package com.aliyun.odps.spark.examples.makedata_ad.v20240620

import com.alibaba.fastjson.{JSON, JSONObject}
import com.aliyun.odps.TableSchema
import com.aliyun.odps.data.Record
import com.aliyun.odps.spark.examples.myUtils.{MyDateUtils, MyHdfsUtils, ParamUtils, env}
import examples.extractor.{ExtractorUtils, RankExtractorFeature_20240530}
import org.apache.hadoop.io.compress.GzipCodec
import org.apache.spark.sql.SparkSession
import org.xm.Similarity

import scala.collection.JavaConversions._
import scala.collection.mutable.ArrayBuffer
import scala.io.Source
/*
   20240608 提取特征
 */

object makedata_ad_33_bucketDataPrint_20240628 {
  def main(args: Array[String]): Unit = {
    val spark = SparkSession
      .builder()
      .appName(this.getClass.getName)
      .getOrCreate()
    val sc = spark.sparkContext

    // 1 读取参数
    val param = ParamUtils.parseArgs(args)
    val tablePart = param.getOrElse("tablePart", "64").toInt
    val beginStr = param.getOrElse("beginStr", "2024061500")
    val endStr = param.getOrElse("endStr", "2024061523")
    val savePath = param.getOrElse("savePath", "/dw/recommend/model/17_for_check/")
    val project = param.getOrElse("project", "loghubods")
    val table = param.getOrElse("table", "alg_recsys_sample_all_new")
    val repartition = param.getOrElse("repartition", "32").toInt
    val readDate = param.getOrElse("readDate", "20240615")


    val loader = getClass.getClassLoader
    val resourceUrl = loader.getResource("20240622_ad_feature_name.txt")
    val content =
      if (resourceUrl != null) {
        val content = Source.fromURL(resourceUrl).getLines().mkString("\n")
        Source.fromURL(resourceUrl).close()
        content
      } else {
        ""
      }
    println(content)
    val contentList = content.split("\n")
      .map(r => r.replace(" ", "").replaceAll("\n", ""))
      .filter(r => r.nonEmpty).toList
    val contentList_br = sc.broadcast(contentList)

    val resourceUrlBucket = loader.getResource("20240622_ad_bucket_249.txt")
    val buckets =
      if (resourceUrlBucket != null) {
        val buckets = Source.fromURL(resourceUrlBucket).getLines().mkString("\n")
        Source.fromURL(resourceUrlBucket).close()
        buckets
      } else {
        ""
      }
    println(buckets)
    val bucketsMap = buckets.split("\n")
      .map(r => r.replace(" ", "").replaceAll("\n", ""))
      .filter(r => r.nonEmpty)
      .map(r => {
        val rList = r.split("\t")
        (rList(0), (rList(1).toDouble, rList(2).split(",").map(_.toDouble)))
      }).toMap
    val bucketsMap_br = sc.broadcast(bucketsMap)

    // 2 读取odps+表信息
    val odpsOps = env.getODPS(sc)

    // 3 循环执行数据生产
    val timeRange = MyDateUtils.getDateHourRange(beginStr, endStr)
    for (dt_hh <- timeRange) {
      val dt = dt_hh.substring(0, 8)
      val hh = dt_hh.substring(8, 10)
      val partition = s"dt=$dt,hh=$hh"
      println("开始执行partiton:" + partition)
      val odpsData = odpsOps.readTable(project = project,
        table = table,
        partition = partition,
        transfer = func,
        numPartition = tablePart)
        .map(record => {

          val ts = record.getString("ts").toInt
          val cid = record.getString("cid")


          val featureMap = new JSONObject()

