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

摘要: 本文尝试使用Spark提供的机器学习算法Gradient-BoostedTrees来预测一个用户是否会点击广告。训练和测试数据使用KaggleAvazuCTR比赛的样例数据，下载地址：https://www.kaggle.com/c/avazu-ct...



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.ListBuffer
import scala.collection.mutable.ArrayBuffer

import org.apache.spark.SparkContext
import org.apache.spark.SparkContext._
import org.apache.spark.SparkConf
import org.apache.spark.rdd.RDD

import org.apache.spark.mllib.classification.NaiveBayes
import org.apache.spark.mllib.regression.LabeledPoint
import org.apache.spark.mllib.linalg.Vectors

import org.apache.spark.mllib.tree.GradientBoostedTrees
import org.apache.spark.mllib.tree.configuration.BoostingStrategy
import 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

本文转载自xw的大数据田地。转载请注明原文链接http://lxw1234.com/archives/2016/01/595.htm