Java IRStatistics类代码示例

import org.apache.mahout.cf.taste.eval.IRStatistics; //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.eval.IRStatistics; //导入依赖的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.eval.IRStatistics; //导入依赖的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());

}
 

import org.apache.mahout.cf.taste.eval.IRStatistics; //导入依赖的package包/类
private static void runItemBasedRecommender(DataModel dataModel)
		throws TasteException {

	TanimotoCoefficientSimilarity tanimotoSimilarity = new TanimotoCoefficientSimilarity(
			dataModel);

	GenericItemBasedRecommender recommender = new GenericItemBasedRecommender(
			dataModel, tanimotoSimilarity);

	RecommenderBuilder recommenderBuilder = EEGVideoRecommender
			.itemBuilder(tanimotoSimilarity);

	for (LongPrimitiveIterator items = dataModel.getItemIDs(); items
			.hasNext();) {
		long itemId = items.nextLong();
		List<RecommendedItem> recommendations = recommender
				.mostSimilarItems(itemId, 5);

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

	}

	// Recommender Evaluation -- Average Absolute Difference Evaluator
	RecommenderEvaluator absoluteDifferenceEvaluator = new AverageAbsoluteDifferenceRecommenderEvaluator();
	double score = absoluteDifferenceEvaluator.evaluate(recommenderBuilder,
			null, dataModel, 0.7, 0.3);
	System.out.println("Item-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("Item-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());

}
 

import org.apache.mahout.cf.taste.eval.IRStatistics; //导入依赖的package包/类
/****************************************** Running Demo for User-based Recommender ******************************************/
private static void runUserRecommender(DataModel dataModel, int n,
		double threshold, double training, double evaluation, int atValue,
		String similarity) throws TasteException {

	System.out.println("Start of Running a User-based Recommender, with:"
			+ similarity + " Similarity and training % = " + training
			+ " and evaluation % = " + evaluation);

	UserSimilarity userSimilarity = null;
	UserNeighborhood neighborhood;

	if (similarity.equals("pearson")) {
		userSimilarity = RecommParametersMeasures
				.getPearsonCorrelation(dataModel);

	} else if (similarity.equals("likelyhood")) {
		userSimilarity = RecommParametersMeasures
				.getLogLikelihoodSimilarity(dataModel);

	} else if (similarity.equals("tanimoto")) {
		userSimilarity = RecommParametersMeasures
				.getTanimotoCoefficientSimilarity(dataModel);
	} else if (similarity.equals("cityblock")) {
		userSimilarity = RecommParametersMeasures
				.getCityBlockSimilarity(dataModel);

	} else if (similarity.equals("ecludian")) {
		userSimilarity = RecommParametersMeasures
				.getEuclideanDistance(dataModel);
	} else if (similarity.equals("uncenteredcosine")) {
		userSimilarity = RecommParametersMeasures.getUncenteredCosine(dataModel);
	}

	if (n > 0) {
		System.out.println("N Size = " + n);
		neighborhood = RecommParametersMeasures.getNearestN(dataModel,
				userSimilarity, n);
	} else {
		System.out.println("Threshold value = " + threshold);

		neighborhood = RecommParametersMeasures.getThreshold(dataModel,
				userSimilarity, threshold);
	}

	RecommenderBuilder recommenderBuilder = EEGVideoRecommender.userBuilder(
			userSimilarity, neighborhood);
	recommenderBuilder.buildRecommender(dataModel).recommend(26, 1);

	// Recommender Evaluation -- Average Absolute Difference Evaluator
	RecommenderEvaluator absoluteDifferenceEvaluator = new AverageAbsoluteDifferenceRecommenderEvaluator();
	double score = absoluteDifferenceEvaluator.evaluate(recommenderBuilder,
			null, dataModel, training, evaluation);
	System.out.println("The Average Score for this recommender is: "
			+ score);

	// Recommender Evaluation -- RMS Evaluator
	RecommenderEvaluator rmsEvaluator = new RMSRecommenderEvaluator();
	double rmsscore = rmsEvaluator.evaluate(recommenderBuilder, null,
			dataModel, training, evaluation);
	System.out.println("The RMS Score for Pearson and threshold is:"
			+ rmsscore);

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