Java DataModel.getUserIDs方法代码示例

import org.apache.mahout.cf.taste.model.DataModel; //导入方法依赖的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.model.DataModel; //导入方法依赖的package包/类
public static void main(String[] args) throws TasteException, IOException {
    String file = "other/testdata/pv.csv";
    DataModel dataModel = RecommendFactory.buildDataModelNoPref(file);
    //基于UserCF城市街区距离（曼哈顿）算法
    RecommenderBuilder rb1 = RecommenderEvaluator.userCityBlock(dataModel);
    //基于ItemCF对数自然相似度算法
    RecommenderBuilder rb2 = RecommenderEvaluator.itemCityBlock(dataModel);

    LongPrimitiveIterator iter = dataModel.getUserIDs();
    while (iter.hasNext()) {
        long uid = iter.nextLong();
        System.out.print("userCityBlock  =>");
        result(uid, rb1, dataModel);
        System.out.print("itemLoglikelihood=>");
        result(uid, rb2, dataModel);
    }
}
 

import org.apache.mahout.cf.taste.model.DataModel; //导入方法依赖的package包/类
public static void displayRecommendation(
		List<RecommendedItem> recommendations, DataModel dataModel)
		throws TasteException {

	for (LongPrimitiveIterator users = dataModel.getUserIDs(); users
			.hasNext();) {
		long userId = users.nextLong();
		// List<RecommendedItem> recommendations =
		// recommender.recommend(userId, 5);

		for (RecommendedItem recommendation : recommendations) {
			System.out.println(userId + "," + recommendation.getItemID()
					+ "," + recommendation.getValue());
		}
	}
}
 

import org.apache.mahout.cf.taste.model.DataModel; //导入方法依赖的package包/类
public static void main(String[] args) throws TasteException, IOException {
    String file = "other/testdata/pv.csv";
    DataModel dataModel = RecommendFactory.buildDataModelNoPref(file);
    RecommenderBuilder rb1 = RecommenderEvaluator.userCityBlock(dataModel);
    RecommenderBuilder rb2 = RecommenderEvaluator.itemLoglikelihood(dataModel);

    LongPrimitiveIterator iter = dataModel.getUserIDs();
    while (iter.hasNext()) {
        long uid = iter.nextLong();
        System.out.print("userCityBlock    =>");
        filterOutdate(uid, rb1, dataModel);
        System.out.print("itemLoglikelihood=>");
        filterOutdate(uid, rb2, dataModel);
    }
}
 

import org.apache.mahout.cf.taste.model.DataModel; //导入方法依赖的package包/类
public static void userCF(DataModel dataModel) throws TasteException {
    UserSimilarity userSimilarity = RecommendFactory.userSimilarity(RecommendFactory.SIMILARITY.CITYBLOCK, dataModel);
    UserNeighborhood userNeighborhood = RecommendFactory.userNeighborhood(RecommendFactory.NEIGHBORHOOD.NEAREST, userSimilarity, dataModel, NEIGHBORHOOD_NUM);
    RecommenderBuilder recommenderBuilder = RecommendFactory.userRecommender(userSimilarity, userNeighborhood, true);

    RecommendFactory.evaluate(RecommendFactory.EVALUATOR.AVERAGE_ABSOLUTE_DIFFERENCE, recommenderBuilder, null, dataModel, 0.7);
    RecommendFactory.statsEvaluator(recommenderBuilder, null, dataModel, 2);

    LongPrimitiveIterator iter = dataModel.getUserIDs();
    while (iter.hasNext()) {
        long uid = iter.nextLong();
        List<RecommendedItem> list = recommenderBuilder.buildRecommender(dataModel).recommend(uid, RECOMMENDER_NUM);
        RecommendFactory.showItems(uid, list, true);
    }
}
 

import org.apache.mahout.cf.taste.model.DataModel; //导入方法依赖的package包/类
public static void itemCF(DataModel dataModel) throws TasteException {
    ItemSimilarity itemSimilarity = RecommendFactory.itemSimilarity(RecommendFactory.SIMILARITY.CITYBLOCK, dataModel);
    RecommenderBuilder recommenderBuilder = RecommendFactory.itemRecommender(itemSimilarity, true);
    RecommendFactory.evaluate(RecommendFactory.EVALUATOR.AVERAGE_ABSOLUTE_DIFFERENCE, recommenderBuilder, null, dataModel, 0.7);
    RecommendFactory.statsEvaluator(recommenderBuilder, null, dataModel, 2);
    LongPrimitiveIterator iter = dataModel.getUserIDs();
    while (iter.hasNext()) {
        long uid = iter.nextLong();
        List<RecommendedItem> list = recommenderBuilder.buildRecommender(dataModel).recommend(uid, RECOMMENDER_NUM);
        RecommendFactory.showItems(uid, list, true);
    }
}
 

import org.apache.mahout.cf.taste.model.DataModel; //导入方法依赖的package包/类
public static void slopeOne(DataModel dataModel) throws TasteException {
    RecommenderBuilder recommenderBuilder = RecommendFactory.slopeOneRecommender();

