Put score back into base class (Accounting for arbitrary deep learning models is not possible)

Author: maxkeller321

modAL/models/base.py

@@ -176,9 +176,19 @@ def query(self, X_pool, *query_args, **query_kwargs) -> Union[Tuple, modALinput]
 
         return query_result, retrieve_rows(X_pool, query_result), query_metrics
 
-    @abc.abstractmethod
-    def score(self, *args, **kwargs) -> None:
-        pass
+    def score(self, X: modALinput, y: modALinput, **score_kwargs) -> Any:
+        """
+        Interface for the score method of the predictor.
+
+        Args:
+            X: The samples for which prediction accuracy is to be calculated.
+            y: Ground truth labels for X.
+            **score_kwargs: Keyword arguments to be passed to the .score() method of the predictor.
+
+        Returns:
+            The score of the predictor.
+        """
+        return self.estimator.score(X, y, **score_kwargs)
 
     @abc.abstractmethod
     def teach(self, *args, **kwargs) -> None:

modAL/models/learners.py

@@ -160,20 +160,6 @@ def fit(self, X: modALinput, y: modALinput, bootstrap: bool = False, **fit_kwarg
         self.X_training, self.y_training = X, y
         return self._fit_to_known(bootstrap=bootstrap, **fit_kwargs)
 
-    def score(self, X: modALinput, y: modALinput, **score_kwargs) -> Any:
-        """
-        Interface for the score method of the predictor.
-
-        Args:
-            X: The samples for which prediction accuracy is to be calculated.
-            y: Ground truth labels for X.
-            **score_kwargs: Keyword arguments to be passed to the .score() method of the predictor.
-
-        Returns:
-            The score of the predictor.
-        """
-        return self.estimator.score(X, y, **score_kwargs)
-
     def teach(self, X: modALinput, y: modALinput, bootstrap: bool = False, only_new: bool = False, **fit_kwargs) -> None:
         """
         Adds X and y to the known training data and retrains the predictor with the augmented dataset.
@@ -245,26 +231,6 @@ def fit(self, X: modALinput, y: modALinput, bootstrap: bool = False, **fit_kwarg
         """            
         return self._fit_on_new(X, y, bootstrap=bootstrap, **fit_kwargs)
 
-    def score(self, X: modALinput, y: modALinput) -> Any:
-        """
-        Interface for the score method of the predictor.
-
-        Args:
-            X: The samples for which prediction accuracy is to be calculated.
-            y: Ground truth labels for X.
-
-        Returns:
-            The score of the predictor.
-        """
-        """
-            sklearn does only accept tensors of different dim for X and Y, if we use
-            Multilabel classifiaction. Using tensors of different sizes for more complex models (e.g. Transformers) 
-            requires to bypass the sklearn checks by directly calling the NeuralNets infer() function.
-        """
-        prediction = self.estimator.infer(X)
-        criterion = self.estimator.criterion()
-        return criterion(prediction, y).item()
-
     def teach(self, X: modALinput, y: modALinput, warm_start: bool = True, bootstrap: bool = False, **fit_kwargs) -> None:
         """
         Adds X and y to the known training data and retrains the predictor with the augmented dataset.