class_docstr: Base class for all anomaly scorers
docstr: 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 actual_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
  ----------
  actual_series
      The actual series to visualize anomalies from.
  pred_series
      The predicted series of `actual_series`.
  actual_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, Scoring function to use. Must be one of "AUC_ROC" and "AUC_PR".
      Default: "AUC_ROC"
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code: def show_anomalies_from_prediction(
      self,
      actual_series: TimeSeries,
      pred_series: TimeSeries,
      scorer_name: str = None,
      actual_anomalies: TimeSeries = None,
      title: str = None,
      metric: str = None,
  ):
      """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 actual_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
      ----------
      actual_series
          The actual series to visualize anomalies from.
      pred_series
          The predicted series of `actual_series`.
      actual_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, Scoring function to use. Must be one of "AUC_ROC" and "AUC_PR".
          Default: "AUC_ROC"
      """
      if isinstance(actual_series, Sequence):
          raise_if_not(
              len(actual_series) == 1,
              "``show_anomalies_from_prediction`` expects only one series for `actual_series`,"
              + f" found a list of length {len(actual_series)} as input.",
          )
  
          actual_series = actual_series[0]
  
      raise_if_not(
          isinstance(actual_series, TimeSeries),
          "``show_anomalies_from_prediction`` expects an input of type TimeSeries,"
          + f" found type {type(actual_series)} for `actual_series`.",
      )
  
      if isinstance(pred_series, Sequence):
          raise_if_not(
              len(pred_series) == 1,
              "``show_anomalies_from_prediction`` expects one series for `pred_series`,"
              + f" found a list of length {len(pred_series)} as input.",
          )
  
          pred_series = pred_series[0]
  
      raise_if_not(
          isinstance(pred_series, TimeSeries),
          "``show_anomalies_from_prediction`` expects an input of type TimeSeries,"
          + f" found type: {type(pred_series)} for `pred_series`.",
      )
  
      anomaly_score = self.score_from_prediction(
          actual_series, pred_series
      )
  
      if title is None:
          title = f"Anomaly results by scorer {self.__str__()}"
  
      if scorer_name is None:
          scorer_name = [f"anomaly score by {self.__str__()}"]
  
      return show_anomalies_from_scores(
          actual_series,
          model_output=pred_series,
          anomaly_scores=anomaly_score,
          window=self.window,
          names_of_scorers=scorer_name,
          actual_anomalies=actual_anomalies,
          title=title,
          metric=metric,
      )
  
imports: from abc import ABC, abstractmethod
  from typing import Any, Sequence, Union
  import numpy
  from darts import TimeSeries
  from darts.ad.utils import _assert_same_length, _assert_timeseries, _intersect, _sanity_check_two_series, _to_list, eval_accuracy_from_scores, show_anomalies_from_scores
  from darts.logging import get_logger, raise_if_not
label_desc: Use image node_id 10 for calling the AnomalyScorer obj's underlying member method code with example usage: obj.show_anomalies_from_prediction(actual_series, pred_series, scorer_name, actual_anomalies, title, metric) and returns: show_anomalies_from_scores