class_docstr: Implementation of AUC metric by Fawcett et al., 2006.
  
  AUC is a ranking metric and  compares the ranking between attributions and a given ground-truth mask
  
  References:
      1) Tom Fawcett: 'An introduction to ROC analysis' "Pattern Recognition Letters" Vol 27, Issue 8, 2006
  
  Attributes:
      -  _name: The name of the metric.
      - _data_applicability: The data types that the metric implementation currently supports.
      - _models: The model types that this metric can work with.
      - score_direction: How to interpret the scores, whether higher/ lower values are considered better.
      - evaluation_category: What property/ explanation quality that this metric measures.
docstr: This method performs XAI evaluation on a single batch of explanations.
  For more information on the specific logic, we refer the metric’s initialisation docstring.
  
  Parameters
  ----------
  a_batch:
      A np.ndarray which contains pre-computed attributions i.e., explanations.
  s_batch:
      A np.ndarray which contains segmentation masks that matches the input.
  kwargs:
      Unused.
  
  Returns
  -------
  scores_batch:
      Evaluation result for batch.
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code: def evaluate_batch(
      self, a_batch: np.ndarray, s_batch: np.ndarray, **kwargs
  ) -> List[float]:
      """
      This method performs XAI evaluation on a single batch of explanations.
      For more information on the specific logic, we refer the metric’s initialisation docstring.
  
      Parameters
      ----------
      a_batch:
          A np.ndarray which contains pre-computed attributions i.e., explanations.
      s_batch:
          A np.ndarray which contains segmentation masks that matches the input.
      kwargs:
          Unused.
  
      Returns
      -------
      scores_batch:
          Evaluation result for batch.
      """
      return [
          self.evaluate_instance(a=a, s=s)
          for a, s in zip(a_batch, s_batch)
      ]
  
file_path: Quantus/quantus/metrics/localisation/auc.py
imports: import sys
  from typing import Any, Callable, Dict, List, Optional
  import numpy
  from sklearn.metrics import auc, roc_curve
  from quantus.helpers import asserts, warn
  from quantus.helpers.enums import DataType, EvaluationCategory, ModelType, ScoreDirection
  from quantus.metrics.base import Metric
label_desc: Use image node_id 5 for calling the AUC obj's underlying member method code with example usage: obj.evaluate_batch(a_batch, s_batch) and returns: unknown