_boolean_hyperparamerer_values, and set_factor_hyperparameter_values

Author: bartzbeielstein

notebooks/00_spotPython_tests.ipynb

@@ -3908,7 +3908,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 3,
+      "execution_count": 1,
       "metadata": {},
       "outputs": [
         {

pyproject.toml

@@ -7,7 +7,7 @@ build-backend = "setuptools.build_meta"
 
 [project]
 name = "spotpython"
-version = "0.14.27"
+version = "0.14.28"
 authors = [
   { name="T. Bartz-Beielstein", email="tbb@bartzundbartz.de" }
 ]

src/spotPython/data/california_housing.py

@@ -19,10 +19,16 @@ class CaliforniaHousing(Dataset):
         - Latitude block group latitude
         - Longitude block group longitude
     * Missing Attribute Values: None
-    * Target: The target variable is the median house value for California districts, expressed in hundreds of thousands of dollars ($100,000).
-    This dataset was obtained from the StatLib repository. https://www.dcc.fc.up.pt/~ltorgo/Regression/cal_housing.html
-    This dataset was derived from the 1990 U.S. census, using one row per census block group. A block group is the smallest geographical unit for which the U.S. Census Bureau publishes sample data (a block group typically has a population of 600 to 3,000 people).
-    A household is a group of people residing within a home. Since the average number of rooms and bedrooms in this dataset are provided per household, these columns may take surprisingly large values for block groups with few households and many empty houses, such as vacation resorts.
+    * Target: The target variable is the median house value for California districts,
+        expressed in hundreds of thousands of dollars ($100,000).
+        This dataset was obtained from the StatLib repository:
+        https://www.dcc.fc.up.pt/~ltorgo/Regression/cal_housing.html
+        This dataset was derived from the 1990 U.S. census, using one row per census block group.
+        A block group is the smallest geographical unit for which the U.S. Census Bureau publishes
+        sample data (a block group typically has a population of 600 to 3,000 people).
+        A household is a group of people residing within a home. Since the average number of rooms
+        and bedrooms in this dataset are provided per household, these columns may take surprisingly
+        large values for block groups with few households and many empty houses, such as vacation resorts.
 
     Args:
         feature_type (torch.dtype): The data type of the features. Defaults to torch.float.

src/spotPython/hyperparameters/values.py

@@ -1466,6 +1466,105 @@ 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 set_int_hyperparameter_values(fun_control, key, lower, upper):
+    """
+    Set the integer hyperparameter values in the fun_control dictionary.
+
+    Args:
+        fun_control (dict):
+            The fun_control dictionary.
+        key (str):
+            The key of the hyperparameter.
+        lower (int):
+            The lower bound of the hyperparameter.
+        upper (int):
+            The upper bound of the hyperparameter.
+    """
+    set_control_hyperparameter_value(
+        fun_control,
+        key,
+        [
+            lower,
+            upper,
+        ],
+    )
+
+
+def set_float_hyperparameter_values(fun_control, key, lower, upper):
+    """
+    Set the float hyperparameter values in the fun_control dictionary.
+
+    Args:
+        fun_control (dict):
+            The fun_control dictionary.
+        key (str):
+            The key of the hyperparameter.
+        lower (float):
+            The lower bound of the hyperparameter.
+        upper (float):
+            The upper bound of the hyperparameter.
+    """
+    set_control_hyperparameter_value(
+        fun_control,
+        key,
+        [
+            lower,
+            upper,
+        ],
+    )
+
+
+def set_boolean_hyperparameter_values(fun_control, key, lower, upper):
+    """
+    Set the boolean hyperparameter values in the fun_control dictionary.
+
+    Args:
+        fun_control (dict):
+            The fun_control dictionary.
+        key (str):
+            The key of the hyperparameter.
+        lower (bool):
+            The lower bound of the hyperparameter.
+        upper (bool):
+            The upper bound of the hyperparameter.
+    """
+    set_control_hyperparameter_value(
+        fun_control,
+        key,
+        [
+            lower,
+            upper,
+        ],
+    )
+
+
+def set_factor_hyperparameter_values(fun_control, key, levels):
+    """
+    Set the factor hyperparameter values in the fun_control dictionary.
+
+    Args:
+        fun_control (dict):
+            The fun_control dictionary.
+        key (str):
+            The key of the hyperparameter.
+        levels (list):
+            The levels of the hyperparameter.
+    """
+    # check if levels is a list of strings. If not, convert it to a list
+    if not isinstance(levels, list):
+        levels = [levels]
+    # check if levels is a list of strings. Othewise, issue a warning and return None
+    if not all(isinstance(x, str) for x in levels):
+        print("!!! Warning: levels should be a list of strings.")
+        return None
+    # check if key "core_model_hyper_dict" exists in fun_control:
+    if "core_model_hyper_dict" not in fun_control.keys():
+        return None
+    else:
+        fun_control["core_model_hyper_dict"][key].update({"levels": levels})
+        fun_control["core_model_hyper_dict"][key].update({"upper": len(levels) - 1})
+
+
 def get_core_model_from_name(core_model_name) -> object:
     """
     Returns the river core model name and instance from a core model name.