class_docstr: Handles MovieLens-100K
docstr: Load user features
  
  Args:
      path (str): Path to the file containing user features information
docstr_tok: ["Load","user","features","Args",":","path","(","str",")",":","Path","to","the","file","containing","user","features","information"]
code: def _load_user_features(self, path):
      """Load user features
  
      Args:
          path (str): Path to the file containing user features information
  
      """
      data = pd.read_csv(
          path,
          delimiter="|",
          names=["user_id", "age", "gender", "occupation", "zip_code"],
      )
      features_df = pd.concat(
          [
              data["user_id"],
              pd.get_dummies(data["user_id"]),
              pd.get_dummies(data["age"]),
              pd.get_dummies(data["gender"]),
              pd.get_dummies(data["occupation"]),
              pd.get_dummies(data["zip_code"]),
          ],
          axis=1,
      )
      features_df.drop(["user_id"], axis=1, inplace=True)
      user_features = np.nan_to_num(features_df.to_numpy())
      return user_features
  
imports: import logging
  import pandas
  import numpy
  from scipy.sparse import coo_matrix, isspmatrix_csr
  from sklearn.model_selection import train_test_split
  from sklearn.preprocessing import normalize
  from recommenders.utils.python_utils import binarize
  from .geoimc_utils import length_normalize, reduce_dims
label_desc: Use image node_id 5 for calling the ML_100K obj's underlying member method code with example usage: obj._load_user_features(path) and returns: user_features