class_docstr: A data interface for private data with meta information.
class_docstr_tok: ["A","data","interface","for","private","data","with","meta","information","."]
docstr: Gets the column indexes categorical features after one-hot-encoding.
docstr_tok: ["Gets","the","column","indexes","categorical","features","after","one-hot-encoding","."]
code: def get_encoded_categorical_feature_indexes(self):
      """Gets the column indexes categorical features after one-hot-encoding."""
      cols = []
      for col_parent in self.categorical_feature_names:
          temp = [
              self.ohe_encoded_feature_names.index(col)
              for col in self.ohe_encoded_feature_names
              if col.startswith(col_parent)
              and col not in self.continuous_feature_names
          ]
          cols.append(temp)
      return cols
  
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 13 for calling the PrivateData obj's underlying member method code with example usage: obj.get_encoded_categorical_feature_indexes() and returns: cols