Spark ALS 协同过滤
本文介绍 Spark MLlib 中的 ALS 协同过滤算法，包括原理、参数设置及代码示例，涵盖 Scala、Java 和 Python 实现。
本文转自http://www.tuicool.com/articles/fANvieZ，所有权力归原作者所有。
本文主要通过Spark官方的例子，理解ALS协同过滤算法的原理和编码过程。
协同过滤
协同过滤 常被应用于推荐系统，旨在补充用户-商品关联矩阵中所缺失的部分。MLlib当前支持基于模型的协同过滤，其中用户和商品通过一小组隐语义因子进行表达，并且这些因子也用于预测缺失的元素。Spark MLlib实现了 交替最小二乘法 (ALS) 来学习这些隐性语义因子。在 MLlib 中的实现有如下的参数:
numBlocks是用于并行化计算的分块个数 (设置为-1，为自动配置)。rank是模型中隐语义因子的个数。iterations是迭代的次数。lambda是ALS的正则化参数。implicitPrefs决定了是用显性反馈ALS的版本还是用适用隐性反馈数据集的版本。alpha是一个针对于隐性反馈 ALS 版本的参数，这个参数决定了偏好行为强度的基准。
可以调整这些参数，不断优化结果，使均方差变小。比如：iterations越多，lambda较小，均方差会较小，推荐结果较优。
隐性反馈 vs 显性反馈
基于矩阵分解的协同过滤的标准方法一般将用户商品矩阵中的元素作为用户对商品的显性偏好。
在许多的现实生活中的很多场景中，我们常常只能接触到隐性的反馈（例如游览，点击，购买，喜欢，分享等等）在 MLlib 中所用到的处理这种数据的方法来源于文献： Collaborative Filtering for Implicit Feedback Datasets 。 本质上，这个方法将数据作为二元偏好值和偏好强度的一个结合，而不是对评分矩阵直接进行建模。因此，评价就不是与用户对商品的显性评分而是和所观察到的用户偏好强度关联了起来。然后，这个模型将尝试找到隐语义因子来预估一个用户对一个商品的偏好。
示例
下面代码的均在 Spark 的home目录下运行，并且示例中加载的文件路径为data/mllib/als/test.data ，文件中每一行包括一个用户id、商品id和评分。我们使用默认的 ALS.train() 方法来构建推荐模型并评估模型的均方差。
查看文件内容：
$ more data/mllib/als/test.data
1,1,5.0
1,2,1.0
1,3,5.0
1,4,1.0
2,1,5.0
2,2,1.0
2,3,5.0
2,4,1.0
3,1,1.0
3,2,5.0
3,3,1.0
3,4,5.0
4,1,1.0
4,2,5.0
4,3,1.0
4,4,5.0
下面例子来自 http://spark.apache.org/docs/latest/mllib-collaborative-filtering.html ，并做了稍许修改。
Scala 示例
下面代码可以在spark-shell中运行：
import org.apache.log4j.Logger
import org.apache.log4j.Level
import org.apache.spark.mllib.recommendation.ALS
import org.apache.spark.mllib.recommendation.Rating
//设置日志级别
Logger.getLogger("org.apache.spark").setLevel(Level.WARN)
Logger.getLogger("org.eclipse.jetty.server").setLevel(Level.OFF)
// 加载并解析数据
val data = sc.textFile("data/mllib/als/test.data")
/**
* Product ratings are on a scale of 1-5:
* 5: Must see
* 4: Will enjoy
* 3: It's okay
* 2: Fairly bad
* 1: Awful
*/
val ratings = data.map(_.split(',') match { case Array(user, product, rate) =>
Rating(user.toInt, product.toInt, rate.toDouble)
})
//使用ALS训练数据建立推荐模型
val rank = 10
val numIterations = 20
val model = ALS.train(ratings, rank, numIterations, 0.01)
//从 ratings 中获得只包含用户和商品的数据集
val usersProducts = ratings.map { case Rating(user, product, rate) =>
(user, product)
}
//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
val predictions =
model.predict(usersProducts).map { case Rating(user, product, rate) =>
((user, product), rate)
}
//将真实评分数据集与预测评分数据集进行合并
val ratesAndPreds = ratings.map { case Rating(user, product, rate) =>
((user, product), rate)
}.join(predictions).sortByKey() //ascending or descending
//然后计算均方差，注意这里没有调用 math.sqrt方法
val MSE = ratesAndPreds.map { case ((user, product), (r1, r2)) =>
val err = (r1 - r2)
err * err
}.mean()
//打印出均方差值
println("Mean Squared Error = " + MSE)
//Mean Squared Error = 1.37797097094789E-5
上面的例子中调用了 ALS.train(ratings, rank, numIterations, 0.01) ，我们还可以设置其他参数，调用方式如下：
val model = new ALS()
.setRank(params.rank)
.setIterations(params.numIterations)
.setLambda(params.lambda)