          val b1: JSONObject = if (record.isNull("b1_feature")) new JSONObject() else
            JSON.parseObject(record.getString("b1_feature"))
          val b2: JSONObject = if (record.isNull("b2_feature")) new JSONObject() else
            JSON.parseObject(record.getString("b2_feature"))
          val b3: JSONObject = if (record.isNull("b3_feature")) new JSONObject() else
            JSON.parseObject(record.getString("b3_feature"))
          val b4: JSONObject = if (record.isNull("b4_feature")) new JSONObject() else
            JSON.parseObject(record.getString("b4_feature"))
          val b5: JSONObject = if (record.isNull("b5_feature")) new JSONObject() else
            JSON.parseObject(record.getString("b5_feature"))
          val b6: JSONObject = if (record.isNull("b6_feature")) new JSONObject() else
            JSON.parseObject(record.getString("b6_feature"))
          val b7: JSONObject = if (record.isNull("b7_feature")) new JSONObject() else
            JSON.parseObject(record.getString("b7_feature"))
          val b8: JSONObject = if (record.isNull("b8_feature")) new JSONObject() else
            JSON.parseObject(record.getString("b8_feature"))
          val b9: JSONObject = if (record.isNull("b9_feature")) new JSONObject() else
            JSON.parseObject(record.getString("b9_feature"))


          featureMap.put("cid_" + cid, 1.0)
          if (b1.containsKey("adid") && b1.getString("adid").nonEmpty) {
            featureMap.put("adid_" + b1.getString("adid"), 1.0)
          }
          if (b1.containsKey("adverid") && b1.getString("adverid").nonEmpty) {
            featureMap.put("adverid_" + b1.getString("adverid"), 1.0)
          }
          if (b1.containsKey("targeting_conversion") && b1.getString("targeting_conversion").nonEmpty) {
            featureMap.put("targeting_conversion_" + b1.getString("targeting_conversion"), 1.0)
          }


          if (b1.containsKey("cpa")) {
            featureMap.put("cpa", b1.getString("cpa").toDouble)
          }

          for ((bn, prefix1) <- List(
            (b2, "b2"), (b3, "b3"), (b4, "b4"), (b5, "b5"), (b8, "b8")
          )) {
            for (prefix2 <- List(
              "3h", "6h", "12h", "1d", "3d", "7d"
            )) {
              val view = if (bn.isEmpty) 0D else bn.getIntValue("ad_view_" + prefix2).toDouble
              val click = if (bn.isEmpty) 0D else bn.getIntValue("ad_click_" + prefix2).toDouble
              val conver = if (bn.isEmpty) 0D else bn.getIntValue("ad_conversion_" + prefix2).toDouble
              val income = if (bn.isEmpty) 0D else bn.getIntValue("ad_income_" + prefix2).toDouble
              val f1 = RankExtractorFeature_20240530.calDiv(click, view)
              val f2 = RankExtractorFeature_20240530.calDiv(conver, view)
              val f3 = RankExtractorFeature_20240530.calDiv(conver, click)
              val f4 = conver
              val f5 = RankExtractorFeature_20240530.calDiv(income * 1000, view)
              featureMap.put(prefix1 + "_" + prefix2 + "_" + "ctr", f1)
              featureMap.put(prefix1 + "_" + prefix2 + "_" + "ctcvr", f2)
              featureMap.put(prefix1 + "_" + prefix2 + "_" + "cvr", f3)
              featureMap.put(prefix1 + "_" + prefix2 + "_" + "conver", f4)
              featureMap.put(prefix1 + "_" + prefix2 + "_" + "ecpm", f5)
            }
          }

          for ((bn, prefix1) <- List(
            (b6, "b6"), (b7, "b7")
          )) {
            for (prefix2 <- List(
              "7d", "14d"
            )) {
              val view = if (bn.isEmpty) 0D else bn.getIntValue("ad_view_" + prefix2).toDouble
              val click = if (bn.isEmpty) 0D else bn.getIntValue("ad_click_" + prefix2).toDouble
              val conver = if (bn.isEmpty) 0D else bn.getIntValue("ad_conversion_" + prefix2).toDouble
              val income = if (bn.isEmpty) 0D else bn.getIntValue("ad_income_" + prefix2).toDouble
              val f1 = RankExtractorFeature_20240530.calDiv(click, view)
              val f2 = RankExtractorFeature_20240530.calDiv(conver, view)
              val f3 = RankExtractorFeature_20240530.calDiv(conver, click)
              val f4 = conver
              val f5 = RankExtractorFeature_20240530.calDiv(income * 1000, view)
              featureMap.put(prefix1 + "_" + prefix2 + "_" + "ctr", f1)
              featureMap.put(prefix1 + "_" + prefix2 + "_" + "ctcvr", f2)
              featureMap.put(prefix1 + "_" + prefix2 + "_" + "cvr", f3)
              featureMap.put(prefix1 + "_" + prefix2 + "_" + "conver", f4)
              featureMap.put(prefix1 + "_" + prefix2 + "_" + "ecpm", f5)
            }
          }

