class_docstr: Implementation of Selectivity test by Montavon et al., 2018.
  
  At each iteration, a patch (e.g., of size 4 x 4) corresponding to the region with
  highest relevance is set to black. The plot keeps track of the function value
  as the features are being progressively removed and computes an average over
  a large number of examples.
  
  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) Grégoire Montavon et al.:
      "Methods for interpreting and understanding deep neural networks."
      Digital Signal Processing 73 (2018): 1-15.
  
  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.
class_docstr_tok: ["Implementation","of","Selectivity","test","by","Montavon","et","al.",",","2018",".","At","each","iteration",",","a","patch","(","e.g.",",","of","size","4","x","4",")","corresponding","to","the","region","with","highest","relevance","is","set","to","black",".","The","plot","keeps","track","of","the","function","value","as","the","features","are","being","progressively","removed","and","computes","an","average","over","a","large","number","of","examples",".","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",")","Gr\u00e9goire","Montavon","et","al",".",":","``","Methods","for","interpreting","and","understanding","deep","neural","networks",".","''","Digital","Signal","Processing","73","(","2018",")",":","1-15",".","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: Parameters
  ----------
  patch_size: integer
      The patch size for masking, default=8.
  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.
  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.
docstr_tok: ["Parameters","--","--","--","--","--","patch_size",":","integer","The","patch","size","for","masking",",","default=8",".","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",".","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","."]
code: def __init__(
      self,
      patch_size: int = 8,
      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,
      default_plot_func: Optional[Callable] = None,
      disable_warnings: bool = False,
      display_progressbar: bool = False,
      **kwargs,
  ):
      """
      Parameters
      ----------
      patch_size: integer
          The patch size for masking, default=8.
      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.
      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_selectivity_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.patch_size = patch_size
      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 patch size for masking"
                  " 'patch_size'"
              ),
              data_domain_applicability=(
                  f"Also, the current implementation only works for 3-dimensional (image) data."
              ),
              citation=(
                  "Montavon, Grégoire, Wojciech Samek, and Klaus-Robert Müller. 'Methods for "
                  "interpreting and understanding deep neural networks.' Digital Signal "
                  "Processing 73 (2018): 1-15"
              ),
          )
  
file_path: Quantus/quantus/metrics/faithfulness/selectivity.py
imports: import itertools
  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 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 Selectivity object from inherited base classes: Metric with example: obj = Selectivity(patch_size, 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)