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@@ -71,403 +71,403 @@ object makedata_31_bucketDataPrint_20240821 {
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(rList(0), (rList(1).toDouble, rList(2).split(",").map(_.toDouble)))
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}).toMap
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val bucketsMap_br = sc.broadcast(bucketsMap)
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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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- if (filterHours.nonEmpty && filterHours.contains(hh)) {
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- println("不执行partiton:" + partition)
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- } else {
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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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- val apptype = record.getString("apptype")
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- val extend: JSONObject = if (record.isNull("extend")) new JSONObject() else
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- JSON.parseObject(record.getString("extend"))
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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, idDefaultValue)
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- if (b1.containsKey("adid") && b1.getString("adid").nonEmpty) {
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- featureMap.put("adid_" + b1.getString("adid"), idDefaultValue)
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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"), idDefaultValue)
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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"), idDefaultValue)
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- }
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-
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- val hour = DateTimeUtil.getHourByTimestamp(ts)
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- featureMap.put("hour_" + hour, idDefaultValue)
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-
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- val dayOfWeek = DateTimeUtil.getDayOrWeekByTimestamp(ts)
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- featureMap.put("dayofweek_" + dayOfWeek, idDefaultValue);
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-
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- featureMap.put("apptype_" + apptype, idDefaultValue);
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-
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- if (extend.containsKey("abcode") && extend.getString("abcode").nonEmpty) {
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- featureMap.put("abcode_" + extend.getString("abcode"), idDefaultValue)
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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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- if (b1.containsKey("weight") && b1.getString("weight").nonEmpty) {
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- featureMap.put("weight", b1.getString("weight").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"), (b9, "b9")
