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.ml;
  
  import org.apache.spark.sql.Dataset;
  import org.apache.spark.sql.Row;
  import org.apache.spark.sql.SparkSession;
  
  // $example on$
  import java.io.Serializable;
  
  import org.apache.spark.api.java.JavaRDD;
  import org.apache.spark.api.java.function.Function;
  import org.apache.spark.ml.evaluation.RegressionEvaluator;
  import org.apache.spark.ml.recommendation.ALS;
  import org.apache.spark.ml.recommendation.ALSModel;
  // $example off$
  
  public class JavaALSExample {
  
    // $example on$
    public static class Rating implements Serializable {
      private int userId;
      private int movieId;
      private float rating;
      private long timestamp;
  
      public Rating() {}
  
      public Rating(int userId, int movieId, float rating, long timestamp) {
        this.userId = userId;
        this.movieId = movieId;
        this.rating = rating;
        this.timestamp = timestamp;
      }
  
      public int getUserId() {
        return userId;
      }
  
      public int getMovieId() {
        return movieId;
      }
  
      public float getRating() {
        return rating;
      }
  
      public long getTimestamp() {
        return timestamp;
      }
  
      public static Rating parseRating(String str) {
        String[] fields = str.split("::");
        if (fields.length != 4) {
          throw new IllegalArgumentException("Each line must contain 4 fields");
        }
        int userId = Integer.parseInt(fields[0]);
        int movieId = Integer.parseInt(fields[1]);
        float rating = Float.parseFloat(fields[2]);
        long timestamp = Long.parseLong(fields[3]);
        return new Rating(userId, movieId, rating, timestamp);
      }
    }
    // $example off$
  
    public static void main(String[] args) {
      SparkSession spark = SparkSession
        .builder()
        .appName("JavaALSExample")
        .getOrCreate();
  
      // $example on$
      JavaRDD<Rating> ratingsRDD = spark
        .read().textFile("data/mllib/als/sample_movielens_ratings.txt").javaRDD()
        .map(new Function<String, Rating>() {
          public Rating call(String str) {
            return Rating.parseRating(str);
          }
        });
      Dataset<Row> ratings = spark.createDataFrame(ratingsRDD, Rating.class);
      Dataset<Row>[] splits = ratings.randomSplit(new double[]{0.8, 0.2});
      Dataset<Row> training = splits[0];
      Dataset<Row> test = splits[1];
  
      // Build the recommendation model using ALS on the training data
      ALS als = new ALS()
        .setMaxIter(5)
        .setRegParam(0.01)
        .setUserCol("userId")
        .setItemCol("movieId")
        .setRatingCol("rating");
      ALSModel model = als.fit(training);
  
      // Evaluate the model by computing the RMSE on the test data
      Dataset<Row> predictions = model.transform(test);
  
      RegressionEvaluator evaluator = new RegressionEvaluator()
        .setMetricName("rmse")
        .setLabelCol("rating")
        .setPredictionCol("prediction");
      Double rmse = evaluator.evaluate(predictions);
      System.out.println("Root-mean-square error = " + rmse);
      // $example off$
      spark.stop();
    }
  }
  