.setImplicitPrefs(params.implicitPrefs)
.setUserBlocks(params.numUserBlocks)
.setProductBlocks(params.numProductBlocks)
.run(training)
Java示例
import scala.Tuple2;
import org.apache.spark.api.java.*;
import org.apache.spark.api.java.function.Function;
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;
public class CollaborativeFiltering {
public static void main(String[] args) {
SparkConf conf = new SparkConf().setAppName("Collaborative Filtering Example");
JavaSparkContext sc = new JavaSparkContext(conf);
// Load and parse the data
String path = "data/mllib/als/test.data";
JavaRDD<String> data = sc.textFile(path);
JavaRDD<Rating> ratings = data.map(
new Function<String, Rating>() {
public Rating call(String 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 = 20;
MatrixFactorizationModel model = ALS.train(JavaRDD.toRDD(ratings), rank, numIterations, 0.01);
// Evaluate the model on rating data
JavaRDD<Tuple2<Object, Object>> userProducts = ratings.map(
new Function<Rating, Tuple2<Object, Object>>() {
public Tuple2<Object, Object> call(Rating r) {
return new Tuple2<Object, Object>(r.user(), r.product());
}
}
);
JavaPairRDD<Tuple2<Integer, Integer>, Double> predictions = JavaPairRDD.fromJavaRDD(
model.predict(JavaRDD.toRDD(userProducts)).toJavaRDD().map(
new Function<Rating, Tuple2<Tuple2<Integer, Integer>, Double>>() {
public Tuple2<Tuple2<Integer, Integer>, Double> call(Rating r){
return new Tuple2<Tuple2<Integer, Integer>, Double>(
new Tuple2<Integer, Integer>(r.user(), r.product()), r.rating());
}
}
));
JavaRDD<Tuple2<Double, Double>> ratesAndPreds =
JavaPairRDD.fromJavaRDD(ratings.map(
new Function<Rating, Tuple2<Tuple2<Integer, Integer>, Double>>() {
public Tuple2<Tuple2<Integer, Integer>, Double> call(Rating r){
return new Tuple2<Tuple2<Integer, Integer>, Double>(
new Tuple2<Integer, Integer>(r.user(), r.product()), r.rating());
}
}
)).join(predictions).values();
double MSE = JavaDoubleRDD.fromRDD(ratesAndPreds.map(
new Function<Tuple2<Double, Double>, Object>() {
public Object call(Tuple2<Double, Double> pair) {
Double err = pair._1() - pair._2();
return err * err;
}
}
).rdd()).mean();
System.out.println("Mean Squared Error = " + MSE);
}
}
Python示例
下面代码可以在 pyspark 中运行：
from pyspark.mllib.recommendation import ALS
from numpy import array
# Load and parse the data
data = sc.textFile("data/mllib/als/test.data")
ratings = data.map(lambda line: array([float(x) for x in line.split(',')]))
# Build the recommendation model using Alternating Least Squares
rank = 10
numIterations = 20
model = ALS.train(ratings, rank, numIterations)
# Evaluate the model on training data
testdata = ratings.map(lambda p: (int(p[0]), int(p[1])))
predictions = model.predictAll(testdata).map(lambda r: ((r[0], r[1]), r[2]))
ratesAndPreds = ratings.map(lambda r: ((r[0], r[1]), r[2])).join(predictions)
MSE = ratesAndPreds.map(lambda r: (r[1][0] - r[1][1])**2).reduce(lambda x, y: x + y)/ratesAndPreds.count()