          val c1: JSONObject = if (record.isNull("c1_feature")) new JSONObject() else
            JSON.parseObject(record.getString("c1_feature"))

          val midActionList = if (c1.containsKey("action") && c1.getString("action").nonEmpty) {
            c1.getString("action").split(",").map(r => {
              val rList = r.split(":")
              (rList(0), (rList(1).toInt, rList(2).toInt, rList(3).toInt, rList(4).toInt, rList(5)))
            }).sortBy(-_._2._1).toList
          } else {
            new ArrayBuffer[(String, (Int, Int, Int, Int, String))]().toList
          }
          // u特征
          val viewAll = midActionList.size.toDouble
          val clickAll = midActionList.map(_._2._2).sum.toDouble
          val converAll = midActionList.map(_._2._3).sum.toDouble
          val incomeAll = midActionList.map(_._2._4).sum.toDouble
          featureMap.put("viewAll", viewAll)
          featureMap.put("clickAll", clickAll)
          featureMap.put("converAll", converAll)
          featureMap.put("incomeAll", incomeAll)
          featureMap.put("ctr_all", RankExtractorFeature_20240530.calDiv(clickAll, viewAll))
          featureMap.put("ctcvr_all", RankExtractorFeature_20240530.calDiv(converAll, viewAll))
          featureMap.put("cvr_all", RankExtractorFeature_20240530.calDiv(clickAll, converAll))
          featureMap.put("ecpm_all", RankExtractorFeature_20240530.calDiv(incomeAll * 1000, viewAll))

          // ui特征
          val midTimeDiff = scala.collection.mutable.Map[String, Double]()
          midActionList.foreach {
            case (cid, (ts_history, click, conver, income, title)) =>
              if (!midTimeDiff.contains("timediff_view_" + cid)) {
                midTimeDiff.put("timediff_view_" + cid, 1.0 / ((ts - ts_history).toDouble / 3600.0 / 24.0))
              }
              if (!midTimeDiff.contains("timediff_click_" + cid) && click > 0) {
                midTimeDiff.put("timediff_click_" + cid, 1.0 / ((ts - ts_history).toDouble / 3600.0 / 24.0))
              }
              if (!midTimeDiff.contains("timediff_conver_" + cid) && conver > 0) {
                midTimeDiff.put("timediff_conver_" + cid, 1.0 / ((ts - ts_history).toDouble / 3600.0 / 24.0))
              }
          }

          val midActionStatic = scala.collection.mutable.Map[String, Double]()
          midActionList.foreach {
            case (cid, (ts_history, click, conver, income, title)) =>
              midActionStatic.put("actionstatic_view_" + cid, 1.0 + midActionStatic.getOrDefault("actionstatic_view_" + cid, 0.0))
              midActionStatic.put("actionstatic_click_" + cid, click + midActionStatic.getOrDefault("actionstatic_click_" + cid, 0.0))
              midActionStatic.put("actionstatic_conver_" + cid, conver + midActionStatic.getOrDefault("actionstatic_conver_" + cid, 0.0))
              midActionStatic.put("actionstatic_income_" + cid, income + midActionStatic.getOrDefault("actionstatic_income_" + cid, 0.0))
          }

