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: Regroup contributions according to features_groups parameter.
  
  Parameters
  ----------
  contributions : pd.DataFrame
      Contributions of each unique feature.
  features_groups : dict
      Python dict that inform which features to regroup.
  
  Returns
  -------
  pd.DataFrame
docstr_tok: ["Regroup","contributions","according","to","features_groups","parameter",".","Parameters","--","--","--","--","--","contributions",":","pd.DataFrame","Contributions","of","each","unique","feature",".","features_groups",":","dict","Python","dict","that","inform","which","features","to","regroup",".","Returns","--","--","--","-","pd.DataFrame"]
code: def compute_grouped_contributions(
      self, contributions, features_groups
  ):
      """
      Regroup contributions according to features_groups parameter.
  
      Parameters
      ----------
      contributions : pd.DataFrame
          Contributions of each unique feature.
      features_groups : dict
          Python dict that inform which features to regroup.
  
      Returns
      -------
      pd.DataFrame
      """
      return group_contributions(contributions, features_groups)
  
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 15 for calling the SmartState obj's underlying member method code with example usage: obj.compute_grouped_contributions(contributions, features_groups) and returns: group_contributions