(self, data_instances)
return self.bin_results.all_split_points
Apply the binning method

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

        Returns
        -------
        split_points : dict.
            Each value represent for the split points for a feature. The element in each row represent for
            the corresponding split point.
            e.g.
            split_points = {'x1': [0.1, 0.2, 0.3, 0.4 ...],    # The first feature
                            'x2': [1, 2, 3, 4, ...],           # The second feature
                            ...]                         # Other features
Apply the binning method
Apply the binning method
def fit_split_points(self, data_instances):
        """
        Apply the binning method

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

        Returns
        -------
        split_points : dict.
            Each value represent for the split points for a feature. The element in each row represent for
            the corresponding split point.
            e.g.
            split_points = {'x1': [0.1, 0.2, 0.3, 0.4 ...],    # The first feature
                            'x2': [1, 2, 3, 4, ...],           # The second feature
                            ...]                         # Other features

        """
        header = data_overview.get_header(data_instances)
        self._default_setting(header)

        # is_sparse = data_overview.is_sparse_data(data_instances)
        # if is_sparse:
        #     raise RuntimeError("Bucket Binning method has not supported sparse data yet.")

        # self._init_cols(data_instances)

        statistics = MultivariateStatisticalSummary(data_instances,
                                                    self.bin_inner_param.bin_indexes,
                                                    abnormal_list=self.abnormal_list)
        max_dict = statistics.get_max()
        min_dict = statistics.get_min()
        for col_name, max_value in max_dict.items():
            min_value = min_dict.get(col_name)
            split_points = []
            L = (max_value - min_value) / self.bin_num
            for k in range(self.bin_num - 1):
                s_p = min_value + (k + 1) * L
                split_points.append(s_p)
            split_points.append(max_value)
            # final_split_points[col_name] = split_point
            self.bin_results.put_col_split_points(col_name, split_points)
        self.fit_category_features(data_instances)
        return self.bin_results.all_split_points
def fit_split_points ( self , data_instances ) : header = data_overview . get_header ( data_instances ) self . _default_setting ( header ) # is_sparse = data_overview.is_sparse_data(data_instances) # if is_sparse: #     raise RuntimeError("Bucket Binning method has not supported sparse data yet.") # self._init_cols(data_instances) statistics = MultivariateStatisticalSummary ( data_instances , self . bin_inner_param . bin_indexes , abnormal_list = self . abnormal_list ) max_dict = statistics . get_max ( ) min_dict = statistics . get_min ( ) for col_name , max_value in max_dict . items ( ) : min_value = min_dict . get ( col_name ) split_points = [ ] L = ( max_value - min_value ) / self . bin_num for k in range ( self . bin_num - 1 ) : s_p = min_value + ( k + 1 ) * L split_points . append ( s_p ) split_points . append ( max_value ) # final_split_points[col_name] = split_point self . bin_results . put_col_split_points ( col_name , split_points ) self . fit_category_features ( data_instances ) return self . bin_results . all_split_points