          if (midTimeDiff.contains("timediff_view_" + cid)) {
            featureMap.put("timediff_view", midTimeDiff.getOrDefault("timediff_view_" + cid, 0.0))
          }
          if (midTimeDiff.contains("timediff_click_" + cid)) {
            featureMap.put("timediff_click", midTimeDiff.getOrDefault("timediff_click_" + cid, 0.0))
          }
          if (midTimeDiff.contains("timediff_conver_" + cid)) {
            featureMap.put("timediff_conver", midTimeDiff.getOrDefault("timediff_conver_" + cid, 0.0))
          }
          if (midActionStatic.contains("actionstatic_view_" + cid)) {
            featureMap.put("actionstatic_view", midActionStatic.getOrDefault("actionstatic_view_" + cid, 0.0))
          }
          if (midActionStatic.contains("actionstatic_click_" + cid)) {
            featureMap.put("actionstatic_click", midActionStatic.getOrDefault("actionstatic_click_" + cid, 0.0))
          }
          if (midActionStatic.contains("actionstatic_conver_" + cid)) {
            featureMap.put("actionstatic_conver", midActionStatic.getOrDefault("actionstatic_conver_" + cid, 0.0))
          }
          if (midActionStatic.contains("actionstatic_income_" + cid)) {
            featureMap.put("actionstatic_income", midActionStatic.getOrDefault("actionstatic_income_" + cid, 0.0))
          }
          if (midActionStatic.contains("actionstatic_view_" + cid) && midActionStatic.contains("timediff_conver_" + cid)) {
            featureMap.put("actionstatic_ctr", RankExtractorFeature_20240530.calDiv(
              midActionStatic.getOrDefault("timediff_conver_" + cid, 0.0),
              midActionStatic.getOrDefault("actionstatic_view_" + cid, 0.0)
            ))
          }
          if (midActionStatic.contains("actionstatic_view_" + cid) && midActionStatic.contains("actionstatic_conver_" + cid)) {
            featureMap.put("actionstatic_ctcvr", RankExtractorFeature_20240530.calDiv(
              midActionStatic.getOrDefault("actionstatic_conver_" + cid, 0.0),
              midActionStatic.getOrDefault("actionstatic_view_" + cid, 0.0)
            ))
          }
          if (midActionStatic.contains("actionstatic_conver_" + cid) && midActionStatic.contains("actionstatic_click_" + cid)) {
            featureMap.put("actionstatic_cvr", RankExtractorFeature_20240530.calDiv(
              midActionStatic.getOrDefault("actionstatic_click_" + cid, 0.0),
              midActionStatic.getOrDefault("timediff_conver_" + cid, 0.0)
            ))
          }

          val e1: JSONObject = if (record.isNull("e1_feature")) new JSONObject() else
            JSON.parseObject(record.getString("e1_feature"))
          val e2: JSONObject = if (record.isNull("e2_feature")) new JSONObject() else
            JSON.parseObject(record.getString("e2_feature"))
          val title = b1.getOrDefault("cidtitle", "").toString
          if (title.nonEmpty) {
            for ((en, prefix1) <- List((e1, "e1"), (e2, "e2"))) {
              for (prefix2 <- List("tags_3d", "tags_7d", "tags_14d")) {
                if (en.nonEmpty && en.containsKey(prefix2) && en.getString(prefix2).nonEmpty) {
                  val (f1, f2, f3, f4) = funcC34567ForTags(en.getString(prefix2), title)
                  featureMap.put(prefix1 + "_" + prefix2 + "_matchnum", f1)
                  featureMap.put(prefix1 + "_" + prefix2 + "_maxscore", f3)
                  featureMap.put(prefix1 + "_" + prefix2 + "_avgscore", f4)

                }
              }
            }
          }

          val d1: JSONObject = if (record.isNull("d1_feature")) new JSONObject() else
            JSON.parseObject(record.getString("d1_feature"))
          val d2: JSONObject = if (record.isNull("d2_feature")) new JSONObject() else
            JSON.parseObject(record.getString("d2_feature"))

