class_docstr: Implementation of Faithfulness Estimate by Alvares-Melis at el., 2018a and 2018b.
  
  Computes the correlations of probability drops and the relevance scores on various points,
  showing the aggregate statistics.
  
  References:
      1) David Alvarez-Melis and Tommi S. Jaakkola.: "Towards robust interpretability with self-explaining
      neural networks." NeurIPS (2018): 7786-7795.
  
  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.
          If None, the default value is used, default=correlation_spearman.
  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,
      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,
      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.
              If None, the default value is used, default=correlation_spearman.
      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.
      """
      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,
      )
  
      # Save metric-specific attributes.
      if similarity_func is None:
          similarity_func = correlation_pearson
      if perturb_func is None:
          perturb_func = baseline_replacement_by_indices
      self.similarity_func = similarity_func
      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' and similarity function "
                  "'similarity_func'"
              ),
              citation=(
                  "Alvarez-Melis, David, and Tommi S. Jaakkola. 'Towards robust interpretability"
                  " with self-explaining neural networks.' arXiv preprint arXiv:1806.07538 (2018)"
              ),
          )
  
file_path: Quantus/quantus/metrics/faithfulness/faithfulness_estimate.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.functions.similarity_func import correlation_pearson
  from quantus.helpers import asserts, 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 FaithfulnessEstimate object from inherited base classes: Metric with example: obj = FaithfulnessEstimate(similarity_func, 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)