class_docstr: Implementation of Monotonicity metric by Arya at el., 2019.
  
  Monotonicity tests if adding more positive evidence increases the probability
  of classification in the specified class.
  
  It captures attributions' faithfulness by incrementally adding each attribute
  in order of increasing importance and evaluating the effect on model performance.
  As more features are added, the performance of the model is expected to increase
  and thus result in monotonically increasing model performance.
  
  References:
      1) Vijay Arya et al.: "One explanation does not fit all: A toolkit and taxonomy of ai explainability
      techniques." arXiv preprint arXiv:1909.03012 (2019).
      2) Ronny Luss et al.: "Generating contrastive explanations with monotonic attribute functions."
      arXiv preprint arXiv:1905.12698 (2019).
  
  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
  ----------
  features_in_step: integer
      The size of the step, default=1.
  abs: boolean
      Indicates whether absolute operation is applied on the attribution, default=True.
  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,
      features_in_step: int = 1,
      abs: bool = True,
      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
      ----------
      features_in_step: integer
          The size of the step, default=1.
      abs: boolean
          Indicates whether absolute operation is applied on the attribution, default=True.
      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,
      )
  
      if perturb_func is None:
          perturb_func = baseline_replacement_by_indices
  
      # Save metric-specific attributes.
      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', also, the monotonicity "
                  "constraint between your given model and explanation method should be assessed"
              ),
              citation=(
                  "Arya, Vijay, et al. 'One explanation does not fit all: A toolkit and taxonomy"
                  " of ai explainability techniques.' arXiv preprint arXiv:1909.03012 (2019)"
              ),
          )
  
file_path: Quantus/quantus/metrics/faithfulness/monotonicity.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 Monotonicity object from inherited base classes: Metric with example: obj = Monotonicity(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)