class_docstr: The SmartPredictor class is an object lighter than SmartExplainer Object with
  additionnal consistency checks.
  
  The SmartPredictor object is provided to deploy the summary of local explanation
  for the operational needs.
  
  Switching from SmartExplainer to SmartPredictor, allows users to reproduce
  the same results automatically on datasets with right structure.
  
  SmartPredictor is designed to make new results understandable:
      - It checks consistency of all parameters
      - It applies preprocessing and postprocessing
      - It computes models contributions
      - It makes predictions
      - It summarizes local explainability
  
  This class allows the user to automatically summarize the results of his model
  on new datasets (prediction, preprocessing and postprocessing linking,
  explainability).
  The SmartPredictor has several methods described below.
  
  The SmartPredictor Attributes :
  
  features_dict: dict
      Dictionary mapping technical feature names to domain names.
  model: model object
      model used to check the different values of target estimate predict_proba
  backend: str or backend object
      backend (explainer) used to compute contributions
  columns_dict: dict
      Dictionary mapping integer column number (in the same order of the trained dataset) to technical feature names.
  features_types: dict
      Dictionary mapping features with the right types needed.
  label_dict: dict (optional)
      Dictionary mapping integer labels to domain names (classification - target values).
  preprocessing: category_encoders, ColumnTransformer, list or dict (optional)
      The processing apply to the original data.
  postprocessing: dict (optional)
      Dictionary of postprocessing modifications to apply in x_init dataframe.
  _case: string
      String that informs if the model used is for classification or regression problem.
  _classes: list, None
      List of labels if the model used is for classification problem, None otherwise.
  mask_params: dict (optional)
      Dictionary that specify how to summarize the explainability.
  
  How to declare a new SmartPredictor object?
  
  Example
  -------
  >>> predictor = SmartPredictor(features_dict=my_features_dict,
  >>>                             model=my_model,
  >>>                             backend=my_backend,
  >>>                             columns_dict=my_columns_dict,
  >>>                             features_types=my_features_type_dict,
  >>>                             label_dict=my_label_dict,
  >>>                             preprocessing=my_preprocess,
  >>>                             postprocessing=my_postprocess)
  
  or the most common syntax
  
  >>> predictor = xpl.to_smartpredictor()
  
  xpl, explainer: object
      SmartExplainer instance to point to.
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docstr: Check if mask_params given respect the expected format.
docstr_tok: ["Check","if","mask_params","given","respect","the","expected","format","."]
code: def check_mask_params(self):
      """
      Check if mask_params given respect the expected format.
      """
      return check_mask_params(self.mask_params)
  
imports: from shapash.utils.check import check_consistency_model_features, check_consistency_model_label
  from shapash.utils.check import check_model, check_preprocessing, check_preprocessing_options
  from shapash.utils.check import check_label_dict, check_mask_params, check_y, check_contribution_object, check_features_name
  import pandas
  from shapash.utils.transform import adapt_contributions
  from shapash.manipulation.select_lines import keep_right_contributions
  from shapash.utils.model import predict_proba
  from shapash.utils.io import save_pickle
  from shapash.utils.transform import apply_preprocessing, apply_postprocessing, preprocessing_tolist
  from shapash.manipulation.filters import hide_contributions
  from shapash.manipulation.filters import cap_contributions
  from shapash.manipulation.filters import sign_contributions
  from shapash.manipulation.filters import cutoff_contributions
  from shapash.manipulation.filters import combine_masks
  from shapash.manipulation.mask import init_mask
  from shapash.manipulation.mask import compute_masked_contributions
  from shapash.manipulation.summarize import summarize, create_grouped_features_values, group_contributions
  from shapash.decomposition.contributions import rank_contributions, assign_contributions
  from shapash.utils.columntransformer_backend import columntransformer
  import copy
  import shapash.explainer.smart_explainer
label_desc: Use image node_id 5 for calling the SmartPredictor obj's underlying member method code with example usage: obj.check_mask_params() and returns: check_mask_params