public static void main(String[] args) throws Exception {
DataModel model =
new GenericBooleanPrefDataModel(
GenericBooleanPrefDataModel.toDataMap(new FileDataModel(new File("ua.base"))));
RecommenderIRStatsEvaluator evaluator = new GenericRecommenderIRStatsEvaluator();
RecommenderBuilder recommenderBuilder =
new RecommenderBuilder() {
@Override
public Recommender buildRecommender(DataModel model) throws TasteException {
UserSimilarity similarity = new LogLikelihoodSimilarity(model);
UserNeighborhood neighborhood = new NearestNUserNeighborhood(10, similarity, model);
return new GenericBooleanPrefUserBasedRecommender(model, neighborhood, similarity);
}
};
DataModelBuilder modelBuilder =
new DataModelBuilder() {
@Override
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());
}
/** statsEvaluator */
public static void statsEvaluator(
RecommenderBuilder rb, DataModelBuilder mb, DataModel m, int topn) throws TasteException {
RecommenderIRStatsEvaluator evaluator = new GenericRecommenderIRStatsEvaluator();
IRStatistics stats =
evaluator.evaluate(
rb, mb, m, null, topn, GenericRecommenderIRStatsEvaluator.CHOOSE_THRESHOLD, 1.0);
// System.out.printf("Recommender IR Evaluator: %s\n", stats);
System.out.printf(
"Recommender IR Evaluator: [Precision:%s,Recall:%s]\n",
stats.getPrecision(), stats.getRecall());
}
public static void prefEvaluate(DataModel m) throws Exception {
System.out.println("==============用户,打分,评估(1)==============");
// 推荐结果
UserSimilarity s = MySimilarity.getPearsonCorrelation(m);
UserNeighborhood n = MyNeighborhood.getNearestN(m, s, 2);
// 算法评估
RecommenderBuilder rb = MyRecommender.userBuilder(s, n);
RecommenderIRStatsEvaluator e = new GenericRecommenderIRStatsEvaluator();
IRStatistics stats =
e.evaluate(rb, null, m, null, 1, GenericRecommenderIRStatsEvaluator.CHOOSE_THRESHOLD, 1);
System.out.println("查准率 : " + stats.getPrecision());
System.out.println("全查率 : " + stats.getRecall());
System.out.println();
}
public static void noPrefEvaluate(DataModel m) throws Exception {
System.out.println("==============用户,无打分,评估(1)==============");
// 推荐结果
UserSimilarity s = new LogLikelihoodSimilarity(m);
UserNeighborhood n = MyNeighborhood.getNearestN(m, s, 2);
// 算法评估
RecommenderBuilder rb = MyRecommender.userBuilder(s, n);
RecommenderIRStatsEvaluator e = new GenericRecommenderIRStatsEvaluator();
DataModelBuilder db = MyDataModel.createNoPrefDataModelBuilder();
IRStatistics stats =
e.evaluate(rb, db, m, null, 1, GenericRecommenderIRStatsEvaluator.CHOOSE_THRESHOLD, 1.0);
System.out.println("查准率 : " + stats.getPrecision());
System.out.println("查全率 : " + stats.getRecall());
System.out.println();
}
