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[SPARK-4378][MLLIB] make ALS more Java-friendly #3240

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6c8bbd1
make ALS more Java-friendly
mengxr Nov 13, 2014
6581503
check number of predictions
mengxr Nov 13, 2014
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17 changes: 11 additions & 6 deletions mllib/src/main/scala/org/apache/spark/mllib/recommendation/ALS.scala
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Expand Up @@ -20,20 +20,20 @@ package org.apache.spark.mllib.recommendation
import scala.collection.mutable
import scala.collection.mutable.ArrayBuffer
import scala.math.{abs, sqrt}
import scala.util.Random
import scala.util.Sorting
import scala.util.{Random, Sorting}
import scala.util.hashing.byteswap32

import org.jblas.{DoubleMatrix, SimpleBlas, Solve}

import org.apache.spark.{HashPartitioner, Logging, Partitioner}
import org.apache.spark.SparkContext._
import org.apache.spark.annotation.{DeveloperApi, Experimental}
import org.apache.spark.api.java.JavaRDD
import org.apache.spark.broadcast.Broadcast
import org.apache.spark.{Logging, HashPartitioner, Partitioner}
import org.apache.spark.storage.StorageLevel
import org.apache.spark.mllib.optimization.NNLS
import org.apache.spark.rdd.RDD
import org.apache.spark.SparkContext._
import org.apache.spark.storage.StorageLevel
import org.apache.spark.util.Utils
import org.apache.spark.mllib.optimization.NNLS

/**
* Out-link information for a user or product block. This includes the original user/product IDs
Expand Down Expand Up @@ -325,6 +325,11 @@ class ALS private (
new MatrixFactorizationModel(rank, usersOut, productsOut)
}

/**
* Java-friendly version of [[ALS.run]].
*/
def run(ratings: JavaRDD[Rating]): MatrixFactorizationModel = run(ratings.rdd)

/**
* Computes the (`rank x rank`) matrix `YtY`, where `Y` is the (`nui x rank`) matrix of factors
* for each user (or product), in a distributed fashion.
Expand Down
15 changes: 11 additions & 4 deletions mllib/src/main/scala/org/apache/spark/mllib/recommendation/MatrixFactorizationModel.scala
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Expand Up @@ -17,13 +17,13 @@

package org.apache.spark.mllib.recommendation

import java.lang.{Integer => JavaInteger}

import org.jblas.DoubleMatrix

import org.apache.spark.annotation.DeveloperApi
import org.apache.spark.api.java.JavaRDD
import org.apache.spark.rdd.RDD
import org.apache.spark.SparkContext._
import org.apache.spark.mllib.api.python.SerDe
import org.apache.spark.api.java.{JavaPairRDD, JavaRDD}
import org.apache.spark.rdd.RDD

/**
* Model representing the result of matrix factorization.
Expand Down Expand Up @@ -65,6 +65,13 @@ class MatrixFactorizationModel private[mllib] (
}
}

/**
* Java-friendly version of [[MatrixFactorizationModel.predict]].
*/
def predict(usersProducts: JavaPairRDD[JavaInteger, JavaInteger]): JavaRDD[Rating] = {
predict(usersProducts.rdd.asInstanceOf[RDD[(Int, Int)]]).toJavaRDD()
}

/**
* Recommends products to a user.
*
Expand Down
74 changes: 31 additions & 43 deletions mllib/src/test/java/org/apache/spark/mllib/recommendation/JavaALSSuite.java
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Expand Up @@ -23,13 +23,14 @@
import scala.Tuple2;
import scala.Tuple3;

import com.google.common.collect.Lists;
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;

Expand All @@ -47,61 +48,48 @@ public void tearDown() {
sc = null;
}

static void validatePrediction(
void validatePrediction(
MatrixFactorizationModel model,
int users,
int products,
int features,
DoubleMatrix trueRatings,
double matchThreshold,
boolean implicitPrefs,
DoubleMatrix truePrefs) {
DoubleMatrix predictedU = new DoubleMatrix(users, features);
List<Tuple2<Object, double[]>> userFeatures = model.userFeatures().toJavaRDD().collect();
for (int i = 0; i < features; ++i) {
for (Tuple2<Object, double[]> userFeature : userFeatures) {
predictedU.put((Integer)userFeature._1(), i, userFeature._2()[i]);
}
}
DoubleMatrix predictedP = new DoubleMatrix(products, features);

