abc trainer

Author: xzrderek

eval_protocol/training/__init__.py

@@ -1,3 +1,3 @@
-from gepa_adapter import GEPATrainer
+from gepa_trainer import GEPATrainer
 
 __all__ = ["GEPATrainer"]

eval_protocol/training/gepa_adapter.py eval_protocol/training/gepa_trainer.pyeval_protocol/training/gepa_adapter.py renamed to eval_protocol/training/gepa_trainer.py

@@ -9,11 +9,11 @@
 
 from eval_protocol.models import EPParameters, EvaluationRow
 from eval_protocol.pytest.types import TestFunction
-from eval_protocol.training.gepa_utils import REFLECTION_LM_CONFIGS
+from eval_protocol.training.trainer import Trainer
 from eval_protocol.training.utils import build_ep_parameters_from_test
 
 
-class GEPATrainer:
+class GEPATrainer(Trainer):
     """
     High-level entrypoint for running GEPA-style training against an existing
     `@evaluation_test`-decorated function.
@@ -30,7 +30,7 @@ def __init__(self, test_fn: TestFunction) -> None:
         Args:
             test_fn: The `@evaluation_test`-decorated function defining the eval.
         """
-        self.test_fn = test_fn
+        super().__init__(test_fn)
         self.ep_params: EPParameters = build_ep_parameters_from_test(test_fn)
 
         self.metric = (

eval_protocol/training/trainer.py

@@ -0,0 +1,16 @@
+from abc import ABC, abstractmethod
+
+from eval_protocol.pytest.types import TestFunction
+
+
+class Trainer(ABC):
+    def __init__(self, test_fn: TestFunction):
+        self.test_fn = test_fn
+
+    @abstractmethod
+    def train(self, *args, **kwargs): ...
+
+    @abstractmethod
+    def evaluate(self, *args, **kwargs):
+        # evaluation logic possibly can be shared since it's EP. TBD
+        ...