(cls, df, cfgname)
return price_or_rent, 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.

        Returns
        -------
        predicted : pandas.Series
            Predicted data in a pandas Series. Will have the index of `data`
            after applying filters and minus any groups that do not have
            models.
        hm : RegressionModel which was used to predict
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.
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 .
def predict_from_cfg(cls, df, cfgname):
        """
        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.

        Returns
        -------
        predicted : pandas.Series
            Predicted data in a pandas Series. Will have the index of `data`
            after applying filters and minus any groups that do not have
            models.
        hm : RegressionModel which was used to predict
        """
        logger.debug('start: predict from configuration {}'.format(cfgname))
        hm = cls.from_yaml(str_or_buffer=cfgname)

        price_or_rent = hm.predict(df)
        print(price_or_rent.describe())

        logger.debug('start: predict from configuration {}'.format(cfgname))
        return price_or_rent, hm
def predict_from_cfg ( cls , df , cfgname ) : logger . debug ( 'start: predict from configuration {}' . format ( cfgname ) ) hm = cls . from_yaml ( str_or_buffer = cfgname ) price_or_rent = hm . predict ( df ) print ( price_or_rent . describe ( ) ) logger . debug ( 'start: predict from configuration {}' . format ( cfgname ) ) return price_or_rent , hm