content: /*
   * Licensed to the Apache Software Foundation (ASF) under one or more
   * contributor license agreements.  See the NOTICE file distributed with
   * this work for additional information regarding copyright ownership.
   * The ASF licenses this file to You under the Apache License, Version 2.0
   * (the "License"); you may not use this file except in compliance with
   * the License.  You may obtain a copy of the License at
   *
   *    http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   */
  
  package org.apache.spark.examples.mllib;
  
  // $example on$
  import scala.Tuple2;
  
  import org.apache.spark.api.java.*;
  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 org.apache.spark.SparkConf;
  // $example off$
  
  public class JavaRecommendationExample {
    public static void main(String[] args) {
      // $example on$
      SparkConf conf = new SparkConf().setAppName("Java Collaborative Filtering Example");
      JavaSparkContext jsc = new JavaSparkContext(conf);
  
      // Load and parse the data
      String path = "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]));
          }
        }
      );
  
      // Build the recommendation model using ALS
      int rank = 10;
      int numIterations = 10;
      MatrixFactorizationModel model = ALS.train(JavaRDD.toRDD(ratings), rank, numIterations, 0.01);
  
      // Evaluate the model on rating data
      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<>(new Tuple2<>(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<>(new Tuple2<>(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);
  
      // Save and load model
      model.save(jsc.sc(), "target/tmp/myCollaborativeFilter");
      MatrixFactorizationModel sameModel = MatrixFactorizationModel.load(jsc.sc(),
        "target/tmp/myCollaborativeFilter");
      // $example off$
  
      jsc.stop();
    }
  }
  