(self, name, model_expression=None, ytransform='default')
Add a new segment with its own model expression and ytransform.

        Parameters
        ----------
        name :
            Segment name. Must match a segment in the groupby of the data.
        model_expression : str or dict, optional
            A patsy model expression that can be used with statsmodels.
            Should contain both the left- and right-hand sides.
            If not given the default model will be used, which must not be
            None.
        ytransform : callable, optional
            A function to call on the array of predicted output.
            For example, if the model relation is predicting the log
            of price, you might pass ``ytransform=np.exp`` so that
            the results reflect actual price.

            If not given the default ytransform will be used.
Add a new segment with its own model expression and ytransform.
Add a new segment with its own model expression and ytransform .
def add_segment(self, name, model_expression=None, ytransform='default'):
        """
        Add a new segment with its own model expression and ytransform.

        Parameters
        ----------
        name :
            Segment name. Must match a segment in the groupby of the data.
        model_expression : str or dict, optional
            A patsy model expression that can be used with statsmodels.
            Should contain both the left- and right-hand sides.
            If not given the default model will be used, which must not be
            None.
        ytransform : callable, optional
            A function to call on the array of predicted output.
            For example, if the model relation is predicting the log
            of price, you might pass ``ytransform=np.exp`` so that
            the results reflect actual price.

            If not given the default ytransform will be used.

        """
        if not model_expression:
            if self.default_model_expr is None:
                raise ValueError(
                    'No default model available, '
                    'you must supply a model experssion.')
            model_expression = self.default_model_expr

        if ytransform == 'default':
            ytransform = self.default_ytransform

        # no fit or predict filters, we'll take care of that this side.
        self._group.add_model_from_params(
            name, None, None, model_expression, ytransform)

        logger.debug('added segment {} to model {}'.format(name, self.name))
def add_segment ( self , name , model_expression = None , ytransform = 'default' ) : if not model_expression : if self . default_model_expr is None : raise ValueError ( 'No default model available, ' 'you must supply a model experssion.' ) model_expression = self . default_model_expr if ytransform == 'default' : ytransform = self . default_ytransform # no fit or predict filters, we'll take care of that this side. self . _group . add_model_from_params ( name , None , None , model_expression , ytransform ) logger . debug ( 'added segment {} to model {}' . format ( name , self . name ) )