qa uses base classes and is testable (Azure#38993)

* qa uses base classes and is testable

* evaluator list input for base class

sdk/evaluation/azure-ai-evaluation/assets.json

@@ -2,5 +2,5 @@
   "AssetsRepo": "Azure/azure-sdk-assets",
   "AssetsRepoPrefixPath": "python",
   "TagPrefix": "python/evaluation/azure-ai-evaluation",
-  "Tag": "python/evaluation/azure-ai-evaluation_326efc986d"
+  "Tag": "python/evaluation/azure-ai-evaluation_23e89ff5ac"
 }

sdk/evaluation/azure-ai-evaluation/azure/ai/evaluation/_evaluators/_common/__init__.py

@@ -5,9 +5,11 @@
 from ._base_eval import EvaluatorBase
 from ._base_prompty_eval import PromptyEvaluatorBase
 from ._base_rai_svc_eval import RaiServiceEvaluatorBase
+from ._base_multi_eval import MultiEvaluatorBase
 
 __all__ = [
     "EvaluatorBase",
     "PromptyEvaluatorBase",
     "RaiServiceEvaluatorBase",
+    "MultiEvaluatorBase",
 ]

sdk/evaluation/azure-ai-evaluation/azure/ai/evaluation/_evaluators/_common/_base_multi_eval.py

@@ -0,0 +1,61 @@
+# ---------------------------------------------------------
+# Copyright (c) Microsoft Corporation. All rights reserved.
+# ---------------------------------------------------------
+from concurrent.futures import as_completed
+from typing import TypeVar, Dict, List
+
+from promptflow.tracing import ThreadPoolExecutorWithContext as ThreadPoolExecutor
+from typing_extensions import override
+
+from azure.ai.evaluation._evaluators._common import EvaluatorBase
+
+T = TypeVar("T")
+
+
+class MultiEvaluatorBase(EvaluatorBase[T]):
+    """
+    Base class for evaluators that contain and run multiple other evaluators to produce a
+    suite of metrics.
+
+    Child classes still need to implement the __call__ methods, but they shouldn't need a _do_eval.
+
+    :param evaluators: The list of evaluators to run when this evaluator is called.
+    :type evaluators: List[~azure.ai.evaluation._evaluators._common.EvaluatorBase]
+    :param kwargs: Additional arguments to pass to the evaluator.
+    :type kwargs: Any
+    :return: An evaluator that runs multiple other evaluators and combines their results.
+    """
+
+    def __init__(self, evaluators: List[EvaluatorBase[T]], **kwargs):
+        super().__init__()
+        self._parallel = kwargs.pop("_parallel", True)
+        self._evaluators = evaluators
+
+    @override
+    async def _do_eval(self, eval_input: Dict) -> Dict[str, T]:
+        """Run each evaluator, possibly in parallel, and combine the results into
+        a single large dictionary containing each evaluation. Inputs are passed
+        directly to each evaluator without additional processing.
+
+
+        :param eval_input: The input to the evaluation function.
+        :type eval_input: Dict
+        :return: The evaluation result.
+        :rtype: Dict
+        """
+        results: Dict[str, T] = {}
+        if self._parallel:
+            with ThreadPoolExecutor() as executor:
+                # pylint: disable=no-value-for-parameter
+                futures = {executor.submit(evaluator, **eval_input): evaluator for evaluator in self._evaluators}
+
+                for future in as_completed(futures):
+                    results.update(future.result())
+        else:
+            for evaluator in self._evaluators:
+                result = evaluator(**eval_input)
+                # Ignore is to avoid mypy getting upset over the amount of duck-typing
+                # that's going on to shove evaluators around like this.
+                results.update(result)  # type: ignore[arg-type]
+
+        return results

sdk/evaluation/azure-ai-evaluation/azure/ai/evaluation/_evaluators/_content_safety/_content_safety.py

@@ -1,13 +1,11 @@
 # ---------------------------------------------------------
 # Copyright (c) Microsoft Corporation. All rights reserved.
 # ---------------------------------------------------------
-from concurrent.futures import as_completed
-from typing import Callable, Dict, List, Union
+from typing import Dict, List, Union
 
-from promptflow.tracing import ThreadPoolExecutorWithContext as ThreadPoolExecutor
 from typing_extensions import overload, override
 
-from azure.ai.evaluation._evaluators._common import EvaluatorBase
+from azure.ai.evaluation._evaluators._common import MultiEvaluatorBase
 from azure.ai.evaluation._model_configurations import Conversation
 from azure.ai.evaluation._common._experimental import experimental
 
@@ -18,7 +16,7 @@
 
 
 @experimental
-class ContentSafetyEvaluator(EvaluatorBase[Union[str, float]]):
+class ContentSafetyEvaluator(MultiEvaluatorBase[Union[str, float]]):
     """
     Initialize a content safety evaluator configured to evaluate content safety metrics for QA scenario.
 
