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+package com.aliyun.odps.spark.examples.makedata_ad
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+
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+import com.alibaba.fastjson.{JSON, JSONObject}
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+import com.aliyun.odps.TableSchema
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+import com.aliyun.odps.data.Record
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+import com.aliyun.odps.spark.examples.myUtils.{MyDateUtils, MyHdfsUtils, ParamUtils, env}
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+import examples.extractor.RankExtractorFeature_20240530
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+import org.apache.hadoop.io.compress.GzipCodec
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+import org.apache.spark.sql.SparkSession
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+import org.xm.Similarity
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+
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+import scala.collection.JavaConversions._
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+import scala.collection.mutable.ArrayBuffer
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+/*
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+ 20240608 提取特征
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+ */
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+
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+object makedata_ad_31_originData_20240620 {
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+ def main(args: Array[String]): Unit = {
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+ val spark = SparkSession
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+ .builder()
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+ .appName(this.getClass.getName)
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+ .getOrCreate()
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+ val sc = spark.sparkContext
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+
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+ // 1 读取参数
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+ val param = ParamUtils.parseArgs(args)
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+ val tablePart = param.getOrElse("tablePart", "64").toInt
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+ val beginStr = param.getOrElse("beginStr", "2024062008")
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+ val endStr = param.getOrElse("endStr", "2024062023")
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+ val savePath = param.getOrElse("savePath", "/dw/recommend/model/31_ad_sample_data/")
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+ val project = param.getOrElse("project", "loghubods")
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+ val table = param.getOrElse("table", "alg_recsys_ad_sample_all")
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+ val repartition = param.getOrElse("repartition", "100").toInt
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+
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+ // 2 读取odps+表信息
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+ val odpsOps = env.getODPS(sc)
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+
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+ // 3 循环执行数据生产
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+ val timeRange = MyDateUtils.getDateHourRange(beginStr, endStr)
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+ for (dt_hh <- timeRange) {
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+ val dt = dt_hh.substring(0, 8)
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+ val hh = dt_hh.substring(8, 10)
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+ val partition = s"dt=$dt,hh=$hh"
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+ println("开始执行partiton:" + partition)
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+ val odpsData = odpsOps.readTable(project = project,
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+ table = table,
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+ partition = partition,
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+ transfer = func,
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+ numPartition = tablePart)
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+ .map(record => {
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+
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+
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+ val ts = record.getString("ts").toInt
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+ val cid = record.getString("cid")
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+
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+
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+ val featureMap = new JSONObject()
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+
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+ val b1: JSONObject = if (record.isNull("b1_feature")) new JSONObject() else
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+ JSON.parseObject(record.getString("b1_feature"))
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+ val b2: JSONObject = if (record.isNull("b2_feature")) new JSONObject() else
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+ JSON.parseObject(record.getString("b2_feature"))
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+ val b3: JSONObject = if (record.isNull("b3_feature")) new JSONObject() else
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+ JSON.parseObject(record.getString("b3_feature"))
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+ val b4: JSONObject = if (record.isNull("b4_feature")) new JSONObject() else