    RecommendFactory.evaluate(RecommendFactory.EVALUATOR.AVERAGE_ABSOLUTE_DIFFERENCE, recommenderBuilder, null, dataModel, 0.7);
    RecommendFactory.statsEvaluator(recommenderBuilder, null, dataModel, 2);

    LongPrimitiveIterator iter = dataModel.getUserIDs();
    while (iter.hasNext()) {
        long uid = iter.nextLong();
        List<RecommendedItem> list = recommenderBuilder.buildRecommender(dataModel).recommend(uid, RECOMMENDER_NUM);
        RecommendFactory.showItems(uid, list, true);
    }
}
 

import org.apache.mahout.cf.taste.model.DataModel; //导入方法依赖的package包/类
public static void itemKNN(DataModel dataModel) throws TasteException {
    ItemSimilarity itemSimilarity = RecommendFactory.itemSimilarity(RecommendFactory.SIMILARITY.EUCLIDEAN, dataModel);
    RecommenderBuilder recommenderBuilder = RecommendFactory.itemKNNRecommender(itemSimilarity, new NonNegativeQuadraticOptimizer(), 10);

    RecommendFactory.evaluate(RecommendFactory.EVALUATOR.AVERAGE_ABSOLUTE_DIFFERENCE, recommenderBuilder, null, dataModel, 0.7);
    RecommendFactory.statsEvaluator(recommenderBuilder, null, dataModel, 2);

    LongPrimitiveIterator iter = dataModel.getUserIDs();
    while (iter.hasNext()) {
        long uid = iter.nextLong();
        List<RecommendedItem> list = recommenderBuilder.buildRecommender(dataModel).recommend(uid, RECOMMENDER_NUM);
        RecommendFactory.showItems(uid, list, true);
    }
}
 

import org.apache.mahout.cf.taste.model.DataModel; //导入方法依赖的package包/类
public static void svd(DataModel dataModel) throws TasteException {
    RecommenderBuilder recommenderBuilder = RecommendFactory.svdRecommender(new ALSWRFactorizer(dataModel, 10, 0.05, 10));

    RecommendFactory.evaluate(RecommendFactory.EVALUATOR.AVERAGE_ABSOLUTE_DIFFERENCE, recommenderBuilder, null, dataModel, 0.7);
    RecommendFactory.statsEvaluator(recommenderBuilder, null, dataModel, 2);

    LongPrimitiveIterator iter = dataModel.getUserIDs();
    while (iter.hasNext()) {
        long uid = iter.nextLong();
        List<RecommendedItem> list = recommenderBuilder.buildRecommender(dataModel).recommend(uid, RECOMMENDER_NUM);
        RecommendFactory.showItems(uid, list, true);
    }
}
 

import org.apache.mahout.cf.taste.model.DataModel; //导入方法依赖的package包/类
public static void treeCluster(DataModel dataModel) throws TasteException {
    UserSimilarity userSimilarity = RecommendFactory.userSimilarity(RecommendFactory.SIMILARITY.LOGLIKELIHOOD, dataModel);
    ClusterSimilarity clusterSimilarity = RecommendFactory.clusterSimilarity(RecommendFactory.SIMILARITY.FARTHEST_NEIGHBOR_CLUSTER, userSimilarity);
    RecommenderBuilder recommenderBuilder = RecommendFactory.treeClusterRecommender(clusterSimilarity, 10);

    RecommendFactory.evaluate(RecommendFactory.EVALUATOR.AVERAGE_ABSOLUTE_DIFFERENCE, recommenderBuilder, null, dataModel, 0.7);
    RecommendFactory.statsEvaluator(recommenderBuilder, null, dataModel, 2);

    LongPrimitiveIterator iter = dataModel.getUserIDs();
    while (iter.hasNext()) {
        long uid = iter.nextLong();
        List<RecommendedItem> list = recommenderBuilder.buildRecommender(dataModel).recommend(uid, RECOMMENDER_NUM);
        RecommendFactory.showItems(uid, list, true);
    }
}
 

import org.apache.mahout.cf.taste.model.DataModel; //导入方法依赖的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.model.DataModel; //导入方法依赖的package包/类
private static void runALSSVDRecommender(DataModel dataModel)
		throws TasteException {

