v0.2.23

new: generate_config_id

Author: bartzbeielstein

pyproject.toml

@@ -7,7 +7,7 @@ build-backend = "setuptools.build_meta"
 
 [project]
 name = "spotPython"
-version = "0.2.22"
+version = "0.2.23"
 authors = [
   { name="T. Bartz-Beielstein", email="tbb@bartzundbartz.de" }
 ]

src/spotPython/fun/hypersklearn.py

@@ -9,6 +9,7 @@
 )
 
 from spotPython.sklearn.traintest import evaluate_cv, evaluate_model_oob, evaluate_hold_out
+from spotPython.utils.eda import generate_config_id
 
 import logging
 from sklearn.metrics import mean_absolute_error
@@ -80,6 +81,7 @@ def fun_sklearn(self, X, fun_control=None):
         self.check_X_shape(X)
         var_dict = assign_values(X, self.fun_control["var_name"])
         for config in generate_one_config_from_var_dict(var_dict, self.fun_control):
+            config_id = generate_config_id(config)
             if self.fun_control["prep_model"] is not None:
                 model = make_pipeline(self.fun_control["prep_model"], self.fun_control["core_model"](**config))
             else:

src/spotPython/fun/hypertorch.py

@@ -8,6 +8,7 @@
     assign_values,
     generate_one_config_from_var_dict,
 )
+from spotPython.utils.eda import generate_config_id
 
 
 logger = logging.getLogger(__name__)
@@ -64,10 +65,7 @@ def fun_torch(self, X, fun_control=None):
         # type information and transformations are considered in generate_one_config_from_var_dict:
         for config in generate_one_config_from_var_dict(var_dict, self.fun_control):
             print(f"\nconfig: {config}")
-            config_id = ""
-            for key in config:
-                config_id += str(config[key]) + "_"
-            config_id = config_id[:-1]
+            config_id = generate_config_id(config)
             if self.fun_control["prep_model"] is not None:
                 model = make_pipeline(self.fun_control["prep_model"], self.fun_control["core_model"](**config))
             else:

src/spotPython/utils/eda.py

@@ -97,3 +97,16 @@ def compare_two_tree_models(model1, model2, headers=["Parameter", "Default", "Sp
     for key, value1, value2 in zip(keys, values1, values2):
         tbl.append([key, value1, value2])
     return tabulate(tbl, headers=headers, numalign="right", tablefmt="github")
+
+
+def generate_config_id(config):
+    """Generates a unique id for a configuration.
+    Args:
+        config (dict): A dictionary with the configuration.
+    Returns:
+        str: A unique id for the configuration.
+    """
+    config_id = ""
+    for key in config:
+        config_id += str(config[key]) + "_"
+    return config_id[:-1]