WIP - Prepare class for label counts

src/intelligence_layer/use_cases/classify/classify.py

@@ -63,27 +63,39 @@ class SingleLabelClassifyEvaluation(BaseModel):
     correct: bool
 
 
+class SingleLabelClassifyLabelCounts(BaseModel):
+    ## TODO: DocString
+    count_correctly_assigned: int = 0
+    count_incorrectly_assigned: int = 0
+
+
 class AggregatedSingleLabelClassifyEvaluation(BaseModel):
     """The aggregated evaluation of a single label classify implementation against a dataset.
 
     Attributes:
         percentage_correct: Percentage of answers that were considered to be correct
+        assignments_per_label: Mapping stating how often the respective label was assigned correctly or incorrectly
     """
 
     percentage_correct: float
+    assignments_per_label: Mapping[str, SingleLabelClassifyLabelCounts]
 
 
 class SingleLabelClassifyAggregationLogic(
     AggregationLogic[
         SingleLabelClassifyEvaluation, AggregatedSingleLabelClassifyEvaluation
     ]
 ):
-    def aggregate(
+    def aggregate( # TODO: Need the specific labels here
         self, evaluations: Iterable[SingleLabelClassifyEvaluation]
     ) -> AggregatedSingleLabelClassifyEvaluation:
         acc = MeanAccumulator()
+        label_counts = Mapping[str, SingleLabelClassifyLabelCounts]
         for evaluation in evaluations:
             acc.add(1.0 if evaluation.correct else 0.0)
+        ## TODO: Add info to label counts
+
+
         return AggregatedSingleLabelClassifyEvaluation(percentage_correct=acc.extract())