scoring_function -> scoring_fn rename, scorer -> scoring_fn rename

llama_stack/apis/scoring/scoring.py

@@ -37,7 +37,7 @@ class ScoreResponse(BaseModel):
 
 
 class ScoringFunctionStore(Protocol):
-    def get_scoring_function(self, name: str) -> ScoringFunctionDefWithProvider: ...
+    def get_scoring_function(self, name: str) -> ScoringFnDefWithProvider: ...
 
 
 @runtime_checkable

llama_stack/apis/scoring_functions/scoring_functions.py

@@ -29,7 +29,7 @@ class LLMAsJudgeContext(BaseModel):
 
 
 @json_schema_type
-class ScoringFunctionDef(BaseModel):
+class ScoringFnDef(BaseModel):
     identifier: str
     description: Optional[str] = None
     metadata: Dict[str, Any] = Field(
@@ -48,7 +48,7 @@ class ScoringFunctionDef(BaseModel):
 
 
 @json_schema_type
-class ScoringFunctionDefWithProvider(ScoringFunctionDef):
+class ScoringFnDefWithProvider(ScoringFnDef):
     provider_id: str = Field(
         description="ID of the provider which serves this dataset",
     )
@@ -57,14 +57,14 @@ class ScoringFunctionDefWithProvider(ScoringFunctionDef):
 @runtime_checkable
 class ScoringFunctions(Protocol):
     @webmethod(route="/scoring_functions/list", method="GET")
-    async def list_scoring_functions(self) -> List[ScoringFunctionDefWithProvider]: ...
+    async def list_scoring_functions(self) -> List[ScoringFnDefWithProvider]: ...
 
     @webmethod(route="/scoring_functions/get", method="GET")
     async def get_scoring_function(
         self, name: str
-    ) -> Optional[ScoringFunctionDefWithProvider]: ...
+    ) -> Optional[ScoringFnDefWithProvider]: ...
 
     @webmethod(route="/scoring_functions/register", method="POST")
     async def register_scoring_function(
-        self, function_def: ScoringFunctionDefWithProvider
+        self, function_def: ScoringFnDefWithProvider
     ) -> None: ...

llama_stack/distribution/datatypes.py

@@ -34,7 +34,7 @@ RoutableObject = Union[
     ShieldDef,
     MemoryBankDef,
     DatasetDef,
-    ScoringFunctionDef,
+    ScoringFnDef,
 ]
 
 RoutableObjectWithProvider = Union[
@@ -42,7 +42,7 @@ RoutableObjectWithProvider = Union[
     ShieldDefWithProvider,
     MemoryBankDefWithProvider,
     DatasetDefWithProvider,
-    ScoringFunctionDefWithProvider,
+    ScoringFnDefWithProvider,
 ]
 
 RoutedProtocol = Union[

llama_stack/distribution/routers/routing_tables.py

@@ -100,7 +100,7 @@ class CommonRoutingTableImpl(RoutingTable):
                 scoring_functions = await p.list_scoring_functions()
                 add_objects(
                     [
-                        ScoringFunctionDefWithProvider(**s.dict(), provider_id=pid)
+                        ScoringFnDefWithProvider(**s.dict(), provider_id=pid)
                         for s in scoring_functions
                     ]
                 )
@@ -239,7 +239,7 @@ class DatasetsRoutingTable(CommonRoutingTableImpl, Datasets):
 
 
 class ScoringFunctionsRoutingTable(CommonRoutingTableImpl, Scoring):
-    async def list_scoring_functions(self) -> List[ScoringFunctionDefWithProvider]:
+    async def list_scoring_functions(self) -> List[ScoringFnDefWithProvider]:
         objects = []
         for objs in self.registry.values():
             objects.extend(objs)
@@ -247,10 +247,10 @@ class ScoringFunctionsRoutingTable(CommonRoutingTableImpl, Scoring):
 
