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JavaRecommendationExample.java
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JavaRecommendationExample.java
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/*
* 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.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(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(r -> new Tuple2<>(r.user(), r.product()));
JavaPairRDD<Tuple2<Integer, Integer>, Double> predictions = JavaPairRDD.fromJavaRDD(
model.predict(JavaRDD.toRDD(userProducts)).toJavaRDD()
.map(r -> new Tuple2<>(new Tuple2<>(r.user(), r.product()), r.rating()))
);
JavaRDD<Tuple2<Double, Double>> ratesAndPreds = JavaPairRDD.fromJavaRDD(
ratings.map(r -> new Tuple2<>(new Tuple2<>(r.user(), r.product()), r.rating())))
.join(predictions).values();
double MSE = ratesAndPreds.mapToDouble(pair -> {
double err = pair._1() - pair._2();
return err * err;
}).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();
}
}