@@ -44,16 +42,14 @@ class ContentSafetyEvaluator(EvaluatorBase[Union[str, float]]):
     id = "content_safety"
     """Evaluator identifier, experimental and to be used only with evaluation in cloud."""
 
-    # TODO address 3579092 to re-enabled parallel evals.
     def __init__(self, credential, azure_ai_project, **kwargs):
-        super().__init__()
-        self._parallel = kwargs.pop("_parallel", True)
-        self._evaluators: List[Callable[..., Dict[str, Union[str, float]]]] = [
+        evaluators = [
             ViolenceEvaluator(credential, azure_ai_project),
             SexualEvaluator(credential, azure_ai_project),
             SelfHarmEvaluator(credential, azure_ai_project),
             HateUnfairnessEvaluator(credential, azure_ai_project),
         ]
+        super().__init__(evaluators=evaluators, **kwargs)
 
     @overload
     def __call__(
@@ -109,36 +105,3 @@ def __call__(  # pylint: disable=docstring-missing-param
         :rtype: Union[Dict[str, Union[str, float]], Dict[str, Union[float, Dict[str, List[Union[str, float]]]]]]
         """
         return super().__call__(*args, **kwargs)
-
-    @override
-    async def _do_eval(self, eval_input: Dict) -> Dict[str, Union[str, float]]:
-        """Perform the evaluation using the Azure AI RAI service.
-        The exact evaluation performed is determined by the evaluation metric supplied
-        by the child class initializer.
-
-        :param eval_input: The input to the evaluation function.
-        :type eval_input: Dict
-        :return: The evaluation result.
-        :rtype: Dict
-        """
-        query = eval_input.get("query", None)
-        response = eval_input.get("response", None)
-        conversation = eval_input.get("conversation", None)
-        results: Dict[str, Union[str, float]] = {}
-        # TODO fix this to not explode on empty optional inputs (PF SKD error)
-        if self._parallel:
-            with ThreadPoolExecutor() as executor:
-                # pylint: disable=no-value-for-parameter
-                futures = {
-                    executor.submit(evaluator, query=query, response=response, conversation=conversation): evaluator
-                    for evaluator in self._evaluators
-                }
-
-                for future in as_completed(futures):
-                    results.update(future.result())
-        else:
-            for evaluator in self._evaluators:
-                result = evaluator(query=query, response=response, conversation=conversation)
-                results.update(result)
-
-        return results

sdk/evaluation/azure-ai-evaluation/azure/ai/evaluation/_evaluators/_qa/_qa.py

@@ -2,10 +2,11 @@
 # Copyright (c) Microsoft Corporation. All rights reserved.
 # ---------------------------------------------------------
 
-from concurrent.futures import as_completed
-from typing import Callable, Dict, List, Union
+from typing import Union
 
-from promptflow.tracing import ThreadPoolExecutorWithContext as ThreadPoolExecutor
+from typing_extensions import overload, override
+
+from azure.ai.evaluation._evaluators._common import MultiEvaluatorBase
 
 from .._coherence import CoherenceEvaluator
 from .._f1_score import F1ScoreEvaluator
@@ -15,7 +16,7 @@
 from .._similarity import SimilarityEvaluator
 
 
-class QAEvaluator:
+class QAEvaluator(MultiEvaluatorBase[Union[str, float]]):
     """
     Initialize a question-answer evaluator configured for a specific Azure OpenAI model.
 