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+ JSON.parseObject(record.getString("b4_feature"))
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+ val b5: JSONObject = if (record.isNull("b5_feature")) new JSONObject() else
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+ JSON.parseObject(record.getString("b5_feature"))
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+ val b6: JSONObject = if (record.isNull("b6_feature")) new JSONObject() else
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+ JSON.parseObject(record.getString("b6_feature"))
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+ val b7: JSONObject = if (record.isNull("b7_feature")) new JSONObject() else
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+ JSON.parseObject(record.getString("b7_feature"))
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+ val b8: JSONObject = if (record.isNull("b8_feature")) new JSONObject() else
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+ JSON.parseObject(record.getString("b8_feature"))
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+ val b9: JSONObject = if (record.isNull("b9_feature")) new JSONObject() else
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+ JSON.parseObject(record.getString("b9_feature"))
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+
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+
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+ featureMap.put("cid_" + cid, 1.0)
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+ if (b1.containsKey("adid") && b1.getString("adid").nonEmpty) {
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+ featureMap.put("adid_" + b1.getString("adid"), 1.0)
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+ }
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+ if (b1.containsKey("adverid") && b1.getString("adverid").nonEmpty) {
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+ featureMap.put("adverid_" + b1.getString("adverid"), 1.0)
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+ }
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+ if (b1.containsKey("targeting_conversion") && b1.getString("targeting_conversion").nonEmpty) {
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+ featureMap.put("targeting_conversion_" + b1.getString("targeting_conversion"), 1.0)
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+ }
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+
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+
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+ if (b1.containsKey("cpa")) {
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+ featureMap.put("cpa", b1.getString("cpa").toDouble)
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+ }
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+
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+ for ((bn, prefix1) <- List(
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+ (b2, "b2"), (b3, "b3"),(b4, "b4"),(b5, "b5"),(b8, "b8")
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+ )){
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+ for (prefix2 <- List(
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+ "3h", "6h", "12h", "1d", "3d", "7d"
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+ )){
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+ val view = if (bn.isEmpty) 0D else bn.getIntValue("ad_view_" + prefix2).toDouble
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+ val click = if (bn.isEmpty) 0D else bn.getIntValue("ad_click_" + prefix2).toDouble
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+ val conver = if (bn.isEmpty) 0D else bn.getIntValue("ad_conversion_" + prefix2).toDouble
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+ val income = if (bn.isEmpty) 0D else bn.getIntValue("ad_income_" + prefix2).toDouble
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+ val f1 = RankExtractorFeature_20240530.calDiv(click, view)
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+ val f2 = RankExtractorFeature_20240530.calDiv(conver, view)
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+ val f3 = RankExtractorFeature_20240530.calDiv(conver, click)
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+ val f4 = conver
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+ val f5 = RankExtractorFeature_20240530.calDiv(income*1000, view)
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+ featureMap.put(prefix1 + "_" + prefix2 + "_" + "ctr", f1)
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+ featureMap.put(prefix1 + "_" + prefix2 + "_" + "ctcvr", f2)
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+ featureMap.put(prefix1 + "_" + prefix2 + "_" + "cvr", f3)
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+ featureMap.put(prefix1 + "_" + prefix2 + "_" + "conver", f4)
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+ featureMap.put(prefix1 + "_" + prefix2 + "_" + "ecpm", f5)
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+ }
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+ }
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+
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+ for ((bn, prefix1) <- List(
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+ (b6, "b6"), (b7, "b7")
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+ )) {
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+ for (prefix2 <- List(
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+ "7d", "14d"
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+ )) {
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+ val view = if (bn.isEmpty) 0D else bn.getIntValue("ad_view_" + prefix2).toDouble
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+ val click = if (bn.isEmpty) 0D else bn.getIntValue("ad_click_" + prefix2).toDouble