     async def get_scoring_function(
         self, name: str
-    ) -> Optional[ScoringFunctionDefWithProvider]:
+    ) -> Optional[ScoringFnDefWithProvider]:
         return self.get_object_by_identifier(name)
 
     async def register_scoring_function(
-        self, function_def: ScoringFunctionDefWithProvider
+        self, function_def: ScoringFnDefWithProvider
     ) -> None:
         await self.register_object(function_def)

llama_stack/providers/datatypes.py

@@ -13,7 +13,7 @@ from pydantic import BaseModel, Field
 from llama_stack.apis.datasets import DatasetDef
 from llama_stack.apis.memory_banks import MemoryBankDef
 from llama_stack.apis.models import ModelDef
-from llama_stack.apis.scoring_functions import ScoringFunctionDef
+from llama_stack.apis.scoring_functions import ScoringFnDef
 from llama_stack.apis.shields import ShieldDef
 
 
@@ -64,11 +64,9 @@ class DatasetsProtocolPrivate(Protocol):
 
 
 class ScoringFunctionsProtocolPrivate(Protocol):
-    async def list_scoring_functions(self) -> List[ScoringFunctionDef]: ...
+    async def list_scoring_functions(self) -> List[ScoringFnDef]: ...
 
-    async def register_scoring_function(
-        self, function_def: ScoringFunctionDef
-    ) -> None: ...
+    async def register_scoring_function(self, function_def: ScoringFnDef) -> None: ...
 
 
 @json_schema_type

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

@@ -13,22 +13,22 @@ from llama_stack.apis.datasetio import *  # noqa: F403
 from llama_stack.apis.datasets import *  # noqa: F403
 
 from llama_stack.providers.datatypes import ScoringFunctionsProtocolPrivate
-from llama_stack.providers.impls.meta_reference.scoring.scorer.equality_scorer import (
-    EqualityScorer,
+from llama_stack.providers.impls.meta_reference.scoring.scoring_fn.equality_scoring_fn import (
+    EqualityScoringFn,
 )
 
-from llama_stack.providers.impls.meta_reference.scoring.scorer.subset_of_scorer import (
-    SubsetOfScorer,
+from llama_stack.providers.impls.meta_reference.scoring.scoring_fn.subset_of_scoring_fn import (
+    SubsetOfScoringFn,
 )
 
 from .config import MetaReferenceScoringConfig
 
-SUPPORTED_SCORERS = [
-    EqualityScorer,
-    SubsetOfScorer,
+SUPPORTED_SCORING_FNS = [
+    EqualityScoringFn,
+    SubsetOfScoringFn,
 ]
 
-SCORER_REGISTRY = {x.scoring_function_def.identifier: x for x in SUPPORTED_SCORERS}
+SCORER_REGISTRY = {x.scoring_function_def.identifier: x for x in SUPPORTED_SCORING_FNS}
 
 
 class MetaReferenceScoringImpl(Scoring, ScoringFunctionsProtocolPrivate):
@@ -46,10 +46,10 @@ class MetaReferenceScoringImpl(Scoring, ScoringFunctionsProtocolPrivate):
 
     async def shutdown(self) -> None: ...
 
-    async def list_scoring_functions(self) -> List[ScoringFunctionDef]:
-        return [x.scoring_function_def for x in SUPPORTED_SCORERS]
+    async def list_scoring_functions(self) -> List[ScoringFnDef]:
+        return [x.scoring_function_def for x in SUPPORTED_SCORING_FNS]
 