@@ -46,18 +47,39 @@ class QAEvaluator:
     """Evaluator identifier, experimental and to be used only with evaluation in cloud."""
 
     def __init__(self, model_config, **kwargs):
-        self._parallel = kwargs.pop("_parallel", False)
-
-        self._evaluators: List[Union[Callable[..., Dict[str, Union[str, float]]], Callable[..., Dict[str, float]]]] = [
+        evaluators = [
             GroundednessEvaluator(model_config),
             RelevanceEvaluator(model_config),
             CoherenceEvaluator(model_config),
             FluencyEvaluator(model_config),
             SimilarityEvaluator(model_config),
             F1ScoreEvaluator(),
         ]
+        super().__init__(evaluators=evaluators, **kwargs)
+
+    @overload  # type: ignore
+    def __call__(self, *, query: str, response: str, context: str, ground_truth: str):
+        """
+        Evaluates question-answering scenario.
+
+        :keyword query: The query to be evaluated.
+        :paramtype query: str
+        :keyword response: The response to be evaluated.
+        :paramtype response: str
+        :keyword context: The context to be evaluated.
+        :paramtype context: str
+        :keyword ground_truth: The ground truth to be evaluated.
+        :paramtype ground_truth: str
+        :return: The scores for QA scenario.
+        :rtype: Dict[str, Union[str, float]]
+        """
 
-    def __call__(self, *, query: str, response: str, context: str, ground_truth: str, **kwargs):
+    @override
+    def __call__(  # pylint: disable=docstring-missing-param
+        self,
+        *args,
+        **kwargs,
+    ):
         """
         Evaluates question-answering scenario.
 
@@ -72,22 +94,5 @@ def __call__(self, *, query: str, response: str, context: str, ground_truth: str
         :return: The scores for QA scenario.
         :rtype: Dict[str, Union[str, float]]
         """
-        results: Dict[str, Union[str, float]] = {}
-        if self._parallel:
-            with ThreadPoolExecutor() as executor:
-                futures = {
-                    executor.submit(
-                        evaluator, query=query, response=response, context=context, ground_truth=ground_truth, **kwargs
-                    ): evaluator
-                    for evaluator in self._evaluators
-                }
-
-                # Collect results as they complete
-                for future in as_completed(futures):
-                    results.update(future.result())
-        else:
-            for evaluator in self._evaluators:
-                result = evaluator(query=query, response=response, context=context, ground_truth=ground_truth, **kwargs)
-                results.update(result)
-
-        return results
+
+        return super().__call__(*args, **kwargs)

sdk/evaluation/azure-ai-evaluation/tests/e2etests/test_mass_evaluate.py

@@ -19,7 +19,7 @@
     RelevanceEvaluator,
     SimilarityEvaluator,
     GroundednessEvaluator,
-    # QAEvaluator,
+    QAEvaluator,
     ContentSafetyEvaluator,
     GroundednessProEvaluator,
     ProtectedMaterialEvaluator,
@@ -88,7 +88,7 @@ def test_evaluate_singleton_inputs(self, model_config, azure_cred, project_scope
             "fluency": FluencyEvaluator(model_config),
             "relevance": RelevanceEvaluator(model_config),
             "similarity": SimilarityEvaluator(model_config),
-            # "qa": QAEvaluator(model_config),
+            "qa": QAEvaluator(model_config),
             "grounded_pro": GroundednessProEvaluator(azure_cred, project_scope),
             "protected_material": ProtectedMaterialEvaluator(azure_cred, project_scope),
             "indirect_attack": IndirectAttackEvaluator(azure_cred, project_scope),
@@ -105,7 +105,7 @@ def test_evaluate_singleton_inputs(self, model_config, azure_cred, project_scope
         row_result_df = pd.DataFrame(result["rows"])
         metrics = result["metrics"]
 
