class_docstr: Calculate predict-level hierarchical aspect importance
  
  Parameters
  ----------
  type : {'default', 'shap'}, optional
      Type of aspect importance/attributions (default is `'default'`, which means 
      the use of simplified LIME method).
  N : int, optional
      Number of observations that will be sampled from the `explainer.data` attribute
      before the calculation of aspect importance (default is `2000`).
  B : int, optional
      Parameter specific for `type == 'shap'`. Number of random paths to calculate aspect
      attributions (default is `25`).
      NOTE: Ignored if `type` is not `'shap'`.
  sample_method : {'default', 'binom'}, optional
      Parameter specific for `type == 'default'`. Sampling method for creating binary matrix 
      used as mask for replacing aspects in data (default is `'default'`, which means 
      it randomly replaces one or two zeros per row; `'binom'` replaces random number of zeros 
      per row).
      NOTE: Ignored if `type` is not `'default'`.
  f : int, optional
      Parameter specific for `type == 'default'` and `sample_method == 'binom'`. Parameter 
      controlling average number of replaced zeros for binomial sampling (default is `2`). 
      NOTE: Ignored if `type` is not `'default'` or `sample_method` is not `'binom'`.
  processes : int, optional
      Number of parallel processes to use in calculations. Iterated over `B`
      (default is `1`, which means no parallel computation).
  random_state : int, optional
      Set seed for random number generator (default is random seed).
  
  Attributes
  -----------
  result : pd.DataFrame
      Main result attribute of an explanation.
  single_variable_importance : pd.DataFrame
      Additional result attribute of an explanation (it contains information 
      about the importance of individual variables).
  prediction : float
      Prediction for `new_observation`.
  intercept : float
      Average prediction for `data`.
  type : {'default', 'shap'}
      Type of aspect importance/attributions to calculate.
  N : int
      Number of observations that will be sampled from the `data` attribute
      before the calculation of aspect importance.
  B : int
      Number of random paths to calculate aspect attributions.
  sample_method : {'default', 'binom'}
      Sampling method for creating binary matrix used as mask for replacing aspects in sampled data.
  f : int
      Average number of replaced zeros for binomial sampling.
  processes : int
      Number of parallel processes to use in calculations. Iterated over `B`.
  random_state : int or None
      Set seed for random number generator.
  
  Notes
  -----
  - https://arxiv.org/abs/2104.03403
code: def __init__(
      self,
      type="default",
      N=2000,
      B=25,
      sample_method="default",
      f=2,
      processes=1,
      random_state=None,
  ):
      types = ("default", "shap")
      aliases = {"simplified_lime": "default", "lime": "default"}
      _type = checks.check_method_type(type, types, aliases)
      self.type = _type
      _processes = checks.check_processes(processes)
      self.processes = _processes
      _random_state = checks.check_random_state(random_state)
      self.random_state = _random_state
      self.N = N
      self.B = checks.check_B(B)
      self.sample_method = sample_method
      self.f = f
      self.result = pd.DataFrame()
      self.single_variable_importance = None
      self._hierarchical_clustering_dendrogram = None
  
file_path: DALEX/python/dalex/dalex/aspect/_predict_triplot/object.py
imports: import numpy
  import pandas
  import plotly.graph_objs
  from copy import deepcopy
  from dalex import _theme, _global_checks
  from dalex._explanation import Explanation
  from dalex.aspect._predict_aspect_importance.object import PredictAspectImportance
  from .None import checks, plot, utils
label_desc: Use image node_id 1 to create a new PredictTriplot object from inherited base classes: Explanation with example: obj = PredictTriplot(type, N, B, sample_method, f, processes, random_state)