spark MLlib collaborativeFilltering学习

 package ML.collaborativeFilltering;

 import org.apache.spark.SparkConf;
 import org.apache.spark.api.java.JavaDoubleRDD;
 import org.apache.spark.api.java.JavaPairRDD;
 import org.apache.spark.api.java.JavaRDD;
 import org.apache.spark.api.java.JavaSparkContext;
 import org.apache.spark.api.java.function.Function;
 import org.apache.spark.mllib.recommendation.ALS;
 import org.apache.spark.mllib.recommendation.MatrixFactorizationModel;
 import org.apache.spark.mllib.recommendation.Rating;
 import scala.Tuple2;

 /**
  * TODO
  *
  * @ClassName: example
  * @author: DingH
  * @since: 2019/4/10 16:03
  */
 public class example {
     public static void main(String[] args) {
         SparkConf conf = new SparkConf().setAppName("Java Collaborative Filtering Example");
         JavaSparkContext jsc = new JavaSparkContext(conf);

         // Load and parse the data
         String path = "D:\\IdeaProjects\\SimpleApp\\src\\main\\resources\\data\\mllib\\als\\test.data";
         JavaRDD<String> data = jsc.textFile(path);
         JavaRDD<Rating> ratings = data.map(new Function<String, Rating>() {
             public Rating call(String s) {
                 String[] sarray = s.split(",");
                 return new Rating(Integer.parseInt(sarray[0]), Integer.parseInt(sarray[1]), Double.parseDouble(sarray[2]));
             }
           }
         );
         int ranks = 10;
         int numIterations = 10;
         MatrixFactorizationModel model = ALS.train(ratings.rdd(), ranks, numIterations);

         JavaRDD<Tuple2<Object, Object>> userProducts = ratings.map(new Function<Rating, Tuple2<Object, Object>>() {
             public Tuple2<Object, Object> call(Rating r) {
               return new Tuple2<Object, Object>(r.user(), r.product());
             }
           }
         );
         JavaPairRDD<Tuple2<Integer, Integer>, Double> predictions = JavaPairRDD.fromJavaRDD(model.predict(JavaRDD.toRDD(userProducts)).toJavaRDD().map(
             new Function<Rating, Tuple2<Tuple2<Integer, Integer>, Double>>() {
               public Tuple2<Tuple2<Integer, Integer>, Double> call(Rating r){
                 return new Tuple2<Tuple2<Integer, Integer>, Double>(
                   new Tuple2<Integer, Integer>(r.user(), r.product()), r.rating());
               }
             }
           ));

         JavaRDD<Tuple2<Double, Double>> ratesAndPreds = JavaPairRDD.fromJavaRDD(ratings.map(
             new Function<Rating, Tuple2<Tuple2<Integer, Integer>, Double>>() {
               public Tuple2<Tuple2<Integer, Integer>, Double> call(Rating r){
                 return new Tuple2<Tuple2<Integer, Integer>, Double>(
                   new Tuple2<Integer, Integer>(r.user(), r.product()), r.rating());
               }
             }
           )).join(predictions).values();

         double MSE = JavaDoubleRDD.fromRDD(ratesAndPreds.map(
           new Function<Tuple2<Double, Double>, Object>() {
             public Object call(Tuple2<Double, Double> pair) {
               Double err = pair._1() - pair._2();
               return err * err;
             }
           }
         ).rdd()).mean();

         System.out.println("Mean Squared Error = " + MSE);

     }
 }