public static void evaluate(
RecommenderEvaluator re,
RecommenderBuilder rb,
DataModelBuilder mb,
DataModel dm,
double trainPt)
throws TasteException {
System.out.printf("Evaluater Score:%s\n", re.evaluate(rb, mb, dm, trainPt, 1.0));
}
@Test
public void testOne() throws TasteException {
RecommenderEvaluator evaluator = new AverageAbsoluteDifferenceRecommenderEvaluator();
FastByIDMap<PreferenceArray> usersData = usersData();
DataModel model = new GenericDataModel(usersData);
double evaluation = evaluator.evaluate(new CrossingRecommenderBuilder(), null, model, 1, 0.5);
log.info(String.valueOf(evaluation));
}
@Test
public void testEvaluate() throws Exception {
DataModel model = getDataModel();
Recommender recommender1 = new SlopeOneRecommender(model);
Recommender recommender2 = new ItemAverageRecommender(model);
RecommenderEvaluator evaluator = new PreferenceBasedRecommenderEvaluator();
RunningAverage tracker = new FullRunningAverage();
evaluator.evaluate(recommender1, recommender2, 100, tracker, Formula.MEANRANK);
double eval = tracker.getAverage();
assertEquals(0.185294508934021, eval, EPSILON);
}
/** 基本算法 */
public static void demo1(DataModel m) throws Exception {
System.out.println("==============用户,打分,平均差值(0)==============");
// 推荐结果
UserSimilarity s = MySimilarity.getPearsonCorrelation(m);
UserNeighborhood n = MyNeighborhood.getNearestN(m, s, 2);
RecommenderBuilder rb = MyRecommender.userBuilder(s, n);
MyRecommender.showItems(rb.buildRecommender(m).recommend(26, 1));
RecommenderEvaluator e = new AverageAbsoluteDifferenceRecommenderEvaluator();
double score = e.evaluate(rb, null, m, 0.9, 1.0); // testing data, all
// data
System.out.println("算法得分: " + score);
System.out.println();
}
public static void demo2(DataModel m) throws Exception {
System.out.println("==============用户,打分,均方根(0)==============");
// 推荐结果
UserSimilarity s = MySimilarity.getPearsonCorrelation(m);
UserNeighborhood n = MyNeighborhood.getNearestN(m, s, 2);
RecommenderBuilder rb = MyRecommender.userBuilder(s, n);
MyRecommender.showItems(rb.buildRecommender(m).recommend(26, 1));
// RandomUtils.useTestSeed();
RecommenderEvaluator e = new RMSRecommenderEvaluator();
double score = e.evaluate(rb, null, m, 0.7, 0.3); // testing data, all
// data
System.out.println("算法得分: " + score);
System.out.println();
}
public static void demo3(DataModel m) throws Exception {
System.out.println("==============用户,无打分,平均差值(0)==============");
// 推荐结果
UserSimilarity s = new LogLikelihoodSimilarity(m);
UserNeighborhood n = MyNeighborhood.getNearestN(m, s, 2);
RecommenderBuilder rb = MyRecommender.userBuilder(s, n);
MyRecommender.showItems(rb.buildRecommender(m).recommend(1, 1));
// 算法得分
// RandomUtils.useTestSeed();
RecommenderEvaluator e = new AverageAbsoluteDifferenceRecommenderEvaluator();
DataModelBuilder db = MyDataModel.createNoPrefDataModelBuilder();
double score = e.evaluate(rb, db, m, 0.7, 1.0); // testing data, all data
System.out.println("算法得分: " + score);
System.out.println();
}