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 one-hot-encoded and min-max normalized data into raw user-fed data format. transformed_data
  should be a dataframe or an array
docstr_tok: ["Transforms","one-hot-encoded","and","min-max","normalized","data","into","raw","user-fed","data","format",".","transformed_data","should","be","a","dataframe","or","an","array"]
code: def get_inverse_ohe_min_max_normalized_data(self, transformed_data):
      """Transforms one-hot-encoded and min-max normalized data into raw user-fed data format. transformed_data
      should be a dataframe or an array"""
      raw_data = self.from_dummies(transformed_data)
      raw_data = self.de_normalize_data(raw_data)
      precisions = self.get_decimal_precisions()
      for ix, feature in enumerate(self.continuous_feature_names):
          raw_data[feature] = (
              raw_data[feature].astype(float).round(precisions[ix])
          )
      raw_data = raw_data[self.feature_names]
      # returns a pandas dataframe
      return raw_data
  
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 24 for calling the PrivateData obj's underlying member method code with example usage: obj.get_inverse_ohe_min_max_normalized_data(transformed_data) and returns: raw_data