(self, choosers, alternatives, debug=False)
return results
Choose from among alternatives for a group of agents after
        segmenting the `choosers` table.

        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.
        debug : bool
            If debug is set to true, will set the variable "sim_pdf" on
            the object to store the probabilities for mapping of the
            outcome.

        Returns
        -------
        choices : pandas.Series
            Mapping of chooser ID to alternative ID. Some choosers
            will map to a nan value when there are not enough alternatives
            for all the choosers.
Choose from among alternatives for a group of agents after
        segmenting the `choosers` table.
Choose from among alternatives for a group of agents after segmenting the choosers table .
def predict(self, choosers, alternatives, debug=False):
        """
        Choose from among alternatives for a group of agents after
        segmenting the `choosers` table.

        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.
        debug : bool
            If debug is set to true, will set the variable "sim_pdf" on
            the object to store the probabilities for mapping of the
            outcome.

        Returns
        -------
        choices : pandas.Series
            Mapping of chooser ID to alternative ID. Some choosers
            will map to a nan value when there are not enough alternatives
            for all the choosers.

        """
        logger.debug(
            'start: predict models in segmented LCM {}'.format(self.name))
        choosers, alternatives = self._filter_choosers_alts(
            choosers, alternatives)

        results = self._group.predict(choosers, alternatives, debug=debug)
        logger.debug(
            'finish: predict models in segmented LCM {}'.format(self.name))
        return results
def predict ( self , choosers , alternatives , debug = False ) : logger . debug ( 'start: predict models in segmented LCM {}' . format ( self . name ) ) choosers , alternatives = self . _filter_choosers_alts ( choosers , alternatives ) results = self . _group . predict ( choosers , alternatives , debug = debug ) logger . debug ( 'finish: predict models in segmented LCM {}' . format ( self . name ) ) return results