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 all data related params for DiCE.
docstr_tok: ["Gets","all","data","related","params","for","DiCE","."]
code: def get_data_params_for_gradient_dice(self):
      """Gets all data related params for DiCE."""
  
      self.create_ohe_params()
      minx, maxx = self.get_minx_maxx(normalized=True)
  
      # get the column indexes of categorical and continuous features after one-hot-encoding
      encoded_categorical_feature_indexes = (
          self.get_encoded_categorical_feature_indexes()
      )
      flattened_indexes = [
          item
          for sublist in encoded_categorical_feature_indexes
          for item in sublist
      ]
      encoded_continuous_feature_indexes = [
          ix
          for ix in range(len(minx[0]))
          if ix not in flattened_indexes
      ]
  
      # min and max for continuous features in original scale
      org_minx, org_maxx = self.get_minx_maxx(normalized=False)
      cont_minx = list(org_minx[0][encoded_continuous_feature_indexes])
      cont_maxx = list(org_maxx[0][encoded_continuous_feature_indexes])
  
      # decimal precisions for continuous features
      cont_precisions = [
          self.get_decimal_precisions()[ix]
          for ix in range(len(self.continuous_feature_names))
      ]
  
      return (
          minx,
          maxx,
          encoded_categorical_feature_indexes,
          encoded_continuous_feature_indexes,
          cont_minx,
          cont_maxx,
          cont_precisions,
      )
  
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 12 for calling the PrivateData obj's underlying member method code with example usage: obj.get_data_params_for_gradient_dice() and returns: minx,  maxx,  encoded_categorical_feature_indexes,  encoded_continuous_feature_indexes,  cont_minx,  cont_maxx,  cont_precisions