(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)
        LOGGER.debug("Header length: {}".format(len(header)))

        self._default_setting(header)
        # self._init_cols(data_instances)
        percent_value = 1.0 / self.bin_num

        # calculate the split points
        percentile_rate = [i * percent_value for i in range(1, self.bin_num)]
        percentile_rate.append(1.0)
        is_sparse = data_overview.is_sparse_data(data_instances)
    
        self._fit_split_point(data_instances, is_sparse, percentile_rate)

        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 ) LOGGER . debug ( "Header length: {}" . format ( len ( header ) ) ) self . _default_setting ( header ) # self._init_cols(data_instances) percent_value = 1.0 / self . bin_num # calculate the split points percentile_rate = [ i * percent_value for i in range ( 1 , self . bin_num ) ] percentile_rate . append ( 1.0 ) is_sparse = data_overview . is_sparse_data ( data_instances ) self . _fit_split_point ( data_instances , is_sparse , percentile_rate ) self . fit_category_features ( data_instances ) return self . bin_results . all_split_points