print("Mean Squared Error = " + str(MSE))
总结
协同过滤ALS算法推荐过程
协同过滤ALS算法推荐过程如下：
- 加载数据到 ratings RDD，每行记录包括：user, product, rate
- 从 ratings 得到用户商品的数据集：(user, product)
- 使用ALS对 ratings 进行训练
- 通过 model 对用户商品进行预测评分：((user, product), rate)
- 从 ratings 得到用户商品的实际评分：((user, product), rate)
- 合并预测评分和实际评分的两个数据集，并求均方差
保存推荐结果
上面的例子只是对训练集并进行了评分，我们还可以进一步的给用户推荐商品。以 Scala 程序为例，在原来代码基础上继续执行下面代码：
//为每个用户进行推荐，推荐的结果可以以用户id为key，结果为value存入redis或者hbase中
val users=data.map(_.split(",") match {
case Array(user, product, rate) => (user)
}).distinct().collect()
//users: Array[String] = Array(4, 2, 3, 1)
users.foreach(
user => {
//依次为用户推荐商品
var rs = model.recommendProducts(user.toInt, numIterations)
var value = ""
var key = 0
//拼接推荐结果
rs.foreach(r => {
key = r.user
value = value + r.product + ":" + r.rating + ","
})
println(key.toString+" " + value)
}
)
//4 4:4.9948551991729,2:4.9948551991729,3:1.0007160894300133,1:1.0007160894300133,
//2 1:4.994747095003154,3:4.994747095003154,2:1.0007376098628127,4:1.0007376098628127,
//3 2:4.9948551991729,4:4.9948551991729,3:1.0007160894300133,1:1.0007160894300133,
//1 3:4.994747095003154,1:4.994747095003154,2:1.0007376098628127,4:1.0007376098628127,
上面的代码调用 model.recommendProducts 方法分别对用户进行推荐，其实在之前的代码中已经计算出了预测的评分，我们可以通过 predictions 或者 ratesAndPreds 来得到最后的推荐结果：
//对预测结果按预测的评分排序
predictions.collect.sortBy(_._2)
//Array[((Int, Int), Double)] = Array(((4,1),1.0007160894300133), ((3,1),1.0007160894300133), ((4,3),1.0007160894300133), ((3,3),1.0007160894300133), ((1,4),1.0007376098628127), ((2,4),1.0007376098628127), ((1,2),1.0007376098628127), ((2,2),1.0007376098628127), ((1,1),4.994747095003154), ((2,1),4.994747095003154), ((1,3),4.994747095003154), ((2,3),4.994747095003154), ((4,4),4.9948551991729), ((3,4),4.9948551991729), ((4,2),4.9948551991729), ((3,2),4.9948551991729))
//对预测结果按用户进行分组，然后合并推荐结果，这部分代码待修正
predictions.map{ case ((user, product), rate) => (user, (product,rate) )}.groupByKey.collect
//格式化测试评分和实际评分的结果
val formatedRatesAndPreds = ratesAndPreds.map {
case ((user, product), (rate, pred)) => user + "," + product + "," + rate + "," + pred
}
//Array(2,1,5.0,4.994747095003154, 4,4,5.0,4.9948551991729, 4,2,5.0,4.9948551991729, 4,1,1.0,1.0007160894300133, 3,4,5.0,4.9948551991729, 1,4,1.0,1.0007376098628127, 3,1,1.0,1.0007160894300133, 2,3,5.0,4.994747095003154, 1,2,1.0,1.0007376098628127, 1,1,5.0,4.994747095003154, 2,2,1.0,1.0007376098628127, 2,4,1.0,1.0007376098628127, 3,2,5.0,4.9948551991729, 3,3,1.0,1.0007160894300133, 4,3,1.0,1.0007160894300133, 1,3,5.0,4.994747095003154)
提示：
因为我对Scala和Spark的语法还不是很熟悉，所以上面的代码中 通过 predictions 或者 ratesAndPreds 来得到最后的推荐结果 的代码尚未给出，待我后续再补充，请见谅！
上面的代码是依次遍历用户然后分别对用户进行推荐，推荐的结果可以以csv、json格式存入到hdfs或者以key/value方式存入到Cassandra、HBase、Redis、Mongodb等分布式数据库。
- 使用Cassandra保存推荐结果
- Simple example on how to use recommenders in Spark / MLlib using the Play framework and Mongodb
将数据集分为训练数据和测试数据
