(self, data, replace_method, replace_value=None)
return cols_transform_value
Parameters
        ----------
        data: input data
        replace_method: dictionary of (column name, replace_method_name) pairs

        Returns
        -------
        list of transform value for each column, length equal to feature count of input data
def __get_cols_transform_value(self, data, replace_method, replace_value=None):
        """

        Parameters
        ----------
        data: input data
        replace_method: dictionary of (column name, replace_method_name) pairs

        Returns
        -------
        list of transform value for each column, length equal to feature count of input data

        """
        summary_obj = MultivariateStatisticalSummary(data, -1, abnormal_list=self.abnormal_value_list)
        header = get_header(data)
        cols_transform_value = {}
        if isinstance(replace_value, list):
            if len(replace_value) != len(header):
                raise ValueError(f"replace value {replace_value} length does not match with header {header}, please check.")
        for i, feature in enumerate(header):
            if replace_method[feature] is None:
                transform_value = 0
            elif replace_method[feature] == consts.MIN:
                transform_value = summary_obj.get_min()[feature]
            elif replace_method[feature] == consts.MAX:
                transform_value = summary_obj.get_max()[feature]
            elif replace_method[feature] == consts.MEAN:
                transform_value = summary_obj.get_mean()[feature]
            elif replace_method[feature] == consts.MEDIAN:
                transform_value = summary_obj.get_median()[feature]
            elif replace_method[feature] == consts.DESIGNATED:
                if isinstance(replace_value, list):
                    transform_value = replace_value[i]
                else:
                    transform_value = replace_value
                LOGGER.debug(f"replace value for feature {feature} is: {transform_value}")
            else:
                raise ValueError("Unknown replace method:{}".format(replace_method))
            cols_transform_value[feature] = transform_value

        LOGGER.debug(f"cols_transform value is: {cols_transform_value}")
        cols_transform_value = [cols_transform_value[key] for key in header]
        # cols_transform_value = {i: round(cols_transform_value[key], 6) for i, key in enumerate(header)}
        LOGGER.debug(f"cols_transform value is: {cols_transform_value}")
        return cols_transform_value
def __get_cols_transform_value ( self , data , replace_method , replace_value = None ) : summary_obj = MultivariateStatisticalSummary ( data , - 1 , abnormal_list = self . abnormal_value_list ) header = get_header ( data ) cols_transform_value = { } if isinstance ( replace_value , list ) : if len ( replace_value ) != len ( header ) : raise ValueError ( f"replace value {replace_value} length does not match with header {header}, please check." ) for i , feature in enumerate ( header ) : if replace_method [ feature ] is None : transform_value = 0 elif replace_method [ feature ] == consts . MIN : transform_value = summary_obj . get_min ( ) [ feature ] elif replace_method [ feature ] == consts . MAX : transform_value = summary_obj . get_max ( ) [ feature ] elif replace_method [ feature ] == consts . MEAN : transform_value = summary_obj . get_mean ( ) [ feature ] elif replace_method [ feature ] == consts . MEDIAN : transform_value = summary_obj . get_median ( ) [ feature ] elif replace_method [ feature ] == consts . DESIGNATED : if isinstance ( replace_value , list ) : transform_value = replace_value [ i ] else : transform_value = replace_value LOGGER . debug ( f"replace value for feature {feature} is: {transform_value}" ) else : raise ValueError ( "Unknown replace method:{}" . format ( replace_method ) ) cols_transform_value [ feature ] = transform_value LOGGER . debug ( f"cols_transform value is: {cols_transform_value}" ) cols_transform_value = [ cols_transform_value [ key ] for key in header ] # cols_transform_value = {i: round(cols_transform_value[key], 6) for i, key in enumerate(header)} LOGGER . debug ( f"cols_transform value is: {cols_transform_value}" ) return cols_transform_value