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+ val conver = if (bn.isEmpty) 0D else bn.getIntValue("ad_conversion_" + prefix2).toDouble
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+ val income = if (bn.isEmpty) 0D else bn.getIntValue("ad_income_" + prefix2).toDouble
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+ val f1 = RankExtractorFeature_20240530.calDiv(click, view)
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+ val f2 = RankExtractorFeature_20240530.calDiv(conver, view)
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+ val f3 = RankExtractorFeature_20240530.calDiv(conver, click)
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+ val f4 = conver
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+ val f5 = RankExtractorFeature_20240530.calDiv(income * 1000, view)
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+ featureMap.put(prefix1 + "_" + prefix2 + "_" + "ctr", f1)
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+ featureMap.put(prefix1 + "_" + prefix2 + "_" + "ctcvr", f2)
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+ featureMap.put(prefix1 + "_" + prefix2 + "_" + "cvr", f3)
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+ featureMap.put(prefix1 + "_" + prefix2 + "_" + "conver", f4)
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+ featureMap.put(prefix1 + "_" + prefix2 + "_" + "ecpm", f5)
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+ }
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+ }
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+
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+ val c1: JSONObject = if (record.isNull("c1_feature")) new JSONObject() else
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+ JSON.parseObject(record.getString("c1_feature"))
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+
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+ val midActionList = if (c1.containsKey("action") && c1.getString("action").nonEmpty){
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+ c1.getString("action").split(",").map(r=>{
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+ val rList = r.split(":")
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+ (rList(0), (rList(1).toInt, rList(2).toInt, rList(3).toInt, rList(4).toInt, rList(5)))
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+ }).sortBy(-_._2._1).toList
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+ }else {
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+ new ArrayBuffer[(String, (Int, Int, Int, Int, String))]().toList
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+ }
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+ // u特征
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+ val viewAll = midActionList.size.toDouble
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+ val clickAll = midActionList.map(_._2._2).sum.toDouble
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+ val converAll = midActionList.map(_._2._3).sum.toDouble
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+ val incomeAll = midActionList.map(_._2._4).sum.toDouble
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+ featureMap.put("viewAll", viewAll)
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+ featureMap.put("clickAll", clickAll)
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+ featureMap.put("converAll", converAll)
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+ featureMap.put("incomeAll", incomeAll)
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+ featureMap.put("ctr_all", RankExtractorFeature_20240530.calDiv(clickAll, viewAll))
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+ featureMap.put("ctcvr_all", RankExtractorFeature_20240530.calDiv(converAll, viewAll))
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+ featureMap.put("cvr_all", RankExtractorFeature_20240530.calDiv(clickAll, converAll))
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+ featureMap.put("ecpm_all", RankExtractorFeature_20240530.calDiv(incomeAll * 1000, viewAll))
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+
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+ // ui特征
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+ val midTimeDiff = scala.collection.mutable.Map[String, Double]()
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+ midActionList.foreach{
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+ case (cid, (ts_history, click, conver, income, title)) =>
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+ if (!midTimeDiff.contains("timediff_view_" + cid)){
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+ midTimeDiff.put("timediff_view_" + cid, 1.0 / ((ts - ts_history).toDouble/3600.0/24.0))
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+ }
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+ if (!midTimeDiff.contains("timediff_click_" + cid) && click > 0) {
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+ midTimeDiff.put("timediff_click_" + cid, 1.0 / ((ts - ts_history).toDouble / 3600.0 / 24.0))
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+ }
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+ if (!midTimeDiff.contains("timediff_conver_" + cid) && conver > 0) {
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+ midTimeDiff.put("timediff_conver_" + cid, 1.0 / ((ts - ts_history).toDouble / 3600.0 / 24.0))
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+ }
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+ }
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+
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+ val midActionStatic = scala.collection.mutable.Map[String, Double]()
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+ midActionList.foreach {
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+ case (cid, (ts_history, click, conver, income, title)) =>
