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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 java.util.*;
import scala.Tuple2;
import org.apache.spark.api.java.*;
import org.apache.spark.api.java.function.Function;
import org.apache.spark.mllib.evaluation.RegressionMetrics;
import org.apache.spark.mllib.evaluation.RankingMetrics;
import org.apache.spark.mllib.recommendation.ALS;
import org.apache.spark.mllib.recommendation.MatrixFactorizationModel;
import org.apache.spark.mllib.recommendation.Rating;
// $example off$
import org.apache.spark.SparkConf;
public class JavaRankingMetricsExample {
public static void main(String[] args) {
SparkConf conf = new SparkConf().setAppName("Java Ranking Metrics Example");
JavaSparkContext sc = new JavaSparkContext(conf);
// $example on$
String path = "data/mllib/sample_movielens_data.txt";
JavaRDD<String> data = sc.textFile(path);
JavaRDD<Rating> ratings = data.map(
new Function<String, Rating>() {
@Override
public Rating call(String line) {
String[] parts = line.split("::");
return new Rating(Integer.parseInt(parts[0]), Integer.parseInt(parts[1]), Double
.parseDouble(parts[2]) - 2.5);
}
}
);
ratings.cache();
// Train an ALS model
final MatrixFactorizationModel model = ALS.train(JavaRDD.toRDD(ratings), 10, 10, 0.01);
// Get top 10 recommendations for every user and scale ratings from 0 to 1
JavaRDD<Tuple2<Object, Rating[]>> userRecs = model.recommendProductsForUsers(10).toJavaRDD();
JavaRDD<Tuple2<Object, Rating[]>> userRecsScaled = userRecs.map(
new Function<Tuple2<Object, Rating[]>, Tuple2<Object, Rating[]>>() {
@Override
public Tuple2<Object, Rating[]> call(Tuple2<Object, Rating[]> t) {
Rating[] scaledRatings = new Rating[t._2().length];
for (int i = 0; i < scaledRatings.length; i++) {
double newRating = Math.max(Math.min(t._2()[i].rating(), 1.0), 0.0);
scaledRatings[i] = new Rating(t._2()[i].user(), t._2()[i].product(), newRating);
}
return new Tuple2<>(t._1(), scaledRatings);
}
}
);
JavaPairRDD<Object, Rating[]> userRecommended = JavaPairRDD.fromJavaRDD(userRecsScaled);
// Map ratings to 1 or 0, 1 indicating a movie that should be recommended
JavaRDD<Rating> binarizedRatings = ratings.map(
new Function<Rating, Rating>() {
@Override
public Rating call(Rating r) {
double binaryRating;
if (r.rating() > 0.0) {
binaryRating = 1.0;
} else {
binaryRating = 0.0;
}
return new Rating(r.user(), r.product(), binaryRating);
}
}
);
// Group ratings by common user
JavaPairRDD<Object, Iterable<Rating>> userMovies = binarizedRatings.groupBy(
new Function<Rating, Object>() {
@Override
public Object call(Rating r) {
return r.user();
}
}
);
// Get true relevant documents from all user ratings
JavaPairRDD<Object, List<Integer>> userMoviesList = userMovies.mapValues(
new Function<Iterable<Rating>, List<Integer>>() {
@Override
public List<Integer> call(Iterable<Rating> docs) {
List<Integer> products = new ArrayList<>();
for (Rating r : docs) {
if (r.rating() > 0.0) {
products.add(r.product());
}
}
return products;
}
}
);
// Extract the product id from each recommendation
JavaPairRDD<Object, List<Integer>> userRecommendedList = userRecommended.mapValues(
new Function<Rating[], List<Integer>>() {
@Override
public List<Integer> call(Rating[] docs) {
List<Integer> products = new ArrayList<>();
for (Rating r : docs) {
products.add(r.product());
}
return products;
}
}
);
JavaRDD<Tuple2<List<Integer>, List<Integer>>> relevantDocs = userMoviesList.join(
userRecommendedList).values();
// Instantiate the metrics object
RankingMetrics<Integer> metrics = RankingMetrics.of(relevantDocs);
// Precision and NDCG at k
Integer[] kVector = {1, 3, 5};
for (Integer k : kVector) {
System.out.format("Precision at %d = %f\n", k, metrics.precisionAt(k));
System.out.format("NDCG at %d = %f\n", k, metrics.ndcgAt(k));
}
// Mean average precision
System.out.format("Mean average precision = %f\n", metrics.meanAveragePrecision());
// Evaluate the model using numerical ratings and regression metrics
JavaRDD<Tuple2<Object, Object>> userProducts = ratings.map(
new Function<Rating, Tuple2<Object, Object>>() {
@Override
public Tuple2<Object, Object> call(Rating r) {
return new Tuple2<Object, Object>(r.user(), r.product());
}
}
);
JavaPairRDD<Tuple2<Integer, Integer>, Object> predictions = JavaPairRDD.fromJavaRDD(
model.predict(JavaRDD.toRDD(userProducts)).toJavaRDD().map(
new Function<Rating, Tuple2<Tuple2<Integer, Integer>, Object>>() {
@Override
public Tuple2<Tuple2<Integer, Integer>, Object> call(Rating r) {
return new Tuple2<Tuple2<Integer, Integer>, Object>(
new Tuple2<>(r.user(), r.product()), r.rating());
}
}
));
JavaRDD<Tuple2<Object, Object>> ratesAndPreds =
JavaPairRDD.fromJavaRDD(ratings.map(
new Function<Rating, Tuple2<Tuple2<Integer, Integer>, Object>>() {
@Override
public Tuple2<Tuple2<Integer, Integer>, Object> call(Rating r) {
return new Tuple2<Tuple2<Integer, Integer>, Object>(
new Tuple2<>(r.user(), r.product()), r.rating());
}
}
)).join(predictions).values();
// Create regression metrics object
RegressionMetrics regressionMetrics = new RegressionMetrics(ratesAndPreds.rdd());
// Root mean squared error
System.out.format("RMSE = %f\n", regressionMetrics.rootMeanSquaredError());
// R-squared
System.out.format("R-squared = %f\n", regressionMetrics.r2());
// $example off$
sc.stop();
}
}
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