-        assert len(row_result_df.keys()) == 48  # 63 with qa
+        assert len(row_result_df.keys()) == 63
         assert len(row_result_df["inputs.query"]) == 3
         assert len(row_result_df["inputs.context"]) == 3
         assert len(row_result_df["inputs.response"]) == 3
@@ -154,23 +154,23 @@ def test_evaluate_singleton_inputs(self, model_config, azure_cred, project_scope
         assert len(row_result_df["outputs.content_safety.violence"]) == 3
         assert len(row_result_df["outputs.content_safety.violence_score"]) == 3
         assert len(row_result_df["outputs.content_safety.violence_reason"]) == 3
-        # assert len(row_result_df["outputs.qa.f1_score"]) == 3
-        # assert len(row_result_df["outputs.qa.groundedness"]) == 3
-        # assert len(row_result_df["outputs.qa.gpt_groundedness"]) == 3
-        # assert len(row_result_df["outputs.qa.groundedness_reason"]) == 3
-        # assert len(row_result_df["outputs.qa.coherence"]) == 3
-        # assert len(row_result_df["outputs.qa.gpt_coherence"]) == 3
-        # assert len(row_result_df["outputs.qa.coherence_reason"]) == 3
-        # assert len(row_result_df["outputs.qa.fluency"]) == 3
-        # assert len(row_result_df["outputs.qa.gpt_fluency"]) == 3
-        # assert len(row_result_df["outputs.qa.fluency_reason"]) == 3
-        # assert len(row_result_df["outputs.qa.relevance"]) == 3
-        # assert len(row_result_df["outputs.qa.gpt_relevance"]) == 3
-        # assert len(row_result_df["outputs.qa.relevance_reason"]) == 3
-        # assert len(row_result_df["outputs.qa.similarity"]) == 3
-        # assert len(row_result_df["outputs.qa.gpt_similarity"]) == 3
+        assert len(row_result_df["outputs.qa.f1_score"]) == 3
+        assert len(row_result_df["outputs.qa.groundedness"]) == 3
+        assert len(row_result_df["outputs.qa.gpt_groundedness"]) == 3
+        assert len(row_result_df["outputs.qa.groundedness_reason"]) == 3
+        assert len(row_result_df["outputs.qa.coherence"]) == 3
+        assert len(row_result_df["outputs.qa.gpt_coherence"]) == 3
+        assert len(row_result_df["outputs.qa.coherence_reason"]) == 3
+        assert len(row_result_df["outputs.qa.fluency"]) == 3
+        assert len(row_result_df["outputs.qa.gpt_fluency"]) == 3
+        assert len(row_result_df["outputs.qa.fluency_reason"]) == 3
+        assert len(row_result_df["outputs.qa.relevance"]) == 3
+        assert len(row_result_df["outputs.qa.gpt_relevance"]) == 3
+        assert len(row_result_df["outputs.qa.relevance_reason"]) == 3
+        assert len(row_result_df["outputs.qa.similarity"]) == 3
+        assert len(row_result_df["outputs.qa.gpt_similarity"]) == 3
 
-        assert len(metrics.keys()) == 28  # 39 with qa
+        assert len(metrics.keys()) == 39
         assert metrics["f1_score.f1_score"] >= 0
         assert metrics["gleu.gleu_score"] >= 0
         assert metrics["bleu.bleu_score"] >= 0
@@ -199,17 +199,17 @@ def test_evaluate_singleton_inputs(self, model_config, azure_cred, project_scope
         assert metrics["protected_material.protected_material_defect_rate"] >= 0
         assert metrics["indirect_attack.xpia_defect_rate"] >= 0
         assert metrics["eci.eci_defect_rate"] >= 0
-        # assert metrics["qa.f1_score"] >= 0
-        # assert metrics["qa.groundedness"] >= 0
-        # assert metrics["qa.gpt_groundedness"] >= 0
-        # assert metrics["qa.coherence"] >= 0
-        # assert metrics["qa.gpt_coherence"] >= 0
-        # assert metrics["qa.fluency"] >= 0
-        # assert metrics["qa.gpt_fluency"] >= 0
-        # assert metrics["qa.relevance"] >= 0
-        # assert metrics["qa.gpt_relevance"] >= 0
-        # assert metrics["qa.similarity"] >= 0
-        # assert metrics["qa.gpt_similarity"] >= 0
+        assert metrics["qa.f1_score"] >= 0
+        assert metrics["qa.groundedness"] >= 0
+        assert metrics["qa.gpt_groundedness"] >= 0
+        assert metrics["qa.coherence"] >= 0
+        assert metrics["qa.gpt_coherence"] >= 0
+        assert metrics["qa.fluency"] >= 0
+        assert metrics["qa.gpt_fluency"] >= 0
+        assert metrics["qa.relevance"] >= 0
+        assert metrics["qa.gpt_relevance"] >= 0
+        assert metrics["qa.similarity"] >= 0
+        assert metrics["qa.gpt_similarity"] >= 0
 
     def test_evaluate_conversation(self, model_config, data_convo_file, azure_cred, project_scope):
         evaluators = {