          if (d1.nonEmpty) {
            for (prefix <- List("3h", "6h", "12h", "1d", "3d", "7d")) {
              val view = if (!d1.containsKey("ad_view_" + prefix)) 0D else d1.getIntValue("ad_view_" + prefix).toDouble
              val click = if (!d1.containsKey("ad_click_" + prefix)) 0D else d1.getIntValue("ad_click_" + prefix).toDouble
              val conver = if (!d1.containsKey("ad_conversion_" + prefix)) 0D else d1.getIntValue("ad_conversion_" + prefix).toDouble
              val income = if (!d1.containsKey("ad_income_" + prefix)) 0D else d1.getIntValue("ad_income_" + prefix).toDouble
              val f1 = RankExtractorFeature_20240530.calDiv(click, view)
              val f2 = RankExtractorFeature_20240530.calDiv(conver, view)
              val f3 = RankExtractorFeature_20240530.calDiv(conver, click)
              val f4 = conver
              val f5 = RankExtractorFeature_20240530.calDiv(income * 1000, view)
              featureMap.put("d1_feature" + "_" + prefix + "_" + "ctr", f1)
              featureMap.put("d1_feature" + "_" + prefix + "_" + "ctcvr", f2)
              featureMap.put("d1_feature" + "_" + prefix + "_" + "cvr", f3)
              featureMap.put("d1_feature" + "_" + prefix + "_" + "conver", f4)
              featureMap.put("d1_feature" + "_" + prefix + "_" + "ecpm", f5)
            }
          }

          val vidRankMaps = scala.collection.mutable.Map[String, scala.collection.immutable.Map[String, Double]]()
          if (d2.nonEmpty) {
            d2.foreach(r => {
              val key = r._1
              val value = d2.getString(key).split(",").map(r => {
                val rList = r.split(":")
                (rList(0), rList(2).toDouble)
              }).toMap
              vidRankMaps.put(key, value)
            })
          }
          for (prefix1 <- List("ctr", "ctcvr", "ecpm")) {
            for (prefix2 <- List("1d", "3d", "7d", "14d")) {
              if (vidRankMaps.contains(prefix1 + "_" + prefix2)) {
                val rank = vidRankMaps(prefix1 + "_" + prefix2).getOrDefault(cid, 0.0)
                if (rank >= 1.0) {
                  featureMap.put("vid_rank_" + prefix1 + "_" + prefix2, 1.0 / rank)
                }
              }
            }
          }


          val flag = record.isNull("metafeature")

          val allfeaturemap = if (record.isNull("allfeature")) new JSONObject() else
            JSON.parseObject(record.getString("allfeature"))
          val apptype = record.getString("apptype")
          val label = record.getString("ad_is_conversion")
          (apptype, "pagesource", "level", label, "abcode", allfeaturemap, featureMap, flag)
        }).filter{
          case (apptype, pagesource, level, label, abcode, allfeaturemap, featureMap, flag) =>
            Set("17").contains(apptype) && !flag
        }.mapPartitions(row => {
          val result = new ArrayBuffer[String]()
          val bucketsMap = bucketsMap_br.value
          row.foreach {
            case (apptype, pagesource, level, label, abcode, allfeaturemap, featureMap, flag) =>
              val offlineFeatrueMap = featureMap.filter(r =>
                bucketsMap.containsKey(r._1) || r._1.startsWith("cid_") || r._1.startsWith("adid_")
                  || r._1.startsWith("adverid_") || r._1.startsWith("targeting_conversion_")
              ).map(r =>{
                val name = r._1
                val score = r._2.toString.toDouble
                if (score > 1E-8) {
                  if (bucketsMap.contains(name)) {
                    val (_, buckets) = bucketsMap(name)
                    val scoreNew = 1.0 / (buckets.length + 1) * (ExtractorUtils.findInsertPosition(buckets, score).toDouble + 1.0)
                    name + ":" + scoreNew.toString
                  } else {
                    name + ":" + score.toString
                  }
                } else {
                  ""
                }
              }).filter(_.nonEmpty)
              result.add((apptype, pagesource, level, label, abcode, allfeaturemap.toString, offlineFeatrueMap.iterator.mkString(","))
              .productIterator.mkString("\t"))
          }
          result.iterator
        })

