add all rows scores to ScoringResult

llama_stack/apis/scoring/scoring.py

@@ -13,7 +13,15 @@ from llama_models.llama3.api.datatypes import *  # noqa: F403
 from llama_stack.apis.scoring_functions import *  # noqa: F403
 
 
-ScoringResult = Dict[str, Any]
+# mapping of metric to value
+ScoringResultRow = Dict[str, Any]
+
+
+@json_schema_type
+class ScoringResult(BaseModel):
+    score_rows: List[ScoringResultRow]
+    # aggregated metrics to value
+    aggregated_results: Dict[str, Any]
 
 
 @json_schema_type

llama_stack/providers/impls/meta_reference/scoring/scorer/base_scorer.py

@@ -26,12 +26,12 @@ class BaseScorer(ABC):
         return self.__class__.__name__
 
     @abstractmethod
-    def score_row(self, input_row: Dict[str, Any]) -> ScoringResult:
+    def score_row(self, input_row: Dict[str, Any]) -> ScoringResultRow:
         raise NotImplementedError()
 
     @abstractmethod
-    def aggregate(self, scoring_results: List[ScoringResult]) -> ScoringResult:
+    def aggregate(self, scoring_results: List[ScoringResultRow]) -> Dict[str, Any]:
         raise NotImplementedError()
 
-    def score(self, input_rows: List[Dict[str, Any]]) -> List[ScoringResult]:
+    def score(self, input_rows: List[Dict[str, Any]]) -> List[ScoringResultRow]:
         return [self.score_row(input_row) for input_row in input_rows]

llama_stack/providers/impls/meta_reference/scoring/scorer/equality_scorer.py

@@ -24,7 +24,7 @@ class EqualityScorer(BaseScorer):
         return_type=NumberType(),
     )
 
-    def score_row(self, input_row: Dict[str, Any]) -> ScoringResult:
+    def score_row(self, input_row: Dict[str, Any]) -> ScoringResultRow:
         assert "expected_answer" in input_row, "Expected answer not found in input row."
         assert (
             "generated_answer" in input_row
@@ -37,7 +37,7 @@ class EqualityScorer(BaseScorer):
             "score": score,
         }
 
-    def aggregate(self, scoring_results: List[ScoringResult]) -> ScoringResult:
+    def aggregate(self, scoring_results: List[ScoringResultRow]) -> Dict[str, Any]:
         assert len(scoring_results) > 0, "Empty scoring results provided."
         num_correct = sum(result["score"] for result in scoring_results)
         avg_score = num_correct / len(scoring_results)

llama_stack/providers/impls/meta_reference/scoring/scoring.py

@@ -99,7 +99,10 @@ class MetaReferenceScoringImpl(Scoring, ScoringFunctionsProtocolPrivate):
             scorer = SCORER_REGISTRY[scoring_fn_id]()
             score_results = scorer.score(input_rows)
             agg_results = scorer.aggregate(score_results)
-            res[scoring_fn_id] = agg_results
+            res[scoring_fn_id] = ScoringResult(
+                score_rows=score_results,
+                aggregated_results=agg_results,
+            )
 
         return ScoreResponse(
             results=res,

llama_stack/providers/tests/scoring/test_scoring.py

@@ -66,3 +66,4 @@ async def test_scoring_score(scoring_settings):
     )
 
     assert len(response.results) == 1
+    assert "equality" in response.results