Java FileDataModel类代码示例

import org.apache.mahout.cf.taste.impl.model.file.FileDataModel; //导入依赖的package包/类
public void buildRecommend(String taskName) {
    String itemmodelsPath = RecommendConfig.class.getResource("/").getPath() + "itemmodels.csv";
    HadoopUtil.download(taskName, itemmodelsPath, true);
    try {
        DataModel dataModel = new FileDataModel(new File(itemmodelsPath));
        UserSimilarity similarity = new SpearmanCorrelationSimilarity(dataModel);
        UserNeighborhood userNeighborhood = new ThresholdUserNeighborhood(0.1, similarity, dataModel);
        LongPrimitiveIterator userIDs = dataModel.getUserIDs();
        while (userIDs.hasNext()) {
            Long userID = userIDs.nextLong();
            long[] neighborhoods = userNeighborhood.getUserNeighborhood(userID);
            for (long neighborhood : neighborhoods) {
                double userSimilarity = similarity.userSimilarity(userID, neighborhood);
                System.out.printf("(%s,%s,%f)", userID, neighborhood, userSimilarity);
                System.out.println();
            }
        }
    } catch (TasteException | IOException e) {
        log.error(e);
    }
}
 

import org.apache.mahout.cf.taste.impl.model.file.FileDataModel; //导入依赖的package包/类
public void IRState(String taskName) {
    String itemmodelsPath = RecommendConfig.class.getResource("/").getPath() + "itemmodels.csv";
    HadoopUtil.download(taskName, itemmodelsPath, false);
    try {
        DataModel fileDataModel = new FileDataModel(new File(itemmodelsPath));
        RecommenderIRStatsEvaluator irStatsEvaluator = new GenericRecommenderIRStatsEvaluator();
        IRStatistics irStatistics = irStatsEvaluator.evaluate(new RecommenderBuilder() {
            @Override
            public org.apache.mahout.cf.taste.recommender.Recommender buildRecommender(final DataModel dataModel) throws TasteException {
                UserSimilarity userSimilarity = new PearsonCorrelationSimilarity(dataModel);
                UserNeighborhood userNeighborhood = new NearestNUserNeighborhood(5, userSimilarity, dataModel);
                return new GenericUserBasedRecommender(dataModel, userNeighborhood, userSimilarity);
            }
        }, new DataModelBuilder() {
            @Override
            public DataModel buildDataModel(final FastByIDMap<PreferenceArray> fastByIDMap) {
                return new GenericDataModel(fastByIDMap);
            }
        }, fileDataModel, null, 5, GenericRecommenderIRStatsEvaluator.CHOOSE_THRESHOLD, 1.0);
        System.out.println("查准率:" + irStatistics.getPrecision());
        System.out.println("查全率:" + irStatistics.getRecall());
    } catch (TasteException | IOException e) {
        e.printStackTrace();
    }
}
 

import org.apache.mahout.cf.taste.impl.model.file.FileDataModel; //导入依赖的package包/类
public static void main(String[] args) throws IOException, TasteException {
	DataModel model  = 
			new FileDataModel(new File("data/ua.base"));
	
	UserSimilarity similarity = 
			new PearsonCorrelationSimilarity(model);
	
	UserNeighborhood neighborhood = 
			new NearestNUserNeighborhood(2, similarity, model);
	
	Recommender recommender = new GenericUserBasedRecommender(
			model, neighborhood, similarity);
	
	List<RecommendedItem> recommendations = recommender.recommend(2, 1);
	
	for (RecommendedItem recommendation : recommendations) {
		logger.info(recommendation.toString());
	}
	
	logger.info("over");
}
 

import org.apache.mahout.cf.taste.impl.model.file.FileDataModel; //导入依赖的package包/类
private static void recommend(String ratingsFile, int ... userIds)
    throws TasteException, IOException {
  DataModel model = new FileDataModel(new File(ratingsFile));

  UserSimilarity similarity = new PearsonCorrelationSimilarity(model);

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

  Recommender recommender =  new GenericUserBasedRecommender(
      model, neighborhood, similarity);

  Recommender cachingRecommender = new CachingRecommender(recommender);

  for(int userId: userIds) {
    System.out.println("UserID " + userId);
    List<RecommendedItem> recommendations =
        cachingRecommender.recommend(userId, 2);
    for(RecommendedItem item: recommendations) {
      System.out.println("  item " + item.getItemID() + " score " + item.getValue());
    }
  }
}
 

import org.apache.mahout.cf.taste.impl.model.file.FileDataModel; //导入依赖的package包/类
public static void main(String[] args) throws TasteException, IOException {
	DataModel model = new FileDataModel(new File("data/dataset.csv"));
   	ItemSimilarity similarity = new LogLikelihoodSimilarity(model);
   	//UserNeighborhood neighborhood = new ThresholdUserNeighborhood(0.1, similarity, model);
   	GenericItemBasedRecommender recommender = new GenericItemBasedRecommender(model, similarity);
   	List<RecommendedItem> recommendations = recommender.mostSimilarItems(18, 3);
   	for (RecommendedItem recommendation : recommendations) {
   	  System.out.println(recommendation);
   	}

