推荐算法流程
预备
wget http://www.iro.umontreal.ca/~lisa/datasets/profiledata_06-May-2005.tar.gz
cd /Users/erichan/garden/spark-1.6.0-bin-hadoop2.6/bin
./spark-shell --master local --driver-memory 6g1 准备数据
val data ="/Users/erichan/AliDrive/ml_spark/data/profiledata_06-May-2005"
val rawUserArtistData = sc.textFile(data+"/user_artist_data.txt",10)
// ALS 需要ID必须为数值型
rawUserArtistData.first
//res3: String = 1092764 1000311
//rawUserArtistData.map(_.split(' ')(0).toDouble).stats()
//res10: org.apache.spark.util.StatCounter = (count: 24296858, mean: 1947573.265353, stdev: 496000.544975, max: 2443548.000000, min: 90.000000)
//rawUserArtistData.map(_.split(' ')(1).toDouble).stats()
//res11: org.apache.spark.util.StatCounter = (count: 24296858, mean: 1718704.093757, stdev: 2539389.040171, max: 10794401.000000, min: 1.000000)
val rawArtistData = sc.textFile(data+"/artist_data.txt")
//rawArtistData.first
//res12: String = 1134999 06Crazy Life
val artistByID = rawArtistData.flatMap { line =>
val (id, name) = line.span(_ != '\t')
if (name.isEmpty) {
None
}else{
try {
Some((id.toInt, name.trim))
} catch {
case e: NumberFormatException => None
}
}
}
val rawArtistAlias = sc.textFile(data+"/artist_alias.txt")
val artistAlias = rawArtistAlias.flatMap { line =>
val tokens = line.split('\t')
if (tokens(0).isEmpty) {
None
}else{
Some((tokens(0).toInt, tokens(1).toInt))
}
}.collectAsMap()
//artistByID.lookup(1000010).head
//res14: String = Aerosmith 2 建模
import org.apache.spark.mllib.recommendation._
val bArtistAlias = sc.broadcast(artistAlias)
val trainData = rawUserArtistData.map { line =>
val Array(userID, artistID, count) = line.split(' ').map(_.toInt)
val finalArtistID = bArtistAlias.value.getOrElse(artistID, artistID)
Rating(userID, finalArtistID, count)
}.cache()
val model = ALS.trainImplicit(trainData, 10, 5, 0.01, 1.0)3 检验
val rawArtistsForUser = rawUserArtistData.map(_.split(' ')).filter {
case Array(user,_,_) => user.toInt == 2093760
}
val existingProducts = rawArtistsForUser.map {
case Array(_,artist,_) => artist.toInt
}.collect().toSet
artistByID.filter {
case (id, name) => existingProducts.contains(id)
}.values.collect().foreach(println)
val recommendations = model.recommendProducts(2093760, 5)
recommendations.foreach(println)
val recommendedProductIDs = recommendations.map(_.product).toSet
artistByID.filter {
case (id, name) => recommendedProductIDs.contains(id)
}.values.collect().foreach(println)
4 评价
:load /Users/erichan/sourcecode/book/aas/ch03-recommender/src/main/scala/RunAUC.scala
val bArtistAlias = sc.broadcast(RunAUC.buildArtistAlias(rawArtistAlias))
val allData = RunAUC.buildRatings(rawUserArtistData, bArtistAlias)
val Array(trainData, cvData) = allData.randomSplit(Array(0.9, 0.1))
trainData.cache()
cvData.cache()
val allItemIDs = allData.map(_.product).distinct().collect()
val bAllItemIDs = sc.broadcast(allItemIDs)
val mostListenedAUC = RunAUC.areaUnderCurve(cvData, bAllItemIDs, RunAUC.predictMostListened(sc, trainData))
println(mostListenedAUC)
//0.9395286660878177
trainData.unpersist()
cvData.unpersist()5 推荐
val someUsers = allData.map(_.user).distinct().take(100)
val someRecommendations = someUsers.map(userID => model.recommendProducts(userID, 5))
someRecommendations.map(
recs => recs.head.user + " -> " + recs.map(_.product).mkString(", ")
).foreach(println)附录
RunAUC.scala
import org.apache.spark.SparkContext
import org.apache.spark.SparkContext._
import org.apache.spark.broadcast.Broadcast
import org.apache.spark.mllib.recommendation._
import org.apache.spark.rdd.RDD
import scala.collection.Map
import scala.collection.mutable.ArrayBuffer
import scala.util.Random
/**
* Created by erichan
* on 16/1/26.
