How to use the lime.explanation.DomainMapper function in lime

import numpy as np
import scipy as sp
import sklearn
import sklearn.preprocessing
from sklearn.utils import check_random_state

from lime.discretize import QuartileDiscretizer
from lime.discretize import DecileDiscretizer
from lime.discretize import EntropyDiscretizer
from lime.discretize import BaseDiscretizer
from lime.discretize import StatsDiscretizer
from . import explanation
from . import lime_base


class TableDomainMapper(explanation.DomainMapper):
    """Maps feature ids to names, generates table views, etc"""

    def __init__(self, feature_names, feature_values, scaled_row,
                 categorical_features, discretized_feature_names=None,
                 feature_indexes=None):
        """Init.

        Args:
            feature_names: list of feature names, in order
            feature_values: list of strings with the values of the original row
            scaled_row: scaled row
            categorical_features: list of categorical features ids (ints)
            feature_indexes: optional feature indexes used in the sparse case
        """
        self.exp_feature_names = feature_names
        self.discretized_feature_names = discretized_feature_names

from functools import partial
import itertools
import json
import re

import numpy as np
import scipy as sp
import sklearn
from sklearn.utils import check_random_state

from . import explanation
from . import lime_base


class TextDomainMapper(explanation.DomainMapper):
    """Maps feature ids to words or word-positions"""

    def __init__(self, indexed_string):
        """Initializer.

        Args:
            indexed_string: lime_text.IndexedString, original string
        """
        self.indexed_string = indexed_string

    def map_exp_ids(self, exp, positions=False):
        """Maps ids to words or word-position strings.

        Args:
            exp: list of tuples [(id, weight), (id,weight)]
            positions: if True, also return word positions