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 Explicit Factor Models"""
  
  import cornac
  from cornac.datasets import amazon_toy
  from cornac.data import SentimentModality
  from cornac.eval_methods import RatioSplit
  
  # Load rating and sentiment information
  rating = amazon_toy.load_feedback()
  sentiment = amazon_toy.load_sentiment()
  
  # Instantiate a SentimentModality, it make it convenient to work with sentiment information
  md = SentimentModality(data=sentiment)
  
  # Define an evaluation method to split feedback into train and test sets
  split_data = RatioSplit(data=rating,
                          test_size=0.15,
                          exclude_unknowns=True, verbose=True,
                          sentiment=md, seed=123)
  
  # Instantiate the EFM model
  efm = cornac.models.EFM(num_explicit_factors=40, num_latent_factors=60, num_most_cared_aspects=15,
                          rating_scale=5.0, alpha=0.85,
                          lambda_x=1, lambda_y=1, lambda_u=0.01, lambda_h=0.01, lambda_v=0.01,
                          max_iter=100, num_threads=1,
                          trainable=True, verbose=True, seed=123)
  
  # Instantiate evaluation metrics
  rmse = cornac.metrics.RMSE()
  ndcg_50 = cornac.metrics.NDCG(k=50)
  auc = cornac.metrics.AUC()
  
  # Put everything together into an experiment and run it
  exp = cornac.Experiment(eval_method=split_data,
                          models=[efm],
                          metrics=[rmse, ndcg_50, auc])
  exp.run()
  