code: def score_(self):
          
          # pylint: disable=access-member-before-definition
          if not hasattr(self, "_concordance_score_"):
              if self.strata:
                  # https://stats.stackexchange.com/questions/133817/stratified-concordance-index-survivalsurvconcordance
                  num_correct, num_tied, num_pairs = 0, 0, 0
                  for _, _df in self._predicted_partial_hazards_.groupby(self.strata):
                      if _df.shape[0] == 1:
                          continue
                      _num_correct, _num_tied, _num_pairs = _concordance_summary_statistics(
                          _df["T"].values, -_df["P"].values, _df["E"].values
                      )
                      num_correct += _num_correct
                      num_tied += _num_tied
                      num_pairs += _num_pairs
              else:
                  df = self._predicted_partial_hazards_
                  num_correct, num_tied, num_pairs = _concordance_summary_statistics(
                      df["T"].values, -df["P"].values, df["E"].values
                  )
  
              self._concordance_score_ = _concordance_ratio(num_correct, num_tied, num_pairs)
              return self._concordance_score_
          return self._concordance_score_
docs: The concordance score (also known as the c-index) of the fit.  The c-index is a generalization of the ROC AUC
          to survival data, including censorships.
  
          For this purpose, the ``score_`` is a measure of the predictive accuracy of the fitted model
          onto the training dataset.
  
          References
          ----------
          https://stats.stackexchange.com/questions/133817/stratified-concordance-index-survivalsurvconcordance