List<Tuple2<Object, double[]>> productFeatures =
model.productFeatures().toJavaRDD().collect();
for (int i = 0; i < features; ++i) {
for (Tuple2<Object, double[]> productFeature : productFeatures) {
predictedP.put((Integer)productFeature._1(), i, productFeature._2()[i]);
List<Tuple2<Integer, Integer>> localUsersProducts =
Lists.newArrayListWithCapacity(users * products);
for (int u=0; u < users; ++u) {
for (int p=0; p < products; ++p) {
localUsersProducts.add(new Tuple2<Integer, Integer>(u, p));
}
}

DoubleMatrix predictedRatings = predictedU.mmul(predictedP.transpose());

JavaPairRDD<Integer, Integer> usersProducts = sc.parallelizePairs(localUsersProducts);
List<Rating> predictedRatings = model.predict(usersProducts).collect();
Assert.assertEquals(users * products, predictedRatings.size());
if (!implicitPrefs) {
for (int u = 0; u < users; ++u) {
for (int p = 0; p < products; ++p) {
double prediction = predictedRatings.get(u, p);
double correct = trueRatings.get(u, p);
Assert.assertTrue(String.format("Prediction=%2.4f not below match threshold of %2.2f",
prediction, matchThreshold), Math.abs(prediction - correct) < matchThreshold);
}
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 (int u = 0; u < users; ++u) {
for (int p = 0; p < products; ++p) {
double prediction = predictedRatings.get(u, p);
double truePref = truePrefs.get(u, p);
double confidence = 1.0 + /* alpha = */ 1.0 * Math.abs(trueRatings.get(u, p));
double err = confidence * (truePref - prediction) * (truePref - prediction);
sqErr += err;
denom += confidence;
}
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);
rmse, matchThreshold), rmse < matchThreshold);
}
}

Expand All @@ -116,7 +104,7 @@ public void runALSUsingStaticMethods() {

JavaRDD<Rating> data = sc.parallelize(testData._1());
MatrixFactorizationModel model = ALS.train(data.rdd(), features, iterations);
validatePrediction(model, users, products, features, testData._2(), 0.3, false, testData._3());
validatePrediction(model, users, products, testData._2(), 0.3, false, testData._3());
}

@Test
Expand All @@ -132,8 +120,8 @@ public void runALSUsingConstructor() {

MatrixFactorizationModel model = new ALS().setRank(features)
.setIterations(iterations)
.run(data.rdd());
validatePrediction(model, users, products, features, testData._2(), 0.3, false, testData._3());
.run(data);
validatePrediction(model, users, products, testData._2(), 0.3, false, testData._3());
}

@Test
Expand All @@ -147,7 +135,7 @@ public void runImplicitALSUsingStaticMethods() {

JavaRDD<Rating> data = sc.parallelize(testData._1());
MatrixFactorizationModel model = ALS.trainImplicit(data.rdd(), features, iterations);
validatePrediction(model, users, products, features, testData._2(), 0.4, true, testData._3());
validatePrediction(model, users, products, testData._2(), 0.4, true, testData._3());
}

@Test
Expand All @@ -165,7 +153,7 @@ public void runImplicitALSUsingConstructor() {
.setIterations(iterations)
.setImplicitPrefs(true)
.run(data.rdd());
validatePrediction(model, users, products, features, testData._2(), 0.4, true, testData._3());
validatePrediction(model, users, products, testData._2(), 0.4, true, testData._3());
}

@Test
Expand All @@ -183,7 +171,7 @@ public void runImplicitALSWithNegativeWeight() {
.setImplicitPrefs(true)
.setSeed(8675309L)
.run(data.rdd());
validatePrediction(model, users, products, features, testData._2(), 0.4, true, testData._3());
validatePrediction(model, users, products, testData._2(), 0.4, true, testData._3());
}

@Test
Expand Down