class_docstr: A data interface for private data with meta information.
class_docstr_tok: ["A","data","interface","for","private","data","with","meta","information","."]
code: def create_ohe_params(self, one_hot_encoded_data=None):
      if len(self.categorical_feature_names) > 0:
          # simulating sklearn's one-hot-encoding
          # continuous features on the left
          self.ohe_encoded_feature_names = [
              feature for feature in self.continuous_feature_names
          ]
          for feature_name in self.categorical_feature_names:
              for category in sorted(
                  self.categorical_levels[feature_name]
              ):
                  self.ohe_encoded_feature_names.append(
                      feature_name + "_" + category
                  )
      else:
          # one-hot-encoded data is same as original data if there is no categorical features.
          self.ohe_encoded_feature_names = [
              feat for feat in self.feature_names
          ]
  
      # base dataframe for doing one-hot-encoding
      # ohe_encoded_feature_names and ohe_base_df are created (and stored as data class's parameters)
      # when get_data_params_for_gradient_dice() is called from gradient-based DiCE explainers
      self.ohe_base_df = self.prepare_df_for_ohe_encoding()
  
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 11 for calling the PrivateData obj's underlying member method code with example usage: obj.create_ohe_params(one_hot_encoded_data) without return types