(data_instances, split_points, bin_cols_map)
return data_bin_dict
Apply the binning method

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

        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

        Returns
        -------
        data_bin_table : Table.

            Each element represent for the corresponding bin number this feature belongs to.
            e.g. it could be:
            [{'x1': 1, 'x2': 5, 'x3': 2}
            ...
             ]
Apply the binning method
Apply the binning method
def get_data_bin(data_instances, split_points, bin_cols_map):
        """
        Apply the binning method

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

        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

        Returns
        -------
        data_bin_table : Table.

            Each element represent for the corresponding bin number this feature belongs to.
            e.g. it could be:
            [{'x1': 1, 'x2': 5, 'x3': 2}
            ...
             ]
        """
        # self._init_cols(data_instances)
        is_sparse = data_overview.is_sparse_data(data_instances)
        header = data_instances.schema.get('header')

        f = functools.partial(BaseBinning.bin_data,
                              split_points=split_points,
                              cols_dict=bin_cols_map,
                              header=header,
                              is_sparse=is_sparse)
        data_bin_dict = data_instances.mapValues(f)
        return data_bin_dict
def get_data_bin ( data_instances , split_points , bin_cols_map ) : # self._init_cols(data_instances) is_sparse = data_overview . is_sparse_data ( data_instances ) header = data_instances . schema . get ( 'header' ) f = functools . partial ( BaseBinning . bin_data , split_points = split_points , cols_dict = bin_cols_map , header = header , is_sparse = is_sparse ) data_bin_dict = data_instances . mapValues ( f ) return data_bin_dict