CategoricalF1Score metric

@synalinks_export(
    [
        "synalinks.metrics.CategoricalF1Score",
        "synalinks.metrics.ListF1Score",
    ]
)
class CategoricalF1Score(CategoricalFBetaScore):
    """Computes F-1 Score on categorical (list / label) structures.

    Formula:
    ```python
        f1_score = 2 * (precision * recall) / (precision + recall)
    ```

    This is the harmonic mean of precision and recall.
    Its output range is `[0, 1]`. It operates at a label level
    and can be used for **classification** or **retrieval pipelines**.

    The difference between this metric and `F1Score` is that this one considers
    each element of the list (or the string value) as **one label**.

    If `labels` is provided, accumulation is performed per-label (sklearn-style)
    and `result()` returns a `{label: score}` dict for `average=None`. See
    `CategoricalFBetaScore` for details.

    Example:

    ```python

        # for single label classification

        class ListClassification(synalinks.DataModel):
            label: Literal["label", "label_1", "label_2"]

        # for multi label classification

        class ListClassification(synalinks.DataModel):
            labels: List[Literal["label", "label_1", "label_2"]]

        # or use it with retrieval pipelines, in that case make sure to mask
        # the correct fields.

        class AnswerWithReferences(synalinks.DataModel):
            sources: List[str]
            answer: str
    ```


    Compilation example:

    ```python
    program.compile(
        metrics=[
            synalinks.metrics.CategoricalF1Score(),
        ],
    )
    ```

    Args:
        average (str): Type of averaging to be performed.
            Acceptable values are `None`, `"micro"`, `"macro"` and
            `"weighted"`. Defaults to `None`.
        labels (list): (Optional) Explicit list of label names to track.
            When provided, accumulation is per-label across all batches and
            `result()` returns a `{label: score}` dict for `average=None`.
        name (str): (Optional) string name of the metric instance.
        in_mask (list): (Optional) list of keys to keep to compute the metric.
        out_mask (list): (Optional) list of keys to remove to compute the metric.
        in_mask_pattern (str): (Optional) Regex pattern; fields whose names match
            are kept (combined with ``in_mask`` via OR).
        out_mask_pattern (str): (Optional) Regex pattern; fields whose names match
            are dropped (combined with ``out_mask`` via OR).
    """

    def __init__(
        self,
        average=None,
        labels=None,
        name="categorical_f1_score",
        in_mask=None,
        out_mask=None,
        in_mask_pattern=None,
        out_mask_pattern=None,
    ):
        super().__init__(
            average=average,
            beta=1.0,
            labels=labels,
            name=name,
            in_mask=in_mask,
            out_mask=out_mask,
            in_mask_pattern=in_mask_pattern,
            out_mask_pattern=out_mask_pattern,
        )

    def get_config(self):
        """Return the serializable config of the metric.

        Returns:
            (dict): The config dict.
        """
        base_config = super().get_config()
        del base_config["beta"]
        return base_config