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+ midActionStatic.put("actionstatic_view_" + cid, 1.0 + midActionStatic.getOrDefault("actionstatic_view_" + cid, 0.0))
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+ midActionStatic.put("actionstatic_click_" + cid, click + midActionStatic.getOrDefault("actionstatic_click_" + cid, 0.0))
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+ midActionStatic.put("actionstatic_conver_" + cid, conver + midActionStatic.getOrDefault("actionstatic_conver_" + cid, 0.0))
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+ midActionStatic.put("actionstatic_income_" + cid, income + midActionStatic.getOrDefault("actionstatic_income_" + cid, 0.0))
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+ }
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+
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+ if (midTimeDiff.contains("timediff_view_" + cid)){
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+ featureMap.put("timediff_view", midTimeDiff.getOrDefault("timediff_view_" + cid, 0.0))
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+ }
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+ if (midTimeDiff.contains("timediff_click_" + cid)) {
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+ featureMap.put("timediff_click", midTimeDiff.getOrDefault("timediff_click_" + cid, 0.0))
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+ }
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+ if (midTimeDiff.contains("timediff_conver_" + cid)) {
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+ featureMap.put("timediff_conver", midTimeDiff.getOrDefault("timediff_conver_" + cid, 0.0))
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+ }
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+ if (midActionStatic.contains("actionstatic_view_" + cid)) {
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+ featureMap.put("actionstatic_view", midActionStatic.getOrDefault("actionstatic_view_" + cid, 0.0))
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+ }
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+ if (midActionStatic.contains("actionstatic_click_" + cid)) {
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+ featureMap.put("actionstatic_click", midActionStatic.getOrDefault("actionstatic_click_" + cid, 0.0))
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+ }
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+ if (midActionStatic.contains("actionstatic_conver_" + cid)) {
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+ featureMap.put("actionstatic_conver", midActionStatic.getOrDefault("actionstatic_conver_" + cid, 0.0))
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+ }
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+ if (midActionStatic.contains("actionstatic_income_" + cid)) {
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+ featureMap.put("actionstatic_income", midActionStatic.getOrDefault("actionstatic_income_" + cid, 0.0))
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+ }
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+ if (midActionStatic.contains("actionstatic_view_" + cid) && midActionStatic.contains("actionstatic_click_" + cid)) {
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+ featureMap.put("actionstatic_ctr", RankExtractorFeature_20240530.calDiv(
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+ midActionStatic.getOrDefault("actionstatic_click_" + cid, 0.0),
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+ midActionStatic.getOrDefault("actionstatic_view_" + cid, 0.0)
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+ ))
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+ }
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+ if (midActionStatic.contains("actionstatic_view_" + cid) && midActionStatic.contains("timediff_conver_" + cid)) {
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+ featureMap.put("actionstatic_ctcvr", RankExtractorFeature_20240530.calDiv(
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+ midActionStatic.getOrDefault("timediff_conver_" + cid, 0.0),
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+ midActionStatic.getOrDefault("actionstatic_view_" + cid, 0.0)
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+ ))
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+ }
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+ if (midActionStatic.contains("actionstatic_conver_" + cid) && midActionStatic.contains("actionstatic_click_" + cid)) {
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+ featureMap.put("actionstatic_cvr", RankExtractorFeature_20240530.calDiv(
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+ midActionStatic.getOrDefault("actionstatic_click_" + cid, 0.0),
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+ midActionStatic.getOrDefault("timediff_conver_" + cid, 0.0)
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+ ))
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+ }
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+
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+ val e1: JSONObject = if (record.isNull("e1_feature")) new JSONObject() else
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+ JSON.parseObject(record.getString("e1_feature"))
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+ val e2: JSONObject = if (record.isNull("e2_feature")) new JSONObject() else
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+ JSON.parseObject(record.getString("e2_feature"))
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+ val title = b1.getOrDefault("cidtitle", "").toString
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+ if (title.nonEmpty){
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+ for ((en, prefix1) <- List((e1, "e1"), (e2, "e2"))){