-    async def register_scoring_function(self, function_def: ScoringFunctionDef) -> None:
+    async def register_scoring_function(self, function_def: ScoringFnDef) -> None:
         raise NotImplementedError(
             "Dynamically registering scoring functions is not supported"
         )
@@ -101,9 +101,9 @@ class MetaReferenceScoringImpl(Scoring, ScoringFunctionsProtocolPrivate):
         for scoring_fn_id in scoring_functions:
             if scoring_fn_id not in SCORER_REGISTRY:
                 raise ValueError(f"Scoring function {scoring_fn_id} is not supported.")
-            scorer = SCORER_REGISTRY[scoring_fn_id]()
-            score_results = scorer.score(input_rows)
-            agg_results = scorer.aggregate(score_results)
+            scoring_fn = SCORER_REGISTRY[scoring_fn_id]()
+            score_results = scoring_fn.score(input_rows)
+            agg_results = scoring_fn.aggregate(score_results)
             res[scoring_fn_id] = ScoringResult(
                 score_rows=score_results,
                 aggregated_results=agg_results,

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

@@ -9,15 +9,15 @@ from llama_stack.apis.scoring_functions import *  # noqa: F401, F403
 from llama_stack.apis.scoring import *  # noqa: F401, F403
 
 
-class BaseScorer(ABC):
+class BaseScoringFn(ABC):
     """
-    Base interface class for all meta-reference scorers.
-    Each scorer needs to implement the following methods:
+    Base interface class for all meta-reference scoring_fns.
+    Each scoring_fn needs to implement the following methods:
     - score_row(self, row)
-    - aggregate(self, scorer_results)
+    - aggregate(self, scoring_fn_results)
     """
 
-    scoring_function_def: ScoringFunctionDef
+    scoring_function_def: ScoringFnDef
 
     def __init__(self, *args, **kwargs) -> None:
         super().__init__(*args, **kwargs)

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

@@ -4,23 +4,23 @@
 # This source code is licensed under the terms described in the LICENSE file in
 # the root directory of this source tree.
 
-from llama_stack.providers.impls.meta_reference.scoring.scorer.base_scorer import (
-    BaseScorer,
+from llama_stack.providers.impls.meta_reference.scoring.scoring_fn.base_scoring_fn import (
+    BaseScoringFn,
 )
 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
-from llama_stack.providers.impls.meta_reference.scoring.scorer.common import (
+from llama_stack.providers.impls.meta_reference.scoring.scoring_fn.common import (
     aggregate_accuracy,
 )
 
 
-class EqualityScorer(BaseScorer):
+class EqualityScoringFn(BaseScoringFn):
     """
-    A scorer that assigns a score of 1.0 if the input string matches the target string, and 0.0 otherwise.
+    A scoring_fn that assigns a score of 1.0 if the input string matches the target string, and 0.0 otherwise.
     """
 
-    scoring_function_def = ScoringFunctionDef(
+    scoring_function_def = ScoringFnDef(
         identifier="equality",
         description="Returns 1.0 if the input is equal to the target, 0.0 otherwise.",
         parameters=[],

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

@@ -4,23 +4,23 @@
 # This source code is licensed under the terms described in the LICENSE file in
 # the root directory of this source tree.
 
-from llama_stack.providers.impls.meta_reference.scoring.scorer.base_scorer import (
-    BaseScorer,
+from llama_stack.providers.impls.meta_reference.scoring.scoring_fn.base_scoring_fn import (
+    BaseScoringFn,
 )
 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
-from llama_stack.providers.impls.meta_reference.scoring.scorer.common import (
+from llama_stack.providers.impls.meta_reference.scoring.scoring_fn.common import (
     aggregate_accuracy,
 )
 
 
-class SubsetOfScorer(BaseScorer):
+class SubsetOfScoringFn(BaseScoringFn):
     """
-    A scorer that assigns a score of 1.0 if the expected string is included in the generated string, and 0.0 otherwise.
+    A scoring_fn that assigns a score of 1.0 if the expected string is included in the generated string, and 0.0 otherwise.
     """
 
-    scoring_function_def = ScoringFunctionDef(
+    scoring_function_def = ScoringFnDef(
         identifier="subset_of",
         description="Returns 1.0 if the expected is included in generated, 0.0 otherwise.",
         parameters=[],