}
 

import org.apache.mahout.cf.taste.impl.model.file.FileDataModel; //导入依赖的package包/类
public static void main(String[] args) throws IOException, TasteException {
	RandomUtils.useTestSeed();
	
	final DataModel model = new FileDataModel(new File("data/intro.csv"));
	
	RecommenderIRStatsEvaluator evaluator = 
			new GenericRecommenderIRStatsEvaluator();
	
	RecommenderBuilder recommenderBuilder = new RecommenderBuilder() {
		
		public Recommender buildRecommender(DataModel dataModel) throws TasteException {
			UserSimilarity similarity = new PearsonCorrelationSimilarity(model);
			UserNeighborhood neighborhood = 
					new NearestNUserNeighborhood(2, similarity, model);
			return new GenericUserBasedRecommender(model, neighborhood, similarity);
		}
	};
	
	IRStatistics stats = evaluator.evaluate(recommenderBuilder, null, model, null, 2, 
			GenericRecommenderIRStatsEvaluator.CHOOSE_THRESHOLD, 1.0);
	
	System.out.println(stats.getPrecision());
	System.out.println(stats.getRecall());
}
 

import org.apache.mahout.cf.taste.impl.model.file.FileDataModel; //导入依赖的package包/类
private void generate() throws IOException, TasteException {
    final DataModel dataModel = new FileDataModel(FileUtils.getFile(this.getFile()));

    EvalItemVO evalItemVO = new EvalItemVO(2);
    evalItemVO.evalMap.put(EvalName.MAE, new EvaluationVO(0.7, 1.0));
    evalItemVO.evalMap.put(EvalName.RMS, new EvaluationVO(0.7, 1.0));
    AbstractAnalyzer eval = new Evaluator(dataModel, evalItemVO);
    eval.analyze();

    UserItemVO userItemVO = new UserItemVO();
    userItemVO.userMap.put(UserName.PEARSON, new UserAffinityVO(2, 6, 1));
    userItemVO.userMap.put(UserName.EUCLIDEAN, new UserAffinityVO(2, 6, 1));
    userItemVO.userMap.put(UserName.COSINE, new UserAffinityVO(2, 6, 1));
    AbstractAnalyzer user = new User(dataModel, userItemVO);
    user.analyze();

    ItemVO itemVO = new ItemVO();
    itemVO.itemMap.put(ItemName.TANIMOTO, new ItemAffinityVO(1, 5));
    itemVO.itemMap.put(ItemName.CITY_BLOCK, new ItemAffinityVO(1, 5));
    itemVO.itemMap.put(ItemName.LOG_LIKE, new ItemAffinityVO(1, 5));
    itemVO.itemMap.put(ItemName.EUCLIDEAN, new ItemAffinityVO(1, 5));
    itemVO.itemMap.put(ItemName.COSINE, new ItemAffinityVO(1, 5));
    AbstractAnalyzer item = new Item(dataModel, itemVO);
    item.analyze();
}
 

import org.apache.mahout.cf.taste.impl.model.file.FileDataModel; //导入依赖的package包/类
public static void main( String[] args ) throws IOException, TasteException
{
	//user based recommender model
	DataModel model = new FileDataModel(new File("data/dataset.csv"));    	
	UserSimilarity similarity = new PearsonCorrelationSimilarity(model);
	UserNeighborhood neighborhood = new ThresholdUserNeighborhood(0.1, similarity, model);
	UserBasedRecommender recommender = new GenericUserBasedRecommender(model, neighborhood, similarity);
	List<RecommendedItem> recommendations = recommender.recommend(2, 3);
	for (RecommendedItem recommendation : recommendations) {
	  System.out.println(recommendation);
	}
}
 

import org.apache.mahout.cf.taste.impl.model.file.FileDataModel; //导入依赖的package包/类
public static void main(String[] args) throws TasteException, IOException {
	//MF recommender model
   	DataModel model = new FileDataModel(new File("data/recodataset.csv"));   
   	//ALSWRFactorizer factorizer = new ALSWRFactorizer(model, 50, 0.065, 15);
   	ParallelSGDFactorizer factorizer = new ParallelSGDFactorizer(model,10,0.1,1);
   	SVDRecommender recommender = new SVDRecommender(model, factorizer);    	
   	
   	List<RecommendedItem> recommendations = recommender.recommend(2, 3);
   	for (RecommendedItem recommendation : recommendations) {
   	  System.out.println(recommendation);
   	}

