/* 

 * 查准率和查全率评估的配置与运行 

 *  

 * */  

package byuser;  

  

import java.io.File;  

  

import org.apache.mahout.cf.taste.common.TasteException;  

import org.apache.mahout.cf.taste.eval.IRStatistics;  

import org.apache.mahout.cf.taste.eval.RecommenderBuilder;  

import org.apache.mahout.cf.taste.eval.RecommenderIRStatsEvaluator;  

import org.apache.mahout.cf.taste.impl.eval.GenericRecommenderIRStatsEvaluator;  

import org.apache.mahout.cf.taste.impl.model.file.FileDataModel;  

import org.apache.mahout.cf.taste.impl.neighborhood.NearestNUserNeighborhood;  

import org.apache.mahout.cf.taste.impl.recommender.GenericUserBasedRecommender;  

import org.apache.mahout.cf.taste.impl.similarity.PearsonCorrelationSimilarity;  

import org.apache.mahout.cf.taste.model.DataModel;  

import org.apache.mahout.cf.taste.neighborhood.UserNeighborhood;  

import org.apache.mahout.cf.taste.recommender.Recommender;  

import org.apache.mahout.cf.taste.similarity.UserSimilarity;  

import org.apache.mahout.common.RandomUtils;  

  

  

public class RecommenderIRStatsEvaluatorTest {  

  

    public RecommenderIRStatsEvaluatorTest(){  

        try{  

            RandomUtils.useTestSeed();  

            DataModel model = new FileDataModel(new File("E:\\mahout项目\\examples\\intro.csv"));  

            RecommenderIRStatsEvaluator evaluator = new GenericRecommenderIRStatsEvaluator();  

            RecommenderBuilder recommenderBuilder = new RecommenderBuilder() {  

                @Override  

                public Recommender buildRecommender(DataModel model) throws TasteException {  

                    // TODO Auto-generated method stub  

                    UserSimilarity similarity = new PearsonCorrelationSimilarity(model);  

                    UserNeighborhood neighborhood = new NearestNUserNeighborhood(2, similarity, model);  

                    return new GenericUserBasedRecommender(model, neighborhood, similarity);  

                }  

            };  

            //推荐两个数据  

            IRStatistics stats = evaluator.evaluate(recommenderBuilder, null, model, null, 2, GenericRecommenderIRStatsEvaluator.CHOOSE_THRESHOLD, 1.0);  

            System.out.println("推荐两个结果的查准率 ：" + stats.getPrecision());  

            System.out.println("推荐两个结果的查全率 ：" + stats.getRecall());  

        }catch(Exception e){  

            e.printStackTrace();  

        }  

    }  

    public static void main(String[] args) {  

        // TODO Auto-generated method stub  

        RecommenderIRStatsEvaluatorTest test = new RecommenderIRStatsEvaluatorTest();  

    }  

  

}  

结果：