      // 4 保存数据到hdfs
      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)
      }
    }

    val data2 = sc.textFile(savePath + "/" + readDate + "*").mapPartitions(row=>{
      val result = new ArrayBuffer[(String,
        Map[String, String], Map[String, String], List[String], List[String], List[String], List[String])]()
      val contentList = contentList_br.value
      row.foreach(r=>{
        val rList = r.split("\t")
        val label = rList(3)
        val allfeaturemap = JSON.parseObject(rList(5)).toMap.map(r => (r._1, r._2.toString))
        val offlineFeatrueMap = rList(6).split(",").map(r => (r.split(":")(0), r.split(":")(1))).toMap

        val v3 = contentList.map(name =>{
          val useOfflineNames = Set(
            "b2_3h_ctr","b2_3h_ctcvr","b2_3h_cvr","b2_3h_conver","b2_3h_ecpm","b2_6h_ctr","b2_6h_ctcvr","b2_6h_cvr","b2_6h_conver","b2_6h_ecpm","b2_12h_ctr","b2_12h_ctcvr","b2_12h_cvr","b2_12h_conver","b2_12h_ecpm","b2_1d_ctr","b2_1d_ctcvr","b2_1d_cvr","b2_1d_conver","b2_1d_ecpm","b2_3d_ctr","b2_3d_ctcvr","b2_3d_cvr","b2_3d_conver","b2_3d_ecpm","b2_7d_ctr","b2_7d_ctcvr","b2_7d_cvr","b2_7d_conver","b2_7d_ecpm","b3_3h_ctr","b3_3h_ctcvr","b3_3h_cvr","b3_3h_conver","b3_3h_ecpm","b3_6h_ctr","b3_6h_ctcvr","b3_6h_cvr","b3_6h_conver","b3_6h_ecpm","b3_12h_ctr","b3_12h_ctcvr","b3_12h_cvr","b3_12h_conver","b3_12h_ecpm","b3_1d_ctr","b3_1d_ctcvr","b3_1d_cvr","b3_1d_conver","b3_1d_ecpm","b3_3d_ctr","b3_3d_ctcvr","b3_3d_cvr","b3_3d_conver","b3_3d_ecpm","b3_7d_ctr","b3_7d_ctcvr","b3_7d_cvr","b3_7d_conver","b3_7d_ecpm","b4_3h_ctr","b4_3h_ctcvr","b4_3h_cvr","b4_3h_conver","b4_3h_ecpm","b4_6h_ctr","b4_6h_ctcvr","b4_6h_cvr","b4_6h_conver","b4_6h_ecpm","b4_12h_ctr","b4_12h_ctcvr","b4_12h_cvr","b4_12h_conver","b4_12h_ecpm","b4_1d_ctr","b4_1d_ctcvr","b4_1d_cvr","b4_1d_conver","b4_1d_ecpm","b4_3d_ctr","b4_3d_ctcvr","b4_3d_cvr","b4_3d_conver","b4_3d_ecpm","b4_7d_ctr","b4_7d_ctcvr","b4_7d_cvr","b4_7d_conver","b4_7d_ecpm","b5_3h_ctr","b5_3h_ctcvr","b5_3h_cvr","b5_3h_conver","b5_3h_ecpm","b5_6h_ctr","b5_6h_ctcvr","b5_6h_cvr","b5_6h_conver","b5_6h_ecpm","b5_12h_ctr","b5_12h_ctcvr","b5_12h_cvr","b5_12h_conver","b5_12h_ecpm","b5_1d_ctr","b5_1d_ctcvr","b5_1d_cvr","b5_1d_conver","b5_1d_ecpm","b5_3d_ctr","b5_3d_ctcvr","b5_3d_cvr","b5_3d_conver","b5_3d_ecpm","b5_7d_ctr","b5_7d_ctcvr","b5_7d_cvr","b5_7d_conver","b5_7d_ecpm","b8_3h_ctr","b8_3h_ctcvr","b8_3h_cvr","b8_3h_conver","b8_3h_ecpm","b8_6h_ctr","b8_6h_ctcvr","b8_6h_cvr","b8_6h_conver","b8_6h_ecpm","b8_12h_ctr","b8_12h_ctcvr","b8_12h_cvr","b8_12h_conver","b8_12h_ecpm","b8_1d_ctr","b8_1d_ctcvr","b8_1d_cvr","b8_1d_conver","b8_1d_ecpm","b8_3d_ctr","b8_3d_ctcvr","b8_3d_cvr","b8_3d_conver","b8_3d_ecpm","b8_7d_ctr","b8_7d_ctcvr","b8_7d_cvr","b8_7d_conver","b8_7d_ecpm"
          ,"b6_7d_ctr","b6_7d_ctcvr","b6_7d_cvr","b6_7d_conver","b6_7d_ecpm","b6_14d_ctr","b6_14d_ctcvr","b6_14d_cvr","b6_14d_conver","b6_14d_ecpm","b7_7d_ctr","b7_7d_ctcvr","b7_7d_cvr","b7_7d_conver","b7_7d_ecpm","b7_14d_ctr","b7_14d_ctcvr","b7_14d_cvr","b7_14d_conver","b7_14d_ecpm"
          )
          if (useOfflineNames.contains(name)){
            if (offlineFeatrueMap.contains(name)){
              name + ":" + offlineFeatrueMap(name)
            }else{
              ""
            }
          }else{
            if (allfeaturemap.contains(name)) {
              name + ":" + allfeaturemap(name)
            } else {
              ""
            }
          }
        }).filter(_.nonEmpty)