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- )) {
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- for (prefix2 <- List(
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- "1h", "2h", "3h", "4h", "5h", "6h", "12h", "1d", "3d", "7d", "today", "yesterday"
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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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- featureMap.put(prefix1 + "_" + prefix2 + "_" + "click", click)
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- featureMap.put(prefix1 + "_" + prefix2 + "_" + "conver*log(view)", conver * RankExtractorFeature_20240530.calLog(view))
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- featureMap.put(prefix1 + "_" + prefix2 + "_" + "conver*ctcvr", conver * f2)
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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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- featureMap.put(prefix1 + "_" + prefix2 + "_" + "click", click)
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- featureMap.put(prefix1 + "_" + prefix2 + "_" + "conver*log(view)", conver * RankExtractorFeature_20240530.calLog(view))
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- featureMap.put(prefix1 + "_" + prefix2 + "_" + "conver*ctcvr", conver * f2)
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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("actionstatic_conver_" + cid)) {
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- featureMap.put("actionstatic_ctcvr", RankExtractorFeature_20240530.calDiv(
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- midActionStatic.getOrDefault("actionstatic_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_conver_" + cid, 0.0),
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- midActionStatic.getOrDefault("actionstatic_click_" + 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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- val d3: JSONObject = if (record.isNull("d3_feature")) new JSONObject() else
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- JSON.parseObject(record.getString("d3_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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- if (d3.nonEmpty) {
|
|
|
- val vTitle = d3.getString("title")
|
|
|
- val score = Similarity.conceptSimilarity(title, vTitle)
|
|
|
- featureMap.put("ctitle_vtitle_similarity", score);
|
|
|
- }
|
|
|
-
|
|
|
- /*
|
|
|
- 广告
|
|
|
- sparse：cid adid adverid targeting_conversion
|
|
|
-
|
|
|
- cpa --> 1个
|
|
|
- adverid下的 3h 6h 12h 1d 3d 7d 、 ctr ctcvr cvr conver ecpm --> 30个
|
|
|
- cid下的 3h 6h 12h 1d 3d 7d 、 ctr ctcvr cvr ecpm conver --> 30个
|
|
|
- 地理//cid下的 3h 6h 12h 1d 3d 7d 、 ctr ctcvr cvr ecpm conver --> 30个
|
|
|
- app//cid下的 3h 6h 12h 1d 3d 7d 、 ctr ctcvr cvr ecpm conver --> 30个
|
|
|
- 手机品牌//cid下的 3h 6h 12h 1d 3d 7d 、 ctr ctcvr cvr ecpm conver --> 30个
|
|
|
- 系统 无数据
|
|
|
- week//cid下的 7d 14d、 ctr ctcvr cvr ecpm conver --> 10个
|
|
|
- hour//cid下的 7d 14d、 ctr ctcvr cvr ecpm conver --> 10个
|
|
|
-
|
|
|
- 用户
|
|
|
- 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
|
|
|
- 用户历史 14d 看过/点过/转化次数/income； ctr cvr ctcvr ecpm； --> 8个
