class_docstr: State pattern attached to SmartExplainer. This is the base class that only handles matrices
  of local contributions. The multi-class case is tackled in SmartMultiState.
class_docstr_tok: ["State","pattern","attached","to","SmartExplainer",".","This","is","the","base","class","that","only","handles","matrices","of","local","contributions",".","The","multi-class","case","is","tackled","in","SmartMultiState","."]
docstr: Compute the summarized contributions of features.
  
  Parameters
  ----------
  s_contrib: pd.DataFrame
      Matrix containing contributions that will be summarized
  var_dict: pd.DataFrame
      Matrix of feature names that will be summarized
  x_sorted: pd.DataFrame
      Matrix containing the value of each feature
  mask: pd.DataFrame
      Mask to apply during the summary step
  columns_dict:
      Dict of column Names, matches column num with column name
  features_dict:
      Dict of column Label, matches column name with column label
  
  Returns
  -------
  pd.DataFrame
      Result of the summarize step
docstr_tok: ["Compute","the","summarized","contributions","of","features",".","Parameters","--","--","--","--","--","s_contrib",":","pd.DataFrame","Matrix","containing","contributions","that","will","be","summarized","var_dict",":","pd.DataFrame","Matrix","of","feature","names","that","will","be","summarized","x_sorted",":","pd.DataFrame","Matrix","containing","the","value","of","each","feature","mask",":","pd.DataFrame","Mask","to","apply","during","the","summary","step","columns_dict",":","Dict","of","column","Names",",","matches","column","num","with","column","name","features_dict",":","Dict","of","column","Label",",","matches","column","name","with","column","label","Returns","--","--","--","-","pd.DataFrame","Result","of","the","summarize","step"]
code: def summarize(
      self,
      s_contrib,
      var_dict,
      x_sorted,
      mask,
      columns_dict,
      features_dict,
  ):
      """
      Compute the summarized contributions of features.
  
      Parameters
      ----------
      s_contrib: pd.DataFrame
          Matrix containing contributions that will be summarized
      var_dict: pd.DataFrame
          Matrix of feature names that will be summarized
      x_sorted: pd.DataFrame
          Matrix containing the value of each feature
      mask: pd.DataFrame
          Mask to apply during the summary step
      columns_dict:
          Dict of column Names, matches column num with column name
      features_dict:
          Dict of column Label, matches column name with column label
  
      Returns
      -------
      pd.DataFrame
          Result of the summarize step
      """
      return summarize(
          s_contrib,
          var_dict,
          x_sorted,
          mask,
          columns_dict,
          features_dict,
      )
  
imports: import numpy
  import pandas
  from shapash.decomposition.contributions import inverse_transform_contributions
  from shapash.decomposition.contributions import rank_contributions, assign_contributions
  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 compute_masked_contributions
  from shapash.manipulation.mask import init_mask
  from shapash.manipulation.summarize import summarize, compute_features_import, group_contributions
label_desc: Use image node_id 13 for calling the SmartState obj's underlying member method code with example usage: obj.summarize(s_contrib, var_dict, x_sorted, mask, columns_dict, features_dict) and returns: summarize