参考 ALSBenchmark.scala ，一个 完整的scala代码 如下：
import scala.collection.mutable
import org.apache.log4j.{Level, Logger}
import scopt.OptionParser
import org.apache.spark.{SparkConf, SparkContext}
import org.apache.spark.SparkContext._
import org.apache.spark.mllib.recommendation.{ALS, MatrixFactorizationModel, Rating}
import org.apache.spark.rdd.RDD
/**
* An example app for ALS on MovieLens data (http://grouplens.org/datasets/movielens/).
* Run with
*
* bin/run-example org.apache.spark.examples.mllib.MovieLensALS
*
* A synthetic dataset in MovieLens format can be found at `data/mllib/sample_movielens_data.txt`.
* If you use it as a template to create your own app, please use `spark-submit` to submit your app.
*/
object MovieLensALS {
case class Params(
input: String = null,
kryo: Boolean = false,
numIterations: Int = 20,
lambda: Double = 1.0,
rank: Int = 10,
numUserBlocks: Int = -1,
numProductBlocks: Int = -1,
implicitPrefs: Boolean = false)
def main(args: Array[String]) {
val defaultParams = Params()
val parser = new OptionParser[Params]("MovieLensALS") {
head("MovieLensALS: an example app for ALS on MovieLens data.")
opt[Int]("rank")
.text(s"rank, default: ${defaultParams.rank}}")
.action((x, c) => c.copy(rank = x))
opt[Int]("numIterations")
.text(s"number of iterations, default: ${defaultParams.numIterations}")
.action((x, c) => c.copy(numIterations = x))
opt[Double]("lambda")
.text(s"lambda (smoothing constant), default: ${defaultParams.lambda}")
.action((x, c) => c.copy(lambda = x))
opt[Unit]("kryo")
.text("use Kryo serialization")
.action((_, c) => c.copy(kryo = true))
opt[Int]("numUserBlocks")
.text(s"number of user blocks, default: ${defaultParams.numUserBlocks} (auto)")
.action((x, c) => c.copy(numUserBlocks = x))
opt[Int]("numProductBlocks")
.text(s"number of product blocks, default: ${defaultParams.numProductBlocks} (auto)")
.action((x, c) => c.copy(numProductBlocks = x))
opt[Unit]("implicitPrefs")
.text("use implicit preference")
.action((_, c) => c.copy(implicitPrefs = true))
arg[String]("<input>")
.required()
.text("input paths to a MovieLens dataset of ratings")
.action((x, c) => c.copy(input = x))
}
parser.parse(args, defaultParams).map { params =>
run(params)
} getOrElse {
System.exit(1)
}
}
def run(params: Params) {
val conf = new SparkConf().setAppName(s"MovieLensALS with $params")
if (params.kryo) {
conf.registerKryoClasses(Array(classOf[mutable.BitSet], classOf[Rating]))
.set("spark.kryoserializer.buffer.mb", "8")
}
val sc = new SparkContext(conf)
Logger.getRootLogger.setLevel(Level.WARN)
//是否显性反馈
val implicitPrefs = params.implicitPrefs
//数据集
val ratings = sc.textFile(params.input).map { line =>
val fields = line.split("::")
if (implicitPrefs) {
/*
* MovieLens ratings are on a scale of 1-5:
* 5: Must see
* 4: Will enjoy
* 3: It's okay
* 2: Fairly bad
* 1: Awful
* So we should not recommend a movie if the predicted rating is less than 3.