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+ for (prefix2 <- List("tags_3d", "tags_7d", "tags_14d")){
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+ if (en.nonEmpty && en.containsKey(prefix2) && en.getString(prefix2).nonEmpty) {
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+ val (f1, f2, f3, f4) = funcC34567ForTags(en.getString(prefix2), title)
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+ featureMap.put(prefix1 + "_" + prefix2 + "_matchnum", f1)
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+ featureMap.put(prefix1 + "_" + prefix2 + "_maxscore", f3)
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+ featureMap.put(prefix1 + "_" + prefix2 + "_avgscore", f4)
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+
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+ }
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+ }
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+ }
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+ }
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+
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+ val d1: JSONObject = if (record.isNull("d1_feature")) new JSONObject() else
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+ JSON.parseObject(record.getString("d1_feature"))
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+ val d2: JSONObject = if (record.isNull("d2_feature")) new JSONObject() else
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+ JSON.parseObject(record.getString("d2_feature"))
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+
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+ if (d1.nonEmpty){
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+ for (prefix <- List("3h", "6h", "12h", "1d", "3d", "7d")) {
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+ val view = if (!d1.containsKey("ad_view_" + prefix)) 0D else d1.getIntValue("ad_view_" + prefix).toDouble
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+ val click = if (!d1.containsKey("ad_click_" + prefix)) 0D else d1.getIntValue("ad_click_" + prefix).toDouble
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+ val conver = if (!d1.containsKey("ad_conversion_" + prefix)) 0D else d1.getIntValue("ad_conversion_" + prefix).toDouble
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+ val income = if (!d1.containsKey("ad_income_" + prefix)) 0D else d1.getIntValue("ad_income_" + prefix).toDouble
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+ val f1 = RankExtractorFeature_20240530.calDiv(click, view)
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+ val f2 = RankExtractorFeature_20240530.calDiv(conver, view)
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+ val f3 = RankExtractorFeature_20240530.calDiv(conver, click)
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+ val f4 = conver
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+ val f5 = RankExtractorFeature_20240530.calDiv(income * 1000, view)
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+ featureMap.put("d1_feature" + "_" + prefix + "_" + "ctr", f1)
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+ featureMap.put("d1_feature" + "_" + prefix + "_" + "ctcvr", f2)
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+ featureMap.put("d1_feature" + "_" + prefix + "_" + "cvr", f3)
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+ featureMap.put("d1_feature" + "_" + prefix + "_" + "conver", f4)
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+ featureMap.put("d1_feature" + "_" + prefix + "_" + "ecpm", f5)
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+ }
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+ }
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+
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+ val vidRankMaps = scala.collection.mutable.Map[String, scala.collection.immutable.Map[String, Double]]()
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+ if (d2.nonEmpty){
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+ d2.foreach(r => {
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+ val key = r._1
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+ val value = d2.getString(key).split(",").map(r=> {
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+ val rList = r.split(":")
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+ (rList(0), rList(2).toDouble)
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+ }).toMap
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+ vidRankMaps.put(key, value)
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+ })
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+ }
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+ for (prefix1 <- List("ctr", "ctcvr", "ecpm")) {
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+ for (prefix2 <- List("1d", "3d", "7d", "14d")) {
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+ if (vidRankMaps.contains(prefix1 + "_" + prefix2)){
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+ val rank = vidRankMaps(prefix1 + "_" + prefix2).getOrDefault(cid, 0.0)
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+ if (rank >= 1.0){
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+ featureMap.put("vid_rank_" + prefix1 + "_" + prefix2, 1.0 / rank)
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+ }
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+ }
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+ }
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+ }
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+
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+
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+ /*
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+ 广告
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+ sparse：cid adid adverid targeting_conversion
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+
