Precision metric

@synalinks_export("synalinks.metrics.Precision")
class Precision(FBetaScore):
    """Computes token-level precision for LM string outputs.

    Formula (per field, SQuAD-style multiset over normalized tokens):

    ```python
    precision = |Counter(y_true_tokens) ∩ Counter(y_pred_tokens)| / |y_pred_tokens|
    ```

    Mirrors `F1Score`: tokenization, masking and `average` modes behave
    identically; only the result formula differs. Use this when you want
    to report precision as a separate signal alongside `Recall` and
    `F1Score`.


    Example:

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

    Args:
        average (str): Type of averaging across per-field results.
            One of `None`, `"micro"`, `"macro"`, `"weighted"`.
        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,
        name="precision",
        in_mask=None,
        out_mask=None,
        in_mask_pattern=None,
        out_mask_pattern=None,
    ):
        super().__init__(
            average=average,
            beta=1.0,
            name=name,
            in_mask=in_mask,
            out_mask=out_mask,
            in_mask_pattern=in_mask_pattern,
            out_mask_pattern=out_mask_pattern,
        )
        self._formula = "precision"

    def get_config(self):
        base_config = super().get_config()
        del base_config["beta"]
        return base_config