apache spark machine learning ALS(Alternating Least Square) from Java - Java Big Data

Java examples for Big Data:apache spark

  1. HOME
  2. Java
  3. Big Data
  4. apache spark

Description

apache spark machine learning ALS(Alternating Least Square) from Java

Demo Code

/*//from  ww  w  . j a v  a2s  . c  o m
 * 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 eduonix.spark.mllib;

import org.apache.spark.SparkConf;
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 java.util.Arrays;
import java.util.regex.Pattern;

import scala.Tuple2;

/**
 * Example using MLlib ALS from Java.
 */
public final class JavaALS {

    static class ParseRating implements Function<String, Rating> {
        private static final Pattern COMMA = Pattern.compile(",");

        @Override
        public Rating call(String line) {
            String[] tok = COMMA.split(line);
            int x = Integer.parseInt(tok[0]);
            int y = Integer.parseInt(tok[1]);
            double rating = Double.parseDouble(tok[2]);
            return new Rating(x, y, rating);
        }
    }

    static class FeaturesToString implements
            Function<Tuple2<Object, double[]>, String> {
        @Override
        public String call(Tuple2<Object, double[]> element) {
            return element._1() + "," + Arrays.toString(element._2());
        }
    }

    public static void main(String[] args) {

        if (args.length < 4) {
            System.err
                    .println("Usage: JavaALS <ratings_file> <rank> <iterations> <output_dir> [<blocks>]");
            System.exit(1);
        }
        SparkConf sparkConf = new SparkConf().setAppName("JavaALS");
        int rank = Integer.parseInt(args[1]);
        int iterations = Integer.parseInt(args[2]);
        String outputDir = args[3];
        int blocks = -1;
        if (args.length == 5) {
            blocks = Integer.parseInt(args[4]);
        }

        JavaSparkContext sc = new JavaSparkContext(sparkConf);
        JavaRDD<String> lines = sc.textFile(args[0]);

        JavaRDD<Rating> ratings = lines.map(new ParseRating());

        MatrixFactorizationModel model = ALS.train(ratings.rdd(), rank,
                iterations, 0.01, blocks);

        model.userFeatures().toJavaRDD().map(new FeaturesToString())
                .saveAsTextFile(outputDir + "/userFeatures");
        model.productFeatures().toJavaRDD().map(new FeaturesToString())
                .saveAsTextFile(outputDir + "/productFeatures");
        System.out.println("Final user/product features written to "
                + outputDir);

        sc.stop();
    }
}

Related Tutorials