class_docstr: Handles MovieLens-100K
docstr: Load dataset
  
  Args:
      path (str): Path to the directory containing ML100K dataset
      e1_path (str): Path to the file containing row (user) features of ML100K dataset
      e2_path (str): Path to the file containing col (movie) features of ML100K dataset
docstr_tok: ["Load","dataset","Args",":","path","(","str",")",":","Path","to","the","directory","containing","ML100K","dataset","e1_path","(","str",")",":","Path","to","the","file","containing","row","(","user",")","features","of","ML100K","dataset","e2_path","(","str",")",":","Path","to","the","file","containing","col","(","movie",")","features","of","ML100K","dataset"]
code: def load_data(self, path):
      """Load dataset
  
      Args:
          path (str): Path to the directory containing ML100K dataset
          e1_path (str): Path to the file containing row (user) features of ML100K dataset
          e2_path (str): Path to the file containing col (movie) features of ML100K dataset
      """
      self.entities = [
          self._load_user_features(f"{path}/u.user"),
          self._load_item_features(f"{path}/u.item"),
      ]
      self.normalize()
      self.reduce_dims()
      self.training_data = DataPtr(
          self._read_from_file(f"{path}/u1.base").tocsr(), self.entities
      )
      self.test_data = DataPtr(
          self._read_from_file(f"{path}/u1.test").tocsr(), self.entities
      )
  
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 4 for calling the ML_100K obj's underlying member method code with example usage: obj.load_data(path) without return types