* To map ratings to confidence scores, we use
* 5 -> 2.5, 4 -> 1.5, 3 -> 0.5, 2 -> -0.5, 1 -> -1.5. This mappings means unobserved
* entries are generally between It's okay and Fairly bad.
* The semantics of 0 in this expanded world of non-positive weights
* are "the same as never having interacted at all".
*/
Rating(fields(0).toInt, fields(1).toInt, fields(2).toDouble - 2.5)
} else {
Rating(fields(0).toInt, fields(1).toInt, fields(2).toDouble)
}
}.cache()
val numRatings = ratings.count()
val numUsers = ratings.map(_.user).distinct().count()
val numMovies = ratings.map(_.product).distinct().count()
println(s"Got $numRatings ratings from $numUsers users on $numMovies movies.")
//拆分数据，80%为训练集，20%为测试集
val splits = ratings.randomSplit(Array(0.8, 0.2))
val training = splits(0).cache()
val test = if (params.implicitPrefs) {
/*
* 0 means "don't know" and positive values mean "confident that the prediction should be 1".
* Negative values means "confident that the prediction should be 0".
* We have in this case used some kind of weighted RMSE. The weight is the absolute value of
* the confidence. The error is the difference between prediction and either 1 or 0,
* depending on whether r is positive or negative.
*/
splits(1).map(x => Rating(x.user, x.product, if (x.rating > 0) 1.0 else 0.0))
} else {
splits(1)
}.cache()
val numTraining = training.count()
val numTest = test.count()
println(s"Training: $numTraining, test: $numTest.")
ratings.unpersist(blocking = false)
val start = System.currentTimeMillis()
val model = new ALS()
.setRank(params.rank)
.setIterations(params.numIterations)
.setLambda(params.lambda)
.setImplicitPrefs(params.implicitPrefs)
.setUserBlocks(params.numUserBlocks)
.setProductBlocks(params.numProductBlocks)
.run(training)
val end = System.currentTimeMillis()
println("Train Time = " + (end-start)*1.0/1000)
val rmse = computeRmse(model, test, params.implicitPrefs)
println(s"Train RMSE = " + computeRmse(model, training,params.implicitPrefs))
println(s"Test RMSE = $rmse.")
sc.stop()
}
/** Compute RMSE (Root Mean Squared Error). */
def computeRmse(model: MatrixFactorizationModel, data: RDD[Rating], implicitPrefs: Boolean) = {
def mapPredictedRating(r: Double) = if (implicitPrefs) math.max(math.min(r, 1.0), 0.0) else r
val predictions: RDD[Rating] = model.predict(data.map(x => (x.user, x.product)))
val predictionsAndRatings = predictions.map{ x =>
((x.user, x.product), mapPredictedRating(x.rating))
}.join(data.map(x => ((x.user, x.product), x.rating))).values
math.sqrt(predictionsAndRatings.map(x => (x._1 - x._2) * (x._1 - x._2)).mean())
}
}
总结
通过本文主要熟悉了如何对输入数据进行推荐和评分，要想完全掌握本文中的示例代码并做到融会贯通，还需要花些时间熟悉和掌握Scala和Spark DataFrame的语法。希望这篇文章能够对你理解Spark的协同过滤算法有所帮助。
后续还需要掌握以下内容：
- ALS中各个参数对推荐结果和评分的影响
- 如何保存推荐结果
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