class_docstr: Implementation of Sensitivity-N test by Ancona et al., 2019.
  
  An attribution method satisfies Sensitivity-n when the sum of the attributions for any subset of features of
  cardinality n is equal to the variation of the output Sc caused removing the features in the subset. The test
  computes the correlation between sum of attributions and delta output.
  
  Pearson correlation coefficient (PCC) is computed between the sum of the attributions and the variation in the
  target output varying n from one to about 80% of the total number of features, where an average across a thousand
  of samples is reported. Sampling is performed using a uniform probability distribution over the features.
  
  References:
      1) Marco Ancona et al.: "Towards better understanding of gradient-based attribution
      methods for deep neural networks." ICLR (Poster) (2018).
  
  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
  ----------
  similarity_func: callable
      Similarity function applied to compare input and perturbed input,
      default=correlation_pearson.
  n_max_percentage: float
      The percentage of features to iteratively evaluatede, fault=0.8.
  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.
  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,
      similarity_func: Optional[Callable] = None,
      n_max_percentage: float = 0.8,
      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: Callable = None,
      perturb_baseline: str = "black",
      perturb_func_kwargs: Optional[Dict[str, Any]] = None,
      return_aggregate: bool = True,
      aggregate_func: Callable = None,
      default_plot_func: Optional[Callable] = None,
      disable_warnings: bool = False,
      display_progressbar: bool = False,
      **kwargs,
  ):
      """
      Parameters
      ----------
      similarity_func: callable
          Similarity function applied to compare input and perturbed input,
          default=correlation_pearson.
      n_max_percentage: float
          The percentage of features to iteratively evaluatede, fault=0.8.
      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.
      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 normalise_func is None:
          normalise_func = normalise_by_max
  
      if default_plot_func is None:
          default_plot_func = plotting.plot_sensitivity_n_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.
      if similarity_func is None:
          similarity_func = correlation_pearson
      self.similarity_func = similarity_func
      self.n_max_percentage = n_max_percentage
      self.features_in_step = features_in_step
      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', the patch size for masking "
                  "'patch_size', similarity function 'similarity_func' and the number "
                  "of features to iteratively evaluate 'n_max_percentage'"
              ),
              citation=(
                  "Ancona, Marco, et al. 'Towards better understanding of gradient-based "
                  "attribution methods for deep neural networks.' arXiv preprint "
                  "arXiv:1711.06104 (2017)"
              ),
          )
  
file_path: Quantus/quantus/metrics/faithfulness/sensitivity_n.py
imports: import sys
  from typing import Any, Callable, Dict, List, Optional
  import numpy
  from quantus.functions.normalise_func import normalise_by_max
  from quantus.functions.perturb_func import baseline_replacement_by_indices
  from quantus.functions.similarity_func import correlation_pearson
  from quantus.helpers import asserts, plotting, 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 SensitivityN object from inherited base classes: Metric with example: obj = SensitivityN(similarity_func, n_max_percentage, features_in_step, 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)