|
|
|
-
|
|
|
- 用户到cid的ui特征 --> 10个
|
|
|
- 1/用户最近看过这个cid的时间间隔
|
|
|
- 1/用户最近点过这个cid的时间间隔
|
|
|
- 1/用户最近转过这个cid的时间间隔
|
|
|
- 用户看过这个cid多少次
|
|
|
- 用户点过这个cid多少次
|
|
|
- 用户转过这个cid多少次
|
|
|
- 用户对这个cid花了多少钱
|
|
|
- 用户对这个cid的ctr ctcvr cvr
|
|
|
-
|
|
|
- 视频
|
|
|
- title与cid的 sim-score-1/-2 无数据
|
|
|
- vid//cid下的 3h 6h 12h 1d 3d 7d 、 ctr ctcvr cvr ecpm conver --> 30个
|
|
|
- vid//cid下的 1d 3d 7d 14d、 ctr ctcvr ecpm 的rank值 倒数 --> 12个
|
|
|
-
|
|
|
- */
|
|
|
-
|
|
|
-
|
|
|
- //4 处理label信息。
|
|
|
- val labels = new JSONObject
|
|
|
- for (labelKey <- List("ad_is_click", "ad_is_conversion")) {
|
|
|
- if (!record.isNull(labelKey)) {
|
|
|
- labels.put(labelKey, record.getString(labelKey))
|
|
|
- }
|
|
|
- }
|
|
|
- //5 处理log key表头。
|
|
|
- val mid = record.getString("mid")
|
|
|
- val allfeature = if (record.isNull("allfeaturemap")) new JSONObject() else
|
|
|
- JSON.parseObject(record.getString("allfeaturemap"))
|
|
|
-
|
|
|
- val headvideoid = record.getString("headvideoid")
|
|
|
- // val logKey = (apptype, mid, cid, ts, headvideoid).productIterator.mkString(",")
|
|
|
- val labelKey = labels.toString()
|
|
|
- val label = record.getString("ad_is_conversion")
|
|
|
- //6 拼接数据，保存。
|
|
|
- (apptype, mid, cid, ts, headvideoid, label, allfeature, featureMap)
|
|
|
- }).filter {
|
|
|
- case (apptype, mid, cid, ts, headvideoid, label, allfeature, featureMap) =>
|
|
|
- !(allfeature.isEmpty || allfeature.containsKey("weight_sum") || allfeature.contains("weight"))
|
|
|
- }.mapPartitions(row => {
|
|
|
- val result = new ArrayBuffer[String]()
|
|
|
- val bucketsMap = bucketsMap_br.value
|
|
|
- row.foreach {
|
|
|
- case (apptype, mid, cid, ts, headvideoid, label, allfeature, featureMap) =>
|
|
|
- val offlineFeatureMap = featureMap.map(r => {
|
|
|
- val score = r._2.toString.toDouble
|
|
|
- val name = r._1
|
|
|
- if (score > 1E-8) {
|
|
|
- if (bucketsMap.contains(name)) {
|
|
|
- val (bucketsNum, buckets) = bucketsMap(name)
|
|
|
- val scoreNew = 1.0 / bucketsNum * (ExtractorUtils.findInsertPosition(buckets, score).toDouble + 1.0)
|
|
|
- name + ":" + scoreNew.toString
|
|
|
- } else {
|
|
|
- name + ":" + score.toString
|
|
|
- }
|
|
|
- } else {
|
|
|
- ""
|
|
|
- }
|
|
|
- }).filter(_.nonEmpty)
|
|
|
- result.add(
|
|
|
- (apptype, mid, cid, ts, headvideoid, label, allfeature.toString(), offlineFeatureMap.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)
|
|
|
- }
|
|
|
- }
|
|
|
- }
|
|
|
+ // // 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"
|
|
|
+ // if (filterHours.nonEmpty && filterHours.contains(hh)) {
|
|
|
+ // println("不执行partiton:" + partition)
|
|
|
+ // } else {
|
|
|
+ // 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 apptype = record.getString("apptype")
|
|
|
+ // val extend: JSONObject = if (record.isNull("extend")) new JSONObject() else
|
|
|
+ // JSON.parseObject(record.getString("extend"))
|
|
|
+ //
|
|
|
+ //
|
|
|
+ // 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, idDefaultValue)
|
|
|
+ // if (b1.containsKey("adid") && b1.getString("adid").nonEmpty) {
|
|
|
+ // featureMap.put("adid_" + b1.getString("adid"), idDefaultValue)
|
|
|
+ // }
|
|
|
+ // if (b1.containsKey("adverid") && b1.getString("adverid").nonEmpty) {
|
|
|