        val v4 = contentList.map(name => {
          val useOfflineNames = Set(
            "e1_tags_3d_matchnum","e1_tags_3d_maxscore","e1_tags_3d_avgscore","e1_tags_7d_matchnum","e1_tags_7d_maxscore","e1_tags_7d_avgscore","e1_tags_14d_matchnum","e1_tags_14d_maxscore","e1_tags_14d_avgscore","e2_tags_3d_matchnum","e2_tags_3d_maxscore","e2_tags_3d_avgscore","e2_tags_7d_matchnum","e2_tags_7d_maxscore","e2_tags_7d_avgscore","e2_tags_14d_matchnum","e2_tags_14d_maxscore","e2_tags_14d_avgscore","d1_feature_3h_ctr","d1_feature_3h_ctcvr","d1_feature_3h_cvr","d1_feature_3h_conver","d1_feature_3h_ecpm","d1_feature_6h_ctr","d1_feature_6h_ctcvr","d1_feature_6h_cvr","d1_feature_6h_conver","d1_feature_6h_ecpm","d1_feature_12h_ctr","d1_feature_12h_ctcvr","d1_feature_12h_cvr","d1_feature_12h_conver","d1_feature_12h_ecpm","d1_feature_1d_ctr","d1_feature_1d_ctcvr","d1_feature_1d_cvr","d1_feature_1d_conver","d1_feature_1d_ecpm","d1_feature_3d_ctr","d1_feature_3d_ctcvr","d1_feature_3d_cvr","d1_feature_3d_conver","d1_feature_3d_ecpm","d1_feature_7d_ctr","d1_feature_7d_ctcvr","d1_feature_7d_cvr","d1_feature_7d_conver","d1_feature_7d_ecpm","vid_rank_ctr_1d","vid_rank_ctr_3d","vid_rank_ctr_7d","vid_rank_ctr_14d","vid_rank_ctcvr_1d","vid_rank_ctcvr_3d","vid_rank_ctcvr_7d","vid_rank_ctcvr_14d","vid_rank_ecpm_1d","vid_rank_ecpm_3d","vid_rank_ecpm_7d","vid_rank_ecpm_14d"
          )
          if (useOfflineNames.contains(name)) {
            if (offlineFeatrueMap.contains(name)) {
              name + ":" + offlineFeatrueMap(name)
            } else {
              ""
            }
          } else {
            if (allfeaturemap.contains(name)) {
              name + ":" + allfeaturemap(name)
            } else {
              ""
            }
          }
        }).filter(_.nonEmpty)

        val v5 = contentList.map(name => {
          val useOfflineNames = Set(
            "viewAll","clickAll","converAll","incomeAll","ctr_all","ctcvr_all","cvr_all","ecpm_all"
          )
          if (useOfflineNames.contains(name)) {
            if (offlineFeatrueMap.contains(name)) {
              name + ":" + offlineFeatrueMap(name)
            } else {
              ""
            }
          } else {
            if (allfeaturemap.contains(name)) {
              name + ":" + allfeaturemap(name)
            } else {
              ""
            }
          }
        }).filter(_.nonEmpty)

