class_docstr: Maps feature ids to names, generates table views, etc
class_docstr_tok: ["Maps","feature","ids","to","names",",","generates","table","views",",","etc"]
docstr: 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
docstr_tok: ["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"]
code: 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
      self.feature_names = feature_names
      self.feature_values = feature_values
      self.feature_indexes = feature_indexes
      self.scaled_row = scaled_row
      if sp.sparse.issparse(scaled_row):
          self.all_categorical = False
      else:
          self.all_categorical = len(categorical_features) == len(
              scaled_row
          )
      self.categorical_features = categorical_features
  
imports: import collections
  import copy
  from functools import partial
  import json
  import warnings
  import numpy
  import scipy
  import sklearn
  import sklearn.preprocessing
  from sklearn.utils import check_random_state
  from pyDOE2 import lhs
  from scipy.stats.distributions import norm
  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 .None import explanation
  from .None import lime_base
label_desc: Use image node_id 1 to create a new TableDomainMapper object from inherited base classes: explanation with example: obj = TableDomainMapper(feature_names, feature_values, scaled_row, categorical_features, discretized_feature_names, feature_indexes)