+ // featureMap.put("adverid_" + b1.getString("adverid"), idDefaultValue)
|
|
|
+ // }
|
|
|
+ // if (b1.containsKey("targeting_conversion") && b1.getString("targeting_conversion").nonEmpty) {
|
|
|
+ // featureMap.put("targeting_conversion_" + b1.getString("targeting_conversion"), idDefaultValue)
|
|
|
+ // }
|
|
|
+ //
|
|
|
+ // val hour = DateTimeUtil.getHourByTimestamp(ts)
|
|
|
+ // featureMap.put("hour_" + hour, idDefaultValue)
|
|
|
+ //
|
|
|
+ // val dayOfWeek = DateTimeUtil.getDayOrWeekByTimestamp(ts)
|
|
|
+ // featureMap.put("dayofweek_" + dayOfWeek, idDefaultValue);
|
|
|
+ //
|
|
|
+ // featureMap.put("apptype_" + apptype, idDefaultValue);
|
|
|
+ //
|
|
|
+ // if (extend.containsKey("abcode") && extend.getString("abcode").nonEmpty) {
|
|
|
+ // featureMap.put("abcode_" + extend.getString("abcode"), idDefaultValue)
|
|
|
+ // }
|
|
|
+ //
|
|
|
+ //
|
|
|
+ // if (b1.containsKey("cpa")) {
|
|
|
+ // featureMap.put("cpa", b1.getString("cpa").toDouble)
|
|
|
+ // }
|
|
|
+ // if (b1.containsKey("weight") && b1.getString("weight").nonEmpty) {
|
|
|
+ // featureMap.put("weight", b1.getString("weight").toDouble)
|
|
|
+ // }
|
|
|
+ //
|
|
|
+ // for ((bn, prefix1) <- List(
|
|
|
+ // (b2, "b2"), (b3, "b3"), (b4, "b4"), (b5, "b5"), (b8, "b8"), (b9, "b9")
|
|
|
+ // )) {
|
|
|
+ // for (prefix2 <- List(
|
|
|
+ // "1h", "2h", "3h", "4h", "5h", "6h", "12h", "1d", "3d", "7d", "today", "yesterday"
|
|
|
+ // )) {
|
|
|
+ // 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)
|
|
|
+ //
|
|
|
+ // featureMap.put(prefix1 + "_" + prefix2 + "_" + "click", click)
|
|
|
+ // featureMap.put(prefix1 + "_" + prefix2 + "_" + "conver*log(view)", conver * RankExtractorFeature_20240530.calLog(view))
|
|
|
+ // featureMap.put(prefix1 + "_" + prefix2 + "_" + "conver*ctcvr", conver * f2)
|
|
|
+ // }
|
|
|
+ // }
|
|
|
+ //
|
|
|
+ // 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)
|
|
|
+ //
|
|
|
+ // featureMap.put(prefix1 + "_" + prefix2 + "_" + "click", click)
|
|
|
+ // featureMap.put(prefix1 + "_" + prefix2 + "_" + "conver*log(view)", conver * RankExtractorFeature_20240530.calLog(view))
|
|
|
+ // featureMap.put(prefix1 + "_" + prefix2 + "_" + "conver*ctcvr", conver * f2)
|
|
|
+ // }
|
|
|
+ // }
|
|
|
+ //
|
|
|
+ // 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("actionstatic_click_" + cid)) {
|
|
|
+ // featureMap.put("actionstatic_ctr", RankExtractorFeature_20240530.calDiv(
|
|
|
+ // midActionStatic.getOrDefault("actionstatic_click_" + 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_conver_" + cid, 0.0),
|
|
|
+ // midActionStatic.getOrDefault("actionstatic_click_" + 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"))
|
|
|
+ // val d3: JSONObject = if (record.isNull("d3_feature")) new JSONObject() else
|
|
|
+ // JSON.parseObject(record.getString("d3_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)
|
|
|
+ // }
|
|
|
+ // }
|
|
|
+ // }
|
|
|
+ // }
|
|
|
+ //
|
|
|
+ // if (d3.nonEmpty) {
|
|
|
+ // val vTitle = d3.getString("title")
|
|
|
+ // val score = Similarity.conceptSimilarity(title, vTitle)
|
|
|
+ // featureMap.put("ctitle_vtitle_similarity", score);
|
|
|
+ // }
|
|
|
+ //
|
|
|
+ // /*
|
|
|
+ // 广告
|
|
|
+ // sparse：cid adid adverid targeting_conversion
