code: def single_feature_logliks(self, feature):
          
          self.assert_less_than_or_equal_1(feature.values)
          self.assert_non_negative(feature.values)
  
          logliks = self._single_feature_logliks_one_step(
              feature, self.one_param_models)
  
          logsumexps = self.logliks_to_logsumexp(logliks)
  
          # If none of the one-parameter models passed, try the two-param models
          if (logsumexps <= self.logbf_thresh).all():
              logliks_two_params = self._single_feature_logliks_one_step(
                  feature, self.two_param_models)
              logliks = pd.concat([logliks, logliks_two_params])
          return logliks
docs: Calculate log-likelihoods of each modality's parameterization
  
          Used for plotting the estimates of a single feature
  
          Parameters
          ----------
          featre : pandas.Series
              A single feature's values. All values must range from 0 to 1.
  
          Returns
          -------
          logliks : pandas.DataFrame
              The log-likelihood the data, for each model, for each
              parameterization
  
          Raises
          ------
          AssertionError
              If any value in ``x`` does not fall only between 0 and 1.