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如何使用Spark ALS实现协同过滤

转载自 JavaChen Blog ,作者: Junez

本文主要记录最近一段时间学习和实现Spark MLlib中的协同过滤的一些总结,希望对大家熟悉Spark ALS算法有所帮助。

更新:

【2016.06.12】Spark1.4.0中MatrixFactorizationModel提供了recommendForAll方法实现离线批量推荐,见 SPARK-3066 。

测试环境

为了测试简单,在本地以local方式运行Spark,你需要做的是下载编译好的压缩包解压即可,可以参考 Spark本地模式运行 。

测试数据使用 MovieLens 的 MovieLens 10M 数据集,下载之后解压到data目录。数据的格式请参考README中的说明,需要注意的是ratings.dat中的数据被处理过, 每个用户至少访问了20个商品

下面的代码均在spark-shell中运行,启动时候可以根据你的机器内存设置JVM参数,例如:

bin/spark-shell --executor-memory 3 g --driver-memory 3 g --driver-java-options '-Xms2g -Xmx2g -XX:+UseCompressedOops'

预测评分

这个例子主要演示如何训练数据、评分并计算根均方差。

准备工作

首先,启动spark-shell,然后引入mllib包,我们需要用到ALS算法类和Rating评分类:

import org.apache.spark.mllib.recommendation.{ALS, Rating}

Spark的日志级别默认为INFO,你可以手动设置为WARN级别,同样先引入log4j依赖:

import org.apache.log4j.{Logger,Level}

然后,运行下面代码:

Logger.getLogger( "org.apache.spark" ).setLevel(Level.WARN)

Logger.getLogger( "org.eclipse.jetty.server" ).setLevel(Level.OFF)

加载数据

spark-shell启动成功之后,sc为内置变量,你可以通过它来加载测试数据:

val data = sc.textFile( "data/ml-1m/ratings.dat" )

接下来解析文件内容,获得用户对商品的评分记录:

val ratings = data.map(_.split( "::" ) match { case Array(user, item, rate, ts) =>

Rating(user.toInt, item.toInt, rate.toDouble)

}).cache()

查看第一条记录:

scala> ratings.first

res81: org.apache.spark.mllib.recommendation.Rating = Rating( 1 , 1193 , 5.0 )

我们可以统计文件中用户和商品数量:

val users = ratings.map(_.user).distinct()

val products = ratings.map(_.product).distinct()

println( "Got " +ratings.count()+ " ratings from " +users.count+ " users on " +products.count+ " products." )

可以看到如下输出:

//Got 1000209 ratings from 6040 users on 3706 products.

你可以对评分数据生成训练集和测试集,例如:训练集和测试集比例为8比2:

val splits = ratings.randomSplit(Array( 0.8 , 0.2 ), seed = 111 l)

val training = splits( 0 ).repartition(numPartitions)

val test = splits( 1 ).repartition(numPartitions)

这里,我们是将评分数据全部当做训练集,并且也为测试集。

训练模型

接下来调用 ALS.train() 方法,进行模型训练:

val rank = 12

val lambda = 0.01

val numIterations = 20

val model = ALS.train(ratings, rank, numIterations, lambda)

训练完后,我们看看model中的用户和商品特征向量:

model.userFeatures

model.userFeatures.count

//res84: Long = 6040

model.productFeatures

model.productFeatures.count

//res86: Long = 3706

评测

我们要对比一下预测的结果,注意:我们将 训练集当作测试集 来进行对比测试。从训练集中获取用户和商品的映射:

val usersProducts= ratings.map { case Rating(user, product, rate) =>

(user, product)

}

显然,测试集的记录数等于评分总记录数,验证一下:

usersProducts.count //Long = 1000209

使用推荐模型对用户商品进行预测评分,得到预测评分的数据集:

var predictions = model.predict(usersProducts).map { case Rating(user, product, rate) =>

((user, product), rate)

}

查看其记录数:

predictions.count //Long = 1000209

将真实评分数据集与预测评分数据集进行合并,这样得到用户对每一个商品的实际评分和预测评分:

val ratesAndPreds = ratings.map { case Rating(user, product, rate) =>

((user, product), rate)

}.join(predictions)

ratesAndPreds.count //Long = 1000209

然后计算根均方差:

val rmse= math.sqrt(ratesAndPreds.map { case ((user, product), (r1, r2)) =>

val err = (r1 - r2)

err * err

}.mean())

println(s "RMSE = $rmse" )

上面这段代码其实就是 对测试集进行评分预测并计算与实际评分的相似度 ,这段代码可以抽象为一个方法,如下:

