class_docstr: A data interface for public data. This class is an interface to DiCE explainers
  and contains methods to transform user-fed raw data into the format a DiCE explainer
  requires, and vice versa.
docstr: Computes Median Absolute Deviation of features. If they are <=0, returns a practical value instead
code: def get_valid_mads(
      self, normalized=False, display_warnings=False, return_mads=True
  ):
      """Computes Median Absolute Deviation of features. If they are <=0, returns a practical value instead"""
      mads = self.get_mads(normalized=normalized)
      for feature in mads:
          if mads[feature] <= 0:
              mads[feature] = 1.0
              if display_warnings:
                  logging.warning(
                      " MAD for feature %s is 0, so replacing it with 1.0 to avoid error.",
                      feature,
                  )
  
      if return_mads:
          return mads
  
imports: import logging
  from collections import defaultdict
  import numpy
  import pandas
  from raiutils.exceptions import UserConfigValidationException
  from sklearn.preprocessing import LabelEncoder
  from dice_ml.data_interfaces.base_data_interface import _BaseData
  from dice_ml.utils.exception import SystemException
label_desc: Use image node_id 14 for calling the PublicData obj's underlying member method code with example usage: obj.get_valid_mads(normalized, display_warnings, return_mads) and returns: mads