Spark MLlib实现的广告点击预测–Gradient-Boosted Trees

来源：互联网发布：淘宝店如何做广告? 编辑：程序博客网时间：2024/05/22 13:14

雪晴数据网

本文尝试使用Spark提供的机器学习算法 Gradient-Boosted Trees来预测一个用户是否会点击广告。

训练和测试数据使用Kaggle Avazu CTR 比赛的样例数据，下载地址：https://www.kaggle.com/c/avazu-ctr-prediction/data

数据格式如下：

包含24个字段：

1-id: ad identifier
2-click: 0/1 for non-click/click
3-hour: format is YYMMDDHH, so 14091123 means 23:00 on Sept. 11, 2014 UTC.
4-C1 — anonymized categorical variable
5-banner_pos
6-site_id
7-site_domain
8-site_category
9-app_id
10-app_domain
11-app_category
12-device_id
13-device_ip
14-device_model
15-device_type
16-device_conn_type
17~24—C14-C21 — anonymized categorical variables

其中5到15列为分类特征，16~24列为数值型特征。

Spark代码如下：

package com.lxw1234.test import scala.collection.mutable.ListBufferimport scala.collection.mutable.ArrayBuffer import org.apache.spark.SparkContextimport org.apache.spark.SparkContext._import org.apache.spark.SparkConfimport org.apache.spark.rdd.RDD import org.apache.spark.mllib.classification.NaiveBayesimport org.apache.spark.mllib.regression.LabeledPointimport org.apache.spark.mllib.linalg.Vectors import org.apache.spark.mllib.tree.GradientBoostedTreesimport org.apache.spark.mllib.tree.configuration.BoostingStrategyimport org.apache.spark.mllib.tree.model.GradientBoostedTreesModel /** * By: lxw * http://lxw1234.com */object CtrPredict {    //input (1fbe01fe,f3845767,28905ebd,ecad2386,7801e8d9)  //output ((0:1fbe01fe),(1:f3845767),(2:28905ebd),(3:ecad2386),(4:7801e8d9))    def parseCatFeatures(catfeatures: Array[String]) :  List[(Int, String)] = {      var catfeatureList = new ListBuffer[(Int, String)]()      for (i <- 0 until catfeatures.length){          catfeatureList += i -> catfeatures(i).toString      }      catfeatureList.toList    }    def main(args: Array[String]) {      val conf = new SparkConf().setMaster("yarn-client")      val sc = new SparkContext(conf)            var ctrRDD = sc.textFile("/tmp/lxw1234/sample.txt",10);      println("Total records : " + ctrRDD.count)            //将整个数据集80%作为训练数据，20%作为测试数据集      var train_test_rdd = ctrRDD.randomSplit(Array(0.8, 0.2), seed = 37L)      var train_raw_rdd = train_test_rdd(0)      var test_raw_rdd = train_test_rdd(1)            println("Train records : " + train_raw_rdd.count)      println("Test records : " + test_raw_rdd.count)            //cache train, test      train_raw_rdd.cache()      test_raw_rdd.cache()            var train_rdd = train_raw_rdd.map{ line =>          var tokens = line.split(",",-1)          //key为id和是否点击广告          var catkey = tokens(0) + "::" + tokens(1)          //第6列到第15列为分类特征，需要One-Hot-Encoding          var catfeatures = tokens.slice(5, 14)          //第16列到24列为数值特征，直接使用          var numericalfeatures = tokens.slice(15, tokens.size-1)          (catkey, catfeatures, numericalfeatures)      }            //拿一条出来看看      train_rdd.take(1)      //scala> train_rdd.take(1)      //res6: Array[(String, Array[String], Array[String])] = Array((1000009418151094273::0,Array(1fbe01fe,       //            f3845767, 28905ebd, ecad2386, 7801e8d9, 07d7df22, a99f214a, ddd2926e, 44956a24),      //              Array(2, 15706, 320, 50, 1722, 0, 35, -1)))            //将分类特征先做特征ID映射      var train_cat_rdd  = train_rdd.map{        x => parseCatFeatures(x._2)      }            train_cat_rdd.take(1)      //scala> train_cat_rdd.take(1)      //res12: Array[List[(Int, String)]] = Array(List((0,1fbe01fe), (1,f3845767), (2,28905ebd),       //        (3,ecad2386), (4,7801e8d9), (5,07d7df22), (6,a99f214a), (7,ddd2926e), (8,44956a24)))            //将train_cat_rdd中的(特征ID：特征)去重，并进行编号      var oheMap = train_cat_rdd.flatMap(x => x).distinct().zipWithIndex().collectAsMap()      //oheMap: scala.collection.Map[(Int, String),Long] = Map((7,608511e9) -> 31527, (7,b2d8fbed) -> 42207,       //  (7,1d3e2fdb) -> 52791      println("Number of features")      println(oheMap.size)            //create OHE for train data      var ohe_train_rdd = train_rdd.map{ case (key, cateorical_features, numerical_features) =>              var cat_features_indexed = parseCatFeatures(cateorical_features)                                      var cat_feature_ohe = new ArrayBuffer[Double]              for (k <- cat_features_indexed) {                if(oheMap contains k){                