data = '/mnt/vss/_work/1/s/sdk/evaluation/azure-ai-evaluation/tests/unittests/data/questions.jsonl'
evaluators = {'half': <test_evaluators.test_inputs_evaluators.HalfOptionalEval object at 0x7f5a2781fd50>, 'no': <test_evaluators.te...tEval object at 0x7f5a278313d0>, 'opt': <test_evaluators.test_inputs_evaluators.OptionalEval object at 0x7f5a278303d0>}
evaluation_name = None, target = None, evaluator_config = None
azure_ai_project = None, output_path = None, kwargs = {'_use_pf_client': True}

    def evaluate(
        *,
        data: Union[str, os.PathLike],
        evaluators: Dict[str, Callable],
        evaluation_name: Optional[str] = None,
        target: Optional[Callable] = None,
        evaluator_config: Optional[Dict[str, EvaluatorConfig]] = None,
        azure_ai_project: Optional[AzureAIProject] = None,
        output_path: Optional[Union[str, os.PathLike]] = None,
        **kwargs,
    ) -> EvaluationResult:
        """Evaluates target or data with built-in or custom evaluators. If both target and data are provided,
            data will be run through target function and then results will be evaluated.
    
        :keyword data: Path to the data to be evaluated or passed to target if target is set.
            Only .jsonl format files are supported.  `target` and `data` both cannot be None. Required.
        :paramtype data: str
        :keyword evaluators: Evaluators to be used for evaluation. It should be a dictionary with key as alias for evaluator
            and value as the evaluator function. Required.
        :paramtype evaluators: Dict[str, Callable]
        :keyword evaluation_name: Display name of the evaluation.
        :paramtype evaluation_name: Optional[str]
        :keyword target: Target to be evaluated. `target` and `data` both cannot be None
        :paramtype target: Optional[Callable]
        :keyword evaluator_config: Configuration for evaluators. The configuration should be a dictionary with evaluator
            names as keys and a values that are dictionaries containing the column mappings. The column mappings should
            be a dictionary with keys as the column names in the evaluator input and values as the column names in the
            input data or data generated by target.
        :paramtype evaluator_config: Optional[Dict[str, ~azure.ai.evaluation.EvaluatorConfig]]
        :keyword output_path: The local folder or file path to save evaluation results to if set. If folder path is provided
              the results will be saved to a file named `evaluation_results.json` in the folder.
        :paramtype output_path: Optional[str]
        :keyword azure_ai_project: Logs evaluation results to AI Studio if set.
        :paramtype azure_ai_project: Optional[~azure.ai.evaluation.AzureAIProject]
        :return: Evaluation results.
        :rtype: ~azure.ai.evaluation.EvaluationResult
    
        :Example:
    
        Evaluate API can be used as follows:
    
        .. code-block:: python
    
                from azure.ai.evaluation import evaluate, RelevanceEvaluator, CoherenceEvaluator
    
    
                model_config = {
                    "azure_endpoint": os.environ.get("AZURE_OPENAI_ENDPOINT"),
                    "api_key": os.environ.get("AZURE_OPENAI_KEY"),
                    "azure_deployment": os.environ.get("AZURE_OPENAI_DEPLOYMENT"),
                }
    
                coherence_eval = CoherenceEvaluator(model_config=model_config)
                relevance_eval = RelevanceEvaluator(model_config=model_config)
    
                path = "evaluate_test_data.jsonl"
                result = evaluate(
                    data=path,
                    evaluators={
                        "coherence": coherence_eval,
                        "relevance": relevance_eval,
                    },
                    evaluator_config={
                        "coherence": {
                            "column_mapping": {
                                "response": "${

self = <test_evaluate.TestEvaluate object at 0x00000159E54B28D0>
questions_file = 'D:\\a\\_work\\1\\s\\sdk\\evaluation\\azure-ai-evaluation\\tests\\unittests\\data\\questions.jsonl'
questions_answers_basic_file = 'D:\\a\\_work\\1\\s\\sdk\\evaluation\\azure-ai-evaluation\\tests\\unittests\\data\\questions_answers_basic.jsonl'
use_pf_client = False

    @pytest.mark.parametrize("use_pf_client", [True, False])
    def test_optional_inputs_with_target(self, questions_file, questions_answers_basic_file, use_pf_client):
        from test_evaluators.test_inputs_evaluators import EchoEval
    
        # Check that target overrides default inputs
        target_answer_results = evaluate(
            data=questions_file,
            target=_new_answer_target,
            evaluators={"echo": EchoEval()},
            _use_pf_client=use_pf_client,
        )  # type: ignore
    
        assert target_answer_results["rows"][0]["outputs.echo.echo_query"] == "How long is flight from Earth to LV-426?"
>       assert target_answer_results["rows"][0]["outputs.echo.echo_response"] == "new response"
E       AssertionError: assert '(Failed)' == 'new response'
E         - new response
E         + (Failed)

D:\a\_work\1\s\sdk\evaluation\azure-ai-evaluation\tests\unittests\test_evaluate.py:621: AssertionError