本文整理汇总了Java中org.apache.mahout.cf.taste.eval.RecommenderBuilder类的典型用法代码示例。如果您正苦于以下问题：Java RecommenderBuilder类的具体用法？Java RecommenderBuilder怎么用？Java RecommenderBuilder使用的例子？那么, 这里精选的类代码示例或许可以为您提供帮助。
RecommenderBuilder类属于org.apache.mahout.cf.taste.eval包，在下文中一共展示了RecommenderBuilder类的15个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的Java代码示例。
示例1: main
import org.apache.mahout.cf.taste.eval.RecommenderBuilder; //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());
}
示例2: main
import org.apache.mahout.cf.taste.eval.RecommenderBuilder; //导入依赖的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);
}
}
示例3: testBuildDefaultRecommender
import org.apache.mahout.cf.taste.eval.RecommenderBuilder; //导入依赖的package包/类
@Test
public void testBuildDefaultRecommender() {
RecommenderBuilder rb = new GenericRecommenderBuilder();
FastByIDMap<PreferenceArray> userData = new FastByIDMap<PreferenceArray>();
userData.put(1, new GenericUserPreferenceArray(Arrays.asList(new GenericPreference(1, 1, 1),
new GenericPreference(1, 2, 1), new GenericPreference(1, 3, 1))));
userData.put(2, new GenericUserPreferenceArray(Arrays.asList(new GenericPreference(2, 1, 1),
new GenericPreference(2, 2, 1), new GenericPreference(2, 4, 1))));
DataModel dm = new GenericDataModel(userData);
Recommender rec = null;
try {
rec = rb.buildRecommender(dm);
} catch (TasteException e) {
e.printStackTrace();
}
assertTrue(rec instanceof RandomRecommender);
}
示例4: evaluateRecommender
import org.apache.mahout.cf.taste.eval.RecommenderBuilder; //导入依赖的package包/类
public static void evaluateRecommender() throws Exception{
StringItemIdFileDataModel dataModel = loadFromFile("data/BX-Book-Ratings.csv",";");
RecommenderEvaluator evaluator = new AverageAbsoluteDifferenceRecommenderEvaluator();
RecommenderBuilder builder = new BookRecommender();
double result = evaluator.evaluate(builder, null, dataModel, 0.9, 1.0);
System.out.println(result);
}
开发者ID:PacktPublishing，项目名称:Machine-Learning-End-to-Endguide-for-Java-developers，代码行数:8，代码来源:BookRecommender.java
示例5: main
import org.apache.mahout.cf.taste.eval.RecommenderBuilder; //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);
}
示例6: evaluate
import org.apache.mahout.cf.taste.eval.RecommenderBuilder; //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
);
}
示例7: main
import org.apache.mahout.cf.taste.eval.RecommenderBuilder; //导入依赖的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);
}
}
示例8: userCF
import org.apache.mahout.cf.taste.eval.RecommenderBuilder; //导入依赖的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);
}
}
示例9: itemCF
import org.apache.mahout.cf.taste.eval.RecommenderBuilder; //导入依赖的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);
}
}
示例10: slopeOne
import org.apache.mahout.cf.taste.eval.RecommenderBuilder; //导入依赖的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);
}
}
示例11: itemKNN
import org.apache.mahout.cf.taste.eval.RecommenderBuilder; //导入依赖的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);
}
}
示例12: svd
import org.apache.mahout.cf.taste.eval.RecommenderBuilder; //导入依赖的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);
}
}
示例13: treeCluster
import org.apache.mahout.cf.taste.eval.RecommenderBuilder; //导入依赖的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);
}
}
示例14: userLoglikelihood
import org.apache.mahout.cf.taste.eval.RecommenderBuilder; //导入依赖的package包/类
/**
* UserCF1
* 1、首先我们需要传递数据模型,然后去构建用户的相似度
* 2、找到用户的近邻
* 3、构建推荐的Builder
* 4、对结果进行评分
* [其他的基本上也是一样]
*/
public static RecommenderBuilder userLoglikelihood(DataModel dataModel) throws TasteException, IOException {
System.out.println("userLoglikelihood");
UserSimilarity userSimilarity = RecommendFactory.userSimilarity(RecommendFactory.SIMILARITY.LOGLIKELIHOOD, dataModel);
UserNeighborhood userNeighborhood = RecommendFactory.userNeighborhood(RecommendFactory.NEIGHBORHOOD.NEAREST, userSimilarity, dataModel, NEIGHBORHOOD_NUM);
RecommenderBuilder recommenderBuilder = RecommendFactory.userRecommender(userSimilarity, userNeighborhood, false);
RecommendFactory.evaluate(RecommendFactory.EVALUATOR.AVERAGE_ABSOLUTE_DIFFERENCE, recommenderBuilder, null, dataModel, 0.7);
RecommendFactory.statsEvaluator(recommenderBuilder, null, dataModel, 2);
return recommenderBuilder;
}
示例15: userCityBlock
import org.apache.mahout.cf.taste.eval.RecommenderBuilder; //导入依赖的package包/类
/**
* Recommender IR Evaluator: [Precision:0.919580419580419,Recall:0.4371584699453552]
*/
public static RecommenderBuilder userCityBlock(DataModel dataModel) throws TasteException, IOException {
System.out.println("userCityBlock");
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, false);
RecommendFactory.evaluate(RecommendFactory.EVALUATOR.AVERAGE_ABSOLUTE_DIFFERENCE, recommenderBuilder, null, dataModel, 0.7);
RecommendFactory.statsEvaluator(recommenderBuilder, null, dataModel, 2);
return recommenderBuilder;
}