(self, choosers, alternatives, current_choice)
Fit and save models based on given data after segmenting
        the `choosers` table.

        Parameters
        ----------
        choosers : pandas.DataFrame
            Table describing the agents making choices, e.g. households.
            Must have a column with the same name as the .segmentation_col
            attribute.
        alternatives : pandas.DataFrame
            Table describing the things from which agents are choosing,
            e.g. buildings.
        current_choice
            Name of column in `choosers` that indicates which alternative
            they have currently chosen.

        Returns
        -------
        log_likelihoods : dict of dict
            Keys will be model names and values will be dictionaries of
            log-liklihood values as returned by MNLDiscreteChoiceModel.fit.
Fit and save models based on given data after segmenting
        the `choosers` table.
Fit and save models based on given data after segmenting the choosers table .
def fit(self, choosers, alternatives, current_choice):
        """
        Fit and save models based on given data after segmenting
        the `choosers` table.

        Parameters
        ----------
        choosers : pandas.DataFrame
            Table describing the agents making choices, e.g. households.
            Must have a column with the same name as the .segmentation_col
            attribute.
        alternatives : pandas.DataFrame
            Table describing the things from which agents are choosing,
            e.g. buildings.
        current_choice
            Name of column in `choosers` that indicates which alternative
            they have currently chosen.

        Returns
        -------
        log_likelihoods : dict of dict
            Keys will be model names and values will be dictionaries of
            log-liklihood values as returned by MNLDiscreteChoiceModel.fit.

        """
        with log_start_finish(
                'fit models in LCM group {}'.format(self.name), logger):
            return {
                name: self.models[name].fit(df, alternatives, current_choice)
                for name, df in self._iter_groups(choosers)}
def fit ( self , choosers , alternatives , current_choice ) : with log_start_finish ( 'fit models in LCM group {}' . format ( self . name ) , logger ) : return { name : self . models [ name ] . fit ( df , alternatives , current_choice ) for name , df in self . _iter_groups ( choosers ) }