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Solve the following problem using Python, implementing the functions described below, one line at a time
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Description:
 def predict_from_cfg(cls, choosers, alternatives, cfgname=None, cfg=None, alternative_ratio=2.0, debug=False):
 """ Simulate choices for the specified choosers Parameters choosers : DataFrame A dataframe of agents doing the choosing. alternatives : DataFrame A dataframe of locations which the choosers are locating in and which have a supply. cfgname : string The name of the yaml config file from which to read the discrete choice model. cfg: string an ordered yaml string of the model discrete choice model configuration. Used to read config from memory in lieu of loading cfgname from disk. alternative_ratio : float, optional Above the ratio of alternatives to choosers (default of 2.0), the alternatives will be sampled to meet this ratio (for performance reasons). debug : boolean, optional (default False) Whether to generate debug information on the model. 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. lcm : MNLDiscreteChoiceModel which was used to predict """

        logger.debug('start: predict from configuration {}'.format(cfgname))
        if cfgname:
            lcm = cls.from_yaml(str_or_buffer=cfgname)
        elif cfg:
            lcm = cls.from_yaml(yaml_str=cfg)
        else:
            msg = 'predict_from_cfg requires a configuration via the cfgname or cfg arguments'
            logger.error(msg)
            raise ValueError(msg)

        if len(alternatives) > len(choosers) * alternative_ratio:
            logger.info(
                ("Alternative ratio exceeded: %d alternatives "
                 "and only %d choosers") %
                (len(alternatives), len(choosers)))
            idxes = np.random.choice(
                alternatives.index, size=int(len(choosers) *
                                             alternative_ratio),
                replace=False)
            alternatives = alternatives.loc[idxes]
            logger.info(
                "  after sampling %d alternatives are available\n" %
                len(alternatives))

        new_units = lcm.predict(choosers, alternatives, debug=debug)
        print("Assigned %d choosers to new units" % len(new_units.dropna()))
        logger.debug('finish: predict from configuration {}'.format(cfgname))
        return new_units, lcm