|
|
|
+ //
|
|
|
+ // cpa --> 1个
|
|
|
+ // adverid下的 3h 6h 12h 1d 3d 7d 、 ctr ctcvr cvr conver ecpm --> 30个
|
|
|
+ // cid下的 3h 6h 12h 1d 3d 7d 、 ctr ctcvr cvr ecpm conver --> 30个
|
|
|
+ // 地理//cid下的 3h 6h 12h 1d 3d 7d 、 ctr ctcvr cvr ecpm conver --> 30个
|
|
|
+ // app//cid下的 3h 6h 12h 1d 3d 7d 、 ctr ctcvr cvr ecpm conver --> 30个
|
|
|
+ // 手机品牌//cid下的 3h 6h 12h 1d 3d 7d 、 ctr ctcvr cvr ecpm conver --> 30个
|
|
|
+ // 系统 无数据
|
|
|
+ // week//cid下的 7d 14d、 ctr ctcvr cvr ecpm conver --> 10个
|
|
|
+ // hour//cid下的 7d 14d、 ctr ctcvr cvr ecpm conver --> 10个
|
|
|
+ //
|
|
|
+ // 用户
|
|
|
+ // 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
|
|
|
+ // 用户历史 14d 看过/点过/转化次数/income； ctr cvr ctcvr ecpm； --> 8个
|
|
|
+ //
|
|
|
+ // 用户到cid的ui特征 --> 10个
|
|
|
+ // 1/用户最近看过这个cid的时间间隔
|
|
|
+ // 1/用户最近点过这个cid的时间间隔
|
|
|
+ // 1/用户最近转过这个cid的时间间隔
|
|
|
+ // 用户看过这个cid多少次
|
|
|
+ // 用户点过这个cid多少次
|
|
|
+ // 用户转过这个cid多少次
|
|
|
+ // 用户对这个cid花了多少钱
|
|
|
+ // 用户对这个cid的ctr ctcvr cvr
|
|
|
+ //
|
|
|
+ // 视频
|
|
|
+ // title与cid的 sim-score-1/-2 无数据
|
|
|
+ // vid//cid下的 3h 6h 12h 1d 3d 7d 、 ctr ctcvr cvr ecpm conver --> 30个
|
|
|
+ // vid//cid下的 1d 3d 7d 14d、 ctr ctcvr ecpm 的rank值 倒数 --> 12个
|
|
|
+ //
|
|
|
+ // */
|
|
|
+ //
|
|
|
+ //
|
|
|
+ // //4 处理label信息。
|
|
|
+ // val labels = new JSONObject
|
|
|
+ // for (labelKey <- List("ad_is_click", "ad_is_conversion")) {
|
|
|
+ // if (!record.isNull(labelKey)) {
|
|
|
+ // labels.put(labelKey, record.getString(labelKey))
|
|
|
+ // }
|
|
|
+ // }
|
|
|
+ // //5 处理log key表头。
|
|
|
+ // val mid = record.getString("mid")
|
|
|
+ // val allfeature = if (record.isNull("allfeaturemap")) new JSONObject() else
|
|
|
+ // JSON.parseObject(record.getString("allfeaturemap"))
|
|
|
+ //
|
|
|
+ // val headvideoid = record.getString("headvideoid")
|
|
|
+ // // val logKey = (apptype, mid, cid, ts, headvideoid).productIterator.mkString(",")
|
|
|
+ // val labelKey = labels.toString()
|
|
|
+ // val label = record.getString("ad_is_conversion")
|
|
|
+ // //6 拼接数据，保存。
|
|
|
+ // (apptype, mid, cid, ts, headvideoid, label, allfeature, featureMap)
|
|
|
+ // }).filter {
|
|
|
+ // case (apptype, mid, cid, ts, headvideoid, label, allfeature, featureMap) =>
|
|
|
+ // !(allfeature.isEmpty || allfeature.containsKey("weight_sum") || allfeature.contains("weight"))
|
|
|
+ // }.mapPartitions(row => {
|
|
|
+ // val result = new ArrayBuffer[String]()
|
|
|
+ // val bucketsMap = bucketsMap_br.value
|
|
|
+ // row.foreach {
|
|
|
+ // case (apptype, mid, cid, ts, headvideoid, label, allfeature, featureMap) =>
|
|
|
+ // val offlineFeatureMap = featureMap.map(r => {
|
|
|
+ // val score = r._2.toString.toDouble
|
|
|
+ // val name = r._1
|
|
|
+ // if (score > 1E-8) {
|
|
|
+ // if (bucketsMap.contains(name)) {
|
|
|
+ // val (bucketsNum, buckets) = bucketsMap(name)
|
|
|
+ // val scoreNew = 1.0 / bucketsNum * (ExtractorUtils.findInsertPosition(buckets, score).toDouble + 1.0)
|
|
|
+ // name + ":" + scoreNew.toString
|
|
|
+ // } else {
|
|
|
+ // name + ":" + score.toString
|
|
|
+ // }
|
|
|
+ // } else {
|
|
|
+ // ""
|
|
|
+ // }
|
|
|
+ // }).filter(_.nonEmpty)
|
|
|
+ // result.add(
|
|
|
