class_docstr: Explanation wrapper for the `shap` package
  
  This object uses the shap package to create the model explanation.
  See ttps://github.com/slundberg/shap
  
  Parameters
  ----------
  type : {'predict_parts', 'model_parts'}
      Method type for calculations.
  
  Attributes
  ----------
  result : list or numpy.ndarray
      Calculated shap values for `new_observation` data.
  shap_explainer : {shap.TreeExplainer, shap.DeepExplainer, shap.GradientExplainer, shap.LinearExplainer, shap.KernelExplainer}
      Explainer object from the `shap` package.
  shap_explainer_type : {'TreeExplainer', 'DeepExplainer',
      'GradientExplainer', 'LinearExplainer', 'KernelExplainer'}
      String name of the Explainer class.
  new_observation : pandas.Series or pandas.DataFrame
      Observations for which the shap values will be calculated
      (later stored in `result`).
  type : {'predict_parts', 'model_parts'}
      Method type for calculations.
  
  Notes
  ----------
  - https://github.com/slundberg/shap
docstr: Plot the Shap Wrapper
  
  Parameters
  ----------
  kwargs : dict
      Keyword arguments passed to one of the:
          - shap.force_plot when type is `'predict_parts'`
          - shap.summary_plot when type is `'model_parts'`
      Exceptions are: `base_value`, `shap_values`,
      `features` and `feature_names`.
  
  Returns
  -----------
  None
  
  Notes
  --------
  - https://github.com/slundberg/shap
docstr_tok: ["Plot","the","Shap","Wrapper","Parameters","--","--","--","--","--","kwargs",":","dict","Keyword","arguments","passed","to","one","of","the",":","-","shap.force_plot","when","type","is","`","'predict_parts","'","`","-","shap.summary_plot","when","type","is","`","'model_parts","'","`","Exceptions","are",":","`","base_value","`",",","`","shap_values","`",",","`","features","`","and","`","feature_names","`",".","Returns","--","--","--","--","--","-","None","Notes","--","--","--","--","-","https",":","\/\/github.com\/slundberg\/shap"]
code: def plot(self, **kwargs):
      """Plot the Shap Wrapper
  
      Parameters
      ----------
      kwargs : dict
          Keyword arguments passed to one of the:
              - shap.force_plot when type is `'predict_parts'`
              - shap.summary_plot when type is `'model_parts'`
          Exceptions are: `base_value`, `shap_values`,
          `features` and `feature_names`.
  
      Returns
      -----------
      None
  
      Notes
      --------
      - https://github.com/slundberg/shap
      """
      from shap import force_plot, summary_plot
  
      if self.type == "predict_parts":
          if isinstance(
              self.shap_explainer.expected_value, (np.ndarray, list)
          ):
              base_value = self.shap_explainer.expected_value[1]
          else:
              base_value = self.shap_explainer.expected_value
  
          shap_values = (
              self.result[1]
              if isinstance(self.result, list)
              else self.result
          )
          force_plot(
              base_value=base_value,
              shap_values=shap_values,
              features=self.new_observation.values,
              feature_names=self.new_observation.columns,
              matplotlib=True,
              **kwargs,
          )
      elif self.type == "model_parts":
          summary_plot(
              shap_values=self.result,
              features=self.new_observation,
              **kwargs,
          )
  
file_path: DALEX/python/dalex/dalex/wrappers/_shap/object.py
imports: import numpy
  from .None import checks
  from ..._explanation import Explanation
label_desc: Use image node_id 3 for calling the ShapWrapper obj's underlying member method code with example usage: obj.plot() without return types