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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.mllib.recommendation;
import java.io.Serializable;
import java.util.ArrayList;
import java.util.List;
import scala.Tuple2;
import scala.Tuple3;
import org.jblas.DoubleMatrix;
import org.junit.After;
import org.junit.Assert;
import org.junit.Before;
import org.junit.Test;
import org.apache.spark.api.java.JavaPairRDD;
import org.apache.spark.api.java.JavaRDD;
import org.apache.spark.api.java.JavaSparkContext;
public class JavaALSSuite implements Serializable {
private transient JavaSparkContext sc;
@Before
public void setUp() {
sc = new JavaSparkContext("local", "JavaALS");
}
@After
public void tearDown() {
sc.stop();
sc = null;
}
void validatePrediction(
MatrixFactorizationModel model,
int users,
int products,
DoubleMatrix trueRatings,
double matchThreshold,
boolean implicitPrefs,
DoubleMatrix truePrefs) {
List<Tuple2<Integer, Integer>> localUsersProducts = new ArrayList(users * products);
for (int u=0; u < users; ++u) {
for (int p=0; p < products; ++p) {
localUsersProducts.add(new Tuple2<Integer, Integer>(u, p));
}
}
JavaPairRDD<Integer, Integer> usersProducts = sc.parallelizePairs(localUsersProducts);
List<Rating> predictedRatings = model.predict(usersProducts).collect();
Assert.assertEquals(users * products, predictedRatings.size());
if (!implicitPrefs) {
for (Rating r: predictedRatings) {
double prediction = r.rating();
double correct = trueRatings.get(r.user(), r.product());
Assert.assertTrue(String.format("Prediction=%2.4f not below match threshold of %2.2f",
prediction, matchThreshold), Math.abs(prediction - correct) < matchThreshold);
}
} else {
// For implicit prefs we use the confidence-weighted RMSE to test
// (ref Mahout's implicit ALS tests)
double sqErr = 0.0;
double denom = 0.0;
for (Rating r: predictedRatings) {
double prediction = r.rating();
double truePref = truePrefs.get(r.user(), r.product());
double confidence = 1.0 +
/* alpha = */ 1.0 * Math.abs(trueRatings.get(r.user(), r.product()));
double err = confidence * (truePref - prediction) * (truePref - prediction);
sqErr += err;
denom += confidence;
}
double rmse = Math.sqrt(sqErr / denom);
Assert.assertTrue(String.format("Confidence-weighted RMSE=%2.4f above threshold of %2.2f",
rmse, matchThreshold), rmse < matchThreshold);
}
}
@Test
public void runALSUsingStaticMethods() {
int features = 1;
int iterations = 15;
int users = 50;
int products = 100;
Tuple3<List<Rating>, DoubleMatrix, DoubleMatrix> testData = ALSSuite.generateRatingsAsJavaList(
users, products, features, 0.7, false, false);
JavaRDD<Rating> data = sc.parallelize(testData._1());
MatrixFactorizationModel model = ALS.train(data.rdd(), features, iterations);
validatePrediction(model, users, products, testData._2(), 0.3, false, testData._3());
}
@Test
public void runALSUsingConstructor() {
int features = 2;
int iterations = 15;
int users = 100;
int products = 200;
Tuple3<List<Rating>, DoubleMatrix, DoubleMatrix> testData = ALSSuite.generateRatingsAsJavaList(
users, products, features, 0.7, false, false);
JavaRDD<Rating> data = sc.parallelize(testData._1());
MatrixFactorizationModel model = new ALS().setRank(features)
.setIterations(iterations)
.run(data);
validatePrediction(model, users, products, testData._2(), 0.3, false, testData._3());
}
@Test
public void runImplicitALSUsingStaticMethods() {
int features = 1;
int iterations = 15;
int users = 80;
int products = 160;
Tuple3<List<Rating>, DoubleMatrix, DoubleMatrix> testData = ALSSuite.generateRatingsAsJavaList(
users, products, features, 0.7, true, false);
JavaRDD<Rating> data = sc.parallelize(testData._1());
MatrixFactorizationModel model = ALS.trainImplicit(data.rdd(), features, iterations);
validatePrediction(model, users, products, testData._2(), 0.4, true, testData._3());
}
@Test
public void runImplicitALSUsingConstructor() {
int features = 2;
int iterations = 15;
int users = 100;
int products = 200;
Tuple3<List<Rating>, DoubleMatrix, DoubleMatrix> testData = ALSSuite.generateRatingsAsJavaList(
users, products, features, 0.7, true, false);
JavaRDD<Rating> data = sc.parallelize(testData._1());
MatrixFactorizationModel model = new ALS().setRank(features)
.setIterations(iterations)
.setImplicitPrefs(true)
.run(data.rdd());
validatePrediction(model, users, products, testData._2(), 0.4, true, testData._3());
}
@Test
public void runImplicitALSWithNegativeWeight() {
int features = 2;
int iterations = 15;
int users = 80;
int products = 160;
Tuple3<List<Rating>, DoubleMatrix, DoubleMatrix> testData = ALSSuite.generateRatingsAsJavaList(
users, products, features, 0.7, true, true);
JavaRDD<Rating> data = sc.parallelize(testData._1());
MatrixFactorizationModel model = new ALS().setRank(features)
.setIterations(iterations)
.setImplicitPrefs(true)
.setSeed(8675309L)
.run(data.rdd());
validatePrediction(model, users, products, testData._2(), 0.4, true, testData._3());
}
@Test
public void runRecommend() {
int features = 5;
int iterations = 10;
int users = 200;
int products = 50;
Tuple3<List<Rating>, DoubleMatrix, DoubleMatrix> testData = ALSSuite.generateRatingsAsJavaList(
users, products, features, 0.7, true, false);
JavaRDD<Rating> data = sc.parallelize(testData._1());
MatrixFactorizationModel model = new ALS().setRank(features)
.setIterations(iterations)
.setImplicitPrefs(true)
.setSeed(8675309L)
.run(data.rdd());
validateRecommendations(model.recommendProducts(1, 10), 10);
validateRecommendations(model.recommendUsers(1, 20), 20);
}
private static void validateRecommendations(Rating[] recommendations, int howMany) {
Assert.assertEquals(howMany, recommendations.length);
for (int i = 1; i < recommendations.length; i++) {
Assert.assertTrue(recommendations[i-1].rating() >= recommendations[i].rating());
}
Assert.assertTrue(recommendations[0].rating() > 0.7);
}
}
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