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

Examples of org.apache.mahout.cf.taste.eval.IRStatistics

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    assertEquals(0.666666666, stats.getNormalizedDiscountedCumulativeGain(), EPSILON);
  }

  @Test
  public void testIRStats() {
    IRStatistics stats = new IRStatisticsImpl(0.3, 0.1, 0.2, 0.05, 0.15);
    assertEquals(0.3, stats.getPrecision(), EPSILON);
    assertEquals(0.1, stats.getRecall(), EPSILON);
    assertEquals(0.15, stats.getF1Measure(), EPSILON);
    assertEquals(0.11538461538462, stats.getFNMeasure(2.0), EPSILON);
    assertEquals(0.05, stats.getNormalizedDiscountedCumulativeGain(), 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.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);
    assertEquals(0.666666666, stats.getF1Measure(), EPSILON);
    assertEquals(0.666666666, stats.getFNMeasure(2.0), EPSILON);
    assertEquals(0.666666666, stats.getNormalizedDiscountedCumulativeGain(), EPSILON);
  }
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    assertEquals(0.666666666, stats.getNormalizedDiscountedCumulativeGain(), EPSILON);
  }

  @Test
  public void testIRStats() {
    IRStatistics stats = new IRStatisticsImpl(0.3, 0.1, 0.2, 0.05, 0.15);
    assertEquals(0.3, stats.getPrecision(), EPSILON);
    assertEquals(0.1, stats.getRecall(), EPSILON);
    assertEquals(0.15, stats.getF1Measure(), EPSILON);
    assertEquals(0.11538461538462, stats.getFNMeasure(2.0), EPSILON);
    assertEquals(0.05, stats.getNormalizedDiscountedCumulativeGain(), 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);
    assertEquals(0.75, stats.getNormalizedDiscountedCumulativeGain(), EPSILON);
  }
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    assertEquals(0.75, stats.getNormalizedDiscountedCumulativeGain(), EPSILON);
  }

  @Test
  public void testIRStats() {
    IRStatistics stats = new IRStatisticsImpl(0.3, 0.1, 0.2, 0.05, 0.15);
    assertEquals(0.3, stats.getPrecision(), EPSILON);
    assertEquals(0.1, stats.getRecall(), EPSILON);
    assertEquals(0.15, stats.getF1Measure(), EPSILON);
    assertEquals(0.11538461538462, stats.getFNMeasure(2.0), EPSILON);
    assertEquals(0.05, stats.getNormalizedDiscountedCumulativeGain(), EPSILON);
  }
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Related Classes of org.apache.mahout.cf.taste.eval.IRStatistics

  • mia.recommender.ch02.IREvaluatorIntro
  • mia.recommender.ch03.IREvaluatorBooleanPrefIntro2
  • mia.recommender.ch05.LibimsetiIREvalRunner
  • org.apache.mahout.cf.taste.example.bookcrossing.BookCrossingBooleanRecommenderEvaluatorRunner
  • org.apache.mahout.cf.taste.impl.eval.GenericRecommenderIRStatsEvaluatorImplTest
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