Folder restructure for evals/datasets/scoring (#419)

* rename evals related stuff

* fix datasetio

* fix scoring test

* localfs -> LocalFS

* refactor scoring

* refactor scoring

* remove 8b_correctness scoring_fn from tests

* tests w/ eval params

* scoring fn braintrust fixture

* import

llama_stack/providers/inline/scoring/braintrust/__init__.py

@@ -0,0 +1,21 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the terms described in the LICENSE file in
+# the root directory of this source tree.
+from typing import Dict
+
+from llama_stack.distribution.datatypes import Api, ProviderSpec
+
+from .config import BraintrustScoringConfig
+
+
+async def get_provider_impl(
+    config: BraintrustScoringConfig,
+    deps: Dict[Api, ProviderSpec],
+):
+    from .braintrust import BraintrustScoringImpl
+
+    impl = BraintrustScoringImpl(config, deps[Api.datasetio], deps[Api.datasets])
+    await impl.initialize()
+    return impl

llama_stack/providers/inline/scoring/braintrust/braintrust.py

@@ -0,0 +1,139 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the terms described in the LICENSE file in
+# the root directory of this source tree.
+from typing import List
+
+from llama_models.llama3.api.datatypes import *  # noqa: F403
+from llama_stack.apis.scoring import *  # noqa: F403
+from llama_stack.apis.scoring_functions import *  # noqa: F403
+from llama_stack.apis.common.type_system import *  # noqa: F403
+from llama_stack.apis.datasetio import *  # noqa: F403
+from llama_stack.apis.datasets import *  # noqa: F403
+
+# from .scoring_fn.braintrust_scoring_fn import BraintrustScoringFn
+from autoevals.llm import Factuality
+from autoevals.ragas import AnswerCorrectness
+from llama_stack.providers.datatypes import ScoringFunctionsProtocolPrivate
+
+from llama_stack.providers.utils.scoring.aggregation_utils import aggregate_average
+
+from .config import BraintrustScoringConfig
+from .scoring_fn.fn_defs.answer_correctness import answer_correctness_fn_def
+from .scoring_fn.fn_defs.factuality import factuality_fn_def
+
+
+class BraintrustScoringImpl(Scoring, ScoringFunctionsProtocolPrivate):
+    def __init__(
+        self,
+        config: BraintrustScoringConfig,
+        datasetio_api: DatasetIO,
+        datasets_api: Datasets,
+    ) -> None:
+        self.config = config
+        self.datasetio_api = datasetio_api
+        self.datasets_api = datasets_api
+
+        self.braintrust_evaluators = {
+            "braintrust::factuality": Factuality(),
+            "braintrust::answer-correctness": AnswerCorrectness(),
+        }
+        self.supported_fn_defs_registry = {
+            factuality_fn_def.identifier: factuality_fn_def,
+            answer_correctness_fn_def.identifier: answer_correctness_fn_def,
+        }
+
+    async def initialize(self) -> None: ...
+
+    async def shutdown(self) -> None: ...
+
+    async def list_scoring_functions(self) -> List[ScoringFnDef]:
+        scoring_fn_defs_list = [x for x in self.supported_fn_defs_registry.values()]
+        for f in scoring_fn_defs_list:
+            assert f.identifier.startswith(
+                "braintrust"
+            ), "All braintrust scoring fn must have identifier prefixed with 'braintrust'! "
+
+        return scoring_fn_defs_list
+
+    async def register_scoring_function(self, function_def: ScoringFnDef) -> None:
+        raise NotImplementedError(
+            "Registering scoring function not allowed for braintrust provider"
+        )
+
+    async def validate_scoring_input_dataset_schema(self, dataset_id: str) -> None:
+        dataset_def = await self.datasets_api.get_dataset(dataset_identifier=dataset_id)
+        if not dataset_def.dataset_schema or len(dataset_def.dataset_schema) == 0:
+            raise ValueError(
+                f"Dataset {dataset_id} does not have a schema defined. Please define a schema for the dataset."
+            )
+
+        for required_column in ["generated_answer", "expected_answer", "input_query"]:
+            if required_column not in dataset_def.dataset_schema:
+                raise ValueError(
+                    f"Dataset {dataset_id} does not have a '{required_column}' column."
+                )
+            if dataset_def.dataset_schema[required_column].type != "string":
+                raise ValueError(
+                    f"Dataset {dataset_id} does not have a '{required_column}' column of type 'string'."
+                )
+
+    async def score_batch(
+        self,
+        dataset_id: str,
+        scoring_functions: List[str],
+        save_results_dataset: bool = False,
+    ) -> ScoreBatchResponse:
+        await self.validate_scoring_input_dataset_schema(dataset_id=dataset_id)
+        all_rows = await self.datasetio_api.get_rows_paginated(
+            dataset_id=dataset_id,
+            rows_in_page=-1,
+        )
+        res = await self.score(
+            input_rows=all_rows.rows, scoring_functions=scoring_functions
+        )
+        if save_results_dataset:
+            # TODO: persist and register dataset on to server for reading
+            # self.datasets_api.register_dataset()
+            raise NotImplementedError("Save results dataset not implemented yet")
+
+        return ScoreBatchResponse(
+            results=res.results,
+        )
+
+    async def score_row(
+        self, input_row: Dict[str, Any], scoring_fn_identifier: Optional[str] = None
+    ) -> ScoringResultRow:
+        assert scoring_fn_identifier is not None, "scoring_fn_identifier cannot be None"
+        expected_answer = input_row["expected_answer"]
+        generated_answer = input_row["generated_answer"]
+        input_query = input_row["input_query"]
+        evaluator = self.braintrust_evaluators[scoring_fn_identifier]
+
+        result = evaluator(generated_answer, expected_answer, input=input_query)
+        score = result.score
+        return {"score": score, "metadata": result.metadata}
+
+    async def score(
+        self, input_rows: List[Dict[str, Any]], scoring_functions: List[str]
+    ) -> ScoreResponse:
+        res = {}
+        for scoring_fn_id in scoring_functions:
+            if scoring_fn_id not in self.supported_fn_defs_registry:
+                raise ValueError(f"Scoring function {scoring_fn_id} is not supported.")
+
+            score_results = [
+                await self.score_row(input_row, scoring_fn_id)
+                for input_row in input_rows
+            ]
+
+            agg_results = aggregate_average(score_results)
+            res[scoring_fn_id] = ScoringResult(
+                score_rows=score_results,
+                aggregated_results=agg_results,
+            )
+
+        return ScoreResponse(
+            results=res,
+        )

