move method to private

Author: SimonBlanke

src/surfaces/test_functions/_base_test_function.py

@@ -43,10 +43,10 @@ class BaseTestFunction:
     default_bounds: Tuple[float, float] = (-5.0, 5.0)
 
     def _create_objective_function_(func):
-        """Decorator that calls create_objective_function after __init__."""
+        """Decorator that calls _create_objective_function after __init__."""
         def wrapper(self, *args, **kwargs):
             func(self, *args, **kwargs)
-            self.create_objective_function()
+            self._create_objective_function()
         return wrapper
 
     @_create_objective_function_
@@ -55,15 +55,15 @@ def __init__(self, metric, sleep, validate=True):
         self.metric = metric
         self._validate = validate
 
-    def create_objective_function(self):
-        raise NotImplementedError("'create_objective_function' must be implemented")
+    def _create_objective_function(self):
+        raise NotImplementedError("'_create_objective_function' must be implemented")
 
     @property
     def default_search_space(self) -> Dict[str, Any]:
         """Default search space for this function (override in subclasses)."""
         raise NotImplementedError("'default_search_space' must be implemented")
 
-    def return_metric(self, value):
+    def _return_metric(self, value):
         """Transform raw value based on metric setting (override in subclasses)."""
         return value
 
@@ -110,7 +110,7 @@ def _evaluate_with_timing(self, params: Dict[str, Any]) -> float:
         """Evaluate with sleep timing applied."""
         time.sleep(self.sleep)
         raw_value = self.pure_objective_function(params)
-        return self.return_metric(raw_value)
+        return self._return_metric(raw_value)
 
     def _evaluate_raw(self, params: Dict[str, Any]) -> float:
         """Evaluate without metric transformation (internal use)."""

src/surfaces/test_functions/machine_learning/_base_machine_learning.py

@@ -74,12 +74,12 @@ def _evaluate_with_timing(self, params: Dict[str, Any]) -> float:
         """Evaluate with sleep timing applied (overrides base class)."""
         time.sleep(self.sleep)
         raw_value = self._get_raw_value(params)
-        return self.return_metric(raw_value)
+        return self._return_metric(raw_value)
 
     # Metrics that behave like scores (higher is better)
     SCORE_LIKE_METRICS = {"score", "accuracy", "r2", "f1", "precision", "recall", "auc"}
 
-    def return_metric(self, score: float) -> float:
+    def _return_metric(self, score: float) -> float:
         """
         Transform raw score value based on metric setting.
 

src/surfaces/test_functions/machine_learning/tabular/classification/test_functions/k_neighbors_classifier.py

@@ -81,7 +81,7 @@ def _search_space(
 
         return search_space
 
-    def create_objective_function(self):
+    def _create_objective_function(self):
         def k_neighbors_classifier(params):
             knc = KNeighborsClassifier(
                 n_neighbors=params["n_neighbors"],

src/surfaces/test_functions/machine_learning/tabular/regression/test_functions/gradient_boosting_regressor.py

@@ -82,7 +82,7 @@ def _search_space(
 
         return search_space
 
-    def create_objective_function(self):
+    def _create_objective_function(self):
         def gradient_boosting_regressor(params):
             knc = GradientBoostingRegressor(
                 n_estimators=params["n_estimators"],

src/surfaces/test_functions/machine_learning/tabular/regression/test_functions/k_neighbors_regressor.py

@@ -82,7 +82,7 @@ def _search_space(
 
         return search_space
 
-    def create_objective_function(self):
+    def _create_objective_function(self):
         def k_neighbors_regressor(params):
             knc = KNeighborsRegressor(
                 n_neighbors=params["n_neighbors"],

src/surfaces/test_functions/mathematical/_base_mathematical_function.py

@@ -62,7 +62,7 @@ def _create_search_space(
         Internal method for creating search spaces with non-default parameters.
         For most use cases, access default_search_space property instead.
         """
-        return self.create_n_dim_search_space(min=min, max=max, size=size, value_types=value_types)
+        return self._create_n_dim_search_space(min=min, max=max, size=size, value_types=value_types)
 
     def __init__(
         self,
@@ -85,7 +85,7 @@ def __init__(
         self.metric = metric
         self.sleep = sleep
 
-    def return_metric(self, loss: float) -> float:
+    def _return_metric(self, loss: float) -> float:
         """
         Transform raw loss value based on metric setting.
 
