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

Class org.apache.mahout.cf.taste.eval.RecommenderIRStatsEvaluator

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

org.apache.mahout.cf.taste.eval.RecommenderIRStatsEvaluator.evaluate()
@param recommenderBuilder object that can build a{@link org.apache.mahout.cf.taste.recommender.Recommender} to test @param dataModel dataset to test on @param rescorer if any, to use when computing recommendations @param at as in, "precision at 5". The number of recommendations to consider when evaluatingprecision, etc. @param relevanceThreshold {@link Item}s whose preference value is at least this value are considered "relevant" for the purposes of computations @return {@link IRStatistics} with resulting precision, recall, etc. @throws TasteException if an error occurs while accessing the {@link DataModel}

      public Recommender buildRecommender(DataModel dataModel) throws TasteException {
        return new SlopeOneRecommender(dataModel);
      }
    };
    RecommenderIRStatsEvaluator evaluator = new GenericRecommenderIRStatsEvaluator();
    IRStatistics stats = evaluator.evaluate(builder, null, model, null, 1, 0.2, 1.0);
    assertNotNull(stats);
    assertEquals(0.75, stats.getPrecision(), EPSILON);
    assertEquals(0.75, stats.getRecall(), EPSILON);
    assertEquals(0.75, stats.getF1Measure(), EPSILON);
    assertEquals(0.75, stats.getNormalizedDiscountedCumulativeGain(), EPSILON);

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      public DataModel buildDataModel(FastByIDMap<PreferenceArray> trainingData) {
        return new GenericBooleanPrefDataModel(GenericBooleanPrefDataModel.toDataMap(trainingData));
      }
    };
    RecommenderIRStatsEvaluator evaluator = new GenericRecommenderIRStatsEvaluator();
    IRStatistics stats = evaluator.evaluate(
        builder, dataModelBuilder, model, null, 1, GenericRecommenderIRStatsEvaluator.CHOOSE_THRESHOLD, 1.0);


    assertNotNull(stats);
    assertEquals(0.666666666, stats.getPrecision(), EPSILON);
    assertEquals(0.666666666, stats.getRecall(), EPSILON);

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      public Recommender buildRecommender(DataModel dataModel) throws TasteException {
        return new SlopeOneRecommender(dataModel);
      }
    };
    RecommenderIRStatsEvaluator evaluator = new GenericRecommenderIRStatsEvaluator();
    IRStatistics stats = evaluator.evaluate(builder, null, model, null, 1, 0.2, 1.0);
    assertNotNull(stats);
    assertEquals(0.75, stats.getPrecision(), EPSILON);
    assertEquals(0.75, stats.getRecall(), EPSILON);
    assertEquals(0.75, stats.getF1Measure(), EPSILON);
    assertEquals(0.75, stats.getFNMeasure(2.0), EPSILON);

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