code: def single_feature_fit(self, feature):
          
          if np.isfinite(feature).sum() == 0:
              series = pd.Series(index=MODALITY_ORDER)
          else:
              logbf_one_param = pd.Series(
                  {k: v.logsumexp_logliks(feature) for
                   k, v in self.one_param_models.items()})
  
              # Check if none of the previous features fit
              if (logbf_one_param <= self.logbf_thresh).all():
                  logbf_two_param = pd.Series(
                      {k: v.logsumexp_logliks(feature)
                       for k, v in self.two_param_models.items()})
                  series = pd.concat([logbf_one_param, logbf_two_param])
                  series[NULL_MODEL] = self.logbf_thresh
              else:
                  series = logbf_one_param
          series.index.name = 'Modality'
          series.name = self.score_name
          return series
docs: Get the log2 bayes factor of the fit for each modality