class_docstr: Handles MovieLens-100K
docstr: Load item features
  
  Args:
      path (str): Path to the file containing item features information
docstr_tok: ["Load","item","features","Args",":","path","(","str",")",":","Path","to","the","file","containing","item","features","information"]
code: def _load_item_features(self, path):
      """Load item features
  
      Args:
          path (str): Path to the file containing item features information
  
      """
      header = [
          "movie_id",
          "movie_title",
          "release_date",
          "video_release_date",
          "IMDb_URL",
          "unknown",
          "Action",
          "Adventure",
          "Animation",
          "Childrens",
          "Comedy",
          "Crime",
          "Documentary",
          "Drama",
          "Fantasy",
          "Film-Noir",
          "Horror",
          "Musical",
          "Mystery",
          "Romance",
          "Sci-Fi",
          "Thriller",
          "War",
          "Western",
      ]
      data = pd.read_csv(
          path, delimiter="|", names=header, encoding="ISO-8859-1"
      )
  
      features_df = pd.concat(
          [
              pd.get_dummies(data["movie_title"]),
              pd.get_dummies(data["release_date"]),
              pd.get_dummies("video_release_date"),
              pd.get_dummies("IMDb_URL"),
              data[header[5:]],
          ],
          axis=1,
      )
      item_features = np.nan_to_num(features_df.to_numpy())
      return item_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 6 for calling the ML_100K obj's underlying member method code with example usage: obj._load_item_features(path) and returns: item_features