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

Examples of org.apache.mahout.cf.taste.impl.eval.GenericRecommenderIRStatsEvaluator.evaluate()

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          UserSimilarity similarity = new TanimotoCoefficientSimilarity(model);
          UserNeighborhood neighborhood = new NearestNUserNeighborhood(2, similarity, model);
          return new GenericBooleanPrefUserBasedRecommender(model, neighborhood, similarity);
        }
      };
      IRStatistics stats = evaluator.evaluate(recommenderBuilder, null, model, null, 10, Double.NaN, 0.1);
      System.out.println(stats);
  }

}
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          new NearestNUserNeighborhood(2, similarity, model);
        return new GenericUserBasedRecommender(model, neighborhood, similarity);
      }
    };
    // Evaluate precision and recall "at 2":
    IRStatistics stats = evaluator.evaluate(recommenderBuilder,
                                            null, model, null, 2,
                                            GenericRecommenderIRStatsEvaluator.CHOOSE_THRESHOLD,
                                            1.0);
    System.out.println(stats.getPrecision());
    System.out.println(stats.getRecall());
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      public DataModel buildDataModel(FastByIDMap<PreferenceArray> trainingData) {
        return new GenericBooleanPrefDataModel(
          GenericBooleanPrefDataModel.toDataMap(trainingData));
      }
    };
    IRStatistics stats = evaluator.evaluate(
        recommenderBuilder, modelBuilder, model, null, 10,
        GenericRecommenderIRStatsEvaluator.CHOOSE_THRESHOLD,
        1.0);
    System.out.println(stats.getPrecision());
    System.out.println(stats.getRecall());
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      model = new BookCrossingDataModel(ratingsFile, true);
    } else {
      model = new BookCrossingDataModel(true);
    }

    IRStatistics evaluation = evaluator.evaluate(
        new BookCrossingBooleanRecommenderBuilder(),
        new BookCrossingDataModelBuilder(),
        model,
        null,
        3,
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    RecommenderIRStatsEvaluator evaluator = new GenericRecommenderIRStatsEvaluator();
    File ratingsFile = TasteOptionParser.getRatings(args);
    DataModel model =
        ratingsFile == null ? new BookCrossingDataModel(true) : new BookCrossingDataModel(ratingsFile, true);

    IRStatistics evaluation = evaluator.evaluate(
        new BookCrossingBooleanRecommenderBuilder(),
        new BookCrossingDataModelBuilder(),
        model,
        null,
        3,
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