_explain to _evalute in base metric

Makefile

@@ -5,7 +5,7 @@ SHELL = /bin/bash
 .PHONY: style
 style:
 	black .
-	python3 -m isort .
+	python -m isort .
 	rm -f .coverage
 	rm -f .coverage.*
 	find . | grep -E "(__pycache__|\.pyc|\.pyo)" | xargs rm -rf

src/metrics/base.py

@@ -24,7 +24,7 @@ def __call__(
 
         1) Universal assertions about the passed arguments, incl. checking that the length of train/test datset and
         explanations match.
-        2) Call the _explain method.
+        2) Call the _evaluate method.
         3) Format the output into a unified format for all metrics, possible using some arguments passed in kwargs.
 
         :param model:
@@ -38,7 +38,7 @@ def __call__(
         raise NotImplementedError
 
     @abstractmethod
-    def _explain(
+    def _evaluate(
         self,
         model: torch.nn.Module,
         train_dataset: torch.utils.data.Dataset,

src/utils/datasets/mark_dataset.py

@@ -17,7 +17,7 @@ def __init__(
         p: float = 0.3,
         cls_to_mark: int = 2,
         mark_fn: Optional[Union[Callable, str]] = None,
-        only_train: bool = False
+        only_train: bool = False,
     ):
         super().__init__()
         self.dataset = dataset