(cls, df, cfgname, debug=False, min_segment_size=None, outcfgname=None)
return hm
Parameters
        ----------
        df : DataFrame
            The dataframe which contains the columns to use for the estimation.
        cfgname : string
            The name of the yaml config file which describes the hedonic model.
        debug : boolean, optional (default False)
            Whether to generate debug information on the model.
        min_segment_size : int, optional
            Set attribute on the model.
        outcfgname : string, optional (default cfgname)
            The name of the output yaml config file where estimation results are written into.

        Returns
        -------
        hm : SegmentedRegressionModel which was used to fit
Parameters
        ----------
        df : DataFrame
            The dataframe which contains the columns to use for the estimation.
        cfgname : string
            The name of the yaml config file which describes the hedonic model.
        debug : boolean, optional (default False)
            Whether to generate debug information on the model.
        min_segment_size : int, optional
            Set attribute on the model.
        outcfgname : string, optional (default cfgname)
            The name of the output yaml config file where estimation results are written into.
Parameters ---------- df : DataFrame The dataframe which contains the columns to use for the estimation . cfgname : string The name of the yaml config file which describes the hedonic model . debug : boolean optional ( default False ) Whether to generate debug information on the model . min_segment_size : int optional Set attribute on the model . outcfgname : string optional ( default cfgname ) The name of the output yaml config file where estimation results are written into .
def fit_from_cfg(cls, df, cfgname, debug=False, min_segment_size=None, outcfgname=None):
        """
        Parameters
        ----------
        df : DataFrame
            The dataframe which contains the columns to use for the estimation.
        cfgname : string
            The name of the yaml config file which describes the hedonic model.
        debug : boolean, optional (default False)
            Whether to generate debug information on the model.
        min_segment_size : int, optional
            Set attribute on the model.
        outcfgname : string, optional (default cfgname)
            The name of the output yaml config file where estimation results are written into.

        Returns
        -------
        hm : SegmentedRegressionModel which was used to fit
        """
        logger.debug('start: fit from configuration {}'.format(cfgname))
        hm = cls.from_yaml(str_or_buffer=cfgname)
        if min_segment_size:
            hm.min_segment_size = min_segment_size

        for k, v in hm.fit(df, debug=debug).items():
            print("REGRESSION RESULTS FOR SEGMENT %s\n" % str(k))
            print(v.summary())
        outcfgname = outcfgname or cfgname
        hm.to_yaml(str_or_buffer=outcfgname)
        logger.debug('finish: fit into configuration {}'.format(outcfgname))
        return hm
def fit_from_cfg ( cls , df , cfgname , debug = False , min_segment_size = None , outcfgname = None ) : logger . debug ( 'start: fit from configuration {}' . format ( cfgname ) ) hm = cls . from_yaml ( str_or_buffer = cfgname ) if min_segment_size : hm . min_segment_size = min_segment_size for k , v in hm . fit ( df , debug = debug ) . items ( ) : print ( "REGRESSION RESULTS FOR SEGMENT %s\n" % str ( k ) ) print ( v . summary ( ) ) outcfgname = outcfgname or cfgname hm . to_yaml ( str_or_buffer = outcfgname ) logger . debug ( 'finish: fit into configuration {}' . format ( outcfgname ) ) return hm