class_docstr: Implementation of IROF (Iterative Removal of Features) by Rieger at el., 2020.
  
  The metric computes the area over the curve per class for sorted mean importances
  of feature segments (superpixels) as they are iteratively removed (and prediction scores are collected),
  averaged over several test samples.
  
  Assumptions:
      - The original metric definition relies on image-segmentation functionality. 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) Laura Rieger and Lars Kai Hansen. "Irof: a low resource evaluation metric for
      explanation methods." arXiv preprint arXiv:2003.08747 (2020).
  
  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: Parameters
  ----------
  segmentation_method: string
      Image segmentation method:'slic' or 'felzenszwalb', default="slic".
  abs: boolean
      Indicates whether absolute operation is applied on the attribution, default=False.
  normalise: boolean
      Indicates whether normalise operation is applied on the attribution, default=True.
  normalise_func: callable
      Attribution normalisation function applied in case normalise=True.
      If normalise_func=None, the default value is used, default=normalise_by_max.
  normalise_func_kwargs: dict
      Keyword arguments to be passed to normalise_func on call, default={}.
  perturb_func: callable
      Input perturbation function. If None, the default value is used,
      default=baseline_replacement_by_indices.
  perturb_baseline: string
      Indicates the type of baseline: "mean", "random", "uniform", "black" or "white",
      default="mean".
  perturb_func_kwargs: dict
      Keyword arguments to be passed to perturb_func, default={}.
  return_aggregate: boolean
      Indicates if an aggregated score should be computed over all instances.
  aggregate_func: callable
      Callable that aggregates the scores given an evaluation call.
  default_plot_func: callable
      Callable that plots the metrics result.
  disable_warnings: boolean
      Indicates whether the warnings are printed, default=False.
  display_progressbar: boolean
      Indicates whether a tqdm-progress-bar is printed, default=False.
  kwargs: optional
      Keyword arguments.
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code: def __init__(
      self,
      segmentation_method: str = "slic",
      abs: bool = False,
      normalise: bool = True,
      normalise_func: Optional[
          Callable[[np.ndarray], np.ndarray]
      ] = None,
      normalise_func_kwargs: Optional[Dict[str, Any]] = None,
      perturb_func: Optional[Callable] = None,
      perturb_baseline: str = "mean",
      perturb_func_kwargs: Optional[Dict[str, Any]] = None,
      return_aggregate: bool = True,
      aggregate_func: Optional[Callable] = None,
      default_plot_func: Optional[Callable] = None,
      disable_warnings: bool = False,
      display_progressbar: bool = False,
      **kwargs,
  ):
      """
      Parameters
      ----------
      segmentation_method: string
          Image segmentation method:'slic' or 'felzenszwalb', default="slic".
      abs: boolean
          Indicates whether absolute operation is applied on the attribution, default=False.
      normalise: boolean
          Indicates whether normalise operation is applied on the attribution, default=True.
      normalise_func: callable
          Attribution normalisation function applied in case normalise=True.
          If normalise_func=None, the default value is used, default=normalise_by_max.
      normalise_func_kwargs: dict
          Keyword arguments to be passed to normalise_func on call, default={}.
      perturb_func: callable
          Input perturbation function. If None, the default value is used,
          default=baseline_replacement_by_indices.
      perturb_baseline: string
          Indicates the type of baseline: "mean", "random", "uniform", "black" or "white",
          default="mean".
      perturb_func_kwargs: dict
          Keyword arguments to be passed to perturb_func, default={}.
      return_aggregate: boolean
          Indicates if an aggregated score should be computed over all instances.
      aggregate_func: callable
          Callable that aggregates the scores given an evaluation call.
      default_plot_func: callable
          Callable that plots the metrics result.
      disable_warnings: boolean
          Indicates whether the warnings are printed, default=False.
      display_progressbar: boolean
          Indicates whether a tqdm-progress-bar is printed, default=False.
      kwargs: optional
          Keyword arguments.
      """
      super().__init__(
          abs=abs,
          normalise=normalise,
          normalise_func=normalise_func,
          normalise_func_kwargs=normalise_func_kwargs,
          return_aggregate=return_aggregate,
          aggregate_func=aggregate_func,
          default_plot_func=default_plot_func,
          display_progressbar=display_progressbar,
          disable_warnings=disable_warnings,
          **kwargs,
      )
  
      if perturb_func is None:
          perturb_func = baseline_replacement_by_indices
  
      # Save metric-specific attributes.
      self.segmentation_method = segmentation_method
      self.nr_channels = None
      self.perturb_func = make_perturb_func(
          perturb_func,
          perturb_func_kwargs,
          perturb_baseline=perturb_baseline,
      )
  
      # Asserts and warnings.
      if not self.disable_warnings:
          warn.warn_parameterisation(
              metric_name=self.__class__.__name__,
              sensitive_params=(
                  "baseline value 'perturb_baseline' and the method to segment "
                  "the image 'segmentation_method' (including all its associated"
                  " hyperparameters), also, IROF only works with image data"
              ),
              data_domain_applicability=(
                  f"Also, the current implementation only works for 3-dimensional (image) data."
              ),
              citation=(
                  "Rieger, Laura, and Lars Kai Hansen. 'Irof: a low resource evaluation metric "
                  "for explanation methods.' arXiv preprint arXiv:2003.08747 (2020)"
              ),
          )
  
file_path: Quantus/quantus/metrics/faithfulness/irof.py
imports: import sys
  from typing import Any, Callable, Dict, List, Optional
  import numpy
  from quantus.functions.perturb_func import baseline_replacement_by_indices
  from quantus.helpers import asserts, utils, 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 1 to create a new IROF object from inherited base classes: Metric with example: obj = IROF(segmentation_method, abs, normalise, normalise_func, normalise_func_kwargs, perturb_func, perturb_baseline, perturb_func_kwargs, return_aggregate, aggregate_func, default_plot_func, disable_warnings, display_progressbar)