equality scorer

llama_stack/apis/scoring/scoring.py

@@ -14,7 +14,6 @@ from llama_stack.apis.scoring_functions import *  # noqa: F403
 
 
 ScoringResult = Dict[str, Any]
-SingleScoringResult = Dict[str, Any]
 
 
 @json_schema_type
@@ -25,7 +24,7 @@ class ScoreBatchResponse(BaseModel):
 @json_schema_type
 class ScoreResponse(BaseModel):
     # each key in the dict is a scoring function name
-    results: List[Dict[str, ScoringResult]]
+    results: Dict[str, ScoringResult]
 
 
 class ScoringFunctionStore(Protocol):

llama_stack/distribution/routers/routers.py

@@ -217,6 +217,7 @@ class ScoringRouter(Scoring):
     async def score(
         self, input_rows: List[Dict[str, Any]], scoring_functions: List[str]
     ) -> ScoreResponse:
+        res = {}
         # look up and map each scoring function to its provider impl
         for fn_identifier in scoring_functions:
             score_response = await self.routing_table.get_provider_impl(
@@ -225,6 +226,6 @@ class ScoringRouter(Scoring):
                 input_rows=input_rows,
                 scoring_functions=[fn_identifier],
             )
-            print(
-                f"fn_identifier={fn_identifier}, score_response={score_response}",
-            )
+            res.update(score_response.results)
+
+        return ScoreResponse(results=res)

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

@@ -17,6 +17,8 @@ class BaseScorer(ABC):
     - aggregate(self, scorer_results)
     """
 
+    scoring_function_def: DeterministicFunctionDef
+
     def __init__(self, *args, **kwargs) -> None:
         super().__init__(*args, **kwargs)
 

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

@@ -7,6 +7,9 @@
 from llama_stack.providers.impls.meta_reference.scoring.scorer.base_scorer import (
     BaseScorer,
 )
+from llama_stack.apis.scoring_functions import *  # noqa: F401, F403
+from llama_stack.apis.scoring import *  # noqa: F401, F403
+from llama_stack.apis.common.type_system import *  # noqa: F403
 
 
 class EqualityScorer(BaseScorer):
@@ -14,11 +17,28 @@ class EqualityScorer(BaseScorer):
     A scorer that assigns a score of 1.0 if the input string matches the target string, and 0.0 otherwise.
     """
 
-    def __init__(self, target: str) -> None:
-        """
-        Initialize the EqualityScorer with a target string.
+    scoring_function_def = DeterministicFunctionDef(
+        identifier="equality",
+        description="Returns 1.0 if the input is equal to the target, 0.0 otherwise.",
+        parameters=[],
+        return_type=NumberType(),
+    )
 
-        Args:
-            target (str): The target string to match against.
-        """
-        self.target = target
+    def score_row(self, input_row: Dict[str, Any]) -> ScoringResult:
+        expected_answer = input_row["expected_answer"]
+        generated_answer = input_row["generated_answer"]
+        score = 1.0 if expected_answer == generated_answer else 0.0
+        return {
+            "score": score,
+        }
+
+    def aggregate(self, scoring_results: List[ScoringResult]) -> ScoringResult:
+        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)
+
+        return {
+            "accuracy": avg_score,
+            "num_correct": num_correct,
+            "num_total": len(scoring_results),
+        }

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

@@ -14,9 +14,18 @@ from llama_stack.apis.datasetio import *  # noqa: F403
 from termcolor import cprint
 
 from llama_stack.providers.datatypes import ScoringFunctionsProtocolPrivate
+from llama_stack.providers.impls.meta_reference.scoring.scorer.equality_scorer import (
+    EqualityScorer,
+)
 
 from .config import MetaReferenceScoringConfig
 
+SUPPORTED_SCORERS = [
+    EqualityScorer,
+]
+
+SCORER_REGISTRY = {x.scoring_function_def.identifier: x for x in SUPPORTED_SCORERS}
+
 
 class MetaReferenceScoringImpl(Scoring, ScoringFunctionsProtocolPrivate):
     def __init__(
@@ -31,14 +40,7 @@ class MetaReferenceScoringImpl(Scoring, ScoringFunctionsProtocolPrivate):
     async def shutdown(self) -> None: ...
 
     async def list_scoring_functions(self) -> List[ScoringFunctionDef]:
-        return [
-            DeterministicFunctionDef(
-                identifier="equality",
-                description="Returns 1.0 if the input is equal to the target, 0.0 otherwise.",
-                parameters=[],
-                return_type=NumberType(),
-            )
-        ]
+        return [x.scoring_function_def for x in SUPPORTED_SCORERS]
 
     async def register_scoring_function(self, function_def: ScoringFunctionDef) -> None:
         raise NotImplementedError(
@@ -53,7 +55,13 @@ class MetaReferenceScoringImpl(Scoring, ScoringFunctionsProtocolPrivate):
     async def score(
         self, input_rows: List[Dict[str, Any]], scoring_functions: List[str]
     ) -> ScoreResponse:
-        print(
-            f"scoring input_rows {input_rows} on scoring_functions {scoring_functions}"
+        res = {}
+        for scoring_fn_id in scoring_functions:
+            scorer = SCORER_REGISTRY[scoring_fn_id]()
+            score_results = scorer.score(input_rows)
+            agg_results = scorer.aggregate(score_results)
+            res[scoring_fn_id] = agg_results
+
+        return ScoreResponse(
+            results=res,
         )
-        return ScoreResponse()