text: # Copyright 2018 The Cornac Authors. All Rights Reserved.
  #
  # Licensed under the Apache License, Version 2.0 (the "License");
  # you may not use this file except in compliance with the License.
  # You may obtain a copy of the License at
  #
  #     http://www.apache.org/licenses/LICENSE-2.0
  #
  # Unless required by applicable law or agreed to in writing, software
  # distributed under the License is distributed on an "AS IS" BASIS,
  # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
  # See the License for the specific language governing permissions and
  # limitations under the License.
  # ============================================================================
  """Example for Matrix Factorization with biases"""
  
  import cornac
  from cornac.datasets import movielens
  from cornac.eval_methods import RatioSplit
  
  # Load MovieLens 1M ratings
  ml_1m = movielens.load_feedback(variant='1M')
  
  # Define an evaluation method to split feedback into train and test sets
  ratio_split = RatioSplit(data=ml_1m,
                           test_size=0.2,
                           exclude_unknowns=False,
                           verbose=True)
  
  # Instantiate the global average baseline and MF model
  global_avg = cornac.models.GlobalAvg()
  mf = cornac.models.MF(k=10, max_iter=25, learning_rate=0.01, lambda_reg=0.02,
                        use_bias=True, early_stop=True, verbose=True)
  
  # Instantiate MAE and RMSE for evaluation
  mae = cornac.metrics.MAE()
  rmse = cornac.metrics.RMSE()
  
  # Put everything together into an experiment and run it
  exp = cornac.Experiment(eval_method=ratio_split,
                          models=[global_avg, mf],
                          metrics=[mae, rmse],
                          user_based=True)
  exp.run()
  