Examples of IRStatistics

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

33
34
35
36
37
38
39
40
          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);
  }
View Full Code Here

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

46
47
48
49
50
51
52
53
54
55
56
57
          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());
  }
View Full Code Here

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

46
47
48
49
50
51
52
53
54
55
56
57
      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());
  }
View Full Code Here

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

35
36
37
38
39
40
41
42
43
44
45
      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);
  }
View Full Code Here

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

46
47
48
49
50
51
52
53
54
55
56
      model = new BookCrossingDataModel(ratingsFile, true);
    } else {
      model = new BookCrossingDataModel(true);
    }

    IRStatistics evaluation = evaluator.evaluate(
        new BookCrossingBooleanRecommenderBuilder(),
        new BookCrossingDataModelBuilder(),
        model,
        null,
        3,
View Full Code Here

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

37
38
39
40
41
42
43
44
45
46
47
      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);
  }
View Full Code Here

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

35
36
37
38
39
40
41
42
43
44
45
      public Recommender buildRecommender(DataModel dataModel) throws TasteException {
        return new SlopeOneRecommender(dataModel);
      }
    };
    RecommenderIRStatsEvaluator evaluator = new GenericRecommenderIRStatsEvaluator();
    IRStatistics stats = evaluator.evaluate(builder, model, null, 5, 0.2, 1.0);
    assertNotNull(stats);
    assertEquals(0.5, stats.getPrecision(), EPSILON);
    assertEquals(1.0, stats.getRecall(), EPSILON);
    assertEquals(0.6666666666666666, stats.getF1Measure(), EPSILON);
  }
View Full Code Here

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

42
43
44
45
46
47
48
49
50
51
52
    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,
View Full Code Here

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

43
44
45
46
47
48
49
50
51
52
53
54
55
      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);
  }
View Full Code Here

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

68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
      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);
  }
View Full Code Here
TOP
Copyright © 2018 www.massapi.com. All rights reserved.
All source code are property of their respective owners. Java is a trademark of Sun Microsystems, Inc and owned by ORACLE Inc. Contact coftware#gmail.com.