class_docstr: A data interface for private data with meta information.
class_docstr_tok: ["A","data","interface","for","private","data","with","meta","information","."]
docstr: Transforms label encoded data back to categorical values
docstr_tok: ["Transforms","label","encoded","data","back","to","categorical","values"]
code: def from_label(self, data):
      """Transforms label encoded data back to categorical values"""
      out = data.copy()
      if isinstance(data, pd.DataFrame) or isinstance(data, dict):
          for column in self.categorical_feature_names:
              out[column] = self.labelencoder[column].inverse_transform(
                  out[column].round().astype(int).tolist()
              )
          return out
      elif isinstance(data, list):
          for column in self.categorical_feature_indexes:
              out[column] = self.labelencoder[
                  self.feature_names[column]
              ].inverse_transform([round(out[column])])[0]
          return out
  
imports: import collections
  import logging
  import sys
  from collections import defaultdict
  import numpy
  import pandas
  from sklearn.preprocessing import LabelEncoder
  from dice_ml.data_interfaces.base_data_interface import _BaseData
label_desc: Use image node_id 16 for calling the PrivateData obj's underlying member method code with example usage: obj.from_label(data) and returns: out, out