def show_anomalies_from_prediction(
        self,
        series: TimeSeries,
        pred_series: TimeSeries,
        scorer_name: str = None,
        anomalies: TimeSeries = None,
        title: str = None,
        metric: Optional[Literal["AUC_ROC", "AUC_PR"]] = None,
        component_wise: bool = False,
    ):
        """Plot the results of the scorer.

        Computes the anomaly score on the two series. And plots the results.

        The plot will be composed of the following:
            - the series and the pred_series.
            - the anomaly score of the scorer.
            - the actual anomalies, if given.

        It is possible to:
            - add a title to the figure with the parameter `title`
            - give personalized name to the scorer with `scorer_name`
            - show the results of a metric for the anomaly score (AUC_ROC or AUC_PR),
              if the actual anomalies is provided.

        Parameters
        ----------
        series
            The actual series to visualize anomalies from.
        pred_series
            The predicted series of `series`.
        anomalies
            The ground truth of the anomalies (1 if it is an anomaly and 0 if not)
        scorer_name
            Name of the scorer.
        title
            Title of the figure
        metric
            Optionally, the name of the metric function to use. Must be one of "AUC_ROC" (Area Under the
            Receiver Operating Characteristic Curve) and "AUC_PR" (Average Precision from scores).
            Default: "AUC_ROC".
        component_wise
            If True, will separately plot each component in case of multivariate anomaly detection.
        """
        series = _check_input(series, name="series", num_series_expected=1)[0]
        pred_series = _check_input(
            pred_series, name="pred_series", num_series_expected=1
        )[0]
        pred_scores = self.score_from_prediction(series, pred_series)

        if title is None:
            title = f"Anomaly results by scorer {str(self)}"

        if scorer_name is None:
            scorer_name = [f"anomaly score by {str(self)}"]

        return show_anomalies_from_scores(
            series=series,
            anomalies=anomalies,
            pred_series=pred_series,
            pred_scores=pred_scores,
            window=self.window,
            names_of_scorers=scorer_name,
            title=title,
            metric=metric,
            component_wise=component_wise,
        )
Plot the results of the scorer.

        Computes the anomaly score on the two series. And plots the results.

        The plot will be composed of the following:
            - the series and the pred_series.
            - the anomaly score of the scorer.
            - the actual anomalies, if given.

        It is possible to:
            - add a title to the figure with the parameter `title`
            - give personalized name to the scorer with `scorer_name`
            - show the results of a metric for the anomaly score (AUC_ROC or AUC_PR),
              if the actual anomalies is provided.

        Parameters
        ----------
        series
            The actual series to visualize anomalies from.
        pred_series
            The predicted series of `series`.
        anomalies
            The ground truth of the anomalies (1 if it is an anomaly and 0 if not)
        scorer_name
            Name of the scorer.
        title
            Title of the figure
        metric
            Optionally, the name of the metric function to use. Must be one of "AUC_ROC" (Area Under the
            Receiver Operating Characteristic Curve) and "AUC_PR" (Average Precision from scores).
            Default: "AUC_ROC".
        component_wise
            If True, will separately plot each component in case of multivariate anomaly detection.
        
darts/ad/scorers/scorers.py