code: def probabilities(self, choosers, alternatives):
          
          logger.debug(
              'start: calculate probabilities in segmented LCM {}'.format(
                  self.name))
          choosers, alternatives = self.apply_predict_filters(
              choosers, alternatives)
          result = self._group.probabilities(choosers, alternatives)
          logger.debug(
              'finish: calculate probabilities in segmented LCM {}'.format(
                  self.name))
          return result
docs: Returns alternative probabilties for each chooser segment as
          a dictionary keyed by segment name.
  
          Parameters
          ----------
          choosers : pandas.DataFrame
              Table describing the agents making choices, e.g. households.
              Must have a column matching the .segmentation_col attribute.
          alternatives : pandas.DataFrame
              Table describing the things from which agents are choosing.
  
          Returns
          -------
          probabilties : dict of pandas.Series