/** Compute RMSE (Root Mean Squared Error). */

def computeRmse(model: MatrixFactorizationModel, data: RDD[Rating]) = {

val usersProducts = data.map { case Rating(user, product, rate) =>

(user, product)

}

val predictions = model.predict(usersProducts).map { case Rating(user, product, rate) =>

((user, product), rate)

}

val ratesAndPreds = data.map { case Rating(user, product, rate) =>

((user, product), rate)

}.join(predictions)

math.sqrt(ratesAndPreds.map { case ((user, product), (r1, r2)) =>

val err = (r1 - r2)

err * err

}.mean())

}

除了RMSE指标,我们还可以计算AUC以及Mean average precision at K (MAPK),关于AUC的计算方法,参考 RunRecommender.scala ,关于MAPK的计算方法可以参考 《Packt.Machine Learning with Spark.2015.pdf》 一书第四章节内容,或者你可以看本文后面内容。

保存真实评分和预测评分

我们还可以保存用户对商品的真实评分和预测评分记录到本地文件:

ratesAndPreds.sortByKey().repartition( 1 ).sortBy(_._1).map({

case ((user, product), (rate, pred)) => (user + "," + product + "," + rate + "," + pred)

}).saveAsTextFile( "/tmp/result" )

上面这段代码先按用户排序,然后重新分区确保目标目录中只生成一个文件。如果你重复运行这段代码,则需要先删除目标路径:

import scala.sys.process._

"rm -r /tmp/result" .!

我们还可以对预测的评分结果按用户进行分组并按评分倒排序:

predictions.map { case ((user, product), rate) =>

(user, (product, rate))

}.groupByKey(numPartitions).map{ case (user_id,list)=>

(user_id,list.toList.sortBy { case (goods_id,rate)=> - rate})

}

给一个用户推荐商品

这个例子主要是记录如何给一个或大量用户进行推荐商品,例如,对用户编号为384的用户进行推荐,查出该用户在测试集中评分过的商品。

找出5个用户:

users.take( 5 )

//Array[Int] = Array(384, 1084, 4904, 3702, 5618)

查看用户编号为384的用户的预测结果中预测评分排前10的商品:

val userId = users.take( 1 )( 0 ) //384

val K = 10

val topKRecs = model.recommendProducts(userId, K)

println(topKRecs.mkString( "/n" ))

// Rating(384,2545,8.354966018818265)

// Rating(384,129,8.113083736094676)

// Rating(384,184,8.038113395650853)

// Rating(384,811,7.983433591425284)

// Rating(384,1421,7.912044967873945)

// Rating(384,1313,7.719639594879865)

// Rating(384,2892,7.53667094600392)

// Rating(384,2483,7.295378004543803)

// Rating(384,397,7.141158013610967)

// Rating(384,97,7.071089782695754)

查看该用户的评分记录:

val goodsForUser=ratings.keyBy(_.user).lookup( 384 )

productsForUser.size //Int = 22

productsForUser.sortBy(-_.rating).take( 10 ).map(rating => (rating.product, rating.rating)).foreach(println)

// (593,5.0)

// (1201,5.0)

// (3671,5.0)

// (1304,5.0)

// (1197,4.0)

// (3037,4.0)

// (1610,4.0)

// (3074,4.0)

// (204,4.0)

// (260,4.0)

可以看到该用户对22个商品评过分以及浏览的商品是哪些。

我们可以该用户对某一个商品的实际评分和预测评分方差为多少:

val actualRating = productsForUser.take( 1 )( 0 )

val predictedRating = model.predict( 384 , actualRating.product)

//predictedRating: Double = 1.9426030777174637

val squaredError = math.pow(predictedRating - actualRating.rating, 2.0 )

//squaredError: Double = 0.0032944066875075172

如何找出和一个已知商品最相似的商品呢?这里,我们可以使用余弦相似度来计算:

import org.jblas.DoubleMatrix

/* Compute the cosine similarity between two vectors */

def cosineSimilarity(vec1: DoubleMatrix, vec2: DoubleMatrix): Double = {

vec1.dot(vec2) / (vec1.norm2() * vec2.norm2())

}

以2055商品为例,计算实际评分和预测评分相似度

val itemId = 2055

val itemFactor = model.productFeatures.lookup(itemId).head

val itemVector = new DoubleMatrix(itemFactor)

cosineSimilarity(itemVector, itemVector)

// res99: Double = 0.9999999999999999

找到和该商品最相似的10个商品:

val sims = model.productFeatures.map{ case (id, factor) =>

val factorVector = new DoubleMatrix(factor)

val sim = cosineSimilarity(factorVector, itemVector)

(id, sim)

}

val sortedSims = sims.top(K)(Ordering.by[(Int, Double), Double] { case (id, similarity) => similarity })

println(sortedSims.mkString( "/n" ))