*/
object RunAUC {
def areaUnderCurve(
positiveData: RDD[Rating],
bAllItemIDs: Broadcast[Array[Int]],
predictFunction: (RDD[(Int,Int)] => RDD[Rating])) = {
// What this actually computes is AUC, per user. The result is actually something
// that might be called "mean AUC".
// Take held-out data as the "positive", and map to tuples
val positiveUserProducts = positiveData.map(r => (r.user, r.product))
// Make predictions for each of them, including a numeric score, and gather by user
val positivePredictions = predictFunction(positiveUserProducts).groupBy(_.user)
// BinaryClassificationMetrics.areaUnderROC is not used here since there are really lots of
// small AUC problems, and it would be inefficient, when a direct computation is available.
// Create a set of "negative" products for each user. These are randomly chosen
// from among all of the other items, excluding those that are "positive" for the user.
val negativeUserProducts = positiveUserProducts.groupByKey().mapPartitions {
// mapPartitions operates on many (user,positive-items) pairs at once
userIDAndPosItemIDs => {
// Init an RNG and the item IDs set once for partition
val random = new Random()
val allItemIDs = bAllItemIDs.value
userIDAndPosItemIDs.map { case (userID, posItemIDs) =>
val posItemIDSet = posItemIDs.toSet
val negative = new ArrayBuffer[Int]()
var i = 0
// Keep about as many negative examples per user as positive.
// Duplicates are OK
while (i < allItemIDs.size && negative.size < posItemIDSet.size) {
val itemID = allItemIDs(random.nextInt(allItemIDs.size))
if (!posItemIDSet.contains(itemID)) {
negative += itemID
}
i += 1
}
// Result is a collection of (user,negative-item) tuples
negative.map(itemID => (userID, itemID))
}
}
}.flatMap(t => t)
// flatMap breaks the collections above down into one big set of tuples
// Make predictions on the rest:
val negativePredictions = predictFunction(negativeUserProducts).groupBy(_.user)
// Join positive and negative by user
positivePredictions.join(negativePredictions).values.map {
case (positiveRatings, negativeRatings) =>
// AUC may be viewed as the probability that a random positive item scores
// higher than a random negative one. Here the proportion of all positive-negative
// pairs that are correctly ranked is computed. The result is equal to the AUC metric.
var correct = 0L
var total = 0L
// For each pairing,
for (positive <- positiveRatings;
negative <- negativeRatings) {
// Count the correctly-ranked pairs
if (positive.rating > negative.rating) {
correct += 1
}
total += 1
}
// Return AUC: fraction of pairs ranked correctly
correct.toDouble / total
}.mean() // Return mean AUC over users
}
def predictMostListened(sc: SparkContext, train: RDD[Rating])(allData: RDD[(Int,Int)]) = {
val bListenCount =
sc.broadcast(train.map(r => (r.product, r.rating)).reduceByKey(_ + _).collectAsMap())
allData.map { case (user, product) =>
Rating(user, product, bListenCount.value.getOrElse(product, 0.0))
}
}
def buildArtistAlias(rawArtistAlias: RDD[String]): Map[Int,Int] =
rawArtistAlias.flatMap { line =>
val tokens = line.split('\t')
if (tokens(0).isEmpty) {
None
} else {
Some((tokens(0).toInt, tokens(1).toInt))
}
}.collectAsMap()
def buildRatings(
rawUserArtistData: RDD[String],
bArtistAlias: Broadcast[Map[Int,Int]]) = {
rawUserArtistData.map { line =>
val Array(userID, artistID, count) = line.split(' ').map(_.toInt)
val finalArtistID = bArtistAlias.value.getOrElse(artistID, artistID)
Rating(userID, finalArtistID, count)
}
}
}