	System.out.println("Start of Running an ALS SVD Recommendation");
	RecommenderBuilder recommenderBuilder = EEGVideoRecommender.buildSVDRecommender();

	SVDRecommender recommender = (SVDRecommender) recommenderBuilder
			.buildRecommender(dataModel);

	RunningAverage runningAverage = new FullRunningAverage();

	LongPrimitiveIterator userIDs = dataModel.getUserIDs();

	while (userIDs.hasNext()) {
		long userID = userIDs.nextLong();

		for (Preference pref : dataModel.getPreferencesFromUser(userID)) {

			double ratingValue = pref.getValue();
			double preferenceEstimate = recommender.estimatePreference(
					userID, pref.getItemID());

			System.out.println(userID + "," + pref.getItemID() + ","
					+ ratingValue);
			double errorValue = ratingValue - preferenceEstimate;
			runningAverage.addDatum(errorValue * errorValue);
		}
	}

	double rmse = Math.sqrt(runningAverage.getAverage());
	System.out.println(rmse);

	// Recommender Evaluation -- Average Absolute Difference Evaluator
	RecommenderEvaluator absoluteDifferenceEvaluator = new AverageAbsoluteDifferenceRecommenderEvaluator();
	double score = absoluteDifferenceEvaluator.evaluate(recommenderBuilder,
			null, dataModel, 0.9, 1.0);
	System.out.println("ALS-based Recommender Average Score is: " + score);

	// Recommender Evaluation -- RMS Evaluator
	RecommenderEvaluator rmsEvaluator = new RMSRecommenderEvaluator();
	double rmsscore = rmsEvaluator.evaluate(recommenderBuilder, null,
			dataModel, 0.9, 1.0);
	System.out.println("ALS-based Recommender RMS Score is:" + rmsscore);

	// Recommender Evaluation -- IRStats Evaluator
	RecommenderIRStatsEvaluator irStatsEvaluator = new GenericRecommenderIRStatsEvaluator();
	IRStatistics stats = irStatsEvaluator.evaluate(recommenderBuilder,
			null, dataModel, null, 2,
			GenericRecommenderIRStatsEvaluator.CHOOSE_THRESHOLD, 1.0);

	System.out.println("Precision Valus is : " + stats.getPrecision());
	System.out.println("Recall Value is : " + stats.getRecall());

	System.out.println("End of Running an ALS SVD Recommendation");
}
 

import org.apache.mahout.cf.taste.model.DataModel; //导入方法依赖的package包/类
private static void runUserBasedRecommender(DataModel dataModel)
		throws TasteException {

	UserSimilarity userSimilarity = RecommParametersMeasures
			.getLogLikelihoodSimilarity(dataModel);

	UserNeighborhood neighborhood = RecommParametersMeasures.getThreshold(
			dataModel, userSimilarity, 0.1);

	RecommenderBuilder recommenderBuilder = EEGVideoRecommender.userBuilder(
			userSimilarity, neighborhood);

	for (LongPrimitiveIterator users = dataModel.getUserIDs(); users
			.hasNext();) {
		long userId = users.nextLong();

		List<RecommendedItem> recommendations = recommenderBuilder
				.buildRecommender(dataModel).recommend(userId, 1);

		for (RecommendedItem recommendation : recommendations) {
			System.out.println(userId + "," + recommendation.getItemID()
					+ "," + recommendation.getValue());
		}

	}

	// Recommender Evaluation -- Average Absolute Difference Evaluator
	RecommenderEvaluator absoluteDifferenceEvaluator = new AverageAbsoluteDifferenceRecommenderEvaluator();
	double score = absoluteDifferenceEvaluator.evaluate(recommenderBuilder,
			null, dataModel, 0.9, 1.0);
	System.out.println("User-based Recommender Average Score is: " + score);

	// Recommender Evaluation -- RMS Evaluator
	RecommenderEvaluator rmsEvaluator = new RMSRecommenderEvaluator();
	double rmsscore = rmsEvaluator.evaluate(recommenderBuilder, null,
			dataModel, 0.7, 0.3);
	System.out.println("User-based Recommende RMS Score is:" + rmsscore);

	// Recommender Evaluation -- IRStats Evaluator
	RecommenderIRStatsEvaluator irStatsEvaluator = new GenericRecommenderIRStatsEvaluator();
	IRStatistics stats = irStatsEvaluator.evaluate(recommenderBuilder,
			null, dataModel, null, 1,
			GenericRecommenderIRStatsEvaluator.CHOOSE_THRESHOLD, 1);
	System.out.println("Precision Valus is : " + stats.getPrecision());
	System.out.println("Recall Value is : " + stats.getRecall());

}