}
 

import org.apache.mahout.cf.taste.impl.model.file.FileDataModel; //导入依赖的package包/类
public static void main(String[] args) throws IOException, TasteException {
	RandomUtils.useTestSeed();
	
	final DataModel model = new FileDataModel(new File("data/ua.base"));
	
	RecommenderEvaluator evaluator = new AverageAbsoluteDifferenceRecommenderEvaluator();
	
	RecommenderBuilder builder = new RecommenderBuilder() {
		
		public Recommender buildRecommender(DataModel dataModel) throws TasteException {
			UserSimilarity similarity = new PearsonCorrelationSimilarity(model);
			UserNeighborhood neighborhood = 
					new NearestNUserNeighborhood(2, similarity, model);
			return new GenericUserBasedRecommender(model, neighborhood, similarity);
		}
	};
	
	double score = evaluator.evaluate(builder, null, model, 0.7, 1.0);
	
	System.out.println(score);
}
 

import org.apache.mahout.cf.taste.impl.model.file.FileDataModel; //导入依赖的package包/类
public static void evaluate(String ratingsFile)
    throws TasteException, IOException {
  DataModel model = new FileDataModel(new File(ratingsFile));
  RecommenderEvaluator evaluator =
      new AverageAbsoluteDifferenceRecommenderEvaluator();
  RecommenderBuilder recommenderBuilder = new MyRecommendBuilder();
  evaluator.evaluate(
      recommenderBuilder,
      null,
      model,
      0.95,
      0.05
  );
}
 

import org.apache.mahout.cf.taste.impl.model.file.FileDataModel; //导入依赖的package包/类
public static void main( String[] args ) throws IOException, TasteException, OptionException
{
    CreateCsvRatingsFile();


    // create data source (model) - from the csv file
    File ratingsFile = new File(outputFile);
    DataModel model = new FileDataModel(ratingsFile);

    // create a simple recommender on our data
    CachingRecommender cachingRecommender = new CachingRecommender(new SlopeOneRecommender(model));

    // for all users
    for (LongPrimitiveIterator it = model.getUserIDs(); it.hasNext();){
        long userId = it.nextLong();

        // get the recommendations for the user
        List<RecommendedItem> recommendations = cachingRecommender.recommend(userId, 10);

        // if empty write something
        if (recommendations.size() == 0){
            System.out.print("User ");
            System.out.print(userId);
            System.out.println(": no recommendations");
        }

        // print the list of recommendations for each
        for (RecommendedItem recommendedItem : recommendations) {
            System.out.print("User ");
            System.out.print(userId);
            System.out.print(": ");
            System.out.println(recommendedItem);
        }
    }
}
 

import org.apache.mahout.cf.taste.impl.model.file.FileDataModel; //导入依赖的package包/类
public List<RecommendedItem> recommend(int userid,int size) throws TasteException, IOException{
		List<RecommendedItem> list = null;
//		MovieDataModel model = new MovieDataModel();
		String file=ServletActionContext.getServletContext().getRealPath("/u1.base");
		DataModel model = new FileDataModel(new File(file));
		Recommender recommender = new CachingRecommender(new SlopeOneRecommender(model));
		list = recommender.recommend(userid, size);
		return list;
	}
 

import org.apache.mahout.cf.taste.impl.model.file.FileDataModel; //导入依赖的package包/类
public List<RecommendedItem> recommend(int userid,int size) throws TasteException, IOException{
		List<RecommendedItem> list = null;
//		MovieDataModel model = new MovieDataModel();
		String file=ServletActionContext.getServletContext().getRealPath("/u1.base");
		DataModel model = new FileDataModel(new File(file));
		UserSimilarity similarity = new PearsonCorrelationSimilarity(model);
		 UserNeighborhood neighborhood = new NearestNUserNeighborhood(25,similarity, model);
		Recommender recommender = new GenericUserBasedRecommender(model, neighborhood, similarity);
		list = recommender.recommend(userid, size);
		return list;
	}
 

import org.apache.mahout.cf.taste.impl.model.file.FileDataModel; //导入依赖的package包/类
/**
	 * 
	 * @param userid	当前用户id
	 * @param size	推荐多少个
	 * @return	推荐列表
	 * @throws TasteException
	 * @throws IOException 
	 */
	public List<RecommendedItem> recommend(int userid,int size) throws TasteException, IOException{
		List<RecommendedItem> list = null;
//        JDBCDataModel model = new MySQLJDBCDataModel();
		String file=ServletActionContext.getServletContext().getRealPath("/u1.base");
		DataModel model = new FileDataModel(new File(file));
		ItemSimilarity similarity = new PearsonCorrelationSimilarity(model);
		Recommender recommender = new GenericItemBasedRecommender(model, similarity);
		list = recommender.recommend(userid, size);
		return list;
	}