(
            self, name, model_expression, sample_size,
            probability_mode='full_product', choice_mode='individual',
            choosers_fit_filters=None, choosers_predict_filters=None,
            alts_fit_filters=None, alts_predict_filters=None,
            interaction_predict_filters=None, estimation_sample_size=None,
            prediction_sample_size=None, choice_column=None)
Add a model by passing parameters through to MNLDiscreteChoiceModel.

        Parameters
        ----------
        name
            Must match a segment in the choosers table.
        model_expression : str, iterable, or dict
            A patsy model expression. Should contain only a right-hand side.
        sample_size : int
            Number of choices to sample for estimating the model.
        probability_mode : str, optional
            Specify the method to use for calculating probabilities
            during prediction.
            Available string options are 'single_chooser' and 'full_product'.
            In "single chooser" mode one agent is chosen for calculating
            probabilities across all alternatives. In "full product" mode
            probabilities are calculated for every chooser across all
            alternatives.
        choice_mode : str or callable, optional
            Specify the method to use for making choices among alternatives.
            Available string options are 'individual' and 'aggregate'.
            In "individual" mode choices will be made separately for each
            chooser. In "aggregate" mode choices are made for all choosers at
            once. Aggregate mode implies that an alternative chosen by one
            agent is unavailable to other agents and that the same
            probabilities can be used for all choosers.
        choosers_fit_filters : list of str, optional
            Filters applied to choosers table before fitting the model.
        choosers_predict_filters : list of str, optional
            Filters applied to the choosers table before calculating
            new data points.
        alts_fit_filters : list of str, optional
            Filters applied to the alternatives table before fitting the model.
        alts_predict_filters : list of str, optional
            Filters applied to the alternatives table before calculating
            new data points.
        interaction_predict_filters : list of str, optional
            Filters applied to the merged choosers/alternatives table
            before predicting agent choices.
        estimation_sample_size : int, optional
            Whether to sample choosers during estimation
            (needs to be applied after choosers_fit_filters)
        prediction_sample_size : int, optional
            Whether (and how much) to sample alternatives during prediction.
            Note that this can lead to multiple choosers picking the same
            alternative.
        choice_column : optional
            Name of the column in the `alternatives` table that choosers
            should choose. e.g. the 'building_id' column. If not provided
            the alternatives index is used.
Add a model by passing parameters through to MNLDiscreteChoiceModel.
Add a model by passing parameters through to MNLDiscreteChoiceModel .
def add_model_from_params(
            self, name, model_expression, sample_size,
            probability_mode='full_product', choice_mode='individual',
            choosers_fit_filters=None, choosers_predict_filters=None,
            alts_fit_filters=None, alts_predict_filters=None,
            interaction_predict_filters=None, estimation_sample_size=None,
            prediction_sample_size=None, choice_column=None):
        """
        Add a model by passing parameters through to MNLDiscreteChoiceModel.

        Parameters
        ----------
        name
            Must match a segment in the choosers table.
        model_expression : str, iterable, or dict
            A patsy model expression. Should contain only a right-hand side.
        sample_size : int
            Number of choices to sample for estimating the model.
        probability_mode : str, optional
            Specify the method to use for calculating probabilities
            during prediction.
            Available string options are 'single_chooser' and 'full_product'.
            In "single chooser" mode one agent is chosen for calculating
            probabilities across all alternatives. In "full product" mode
            probabilities are calculated for every chooser across all
            alternatives.
        choice_mode : str or callable, optional
            Specify the method to use for making choices among alternatives.
            Available string options are 'individual' and 'aggregate'.
            In "individual" mode choices will be made separately for each
            chooser. In "aggregate" mode choices are made for all choosers at
            once. Aggregate mode implies that an alternative chosen by one
            agent is unavailable to other agents and that the same
            probabilities can be used for all choosers.
        choosers_fit_filters : list of str, optional
            Filters applied to choosers table before fitting the model.
        choosers_predict_filters : list of str, optional
            Filters applied to the choosers table before calculating
            new data points.
        alts_fit_filters : list of str, optional
            Filters applied to the alternatives table before fitting the model.
        alts_predict_filters : list of str, optional
            Filters applied to the alternatives table before calculating
            new data points.
        interaction_predict_filters : list of str, optional
            Filters applied to the merged choosers/alternatives table
            before predicting agent choices.
        estimation_sample_size : int, optional
            Whether to sample choosers during estimation
            (needs to be applied after choosers_fit_filters)
        prediction_sample_size : int, optional
            Whether (and how much) to sample alternatives during prediction.
            Note that this can lead to multiple choosers picking the same
            alternative.
        choice_column : optional
            Name of the column in the `alternatives` table that choosers
            should choose. e.g. the 'building_id' column. If not provided
            the alternatives index is used.

        """
        logger.debug('adding model {} to LCM group {}'.format(name, self.name))
        self.models[name] = MNLDiscreteChoiceModel(
            model_expression, sample_size,
            probability_mode, choice_mode,
            choosers_fit_filters, choosers_predict_filters,
            alts_fit_filters, alts_predict_filters,
            interaction_predict_filters, estimation_sample_size,
            prediction_sample_size, choice_column, name)
def add_model_from_params ( self , name , model_expression , sample_size , probability_mode = 'full_product' , choice_mode = 'individual' , choosers_fit_filters = None , choosers_predict_filters = None , alts_fit_filters = None , alts_predict_filters = None , interaction_predict_filters = None , estimation_sample_size = None , prediction_sample_size = None , choice_column = None ) : logger . debug ( 'adding model {} to LCM group {}' . format ( name , self . name ) ) self . models [ name ] = MNLDiscreteChoiceModel ( model_expression , sample_size , probability_mode , choice_mode , choosers_fit_filters , choosers_predict_filters , alts_fit_filters , alts_predict_filters , interaction_predict_filters , estimation_sample_size , prediction_sample_size , choice_column , name )