cat_feature_ohe += (oheMap get (k)).get.toDouble                }else {                  cat_feature_ohe += 0.0                }                             }              var numerical_features_dbl  = numerical_features.map{                        x =>                           var x1 = if (x.toInt < 0) "0" else x                        x1.toDouble              }              var features = cat_feature_ohe.toArray ++  numerical_features_dbl                         LabeledPoint(key.split("::")(1).toInt, Vectors.dense(features))                                                    }           ohe_train_rdd.take(1)     //res15: Array[org.apache.spark.mllib.regression.LabeledPoint] =      //  Array((0.0,[43127.0,50023.0,57445.0,13542.0,31092.0,14800.0,23414.0,54121.0,     //     17554.0,2.0,15706.0,320.0,50.0,1722.0,0.0,35.0,0.0]))          //训练模型     //val boostingStrategy = BoostingStrategy.defaultParams("Regression")     val boostingStrategy = BoostingStrategy.defaultParams("Classification")     boostingStrategy.numIterations = 100     boostingStrategy.treeStrategy.numClasses = 2     boostingStrategy.treeStrategy.maxDepth = 10     boostingStrategy.treeStrategy.categoricalFeaturesInfo = Map[Int, Int]()               val model = GradientBoostedTrees.train(ohe_train_rdd, boostingStrategy)     //保存模型     model.save(sc, "/tmp/myGradientBoostingClassificationModel")     //加载模型     val sameModel = GradientBoostedTreesModel.load(sc,"/tmp/myGradientBoostingClassificationModel")          //将测试数据集做OHE     var test_rdd = test_raw_rdd.map{ line =>        var tokens = line.split(",")        var catkey = tokens(0) + "::" + tokens(1)        var catfeatures = tokens.slice(5, 14)        var numericalfeatures = tokens.slice(15, tokens.size-1)        (catkey, catfeatures, numericalfeatures)     }          var ohe_test_rdd = test_rdd.map{ case (key, cateorical_features, numerical_features) =>            var cat_features_indexed = parseCatFeatures(cateorical_features)                  var cat_feature_ohe = new ArrayBuffer[Double]            for (k <- cat_features_indexed) {                             if(oheMap contains k){                cat_feature_ohe += (oheMap get (k)).get.toDouble              }else {                cat_feature_ohe += 0.0              }            }          var numerical_features_dbl  = numerical_features.map{x =>                               var x1 = if (x.toInt < 0) "0" else x                              x1.toDouble}            var features = cat_feature_ohe.toArray ++  numerical_features_dbl                       LabeledPoint(key.split("::")(1).toInt, Vectors.dense(features))                                                    }          //验证测试数据集     var b = ohe_test_rdd.map {        y => var s = model.predict(y.features)        (s,y.label,y.features)     }          b.take(10).foreach(println)          //预测准确率      var predictions = ohe_test_rdd.map(lp => sameModel.predict(lp.features))      predictions.take(10).foreach(println)      var predictionAndLabel = predictions.zip( ohe_test_rdd.map(_.label))      var accuracy = 1.0 * predictionAndLabel.filter(x => x._1 == x._2 ).count/ohe_test_rdd.count      println("GBTR accuracy " + accuracy)      //GBTR accuracy 0.8227084119200302      }  }

其中，训练数据集： Train records : 104558，测试数据集：Test records : 26510

程序主要输出：

scala> train_rdd.take(1)res23: Array[(String, Array[String], Array[String])] = Array((1000009418151094273::0,        Array(1fbe01fe, f3845767, 28905ebd, ecad2386, 7801e8d9, 07d7df22, a99f214a, ddd2926e, 44956a24),        Array(2, 15706, 320, 50, 1722, 0, 35, -1)))  scala> train_cat_rdd.take(1)res24: Array[List[(Int, String)]] = Array(List((0,1fbe01fe), (1,f3845767), (2,28905ebd),         (3,ecad2386), (4,7801e8d9), (5,07d7df22), (6,a99f214a), (7,ddd2926e), (8,44956a24)))  scala> println("Number of features")Number of features scala> println(oheMap.size)57606  scala> ohe_train_rdd.take(1)res27: Array[org.apache.spark.mllib.regression.LabeledPoint] = Array(        (0.0,[11602.0,22813.0,11497.0,16828.0,30657.0,23893.0,13182.0,31723.0,39722.0,2.0,15706.0,320.0,50.0,1722.0,0.0,35.0,0.0]))  scala> println("GBTR accuracy " + accuracy)GBTR accuracy 0.8227084119200302

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