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 _repr_html_(self):
      return self.result._repr_html_()
  
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 2 for calling the PredictTriplot obj's underlying member method code with example usage: obj._repr_html_() and returns: self