        val v6 = contentList.map(name => {
          val useOfflineNames = Set(
            "timediff_view","timediff_click","timediff_conver","actionstatic_view","actionstatic_click","actionstatic_conver","actionstatic_income","actionstatic_ctr","actionstatic_ctcvr","actionstatic_cvr"
          )
          if (useOfflineNames.contains(name)) {
            if (offlineFeatrueMap.contains(name)) {
              name + ":" + offlineFeatrueMap(name)
            } else {
              ""
            }
          } else {
            if (allfeaturemap.contains(name)) {
              name + ":" + allfeaturemap(name)
            } else {
              ""
            }
          }
        }).filter(_.nonEmpty)


        result.add((label, offlineFeatrueMap, allfeaturemap, v3, v4, v5, v6))

      })
      result.iterator
    })

    val saveV1 = "/dw/recommend/model/33_for_check_v1/" + readDate
    if (saveV1.nonEmpty && saveV1.startsWith("/dw/recommend/model/")) {
      println("删除路径并开始数据写入:" + saveV1)
      MyHdfsUtils.delete_hdfs_path(saveV1)
      data2.map(r => r._1 + "\t" + r._2.map(r=> r._1 + ":" + r._2).mkString("\t")).saveAsTextFile(saveV1, classOf[GzipCodec])
    } else {
      println("路径不合法，无法写入:" + saveV1)
    }

    val saveV2 = "/dw/recommend/model/33_for_check_v2/" + readDate
    if (saveV2.nonEmpty && saveV2.startsWith("/dw/recommend/model/")) {
      println("删除路径并开始数据写入:" + saveV2)
      MyHdfsUtils.delete_hdfs_path(saveV2)
      data2.map(r => r._1 + "\t" + r._3.map(r=> r._1 + ":" + r._2).mkString("\t")).saveAsTextFile(saveV2, classOf[GzipCodec])
    } else {
      println("路径不合法，无法写入:" + saveV2)
    }

    val saveV3 = "/dw/recommend/model/33_for_check_v3/" + readDate
    if (saveV3.nonEmpty && saveV3.startsWith("/dw/recommend/model/")) {
      println("删除路径并开始数据写入:" + saveV3)
      MyHdfsUtils.delete_hdfs_path(saveV3)
      data2.map(r => r._1 + "\t" + r._4.mkString("\t")).saveAsTextFile(saveV3, classOf[GzipCodec])
    } else {
      println("路径不合法，无法写入:" + saveV3)
    }

    val saveV4 = "/dw/recommend/model/33_for_check_v4/" + readDate
    if (saveV4.nonEmpty && saveV4.startsWith("/dw/recommend/model/")) {
      println("删除路径并开始数据写入:" + saveV4)
      MyHdfsUtils.delete_hdfs_path(saveV4)
      data2.map(r => r._1 + "\t" + r._5.mkString("\t")).saveAsTextFile(saveV4, classOf[GzipCodec])
    } else {
      println("路径不合法，无法写入:" + saveV4)
    }

    val saveV5 = "/dw/recommend/model/33_for_check_v5/" + readDate
    if (saveV5.nonEmpty && saveV5.startsWith("/dw/recommend/model/")) {
      println("删除路径并开始数据写入:" + saveV5)
      MyHdfsUtils.delete_hdfs_path(saveV5)
      data2.map(r => r._1 + "\t" + r._6.mkString("\t")).saveAsTextFile(saveV5, classOf[GzipCodec])
    } else {
      println("路径不合法，无法写入:" + saveV5)
    }

    val saveV6 = "/dw/recommend/model/33_for_check_v6/" + readDate
    if (saveV6.nonEmpty && saveV6.startsWith("/dw/recommend/model/")) {
      println("删除路径并开始数据写入:" + saveV6)
      MyHdfsUtils.delete_hdfs_path(saveV6)
      data2.map(r => r._1 + "\t" + r._7.mkString("\t")).saveAsTextFile(saveV6, classOf[GzipCodec])
    } else {
      println("路径不合法，无法写入:" + saveV6)
    }



  }



  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)
  }
}
