BinaryAccuracy metric

@synalinks_export("synalinks.metrics.BinaryAccuracy")
class BinaryAccuracy(Accuracy):
    """Computes accuracy on binary structures.

    Its output range is `[0, 1]`. It operates at a field level
    and can be used for **multi-class and multi-label classification**.

    Each field of `y_true` and `y_pred` should be a boolean or a float in
    `[0, 1]`. Float fields are thresholded against `threshold` to become
    binary.

    Per-field accuracy is `1` when the binarized values agree and `0`
    otherwise. Results are aggregated according to `average`.

    Example:

    ```python

    class MultiClassClassification(synalinks.DataModel):
        label_1: bool = synalinks.Field(
            description="The first label",
        )
        label_2: bool = synalinks.Field(
            description="The second label",
        )
        label_3: bool = synalinks.Field(
            description="The third label",
        )

    # OR you can also use floats between 0 and 1
    # The `Score`, enforce a float between 0.0 and 1.0 using constrained decoding

    class MultiClassClassification(synalinks.DataModel):
        label_1: synalinks.Score = synalinks.Field(
            description="The first label",
        )
        label_2: synalinks.Score = synalinks.Field(
            description="The second label",
        )
        label_3: synalinks.Score = synalinks.Field(
            description="The third label",
        )

    ```


    Compilation example:

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

    Args:
        average (str): Type of averaging to be performed across per-class results
            in the multi-class case.
            Acceptable values are `None`, `"micro"`, `"macro"` and
            `"weighted"`. Defaults to `None`.
            If `None`, no averaging is performed and `result()` will return
            the score for each class.
            If `"micro"`, compute the metric globally by aggregating
            counts across all fields.
            If `"macro"`, compute the metric for each field, and return their
            unweighted mean.
            If `"weighted"`, compute the metric for each field, and return their
            mean weighted by support (the number of positive labels per field).
        threshold (float): (Optional) Float representing the threshold for deciding
            whether a value is `1` or `0`. Elements of `y_pred` and `y_true`
            greater than `threshold` are converted to `1`, the rest to `0`.
            Defaults to `0.5`.
        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,
        threshold=0.5,
        name="binary_accuracy",
        in_mask=None,
        out_mask=None,
        in_mask_pattern=None,
        out_mask_pattern=None,
    ):
        super().__init__(
            average=average,
            name=name,
            in_mask=in_mask,
            out_mask=out_mask,
            in_mask_pattern=in_mask_pattern,
            out_mask_pattern=out_mask_pattern,
        )
        if not isinstance(threshold, float):
            raise ValueError(
                "Invalid `threshold` argument value. "
                "It should be a Python float. "
                f"Received: threshold={threshold} "
                f"of type '{type(threshold)}'"
            )
        if threshold > 1.0 or threshold <= 0.0:
            raise ValueError(
                "Invalid `threshold` argument value. "
                "It should verify 0 < threshold <= 1. "
                f"Received: threshold={threshold}"
            )
        self.threshold = threshold

    async def update_state(self, y_true, y_pred):
        y_pred = tree.map_structure(lambda x: ops.convert_to_json_data_model(x), y_pred)
        y_true = tree.map_structure(lambda x: ops.convert_to_json_data_model(x), y_true)

        if self.in_mask or self.in_mask_pattern:
            y_pred = tree.map_structure(
                lambda x: (
                    x.in_mask(mask=self.in_mask, pattern=self.in_mask_pattern)
                    if x is not None
                    else x
                ),
                y_pred,
            )
            y_true = tree.map_structure(
                lambda x: (
                    x.in_mask(mask=self.in_mask, pattern=self.in_mask_pattern)
                    if x is not None
                    else x
                ),
                y_true,
            )
        if self.out_mask or self.out_mask_pattern:
            y_pred = tree.map_structure(
                lambda x: (
                    x.out_mask(mask=self.out_mask, pattern=self.out_mask_pattern)
                    if x is not None
                    else x
                ),
                y_pred,
            )
            y_true = tree.map_structure(
                lambda x: (
                    x.out_mask(mask=self.out_mask, pattern=self.out_mask_pattern)
                    if x is not None
                    else x
                ),
                y_true,
            )

        def convert_to_binary(x):
            if isinstance(x, bool):
                return 1.0 if x is True else 0.0
            elif isinstance(x, float):
                return 1.0 if x > self.threshold else 0.0
            else:
                raise ValueError(
                    "All `y_true` and y_pred` fields should be booleans or floats. "
                    "Use `in_mask` or `out_mask` to remove the other fields."
                )

        if y_true is None or y_pred is None:
            # A failed prediction yields `y_pred is None`; there is nothing to
            # compare, so skip the sample instead of calling `.get_json()` on None.
            return
        y_true = tree.flatten(
            tree.map_structure(lambda x: convert_to_binary(x), y_true.get_json())
        )
        y_pred = tree.flatten(
            tree.map_structure(lambda x: convert_to_binary(x), y_pred.get_json())
        )
        y_true = np.convert_to_tensor(y_true)
        y_pred = np.convert_to_tensor(y_pred)

        # zip_longest, not zip: a structure mismatch between pred and gold
        # (variable-length arrays) must count the unmatched leaves as wrong,
        # not silently drop them. The None fill never equals a 0.0/1.0 leaf.
        y_true_leaves = y_true.tolist()
        y_pred_leaves = y_pred.tolist()
        size = max(len(y_true_leaves), len(y_pred_leaves))
        correct = np.convert_to_tensor(
            [
                1.0 if yt == yp else 0.0
                for yt, yp in zip_longest(y_true_leaves, y_pred_leaves)
            ]
        )
        total = np.convert_to_tensor([1.0] * size)
        intermediate_weights = np.convert_to_tensor(
            y_true_leaves + [0.0] * (size - len(y_true_leaves))
        )

        current_correct = self.state.get("correct")
        if current_correct:
            correct = ragged_add(current_correct, correct)

        current_total = self.state.get("total")
        if current_total:
            total = ragged_add(current_total, total)

        current_intermediate_weights = self.state.get("intermediate_weights")
        if current_intermediate_weights:
            intermediate_weights = ragged_add(
                current_intermediate_weights, intermediate_weights
            )

        self.state.update(
            {
                "correct": correct.tolist(),
                "total": total.tolist(),
                "intermediate_weights": intermediate_weights.tolist(),
            }
        )

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

        Returns:
            (dict): The config dict.
        """
        config = {
            "threshold": self.threshold,
            "name": self.name,
        }
        base_config = super().get_config()
        return {**base_config, **config}