diff --git a/README.md b/README.md index 47e554896abb1ec9c452428bfa8e540902027189..ceb2ccddd12edbbc674e406dc32aacf98f746d92 100644 --- a/README.md +++ b/README.md @@ -5,10 +5,11 @@ # 目录规划 -data 测试用数据集合 -standalone 相关实践的单机实现版本（主要为python实现） -spark 相关实践的spark版本(主要为scala实现) -manual 相关资料集合 +* data 测试用数据集合 +* standalone 相关实践的单机实现版本（主要为python实现） +* spark 相关实践的spark版本(主要为scala实现) + 已经完成特征工程，ItemCF 部分 +* manual 相关资料集合 # 计划项(恩 就是挖坑的意思) ## 推荐算实现 @@ -30,8 +31,6 @@ Markov Chain 社交网络 .... -## paper阅读笔记 - ## 评价系统实现 diff --git a/data/DFPath/._SUCCESS.crc b/data/DFPath/._SUCCESS.crc new file mode 100644 index 0000000000000000000000000000000000000000..3b7b044936a890cd8d651d349a752d819d71d22c Binary files /dev/null and b/data/DFPath/._SUCCESS.crc differ diff --git a/data/DFPath/._common_metadata.crc b/data/DFPath/._common_metadata.crc new file mode 100644 index 0000000000000000000000000000000000000000..753ebae2ba339201807d19be489657a45b91e867 Binary files /dev/null and b/data/DFPath/._common_metadata.crc 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index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/data/libSVMPath/part-00000 b/data/libSVMPath/part-00000 new file mode 100644 index 0000000000000000000000000000000000000000..5489e316ed669bff5deb47ef09a4b94b5e4c93cf --- /dev/null +++ b/data/libSVMPath/part-00000 @@ -0,0 +1,2 @@ +4 260:5 480:4 +2 21:1 95:2 110:5 163:4 165:3 235:3 265:4 292:3 318:5 349:4 356:5 368:4 380:5 434:2 442:3 457:4 459:3 480:5 498:3 515:5 589:4 590:5 593:5 647:3 648:4 736:4 780:3 902:2 920:5 982:4 diff --git a/data/ratingslibsvm b/data/libSVMPath/part-00001 similarity index 71% rename from data/ratingslibsvm rename to data/libSVMPath/part-00001 index 6add3fbb1ec2e37c29716ef96e1e54122b7686d1..8f4a49dd4c4782eb3286dfedf9810787d4967979 100644 --- a/data/ratingslibsvm +++ b/data/libSVMPath/part-00001 @@ -1,5 +1,3 @@ -4 260:5 480:4 -2 21:1 95:2 110:5 163:4 165:3 235:3 265:4 292:3 318:5 349:4 356:5 368:4 380:5 434:2 442:3 457:4 459:3 480:5 498:3 515:5 589:4 590:5 593:5 647:3 648:4 736:4 780:3 902:2 920:5 982:4 5 6:2 16:3 24:1 29:5 32:4 34:4 36:3 39:3 41:4 47:3 50:5 52:2 150:2 162:4 176:4 194:3 202:2 215:3 224:3 229:3 265:3 272:3 288:2 296:4 299:3 318:3 321:3 348:4 353:2 356:1 357:2 377:4 412:2 461:3 497:3 501:1 506:4 509:4 515:4 551:4 562:4 581:3 593:4 608:4 714:4 728:4 733:1 800:2 860:2 866:4 896:4 908:4 913:5 919:4 968:3 994:5 3 104:4 260:5 480:4 552:4 590:4 593:3 648:3 653:4 733:5 1 1:5 48:5 150:5 260:4 527:5 531:4 588:4 594:4 595:5 608:4 661:3 720:3 745:3 783:4 914:3 919:4 938:4 diff --git a/data/hot_movies.csv b/data/ml-1m/hot_movies.csv similarity index 100% rename from data/hot_movies.csv rename to data/ml-1m/hot_movies.csv diff --git a/data/ratings b/data/ml-1m/ratings similarity index 100% rename from data/ratings rename to data/ml-1m/ratings diff --git a/data/user_movies.csv b/data/ml-1m/user_movies.csv similarity index 100% rename from data/user_movies.csv rename to data/ml-1m/user_movies.csv diff --git a/spark/README.md b/spark/README.md new file mode 100644 index 0000000000000000000000000000000000000000..cf348c580ba7ec43d6d7c5e3ca3a23235d6e6c81 --- /dev/null +++ b/spark/README.md @@ -0,0 +1,41 @@ +# 特征工程部分 +代码位于Features 类 +因为现实系统的rating 值，非常稀疏，为了节省存储空间和提升效率，在特征存储结构上，需要进行一些改动。主要思路如下： + +## 将rating 转为libsvm的方式存储 +用户对物品打分的数据，针对单个用户而言，对不同的物品的打分是很稀疏的。可以用libsvm格式来进行存储。 +``` +例如： 输入为 +1::661::3::978302109 +1::914::3::978301968 +转化之后结果为 +1 661:3 914:3 +``` + +## 将rating 转为 格式的DataFrame +id 为String +features 为 SparseVector + +# ItemCF +代码位于ItemCF 类 +## 相似度计算 +实现了两种方式，Jaccard 相似度 和 余弦相似度 + +## Jaccard +使用BitSet 存储每个用户的对该Item 是否有Ratting 的情况。 + +## 余弦相似度 +使用自带API实现 + + +## 基于Item 相似度 推荐单个物品 +选取该物品和其他物品的相似度向量。使用特征向量和相似度向量点乘即可。 + + +## 基于Item 相似度 推荐topK 的物品 +挖坑 + +# UserCF +挖坑 +## 根据topN的相似用户推荐 + \ No newline at end of file diff --git a/spark/src/main/scala/com/apachecn/recommand/colfliter/Features.scala b/spark/src/main/scala/com/apachecn/recommand/colfliter/Features.scala index 2f6374855f8add33ffe20b42f3ff6a86991b5b6c..141af349c74aee8082c4bab111efa45619b5db77 100644 --- a/spark/src/main/scala/com/apachecn/recommand/colfliter/Features.scala +++ b/spark/src/main/scala/com/apachecn/recommand/colfliter/Features.scala @@ -53,14 +53,15 @@ object Features{ def main(args: Array[String]): Unit = { /** + * 如需使用集群模式，修改入参，使用如下命令提交即可 * 提交命令 spark-submit --master yarn-cluster */ - val conf = new SparkConf().setAppName("Features Prepare") + val conf = new SparkConf().setAppName("Features Prepare").setMaster("local[*]") val sc = new SparkContext(conf) - val ratingsPath = args(0) - val libSVMOutPath = args(1) - val dfOutPath = args(2) - val featureSize = args(3).toInt + val ratingsPath = "..//data//ml-1m//ratings" + val libSVMOutPath = "..//data//libSVMPath" + val dfOutPath = "..//data//DFPath" + val featureSize = 3953 val testF = new Features @@ -69,3 +70,4 @@ object Features{ } } + diff --git a/spark/src/main/scala/com/apachecn/recommand/colfliter/ItemCF.scala b/spark/src/main/scala/com/apachecn/recommand/colfliter/ItemCF.scala index 852b19b7154be3f901a94061b7cdcf20ccb0a541..41e69da1c4749a7a6d2be844b8c00d7e87833eee 100644 --- a/spark/src/main/scala/com/apachecn/recommand/colfliter/ItemCF.scala +++ b/spark/src/main/scala/com/apachecn/recommand/colfliter/ItemCF.scala @@ -21,7 +21,7 @@ class ItemCF { val sqlContext = new SQLContext(sc) val rdd = sqlContext.read.parquet(featurePath).select("features") .rdd.map(x=>x(0).asInstanceOf[org.apache.spark.mllib.linalg.SparseVector]) - .map(x=>(x.indices)) + .map(x=>x.indices) .zipWithIndex() .map(x=>{ for (i <- x._1) yield { @@ -104,6 +104,13 @@ class ItemCF { } + /** + * 载入相似度矩阵 + * @param sc + * @param simPath + * @param featruesSize + * @return + */ def loadSimMatrix(sc: SparkContext, simPath: String, featruesSize: Int @@ -130,59 +137,144 @@ class ItemCF { } - def predictByMatrix(sc: SparkContext, - simMatrix: breeze.linalg.Matrix[Double], - featuresSize: Int, - featurePath: String, - resultPath: String - ): Unit ={ - val rdd = sc.textFile(featurePath) - .map(_.split(" ")) - .map(x=>(x.filter(g=>g.contains(":")))) - .map(x=>(x.map(_.split(":")).map(ar => (ar(0).toInt,ar(1).toDouble)))) - .map(x=>{ - val idx = x.map(_._1) - val v = x.map(_._2) - val vec: SparseVector[Double] = new SparseVector(idx, v, featuresSize) - vec + /** + * 根据Item 编号，从相似矩阵中获取该Item 的相似向量 + * @param simMatrix + * @param itemNum + * @return + */ + def getSimVecFromMatrix(simMatrix: SparseMatrix, itemNum: Int):Array[Double] ={ + val arr1 = for (i <- 0 until itemNum) yield { + simMatrix(i, itemNum) + } + val arr2 = for (i <- itemNum until simMatrix.numRows) yield { + simMatrix(itemNum, i) + } + (arr1 ++ arr2).toArray + } + + /** + * 基于Item 相似度向量 计算推荐单个物品时的得分，输出结果按得分降序排序 + * @param sc + * @param sim + * @param featurePath + * @return + */ + def predictBySimVecWithLibSVM(sc: SparkContext, + sim: Array[Double], + featurePath: String): RDD[(String, Double)] ={ + sc.textFile(featurePath).map(_.split(" ")).map(x=>{ + val id = x(0) + var score = 0.0 + for (i <- 1 until x.length){ + val idx = x(i).split(":")(0) + val value = x(i).split(":")(1) + score += value.toDouble * sim(idx.toInt) + } + (id,score) + }).sortBy(_._2,false) + } + + + /** + * 基于Item 相似度向量 计算推荐单个物品时的得分，输出结果按得分降序排序 + * @param sc + * @param sim + * @param featurePath + * @return + */ + def predictBySimVecWithDF(sc: SparkContext, + sim: Array[Double], + featurePath: String): RDD[(String, Double)] ={ + + val sqlContext = new SQLContext(sc) + sqlContext.read.parquet(featurePath).select("id","features") + .rdd.map(x=>{ + val p = x(0).toString + val v = x(1).asInstanceOf[org.apache.spark.mllib.linalg.SparseVector] + + val idxs = v.toSparse.indices + val values = v.toSparse.values + var score = 0.0 + for (i <- 0 until idxs.length){ + score += values(i) * sim(idxs(i)) + } + (p,score) }) -// .map(x=>(x.toDenseVector.toDenseMatrix.dot(simMatrix))) + .sortBy(_._2,false) } + } object ItemCF extends ItemCF{ + + + def main(args: Array[String]): Unit = { val conf = new SparkConf().setAppName("utils").setMaster("local[8]") val sc = new SparkContext(conf) val sqlContext = new SQLContext(sc) - val libsvmFeaturePath = "C:\\workspace\\data\\apacheCN\\libsvmOut" - val dfFeaturePath = "C:\\workspace\\data\\apacheCN\\dfOut" - val simPath = "C:\\workspace\\data\\apacheCN\\simPath" + val libsvmFeaturePath = "..//data//libSVMPath" + val dfFeaturePath = "..//data//DFPath" + val simPath = "..//data//SimPath" -// val JaccardSimPath = "..//data//jaccardSim" - val CosSimPath = "..//data//cosSim" val featureSize = 3953 -// -// val sim1 = computeJaccardSimWithLibSVM(sc,libsvmFeaturePath) -// sim1.entries.take(10) + testComputeSim() + testSaveAndLoadSimMatrix() + + + def testComputeSim(): Unit ={ + println("Test Compute Jaccard Sim With LibSVM ") + val sim1 = computeJaccardSimWithLibSVM(sc,libsvmFeaturePath) + sim1.entries.take(3).foreach(println) - val sim2 = computeCosSimWithLibSVM(sc,featureSize,libsvmFeaturePath) -// sim2.entries.take(10).foreach(println) -// saveSimMatrix(sc,simPath,sim2) + println("Test Compute Cossin Sim With LibSVM ") + val sim2 = computeCosSimWithLibSVM(sc,featureSize,libsvmFeaturePath) + sim2.entries.take(3).foreach(println) + + println("Test Compute Jaccard Sim With DataFrame ") val sim3 = computeJaccardSimWithDF(sc,dfFeaturePath) - sim3.entries.take(10) + sim3.entries.take(3).foreach(println) + + println("Test Compute Cossin Sim With DataFrame ") + val sim4 = computeCosSimWithDF(sc,dfFeaturePath) + sim4.entries.take(3).foreach(println) + } - val sim4 = computeCosSimWithDF(sc,dfFeaturePath) - sim4.entries.take(10) -// computeItemJaccardSim(sc,featurePath, JaccardSimPath) -// computeItemCosSim(sc,100,featurePath, CosSimPath) - val simMatrix = loadSimMatrix(sc, simPath, featureSize) -// val score = predict() + def testSaveAndLoadSimMatrix(): Unit ={ + val sim = computeCosSimWithLibSVM(sc,featureSize,libsvmFeaturePath) + saveSimMatrix(sc,simPath,sim) + + println("Save The SimMatrix") + + val simLoad = loadSimMatrix(sc, simPath, featureSize) + println(s"Load The SimMatrix. The Row Num Is ${simLoad.numRows} The Col Num Is ${simLoad.numCols}") + } + + def testPredict(): Unit ={ + val simMatrix = loadSimMatrix(sc, simPath, featureSize) + println(s"Load The SimMatrix. The Row Num Is ${simMatrix.numRows} The Col Num Is ${simMatrix.numCols}") + + val itemNum = 800 + val simVec = getSimVecFromMatrix(simMatrix,itemNum) + + println("Test Predict By SimVec With LibSVM ") + val score1 = predictBySimVecWithLibSVM(sc, simVec, libsvmFeaturePath) + score1.take(3).foreach(println) + + println("Test Predict By SimVec With DataFrame ") + val score2 = predictBySimVecWithDF(sc, simVec, dfFeaturePath) + score2.take(3).foreach(println) + + + + } + } }