use *args, **kwargs schema to centralize default arguments

src/surfaces/test_functions/machine_learning/_base_machine_learning.py

@@ -10,8 +10,8 @@
 
 
 class MachineLearningFunction(BaseTestFunction):
-    def __init__(self, metric, sleep, evaluate_from_data):
-        super().__init__(metric, sleep)
+    def __init__(self, *args, sleep=0, evaluate_from_data=False, **kwargs):
+        super().__init__(*args, sleep, **kwargs)
 
         if evaluate_from_data:
             self.sdc = SurfacesDataCollector()

src/surfaces/test_functions/machine_learning/tabular/_base_tabular.py

@@ -7,5 +7,5 @@
 
 
 class BaseTabular(MachineLearningFunction):
-    def __init__(self, metric, sleep, evaluate_from_data):
-        super().__init__(metric, sleep, evaluate_from_data)
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)

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

@@ -7,7 +7,5 @@
 
 
 class BaseClassification(BaseTabular):
-    metric = "accuracy"  # called 'scoring' in sklearn
-
-    def __init__(self, metric, sleep, evaluate_from_data):
-        super().__init__(metric, sleep, evaluate_from_data)
+    def __init__(self, *args, metric="accuracy", **kwargs):
+        super().__init__(*args, metric, **kwargs)

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

@@ -20,13 +20,8 @@ class KNeighborsClassifierFunction(BaseClassification):
     cv_default = [2, 3, 4, 5, 8, 10]
     dataset_default = [digits_data, wine_data, iris_data]
 
-    def __init__(
-        self,
-        metric=None,
-        sleep=0,
-        evaluate_from_data=False,
-    ):
-        super().__init__(metric, sleep, evaluate_from_data)
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
 
     def search_space(
         self,

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

@@ -7,7 +7,5 @@
 
 
 class BaseRegression(BaseTabular):
-    metric = "r2"  # called 'scoring' in sklearn
-
-    def __init__(self, metric, sleep, evaluate_from_data):
-        super().__init__(metric, sleep, evaluate_from_data)
+    def __init__(self, *args, metric="r2", **kwargs):
+        super().__init__(*args, metric, **kwargs)

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

@@ -25,13 +25,8 @@ class GradientBoostingRegressorFunction(BaseRegression):
     cv_default = [2, 3, 4, 5, 8, 10]
     dataset_default = [diabetes_data]
 
-    def __init__(
-        self,
-        metric=None,
-        sleep=0,
-        evaluate_from_data=False,
-    ):
-        super().__init__(metric, sleep, evaluate_from_data)
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
 
     def search_space(
         self,

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

@@ -25,13 +25,8 @@ class KNeighborsRegressorFunction(BaseRegression):
     cv_default = [2, 3, 4, 5, 8, 10]
     dataset_default = [diabetes_data]
 
-    def __init__(
-        self,
-        metric=None,
-        sleep=0,
-        evaluate_from_data=False,
-    ):
-        super().__init__(metric, sleep, evaluate_from_data)
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
 
     def search_space(
         self,