llama_stack/providers/inline/scoring/braintrust/config.py

@@ -0,0 +1,9 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the terms described in the LICENSE file in
+# the root directory of this source tree.
+from llama_stack.apis.scoring import *  # noqa: F401, F403
+
+
+class BraintrustScoringConfig(BaseModel): ...

llama_stack/providers/inline/scoring/braintrust/scoring_fn/__init__.py

@@ -0,0 +1,5 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the terms described in the LICENSE file in
+# the root directory of this source tree.

llama_stack/providers/inline/scoring/braintrust/scoring_fn/fn_defs/__init__.py

@@ -0,0 +1,5 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the terms described in the LICENSE file in
+# the root directory of this source tree.

llama_stack/providers/inline/scoring/braintrust/scoring_fn/fn_defs/answer_correctness.py

@@ -0,0 +1,16 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the terms described in the LICENSE file in
+# the root directory of this source tree.
+
+from llama_stack.apis.common.type_system import NumberType
+from llama_stack.apis.scoring_functions import ScoringFnDef
+
+
+answer_correctness_fn_def = ScoringFnDef(
+    identifier="braintrust::answer-correctness",
+    description="Test whether an output is factual, compared to an original (`expected`) value. One of Braintrust LLM basd scorer https://github.com/braintrustdata/autoevals/blob/main/py/autoevals/llm.py",
+    parameters=[],
+    return_type=NumberType(),
+)

llama_stack/providers/inline/scoring/braintrust/scoring_fn/fn_defs/factuality.py

@@ -0,0 +1,16 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the terms described in the LICENSE file in
+# the root directory of this source tree.
+
+from llama_stack.apis.common.type_system import NumberType
+from llama_stack.apis.scoring_functions import ScoringFnDef
+
+
+factuality_fn_def = ScoringFnDef(
+    identifier="braintrust::factuality",
+    description="Test whether an output is factual, compared to an original (`expected`) value. One of Braintrust LLM basd scorer https://github.com/braintrustdata/autoevals/blob/main/py/autoevals/llm.py",
+    parameters=[],
+    return_type=NumberType(),
+)