move 'evaluate_from_data'-parameter to ml-class only

src/surfaces/test_functions/_base_test_function.py

@@ -7,8 +7,6 @@
 import numpy as np
 import pandas as pd
 
-from ..data_collector import SurfacesDataCollector
-
 
 class BaseTestFunction:
     explanation = """ """
@@ -20,18 +18,12 @@ class BaseTestFunction:
     objective_function: callable
     pure_objective_function: callable
 
-    def __init__(self, metric, sleep, evaluate_from_data):
+    def __init__(self, metric, sleep):
         self.sleep = sleep
         self.metric = metric
 
         self.create_objective_function()
 
-        if evaluate_from_data:
-            self.sdc = SurfacesDataCollector()
-            self._objective_function_ = self.objective_function_loaded
-        else:
-            self._objective_function_ = self.pure_objective_function
-
     def create_objective_function(self):
         e_msg = "'create_objective_function'-method is not implemented"
         raise NotImplementedError(e_msg)
@@ -61,30 +53,4 @@ def objective_function(self, *input):
         """
 
         metric = self.pure_objective_function(*input)
-        print("\n metric ", metric, "\n")
         return self.return_metric(metric)
-
-    def objective_function_loaded(self, params):
-        try:
-            parameter_d = params.para_dict
-        except AttributeError:
-            parameter_d = params
-
-        for para_names, dim_value in parameter_d.items():
-            try:
-                parameter_d[para_names] = dim_value.__name__
-            except AttributeError:
-                pass
-
-        search_data = self.sdc.load(self.__name__)
-        if search_data is None:
-            msg = "Search Data is empty"
-            raise TypeError(msg)
-
-        params_df = pd.DataFrame(parameter_d, index=[0])
-
-        para_df_row = search_data[
-            np.all(search_data[self.para_names].values == params_df.values, axis=1)
-        ]
-        score = para_df_row["score"].values[0]
-        return score

src/surfaces/test_functions/machine_learning/base_machine_learning_function.py

@@ -4,8 +4,40 @@
 
 
 from .._base_test_function import BaseTestFunction
+from ...data_collector import SurfacesDataCollector
 
 
 class MachineLearningFunction(BaseTestFunction):
     def __init__(self, metric, sleep, evaluate_from_data):
-        super().__init__(metric, sleep, evaluate_from_data)
+        super().__init__(metric, sleep)
+
+        if evaluate_from_data:
+            self.sdc = SurfacesDataCollector()
+            self._objective_function_ = self.objective_function_loaded
+        else:
+            self._objective_function_ = self.pure_objective_function
+
+    def objective_function_loaded(self, params):
+        try:
+            parameter_d = params.para_dict
+        except AttributeError:
+            parameter_d = params
+
+        for para_names, dim_value in parameter_d.items():
+            try:
+                parameter_d[para_names] = dim_value.__name__
+            except AttributeError:
+                pass
+
+        search_data = self.sdc.load(self.__name__)
+        if search_data is None:
+            msg = "Search Data is empty"
+            raise TypeError(msg)
+
+        params_df = pd.DataFrame(parameter_d, index=[0])
+
+        para_df_row = search_data[
+            np.all(search_data[self.para_names].values == params_df.values, axis=1)
+        ]
+        score = para_df_row["score"].values[0]
+        return score

src/surfaces/test_functions/mathematical/_base_mathematical_function.py

@@ -19,13 +19,14 @@ def __init__(
         self,
         metric="loss",
         sleep=0,
-        evaluate_from_data=False,
     ):
-        super().__init__(metric, sleep, evaluate_from_data)
+        super().__init__(metric, sleep)
 
         self.metric = metric
         self.sleep = sleep
 
+        self._objective_function_ = self.pure_objective_function
+
     def return_metric(self, loss):
         if self.metric == "score":
             return -loss