// (2055,0.9999999999999999)

// (2051,0.9138311231145874)

// (3520,0.8739823400539756)

// (2190,0.8718466671129721)

// (2050,0.8612639515847019)

// (1011,0.8466911667526461)

// (2903,0.8455764332511272)

// (3121,0.8227325520485377)

// (3674,0.8075743004357392)

// (2016,0.8063817280259447)

显然第一个最相似的商品即为该商品本身,即2055,我们可以修改下代码,取前k+1个商品,然后排除第一个:

val sortedSims2 = sims.top(K + 1 )(Ordering.by[(Int, Double), Double] { case (id, similarity) => similarity })

sortedSims2.slice( 1 , 11 ).map{ case (id, sim) => (id, sim) }.mkString( "/n" )

// (2051,0.9138311231145874)

// (3520,0.8739823400539756)

// (2190,0.8718466671129721)

// (2050,0.8612639515847019)

// (1011,0.8466911667526461)

// (2903,0.8455764332511272)

// (3121,0.8227325520485377)

// (3674,0.8075743004357392)

// (2016,0.8063817280259447)

// (3672,0.8016276723120674)

接下来,我们可以计算给该用户推荐的前K个商品的平均准确度MAPK,该算法定义如下(该算法是否正确还有待考证):

def avgPrecisionK(actual: Seq[Int], predicted: Seq[Int], k: Int): Double = {

val predK = predicted.take(k)

var score = 0.0

var numHits = 0.0

for ((p, i) <- predK.zipWithIndex) {

if (actual.contains(p)) {

numHits += 1.0

score += numHits / (i.toDouble + 1.0 )

}

}

if (actual.isEmpty) {

1.0

} else {

score / scala.math.min(actual.size, k).toDouble

}

}

给该用户推荐的商品为:

val actualProducts = productsForUser.map(_.product)

给该用户预测的商品为:

val predictedProducts = topKRecs.map(_.product)

最后的准确度为:

val apk10 = avgPrecisionK(actualProducts, predictedProducts, 10 )

// apk10: Double = 0.0

批量推荐

你可以评分记录中获得所有用户然后依次给每个用户推荐:

val users = ratings.map(_.user).distinct()

users.collect.flatMap { user =>

model.recommendProducts(user, 10 )

}

这种方式是遍历内存中的一个集合然后循环调用RDD的操作,运行会比较慢,另外一种方式是直接操作model中的userFeatures和productFeatures,代码如下:

val itemFactors = model.productFeatures.map { case (id, factor) => factor }.collect()

val itemMatrix = new DoubleMatrix(itemFactors)

println(itemMatrix.rows, itemMatrix.columns)

//(3706,12)

// broadcast the item factor matrix

val imBroadcast = sc.broadcast(itemMatrix)

//获取商品和索引的映射

var idxProducts=model.productFeatures.map { case (prodcut, factor) => prodcut }.zipWithIndex().map{ case (prodcut, idx) => (idx,prodcut)}.collectAsMap()

val idxProductsBroadcast = sc.broadcast(idxProducts)

val allRecs = model.userFeatures.map{ case (user, array) =>

val userVector = new DoubleMatrix(array)

val scores = imBroadcast.value.mmul(userVector)

val sortedWithId = scores.data.zipWithIndex.sortBy(-_._1)

//根据索引取对应的商品id

val recommendedProducts = sortedWithId.map(_._2).map{idx=>idxProductsBroadcast.value.get(idx).get}

(user, recommendedProducts)

}

这种方式其实还不是最优方法,更好的方法可以参考 Personalised recommendations using Spark ,当然这篇文章中的代码还可以继续优化一下。我修改后的代码如下,供大家参考:

val productFeatures = model.productFeatures.collect()

var productArray = ArrayBuffer[Int]()

var productFeaturesArray = ArrayBuffer[Array[Double]]()

for ((product, features) <- productFeatures) {

productArray += product

productFeaturesArray += features

}

val productArrayBroadcast = sc.broadcast(productArray)

val productFeatureMatrixBroadcast = sc.broadcast( new DoubleMatrix(productFeaturesArray.toArray).transpose())

start = System.currentTimeMillis()

val allRecs = model.userFeatures.mapPartitions { iter =>

// Build user feature matrix for jblas

var userFeaturesArray = ArrayBuffer[Array[Double]]()

var userArray = new ArrayBuffer[Int]()

while (iter.hasNext) {

val (user, features) = iter.next()

userArray += user

userFeaturesArray += features

}

var userFeatureMatrix = new DoubleMatrix(userFeaturesArray.toArray)

var userRecommendationMatrix = userFeatureMatrix.mmul(productFeatureMatrixBroadcast.value)

var productArray=productArrayBroadcast.value

var mappedUserRecommendationArray = new ArrayBuffer[String](params.topk)

