ItemCF Finish

8b65e9f7 · Yao544303 · d31d19f1 · 8b65e9f7 · 8b65e9f7 · 8b65e9f7
27 changed file
--- 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


--- a/data/DFPath/._SUCCESS.crc
+++ b/data/DFPath/._SUCCESS.crc
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--- a/data/libSVMPath/._SUCCESS.crc
+++ b/data/libSVMPath/._SUCCESS.crc
--- a/data/libSVMPath/.part-00000.crc
+++ b/data/libSVMPath/.part-00000.crc
--- a/data/libSVMPath/.part-00001.crc
+++ b/data/libSVMPath/.part-00001.crc
--- a/data/libSVMPath/_SUCCESS
+++ b/data/libSVMPath/_SUCCESS
--- a/data/libSVMPath/part-00000
+++ b/data/libSVMPath/part-00000
+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
--- a/data/ratingslibsvm
+++ b/data/ratingslibsvm
-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
--- a/data/hot_movies.csv
+++ b/data/hot_movies.csv
--- a/data/ratings
+++ b/data/ratings
--- a/data/user_movies.csv
+++ b/data/user_movies.csv
--- a/spark/README.md
+++ b/spark/README.md
+# 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<id, features> 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
--- 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 <ratingsPath> <libSVMOutPath> <dfOutPath> <featureSize>
     */
-    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{

  }
 }
+
--- 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)
+
+
+
+    }
+
  }
 }