@@ -118,14 +118,14 @@ def _to_score(self, raw_value: float) -> float:
         return -raw_value
 
     @staticmethod
-    def conv_arrays2lists(search_space: Dict[str, Any]) -> Dict[str, list]:
+    def _conv_arrays2lists(search_space: Dict[str, Any]) -> Dict[str, list]:
         """Convert array-valued search space to list-valued."""
         return {
             para_name: list(dim_values)
             for para_name, dim_values in search_space.items()
         }
 
-    def create_n_dim_search_space(
+    def _create_n_dim_search_space(
         self,
         min: Union[float, list] = -5,
         max: Union[float, list] = 5,

src/surfaces/test_functions/mathematical/test_functions_1d/gramacy_and_lee_function.py

@@ -61,7 +61,7 @@ def __init__(self, metric="score", sleep=0):
         super().__init__(metric, sleep)
         self.n_dim = 1
 
-    def create_objective_function(self):
+    def _create_objective_function(self):
         def gramacy_and_lee_function(params):
             x = params["x0"]
 
@@ -70,6 +70,6 @@ def gramacy_and_lee_function(params):
         self.pure_objective_function = gramacy_and_lee_function
 
     def _search_space(self, min=0.5, max=2.5, value_types="array", size=10000):
-        return super().create_n_dim_search_space(
+        return super()._create_n_dim_search_space(
             min=min, max=max, size=size, value_types=value_types
         )

src/surfaces/test_functions/mathematical/test_functions_2d/ackley_function.py

@@ -77,7 +77,7 @@ def __init__(
         self.A = A
         self.angle = angle
 
-    def create_objective_function(self):
+    def _create_objective_function(self):
         def ackley_function(params):
             x = params["x0"]
             y = params["x1"]
@@ -92,6 +92,6 @@ def ackley_function(params):
         self.pure_objective_function = ackley_function
 
     def _search_space(self, min=-5, max=5, value_types="array", size=10000):
-        return super().create_n_dim_search_space(
+        return super()._create_n_dim_search_space(
             min=min, max=max, size=size, value_types=value_types
         )

src/surfaces/test_functions/mathematical/test_functions_2d/beale_function.py

@@ -70,7 +70,7 @@ def __init__(self, A=1.5, B=2.25, C=2.652, metric="score", sleep=0):
         self.B = B
         self.C = C
 
-    def create_objective_function(self):
+    def _create_objective_function(self):
         def beale_function(params):
             x = params["x0"]
             y = params["x1"]
@@ -84,6 +84,6 @@ def beale_function(params):
         self.pure_objective_function = beale_function
 
     def _search_space(self, min=-4.5, max=4.5, value_types="array", size=10000):
-        return super().create_n_dim_search_space(
+        return super()._create_n_dim_search_space(
             min=min, max=max, size=size, value_types=value_types
         )

src/surfaces/test_functions/mathematical/test_functions_2d/booth_function.py

@@ -55,7 +55,7 @@ def __init__(self, metric="score", sleep=0):
         super().__init__(metric, sleep)
         self.n_dim = 2
 
-    def create_objective_function(self):
+    def _create_objective_function(self):
 
         def booth_function(params):
             x = params["x0"]
@@ -69,6 +69,6 @@ def booth_function(params):
         self.pure_objective_function = booth_function
 
     def _search_space(self, min=-10, max=10, value_types="array", size=10000):
-        return super().create_n_dim_search_space(
+        return super()._create_n_dim_search_space(
             min=min, max=max, size=size, value_types=value_types
         )