推薦系統-02-評價技術
阿新 • • 發佈:2018-05-16
del nts all AI try absolute out gen new
下面簡單通過在測試集上驗證錯誤值 (JAVA)
package xyz.pl8.evaluatorintro; import org.apache.mahout.cf.taste.common.TasteException; import org.apache.mahout.cf.taste.eval.RecommenderBuilder; import org.apache.mahout.cf.taste.eval.RecommenderEvaluator; import org.apache.mahout.cf.taste.impl.eval.AverageAbsoluteDifferenceRecommenderEvaluator; import org.apache.mahout.cf.taste.impl.eval.RMSRecommenderEvaluator; 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 java.io.File; public class EvaluatorIntro { public static void main(String[] args){ try{ DataModel model = new FileDataModel(new File("/home/hadoop/ua.base")); System.out.println(model); Recommender recommender = null; RecommenderBuilder recommenderBuilder = new RecommenderBuilder() { public Recommender buildRecommender(DataModel model) throws TasteException { UserSimilarity similarity = new PearsonCorrelationSimilarity(model); UserNeighborhood neighborhood = new NearestNUserNeighborhood(100, similarity, model ); return new GenericUserBasedRecommender(model, neighborhood, similarity); } }; RecommenderEvaluator avgEvaluator = new AverageAbsoluteDifferenceRecommenderEvaluator(); RecommenderEvaluator rmsEvaluator = new RMSRecommenderEvaluator(); // 平均絕對值誤差 double avgScore = avgEvaluator.evaluate(recommenderBuilder, null, model, 0.7, 1.0); // 根方差錯誤 double rmsScore = rmsEvaluator.evaluate(recommenderBuilder, null, model, 0.7, 1.0); System.out.println(avgScore); System.out.println(rmsScore); }catch (Exception e){ e.printStackTrace(); } } }
以下是通過信息檢索, 進行多維度的評價模型的優劣度(java)
package xyz.pl8.irevaluatorintro; 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.RecommenderEvaluator; import org.apache.mahout.cf.taste.eval.RecommenderIRStatsEvaluator; import org.apache.mahout.cf.taste.impl.eval.AverageAbsoluteDifferenceRecommenderEvaluator; import org.apache.mahout.cf.taste.impl.eval.GenericRecommenderIRStatsEvaluator; import org.apache.mahout.cf.taste.impl.eval.RMSRecommenderEvaluator; 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 java.io.File; public class IREvaluatorIntro { public static void main(String[] args){ try{ DataModel model = new FileDataModel(new File("/home/hadoop/ua.base")); Recommender recommender = null; RecommenderBuilder recommenderBuilder = new RecommenderBuilder() { public Recommender buildRecommender(DataModel model) throws TasteException { UserSimilarity similarity = new PearsonCorrelationSimilarity(model); UserNeighborhood neighborhood = new NearestNUserNeighborhood(100, similarity, model ); return new GenericUserBasedRecommender(model, neighborhood, similarity); } }; // 構造信息檢索評估器 RecommenderIRStatsEvaluator evaluator = new GenericRecommenderIRStatsEvaluator(); // 進行評估 IRStatistics stats = evaluator.evaluate(recommenderBuilder, null, model, null, 5, 0.7, 1.0); // 輸出精確度,召回率, F1測度 System.out.println(stats.getPrecision()); System.out.println(stats.getRecall()); System.out.println(stats.getF1Measure()); }catch (Exception e){ e.printStackTrace(); } } }
推薦系統-02-評價技術