Package org.apache.mahout.cf.taste.impl.eval

Examples of org.apache.mahout.cf.taste.impl.eval.AverageAbsoluteDifferenceRecommenderEvaluator

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  public static void main(String[] args) throws Exception {
    DataModel model = new FileDataModel(new File("ratings.dat"));

    RecommenderEvaluator evaluator =
      new AverageAbsoluteDifferenceRecommenderEvaluator();

    RecommenderBuilder recommenderBuilder = new RecommenderBuilder() {
      @Override
      public Recommender buildRecommender(DataModel model) throws TasteException {
        try {
          return new LibimsetiRecommender(model);
        } catch (IOException ioe) {
          throw new TasteException(ioe);
        }

      }
    };
    double score = evaluator.evaluate(recommenderBuilder, null, model, 0.95, 0.1);
    System.out.println(score);
  }
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  public static void main(String[] args) throws Exception {
    DataModel model = new GroupLensDataModel(new File("ratings.dat"));

    RecommenderEvaluator evaluator =
      new AverageAbsoluteDifferenceRecommenderEvaluator();
    RecommenderBuilder recommenderBuilder = new RecommenderBuilder() {
      @Override
      public Recommender buildRecommender(DataModel model) throws TasteException {
        UserSimilarity similarity = new PearsonCorrelationSimilarity(model);
        UserNeighborhood neighborhood =
          new NearestNUserNeighborhood(100, similarity, model);
        return new GenericUserBasedRecommender(model, neighborhood, similarity);
      }
    };
    double score = evaluator.evaluate(recommenderBuilder, null, model, 0.95, 0.05);
    System.out.println(score);
  }
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    DataModel model = new GenericBooleanPrefDataModel(
        GenericBooleanPrefDataModel.toDataMap(
          new FileDataModel(new File("ua.base"))));

    RecommenderEvaluator evaluator =
      new AverageAbsoluteDifferenceRecommenderEvaluator();
    RecommenderBuilder recommenderBuilder = new RecommenderBuilder() {
      @Override
      public Recommender buildRecommender(DataModel model) throws TasteException {
        UserSimilarity similarity = new PearsonCorrelationSimilarity(model);
        UserNeighborhood neighborhood =
          new NearestNUserNeighborhood(10, similarity, model);
        return new GenericUserBasedRecommender(model, neighborhood, similarity);
      }
    };
    DataModelBuilder modelBuilder = new DataModelBuilder() {
      @Override
      public DataModel buildDataModel(FastByIDMap<PreferenceArray> trainingData) {
        return new GenericBooleanPrefDataModel(
          GenericBooleanPrefDataModel.toDataMap(trainingData));
      }
    };
    double score = evaluator.evaluate(
        recommenderBuilder, modelBuilder, model, 0.9, 1.0);
    System.out.println(score);
  }
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    System.exit(1);
  }
    DataModel model = new FileDataModel(modelFile);

    RecommenderEvaluator evaluator =
      new AverageAbsoluteDifferenceRecommenderEvaluator();
    // Build the same recommender for testing that we did last time:
    RecommenderBuilder recommenderBuilder = new RecommenderBuilder() {
      @Override
      public Recommender buildRecommender(DataModel model) throws TasteException {
        UserSimilarity similarity = new PearsonCorrelationSimilarity(model);
        UserNeighborhood neighborhood =
          new NearestNUserNeighborhood(2, similarity, model);
        return new GenericUserBasedRecommender(model, neighborhood, similarity);
      }
    };
    // Use 70% of the data to train; test using the other 30%.
    double score = evaluator.evaluate(recommenderBuilder, null, model, 0.7, 1.0);
    System.out.println(score);
  }
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  private JesterRecommenderEvaluatorRunner() {
    // do nothing
  }
 
  public static void main(String... args) throws IOException, TasteException, OptionException {
    RecommenderEvaluator evaluator = new AverageAbsoluteDifferenceRecommenderEvaluator();
    DataModel model;
    File ratingsFile = TasteOptionParser.getRatings(args);
    if (ratingsFile != null) {
      model = new JesterDataModel(ratingsFile);
    } else {
      model = new JesterDataModel();
    }
    double evaluation = evaluator.evaluate(new JesterRecommenderBuilder(),
      null,
      model,
      0.9,
      0.3);
    log.info(String.valueOf(evaluation));
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  private GroupLensRecommenderEvaluatorRunner() {
    // do nothing
  }
 
