code: def get_Processed_NHANES_Data(filename):
      """
      Args:
          filename (str): Enter NHANES filename
  
      Returns:
          One hot encoded features and original input
      """
      # returns original and one hot encoded data
      # Input: XPT filename e.g. 2_H.XPT)
      # output:
      # One hot endcoded, e.g. (5924 x 145)
      # original, e.g. (5924 x 9)
  
      with open(filename, "rb") as f:
          original = xport.to_numpy(f)
  
      # replace nan's with 0's.
      original[np.isnan(original)] = 0
  
      # delete 1st column (contains sequence numbers)
      original = original[:, 1:]
  
      # one hot encoding of all columns/features
      onehot_encoder = OneHotEncoder(sparse=False)
      onehot_encoded = onehot_encoder.fit_transform(original)
  
      # return one hot encoded and original data
      return (onehot_encoded, original)
  
file_path: explainx/lib/PDASH_utils.py
imports: from __future__ import print_function
  import numpy
  from sklearn.preprocessing import OneHotEncoder
  from cvxopt.solvers import qp
  from cvxopt import matrix, spmatrix
  from numpy import array, ndarray
  from scipy.spatial.distance import cdist
label_desc: Use image node_id 4 for calling a global function with example usage: get_Processed_NHANES_Data(filename) and returns: onehot_encoded,  original