dspy.Evaluate

dspy.Evaluate(*, devset: List[dspy.Example], metric: Optional[Callable] = None, num_threads: Optional[int] = None, display_progress: bool = False, display_table: Union[bool, int] = False, max_errors: Optional[int] = None, provide_traceback: Optional[bool] = None, failure_score: float = 0.0, **kwargs)

DSPy Evaluate class.

This class is used to evaluate the performance of a DSPy program. Users need to provide a evaluation dataset and a metric function in order to use this class. This class supports parallel evaluation on the provided dataset.

Parameters:

Name	Type	Description	Default
devset	List[Example]	the evaluation dataset.	required
metric	Callable	The metric function to use for evaluation.	None
num_threads	Optional[int]	The number of threads to use for parallel evaluation.	None
display_progress	bool	Whether to display progress during evaluation.	False
display_table	Union[bool, int]	Whether to display the evaluation results in a table. If a number is passed, the evaluation results will be truncated to that number before displayed.	False
max_errors	Optional[int]	The maximum number of errors to allow before stopping evaluation. If None, inherits from dspy.settings.max_errors.	None
provide_traceback	Optional[bool]	Whether to provide traceback information during evaluation.	None
failure_score	float	The default score to use if evaluation fails due to an exception.	0.0

Source code in dspy/evaluate/evaluate.py

def __init__(
    self,
    *,
    devset: List["dspy.Example"],
    metric: Optional[Callable] = None,
    num_threads: Optional[int] = None,
    display_progress: bool = False,
    display_table: Union[bool, int] = False,
    max_errors: Optional[int] = None,
    provide_traceback: Optional[bool] = None,
    failure_score: float = 0.0,
    **kwargs,
):
    """
    Args:
        devset (List[dspy.Example]): the evaluation dataset.
        metric (Callable): The metric function to use for evaluation.
        num_threads (Optional[int]): The number of threads to use for parallel evaluation.
        display_progress (bool): Whether to display progress during evaluation.
        display_table (Union[bool, int]): Whether to display the evaluation results in a table.
            If a number is passed, the evaluation results will be truncated to that number before displayed.
        max_errors (Optional[int]): The maximum number of errors to allow before
            stopping evaluation. If ``None``, inherits from ``dspy.settings.max_errors``.
        provide_traceback (Optional[bool]): Whether to provide traceback information during evaluation.
        failure_score (float): The default score to use if evaluation fails due to an exception.
    """
    self.devset = devset
    self.metric = metric
    self.num_threads = num_threads
    self.display_progress = display_progress
    self.display_table = display_table
    self.max_errors = max_errors
    self.provide_traceback = provide_traceback
    self.failure_score = failure_score

Functions

__call__(program: dspy.Module, metric: Optional[Callable] = None, devset: Optional[List[dspy.Example]] = None, num_threads: Optional[int] = None, display_progress: Optional[bool] = None, display_table: Optional[Union[bool, int]] = None, callback_metadata: Optional[dict[str, Any]] = None) -> EvaluationResult

Parameters:

Name	Type	Description	Default
program	Module	The DSPy program to evaluate.	required
metric	Callable	The metric function to use for evaluation. if not provided, use self.metric.	None
devset	List[Example]	the evaluation dataset. if not provided, use self.devset.	None
num_threads	Optional[int]	The number of threads to use for parallel evaluation. if not provided, use self.num_threads.	None
display_progress	bool	Whether to display progress during evaluation. if not provided, use self.display_progress.	None
display_table	Union[bool, int]	Whether to display the evaluation results in a table. if not provided, use self.display_table. If a number is passed, the evaluation results will be truncated to that number before displayed.	None
callback_metadata	dict	Metadata to be used for evaluate callback handlers.	None

Returns:

Type	Description
EvaluationResult	The evaluation results are returned as a dspy.EvaluationResult object containing the following attributes:
EvaluationResult	score: A float percentage score (e.g., 67.30) representing overall performance
EvaluationResult	results: a list of (example, prediction, score) tuples for each example in devset

Source code in dspy/evaluate/evaluate.py

@with_callbacks
def __call__(
    self,
    program: "dspy.Module",
    metric: Optional[Callable] = None,
    devset: Optional[List["dspy.Example"]] = None,
    num_threads: Optional[int] = None,
    display_progress: Optional[bool] = None,
    display_table: Optional[Union[bool, int]] = None,
    callback_metadata: Optional[dict[str, Any]] = None,
) -> EvaluationResult:
    """
    Args:
        program (dspy.Module): The DSPy program to evaluate.
        metric (Callable): The metric function to use for evaluation. if not provided, use `self.metric`.
        devset (List[dspy.Example]): the evaluation dataset. if not provided, use `self.devset`.
        num_threads (Optional[int]): The number of threads to use for parallel evaluation. if not provided, use
            `self.num_threads`.
        display_progress (bool): Whether to display progress during evaluation. if not provided, use
            `self.display_progress`.
        display_table (Union[bool, int]): Whether to display the evaluation results in a table. if not provided, use
            `self.display_table`. If a number is passed, the evaluation results will be truncated to that number before displayed.
        callback_metadata (dict): Metadata to be used for evaluate callback handlers.

    Returns:
        The evaluation results are returned as a dspy.EvaluationResult object containing the following attributes:

        - score: A float percentage score (e.g., 67.30) representing overall performance

        - results: a list of (example, prediction, score) tuples for each example in devset
    """
    metric = metric if metric is not None else self.metric
    devset = devset if devset is not None else self.devset
    num_threads = num_threads if num_threads is not None else self.num_threads
    display_progress = display_progress if display_progress is not None else self.display_progress
    display_table = display_table if display_table is not None else self.display_table

    if callback_metadata:
        logger.debug(f"Evaluate is called with callback metadata: {callback_metadata}")

    tqdm.tqdm._instances.clear()

    executor = ParallelExecutor(
        num_threads=num_threads,
        disable_progress_bar=not display_progress,
        max_errors=(
            self.max_errors
            if self.max_errors is not None
            else dspy.settings.max_errors
        ),
        provide_traceback=self.provide_traceback,
        compare_results=True,
    )

    def process_item(example):
        prediction = program(**example.inputs())
        score = metric(example, prediction)

        # Increment assert and suggest failures to program's attributes
        if hasattr(program, "_assert_failures"):
            program._assert_failures += dspy.settings.get("assert_failures")
        if hasattr(program, "_suggest_failures"):
            program._suggest_failures += dspy.settings.get("suggest_failures")

        return prediction, score

    results = executor.execute(process_item, devset)
    assert len(devset) == len(results)

    results = [((dspy.Prediction(), self.failure_score) if r is None else r) for r in results]
    results = [(example, prediction, score) for example, (prediction, score) in zip(devset, results)]
    ncorrect, ntotal = sum(score for *_, score in results), len(devset)

    logger.info(f"Average Metric: {ncorrect} / {ntotal} ({round(100 * ncorrect / ntotal, 1)}%)")

    if display_table:
        if importlib.util.find_spec("pandas") is not None:
            # Rename the 'correct' column to the name of the metric object
            metric_name = metric.__name__ if isinstance(metric, types.FunctionType) else metric.__class__.__name__
            # Construct a pandas DataFrame from the results
            result_df = self._construct_result_table(results, metric_name)

            self._display_result_table(result_df, display_table, metric_name)
        else:
            logger.warning("Skipping table display since `pandas` is not installed.")

    return EvaluationResult(
        score=round(100 * ncorrect / ntotal, 2),
        results=results,
    )