Now sets classes when fitting committee

Author: Oskar Liew

modAL/models/learners.py

@@ -312,6 +312,22 @@ def _set_classes(self):
     def _add_training_data(self, X: modALinput, y: modALinput):
         super()._add_training_data(X, y)
 
+    def fit(self, X: modALinput, y: modALinput, **fit_kwargs) -> 'BaseCommittee':
+        """
+        Fits every learner to a subset sampled with replacement from X. Calling this method makes the learner forget the
+        data it has seen up until this point and replaces it with X! If you would like to perform bootstrapping on each
+        learner using the data it has seen, use the method .rebag()!
+
+        Calling this method makes the learner forget the data it has seen up until this point and replaces it with X!
+
+        Args:
+            X: The samples to be fitted on.
+            y: The corresponding labels.
+            **fit_kwargs: Keyword arguments to be passed to the fit method of the predictor.
+        """
+        super().fit(X, y, **fit_kwargs)
+        self._set_classes()
+
     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 for each learner and retrains learners with the augmented dataset.
@@ -323,7 +339,6 @@ def teach(self, X: modALinput, y: modALinput, bootstrap: bool = False, only_new:
             only_new: If True, the model is retrained using only X and y, ignoring the previously provided examples.
             **fit_kwargs: Keyword arguments to be passed to the fit method of the predictor.
         """
-
         super().teach(X, y, bootstrap=bootstrap, only_new=only_new, **fit_kwargs)
         self._set_classes()