(data_instances, params, cols_dict, abnormal_list, header, is_sparse)
return summary_dict
Calculates each quantile information

        Parameters
        ----------
        data_instances : Table
            The input data

        cols_dict: dict
            Record key, value pairs where key is cols' name, and value is cols' index.

        params : FeatureBinningParam object,
                Parameters that user set.

        abnormal_list: list, default: None
            Specify which columns are abnormal so that will not static when traveling.

        header: list,
            Storing the header information.

        is_sparse: bool
            Specify whether data_instance is in sparse type

        Returns
        -------
        summary_dict: dict
            {'col_name1': summary1,
             'col_name2': summary2,
             ...
             }
Calculates each quantile information
Calculates each quantile information
def approxi_quantile(data_instances, params, cols_dict, abnormal_list, header, is_sparse):
        """
        Calculates each quantile information

        Parameters
        ----------
        data_instances : Table
            The input data

        cols_dict: dict
            Record key, value pairs where key is cols' name, and value is cols' index.

        params : FeatureBinningParam object,
                Parameters that user set.

        abnormal_list: list, default: None
            Specify which columns are abnormal so that will not static when traveling.

        header: list,
            Storing the header information.

        is_sparse: bool
            Specify whether data_instance is in sparse type

        Returns
        -------
        summary_dict: dict
            {'col_name1': summary1,
             'col_name2': summary2,
             ...
             }

        """

        summary_dict = {}

        summary_param = {'compress_thres': params.compress_thres,
                         'head_size': params.head_size,
                         'error': params.error,
                         'abnormal_list': abnormal_list}

        for col_name, col_index in cols_dict.items():
            quantile_summaries = quantile_summary_factory(is_sparse=is_sparse, param_dict=summary_param)
            summary_dict[col_name] = quantile_summaries

        QuantileBinning.insert_datas(data_instances, summary_dict, cols_dict, header, is_sparse)
        for _, summary_obj in summary_dict.items():
            summary_obj.compress()
        return summary_dict
def approxi_quantile ( data_instances , params , cols_dict , abnormal_list , header , is_sparse ) : summary_dict = { } summary_param = { 'compress_thres' : params . compress_thres , 'head_size' : params . head_size , 'error' : params . error , 'abnormal_list' : abnormal_list } for col_name , col_index in cols_dict . items ( ) : quantile_summaries = quantile_summary_factory ( is_sparse = is_sparse , param_dict = summary_param ) summary_dict [ col_name ] = quantile_summaries QuantileBinning . insert_datas ( data_instances , summary_dict , cols_dict , header , is_sparse ) for _ , summary_obj in summary_dict . items ( ) : summary_obj . compress ( ) return summary_dict