class_docstr: Implementation of Pixel-Flipping experiment by Bach et al., 2015.
  
  The basic idea is to compute a decomposition of a digit for a digit class
  and then flip pixels with highly positive, highly negative scores or pixels
  with scores close to zero and then to evaluate the impact of these flips
  onto the prediction scores (mean prediction is calculated).
  
  References:
      1) Sebastian Bach et al.: "On pixel-wise explanations for non-linear classifier
      decisions by layer-wise relevance propagation." PloS one 10.7 (2015): e0130140.
  
  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
  ----------
  features_in_step: integer
      The size of the step, default=1.
  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="black".
  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.
  return_auc_per_sample: boolean
      Indicates if an AUC score should be computed over the curve and returned.
  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,
      features_in_step: int = 1,
      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 = "black",
      perturb_func_kwargs: Optional[Dict[str, Any]] = None,
      return_aggregate: bool = False,
      aggregate_func: Optional[Callable] = None,
      return_auc_per_sample: bool = False,
      default_plot_func: Optional[Callable] = None,
      disable_warnings: bool = False,
      display_progressbar: bool = False,
      **kwargs,
  ):
      """
      Parameters
      ----------
      features_in_step: integer
          The size of the step, default=1.
      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="black".
      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.
      return_auc_per_sample: boolean
          Indicates if an AUC score should be computed over the curve and returned.
      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.
      """
      if default_plot_func is None:
          default_plot_func = plotting.plot_pixel_flipping_experiment
  
      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.features_in_step = features_in_step
      self.return_auc_per_sample = return_auc_per_sample
      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'",
              citation=(
                  "Bach, Sebastian, et al. 'On pixel-wise explanations for non-linear classifier"
                  " decisions by layer - wise relevance propagation.' PloS one 10.7 (2015) "
                  "e0130140"
              ),
          )
  
file_path: Quantus/quantus/metrics/faithfulness/pixel_flipping.py
imports: import sys
  from typing import Any, Callable, Dict, List, Optional, Union
  import numpy
  from quantus.functions.perturb_func import baseline_replacement_by_indices
  from quantus.helpers import asserts, plotting, 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 PixelFlipping object from inherited base classes: Metric with example: obj = PixelFlipping(features_in_step, abs, normalise, normalise_func, normalise_func_kwargs, perturb_func, perturb_baseline, perturb_func_kwargs, return_aggregate, aggregate_func, return_auc_per_sample, default_plot_func, disable_warnings, display_progressbar)