class_docstr: Stores the explainability results of a :class:`ShapExplainer <darts.explainability.shap_explainer.ShapExplainer>`
  with convenient access to the results. It extends the :class:`HorizonBasedExplainabilityResult
  <HorizonBasedExplainabilityResult>` and carries additional information specific to the Shap explainers.
  In particular, in addition to the `explained_forecasts` (which in the case of the `ShapExplainer` are the
  shap values), it also provides access to the corresponding `feature_values` and the underlying `shap.Explanation`
  object.
  
  - :func:`get_explanation() <ShapExplainabilityResult.get_explanation>`: explained forecast for a given horizon
    (and target component)
  - :func:`get_feature_values() <ShapExplainabilityResult.get_feature_values>`: feature values for a given horizon
    (and target component).
  - :func:`get_shap_explanation_object() <ShapExplainabilityResult.get_shap_explanation_object>`: `shap.Explanation`
    object for a given horizon (and target component).
  
  Examples
  --------
  >>> explainer = ShapExplainer(model)  # requires `background` if model was trained on multiple series
  >>> explain_results = explainer.explain()
  >>> exlained_fc = explain_results.get_explanation(horizon=1)
  >>> feature_values = explain_results.get_feature_values(horizon=1)
  >>> shap_objects = explain_results.get_shap_explanation_objects(horizon=1)
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code: def __init__(
      self,
      explained_forecasts: Union[
          Dict[int, Dict[str, TimeSeries]],
          List[Dict[int, Dict[str, TimeSeries]]],
      ],
      feature_values: Union[
          Dict[int, Dict[str, TimeSeries]],
          List[Dict[int, Dict[str, TimeSeries]]],
      ],
      shap_explanation_object: Union[
          Dict[int, Dict[str, shap.Explanation]],
          List[Dict[int, Dict[str, shap.Explanation]]],
      ],
  ):
      super().__init__(explained_forecasts)
      self.feature_values = feature_values
      self.shap_explanation_object = shap_explanation_object
  
imports: from abc import ABC, abstractmethod
  from typing import Any, Dict, List, Optional, Union
  import pandas
  import shap
  from darts import TimeSeries
  from darts.logging import get_logger, raise_if, raise_if_not, raise_log
label_desc: Use image node_id 1 to create a new ShapExplainabilityResult object from inherited base classes: HorizonBasedExplainabilityResult with example: obj = ShapExplainabilityResult(explained_forecasts, feature_values, shap_explanation_object)