/*
* 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.mllib.examples;
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 != 5 && args.length != 6) {
System.err.println(
"Usage: JavaALS <master> <ratings_file> <rank> <iterations> <output_dir> [<blocks>]");
System.exit(1);
}
int rank = Integer.parseInt(args[2]);
int iterations = Integer.parseInt(args[3]);
String outputDir = args[4];
int blocks = -1;
if (args.length == 6) {
blocks = Integer.parseInt(args[5]);
}
JavaSparkContext sc = new JavaSparkContext(args[0], "JavaALS",
System.getenv("SPARK_HOME"), JavaSparkContext.jarOfClass(JavaALS.class));
JavaRDD<String> lines = sc.textFile(args[1]);
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);
System.exit(0);
}
}