class_docstr: Implementation of ROAD evaluation strategy by Rong et al., 2022.
  
  The ROAD approach measures the accuracy of the model on the provided test set at each step of an iterative process
  of removing k most important pixels. At each step k most relevant pixels (MoRF order) are replaced with noisy linear
  imputations which removes bias.
  
  Assumptions:
      - The original metric definition relies on perturbation functionality suited only for images.
      Therefore, only apply the metric to 3-dimensional (image) data. To extend the applicablity
      to other data domains, adjustments to the current implementation might be necessary.
  
  References:
      1) Leemann Rong et al.: "Evaluating Feature Attribution: An Information-Theoretic Perspective." arXiv preprint
      arXiv:2202.00449 (2022).
  
  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.
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docstr: Post-process the evaluation results.
  
  Parameters
  ----------
  kwargs:
      Unused.
  
  Returns
  -------
  None
docstr_tok: ["Post-process","the","evaluation","results",".","Parameters","--","--","--","--","--","kwargs",":","Unused",".","Returns","--","--","--","-","None"]
code: def custom_postprocess(
      self,
      **kwargs,
  ) -> None:
      """
      Post-process the evaluation results.
  
      Parameters
      ----------
      kwargs:
          Unused.
  
      Returns
      -------
      None
      """
  
      # Calculate accuracy for every number of most important pixels removed.
      self.evaluation_scores = {
          percentage: np.mean(np.array(self.evaluation_scores)[:, p_ix])
          for p_ix, percentage in enumerate(self.percentages)
      }
  
file_path: Quantus/quantus/metrics/faithfulness/road.py
imports: import sys
  from typing import Any, Callable, Dict, List, Optional
  import numpy
  from quantus.functions.perturb_func import noisy_linear_imputation
  from quantus.helpers import warn
  from quantus.helpers.enums import DataType, EvaluationCategory, ModelType, ScoreDirection
  from quantus.helpers.model.model_interface import ModelInterface
  from quantus.helpers.perturbation_utils import make_perturb_func
  from quantus.metrics.base import Metric
label_desc: Use image node_id 5 for calling the ROAD obj's underlying member method code with example usage: obj.custom_postprocess() without return types