  public static void main(String... args) throws IOException, TasteException, OptionException {
    RecommenderEvaluator evaluator = new AverageAbsoluteDifferenceRecommenderEvaluator();
    DataModel model;
    File ratingsFile = TasteOptionParser.getRatings(args);
    if (ratingsFile != null) {
      model = new GroupLensDataModel(ratingsFile);
    } else {
      model = new GroupLensDataModel();
    }
    double evaluation = evaluator.evaluate(new GroupLensRecommenderBuilder(),
      null,
      model,
      0.9,
      0.3);
    log.info(String.valueOf(evaluation));
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  private BookCrossingRecommenderEvaluatorRunner() {
    // do nothing
  }
 
  public static void main(String... args) throws IOException, TasteException, OptionException {
    RecommenderEvaluator evaluator = new AverageAbsoluteDifferenceRecommenderEvaluator();
    DataModel model;
    File ratingsFile = TasteOptionParser.getRatings(args);
    if (ratingsFile != null) {
      model = new BookCrossingDataModel(ratingsFile, false);
    } else {
      model = new BookCrossingDataModel(false);
    }
   
    double evaluation = evaluator.evaluate(new BookCrossingRecommenderBuilder(),
      null,
      model,
      0.9,
      0.3);
    log.info(String.valueOf(evaluation));
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  private NetflixRecommenderEvaluatorRunner() {
    // do nothing
  }
 
  public static void main(String... args) throws IOException, TasteException, OptionException {
    RecommenderEvaluator evaluator = new AverageAbsoluteDifferenceRecommenderEvaluator();
    File ratingsFile = TasteOptionParser.getRatings(args);
    if (ratingsFile != null) {
      DataModel model = new NetflixDataModel(ratingsFile, true);
      double evaluation = evaluator.evaluate(new NetflixRecommenderBuilder(), null, model, 0.9, 0.1);
      log.info(String.valueOf(evaluation));
    } else {
      log.error("Netflix Recommender needs a ratings file to work. Please provide it with the -i command line option.");
    }
  }
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  private BookCrossingRecommenderEvaluatorRunner() {
    // do nothing
  }

  public static void main(String... args) throws IOException, TasteException {
    RecommenderEvaluator evaluator = new AverageAbsoluteDifferenceRecommenderEvaluator();
    BookCrossingDataModel model = new BookCrossingDataModel();
    double evaluation = evaluator.evaluate(new BookCrossingRecommenderBuilder(model),
                                                 model,
                                                 0.9,
                                                 0.1);
    log.info(String.valueOf(evaluation));
  }
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  private JesterRecommenderEvaluatorRunner() {
    // do nothing
  }

  public static void main(String... args) throws IOException, TasteException {
    RecommenderEvaluator evaluator = new AverageAbsoluteDifferenceRecommenderEvaluator();
    DataModel model = new JesterDataModel();
    double evaluation = evaluator.evaluate(new JesterRecommenderBuilder(),
                                                 model,
                                                 0.9,
                                                 1.0);
    log.info(String.valueOf(evaluation));
  }
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  • mia.recommender.ch03.IREvaluatorBooleanPrefIntro1
  • mia.recommender.ch04.GroupLens10MEvalIntro
  • mia.recommender.ch05.LibimsetiEvalRunner
  • org.apache.mahout.cf.taste.example.bookcrossing.BookCrossingRecommenderEvaluatorRunner
  • org.apache.mahout.cf.taste.example.grouplens.GroupLensRecommenderEvaluatorRunner
  • org.apache.mahout.cf.taste.example.jester.JesterRecommenderEvaluatorRunner
  • org.apache.mahout.cf.taste.example.netflix.NetflixRecommenderEvaluatorRunner
  • org.apache.mahout.cf.taste.impl.common.FullRunningAverage
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