// Extract ratings from the matrix

for (i <- 0 until userArray.length) {

var ratingSet = mutable.TreeSet.empty(Ordering.fromLessThan[(Int,Double)](_._2 > _._2))

for (j <- 0 until productArray.length) {

var rating = (productArray(j), userRecommendationMatrix.get(i,j))

ratingSet += rating

}

mappedUserRecommendationArray += userArray(i)+ "," +ratingSet.take(params.topk).mkString( "," )

}

mappedUserRecommendationArray.iterator

}

2015.06.12 更新:

悲哀的是,上面的方法还是不能解决问题,因为矩阵相乘会撑爆集群内存;可喜的是,如果你关注Spark最新动态,你会发现Spark1.4.0中MatrixFactorizationModel提供了 recommendForAll 方法实现离线批量推荐,详细说明见 SPARK-3066 。因为,我使用的Hadoop版本是CDH-5.4.0,其中Spark版本还是1.3.0,所以暂且不能在集群上测试Spark1.4.0中添加的新方法。

如果上面结果跑出来了,就可以验证推荐结果是否正确 。还是以384用户为例:

allRecs.lookup( 384 ).head.take( 10 )

topKRecs.map(_.product)

接下来,我们可以计算所有推荐结果的准确度了,首先,得到每个用户评分过的所有商品:

val userProducts = ratings.map{ case Rating(user, product, rating) => (user, product) }.groupBy(_._1)

然后,预测的商品和实际商品关联求准确度:

val MAPK = allRecs.join(userProducts).map{ case (userId, (predicted, actualWithIds)) =>

val actual = actualWithIds.map(_._2).toSeq

avgPrecisionK(actual, predicted, K)

}.reduce(_ + _) / allRecs.count

println( "Mean Average Precision at K = " + MAPK)

//Mean Average Precision at K = 0.018827551771260383

其实,我们也可以使用Spark内置的算法计算RMSE和MAE:

// MSE, RMSE and MAE

import org.apache.spark.mllib.evaluation.RegressionMetrics

val predictedAndTrue = ratesAndPreds.map { case ((user, product), (actual, predicted)) => (actual, predicted) }

val regressionMetrics = new RegressionMetrics(predictedAndTrue)

println( "Mean Squared Error = " + regressionMetrics.meanSquaredError)

println( "Root Mean Squared Error = " + regressionMetrics.rootMeanSquaredError)

// Mean Squared Error = 0.5490153087908566

// Root Mean Squared Error = 0.7409556726220918

// MAPK

import org.apache.spark.mllib.evaluation.RankingMetrics

val predictedAndTrueForRanking = allRecs.join(userProducts).map{ case (userId, (predicted, actualWithIds)) =>

val actual = actualWithIds.map(_._2)

(predicted.toArray, actual.toArray)

}

val rankingMetrics = new RankingMetrics(predictedAndTrueForRanking)

println( "Mean Average Precision = " + rankingMetrics.meanAveragePrecision)

// Mean Average Precision = 0.04417535679520426

计算推荐2000个商品时的准确度为:

val MAPK2000 = allRecs.join(userProducts).map{ case (userId, (predicted, actualWithIds)) =>

val actual = actualWithIds.map(_._2).toSeq

avgPrecisionK(actual, predicted, 2000 )

}.reduce(_ + _) / allRecs.count

println( "Mean Average Precision = " + MAPK2000)

//Mean Average Precision = 0.025228311843069083

保存和加载推荐模型

对与实时推荐,我们需要启动一个web server,在启动的时候生成或加载训练模型,然后提供API接口返回推荐接口,需要调用的相关方法为:

save(model: MatrixFactorizationModel, path: String)

load(sc: SparkContext, path: String)

model中的userFeatures和productFeatures也可以保存起来:

val outputDir= "/tmp"

model.userFeatures.map{ case (id, vec) => id + "/t" + vec.mkString( "," ) }.saveAsTextFile(outputDir + "/userFeatures" )

model.productFeatures.map{ case (id, vec) => id + "/t" + vec.mkString( "," ) }.saveAsTextFile(outputDir + "/productFeatures" )

总结

本文主要记录如何使用ALS算法实现协同过滤并给用户推荐商品,以上代码在Github仓库中的 ScalaLocalALS.scala 文件。

如果你想更加深入了解Spark MLlib算法的使用,可以看看 Packt.Machine Learning with Spark.2015.pdf 这本电子书并下载书中的源码,本文大部分代码参考自该电子书。

参考资料

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