class_docstr: Calculate predict-level variable attributions as Shapley Values
  
  Parameters
  -----------
  path : list of int, optional
      If specified, then attributions for this path will be plotted
      (default is `'average'`, which plots attribution means for `B` random paths).
  B : int, optional
      Number of random paths to calculate variable attributions (default is `25`).
  keep_distributions : bool, optional
      Save the distribution of partial predictions (default is `False`).
  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.
  prediction : float
      Prediction for `new_observation`.
  intercept : float
      Average prediction for `data`.
  path : list of int or 'average'
      Path for which the attributions will be plotted.
  B : int
      Number of random paths to calculate variable attributions.
  keep_distributions : bool
      Save the distribution of partial predictions.
  yhats_distributions : pd.DataFrame or None
      The distribution of partial predictions.
  processes : int
      Number of parallel processes to use in calculations. Iterated over `B`.
  random_state : int or None
      Seed that was set for random number generator.
  
  Notes
  --------
  - https://pbiecek.github.io/ema/shapley.html
code: def __init__(
      self,
      path="average",
      B=25,
      keep_distributions=False,
      processes=1,
      random_state=None,
  ):
      _path = checks.check_path(path)
      _processess = checks.check_processes(processes)
      _random_state = checks.check_random_state(random_state)
  
      self.path = _path
      self.keep_distributions = keep_distributions
      self.B = B
      self.result = pd.DataFrame()
      self.yhats_distributions = None
      self.prediction = None
      self.intercept = None
      self.processes = _processess
      self.random_state = _random_state
  
file_path: DALEX/python/dalex/dalex/predict_explanations/_shap/object.py
imports: import numpy
  import pandas
  from plotly.subplots import make_subplots
  from .None import checks, plot, utils
  from ...None import _theme, _global_checks
  from ..._explanation import Explanation
label_desc: Use image node_id 1 to create a new Shap object from inherited base classes: Explanation with example: obj = Shap(path, B, keep_distributions, processes, random_state)