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+ cpa --> 1个
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+ adverid下的 3h 6h 12h 1d 3d 7d 、 ctr ctcvr cvr conver ecpm --> 30个
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+ cid下的 3h 6h 12h 1d 3d 7d 、 ctr ctcvr cvr ecpm conver --> 30个
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+ 地理//cid下的 3h 6h 12h 1d 3d 7d 、 ctr ctcvr cvr ecpm conver --> 30个
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+ app//cid下的 3h 6h 12h 1d 3d 7d 、 ctr ctcvr cvr ecpm conver --> 30个
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+ 手机品牌//cid下的 3h 6h 12h 1d 3d 7d 、 ctr ctcvr cvr ecpm conver --> 30个
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+ 系统 无数据
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+ week//cid下的 7d 14d、 ctr ctcvr cvr ecpm conver --> 10个
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+ hour//cid下的 7d 14d、 ctr ctcvr cvr ecpm conver --> 10个
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+
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+ 用户
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+ 用户历史 点击/转化 的title tag；3d 7d 14d； cid的title； 数量/最高分/平均分 --> 18个
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+ 用户历史 14d 看过/点过/转化次数/income； ctr cvr ctcvr ecpm； --> 8个
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+
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+ 用户到cid的ui特征 --> 10个
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+ 1/用户最近看过这个cid的时间间隔
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+ 1/用户最近点过这个cid的时间间隔
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+ 1/用户最近转过这个cid的时间间隔
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+ 用户看过这个cid多少次
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+ 用户点过这个cid多少次
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+ 用户转过这个cid多少次
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+ 用户对这个cid花了多少钱
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+ 用户对这个cid的ctr ctcvr cvr
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+
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+ 视频
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+ title与cid的 sim-score-1/-2 无数据
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|
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+ vid//cid下的 3h 6h 12h 1d 3d 7d 、 ctr ctcvr cvr ecpm conver --> 30个
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+ vid//cid下的 1d 3d 7d 14d、 ctr ctcvr ecpm 的rank值 倒数 --> 12个
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+
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+ */
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+
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+
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+
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+ //4 处理label信息。
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+ val labels = new JSONObject
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|
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+ for (labelKey <- List("ad_is_click", "ad_is_conversion")){
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|
|
+ if (!record.isNull(labelKey)){
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+ labels.put(labelKey, record.getString(labelKey))
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|
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+ }
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+ }
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|
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+ //5 处理log key表头。
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|
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+ val apptype = record.getString("apptype")
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+ val mid = record.getString("mid")
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+ val headvideoid = record.getString("headvideoid")
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+ val logKey = (apptype, mid, cid, ts, headvideoid).productIterator.mkString(",")
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+ val labelKey = labels.toString()
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+ val featureKey = featureMap.toString()
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|
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+ //6 拼接数据，保存。
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|
|
+ logKey + "\t" + labelKey + "\t" + featureKey
|
|
|
+ })
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+
|
|
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+ // 4 保存数据到hdfs
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|
|
+ val savePartition = dt + hh
|
|
|
+ val hdfsPath = savePath + "/" + savePartition
|
|
|
+ if (hdfsPath.nonEmpty && hdfsPath.startsWith("/dw/recommend/model/")){
|
|
|
+ println("删除路径并开始数据写入:" + hdfsPath)
|
|
|
+ MyHdfsUtils.delete_hdfs_path(hdfsPath)
|
|
|
+ odpsData.coalesce(repartition).saveAsTextFile(hdfsPath, classOf[GzipCodec])
|
|
|
+ }else{
|
|
|
+ println("路径不合法，无法写入:" + hdfsPath)
|
|
|
+ }
|
|
|
+ }
|
|
|
+ }
|
|
|
+
|
|
|
+ def func(record: Record, schema: TableSchema): Record = {
|
|
|
+ record
|
|
|
+ }
|
|
|
+
|
|
|
+ def funcC34567ForTags(tags: String, title: String): Tuple4[Double, String, Double, Double] = {
|
|
|
+ // 匹配数量 匹配词 语义最高相似度分 语义平均相似度分
|
|
|
+ val tagsList = tags.split(",")
|
|
|
+ var d1 = 0.0
|
|
|
+ val d2 = new ArrayBuffer[String]()
|
|
|
+ var d3 = 0.0
|
|
|
+ var d4 = 0.0
|
|
|
+ for (tag <- tagsList) {
|
|
|
+ if (title.contains(tag)) {
|
|
|
+ d1 = d1 + 1.0
|
|
|
+ d2.add(tag)
|
|
|
+ }
|
|
|
+ val score = Similarity.conceptSimilarity(tag, title)
|
|
|
+ d3 = if (score > d3) score else d3
|
|
|
+ d4 = d4 + score
|
|
|
+ }
|
|
|
+ d4 = if (tagsList.nonEmpty) d4 / tagsList.size else d4
|
|
|
+ (d1, d2.mkString(","), d3, d4)
|
|
|
+ }
|
|
|
+}
|
zhangbo