Implemented abstract base class for custom optimization algorithm strategies

Author: fjwillemsen

kernel_tuner/strategies/wrapper.py

@@ -1,27 +1,48 @@
 """Wrapper intended for user-defined custom optimization methods"""
 
+from abc import ABC, abstractmethod
+
 from kernel_tuner import util
 from kernel_tuner.searchspace import Searchspace
 from kernel_tuner.strategies.common import CostFunc
 
 
+class OptAlg(ABC):
+    """Base class for user-defined optimization algorithms."""
+
+    def __init__(self):
+        self.costfunc_kwargs = {"scaling": True, "snap": True}
+
+    @abstractmethod
+    def __call__(self, func: CostFunc, searchspace: Searchspace, budget_spent_fraction: float) -> tuple[tuple, float]:
+        """_summary_
+
+        Args:
+            func (CostFunc): Cost function to be optimized.
+            searchspace (Searchspace): Search space containing the parameters to be optimized.
+            budget_spent_fraction (float): Fraction of the budget that has already been spent.
+
+        Returns:
+            tuple[tuple, float]: tuple of the best parameters and the corresponding cost value
+        """
+        pass
+
+
 class OptAlgWrapper:
     """Wrapper class for user-defined optimization algorithms"""
 
-    def __init__(self, optimizer, scaling=True):
-        self.optimizer = optimizer
-        self.scaling = scaling
-
+    def __init__(self, optimizer: OptAlg):
+        self.optimizer: OptAlg = optimizer
 
     def tune(self, searchspace: Searchspace, runner, tuning_options):
-        cost_func = CostFunc(searchspace, tuning_options, runner, scaling=self.scaling)
+        cost_func = CostFunc(searchspace, tuning_options, runner, **self.optimizer.costfunc_kwargs)
 
-        if self.scaling:
+        if self.optimizer.costfunc_kwargs.get('scaling', True):
             # Initialize costfunc for scaling
             cost_func.get_bounds_x0_eps()
 
         try:
-            self.optimizer(cost_func)
+            self.optimizer(cost_func, searchspace)
         except util.StopCriterionReached as e:
             if tuning_options.verbose:
                 print(e)