parameters: (self)
return_statement: return consistency_ratio
docstring: Calculates the consistency ratio, indicating how consistent the decision maker is being with their pair-wise
          comparisons.
  
          Returns:
              float:  Consistency ratio indicating the consistency of the pair-wise comparison.
docstring_summary: Calculates the consistency ratio, indicating how consistent the decision maker is being with their pair-wise
          comparisons.
docstring_tokens: Calculates the consistency ratio indicating how consistent the decision maker is being with their pair - wise comparisons .
function: def _consistency_ratio(self):
          """ Calculates the consistency ratio, indicating how consistent the decision maker is being with their pair-wise
          comparisons.
  
          Returns:
              float:  Consistency ratio indicating the consistency of the pair-wise comparison.
  
          """
          comparison = self.pairwise_tp(score=self._importance)
          squared_comparison_matrix = self._square_matrix(score=comparison)
          eigenvector_ranking = self._calculate_eigenvector(squared_comparison_matrix=squared_comparison_matrix)
          sum_comparison = np.sum(squared_comparison_matrix, axis=0)
          lambda_max = sum(sum_comparison * np.array(eigenvector_ranking))
          consistency_index = (lambda_max - len(self.criteria)) / (len(self.criteria) - 1)
          consistency_ratio = consistency_index / self.__RANDOM_INDICES.get(len(self.criteria))
          return consistency_ratio
function_tokens: def _consistency_ratio ( self ) : comparison = self . pairwise_tp ( score = self . _importance ) squared_comparison_matrix = self . _square_matrix ( score = comparison ) eigenvector_ranking = self . _calculate_eigenvector ( squared_comparison_matrix = squared_comparison_matrix ) sum_comparison = np . sum ( squared_comparison_matrix , axis = 0 ) lambda_max = sum ( sum_comparison * np . array ( eigenvector_ranking ) ) consistency_index = ( lambda_max - len ( self . criteria ) ) / ( len ( self . criteria ) - 1 ) consistency_ratio = consistency_index / self . __RANDOM_INDICES . get ( len ( self . criteria ) ) return consistency_ratio