+ // (apptype, mid, cid, ts, headvideoid, label, allfeature.toString(), offlineFeatureMap.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, List[String], List[String], List[String], List[String])]()
|
|
|
+ val result = new ArrayBuffer[(String, List[String], List[String], List[String], List[String], List[String])]()
|
|
|
// 680实验，517个特征
|
|
|
row.foreach(r => {
|
|
|
val rList = r.split("\t")
|
|
|
@@ -475,6 +475,9 @@ object makedata_31_bucketDataPrint_20240821 {
|
|
|
val label = rList(5).toString
|
|
|
val allFeatureMap = JSON.parseObject(rList(6)).toMap.map(r => (r._1, r._2.toString))
|
|
|
val offlineFeature = rList(7).split(",").map(r => (r.split(":")(0), r.split(":")(1))).toMap
|
|
|
+
|
|
|
+ val contentList = contentList_br.value
|
|
|
+
|
|
|
if (!allFeatureMap.containsKey("cid_" + cid)) {
|
|
|
allFeatureMap.put("cid_" + cid, "0.1");
|
|
|
}
|
|
|
@@ -501,17 +504,34 @@ object makedata_31_bucketDataPrint_20240821 {
|
|
|
}
|
|
|
}.filter(_.nonEmpty).toList
|
|
|
|
|
|
+ val allFeatureV3 = contentList.map {
|
|
|
+ case (name) =>
|
|
|
+ val b9FeatureSet = Set("b9_1h_ctr", "b9_1h_ctcvr", "b9_1h_cvr", "b9_1h_conver", "b9_1h_click", "b9_1h_conver*log(view)", "b9_1h_conver*ctcvr", "b9_2h_ctr", "b9_2h_ctcvr", "b9_2h_cvr", "b9_2h_conver", "b9_2h_click", "b9_2h_conver*log(view)", "b9_2h_conver*ctcvr", "b9_3h_ctr", "b9_3h_ctcvr", "b9_3h_cvr", "b9_3h_conver", "b9_3h_click", "b9_3h_conver*log(view)", "b9_3h_conver*ctcvr", "b9_6h_ctr", "b9_6h_ctcvr", "b9_6h_cvr", "b9_6h_conver", "b9_6h_click", "b9_6h_conver*log(view)", "b9_6h_conver*ctcvr", "b9_12h_ctr", "b9_12h_ctcvr", "b9_12h_cvr", "b9_12h_conver", "b9_12h_click", "b9_12h_conver*log(view)", "b9_12h_conver*ctcvr", "b9_1d_ctr", "b9_1d_ctcvr", "b9_1d_cvr", "b9_1d_conver", "b9_1d_click", "b9_1d_conver*log(view)", "b9_1d_conver*ctcvr", "b9_3d_ctr", "b9_3d_ctcvr", "b9_3d_cvr", "b9_3d_conver", "b9_3d_click", "b9_3d_conver*log(view)", "b9_3d_conver*ctcvr", "b9_7d_ctr", "b9_7d_ctcvr", "b9_7d_cvr", "b9_7d_conver", "b9_7d_click", "b9_7d_conver*log(view)", "b9_7d_conver*ctcvr", "b9_yesterday_ctr", "b9_yesterday_ctcvr", "b9_yesterday_cvr", "b9_yesterday_conver", "b9_yesterday_click", "b9_yesterday_conver*log(view)", "b9_yesterday_conver*ctcvr", "b9_today_ctr", "b9_today_ctcvr", "b9_today_cvr", "b9_today_conver", "b9_today_click", "b9_today_conver*log(view)", "b9_today_conver*ctcvr")
|
|
|
+ if (b9FeatureSet.contains(name)) {
|
|
|
+ if (offlineFeature.contains(name)) {
|
|
|
+ name + ":" + offlineFeature(name)
|
|
|
+ } else {
|
|
|
+ ""
|
|
|
+ }
|
|
|
+ } else {
|
|
|
+ if (allFeatureMap.contains(name)) {
|
|
|
+ name + ":" + allFeatureMap(name)
|
|
|
+ } else {
|
|
|
+ ""
|
|
|
+ }
|
|
|
+ }
|
|
|
+ }
|
|
|
+
|
|
|
val ctcvrFeature = offlineFeature.map {
|
|
|
- case (key, value) => {
|
|
|
+ case (key, value) =>
|
|
|
if (key.contains("ctcvr") || key.contains("Ctcvr")) {
|
|
|
key + ":" + value
|
|
|
} else {
|
|
|
""
|
|
|
}
|
|
|
- }
|
|
|
}.filter(_.nonEmpty).toList
|
|
|
|
|
|
- result.add((label, offlineFeatureList, allFeatureV1, allFeatureV2, ctcvrFeature))
|
|
|
+ result.add((label, offlineFeatureList, allFeatureV1, allFeatureV2, ctcvrFeature, allFeatureV3))
|
|
|
})
|
|
|
|
|
|
result.iterator
|
|
|
@@ -553,6 +573,15 @@ object makedata_31_bucketDataPrint_20240821 {
|
|
|
println("路径不合法，无法写入:" + ctcvrFeature)
|
|
|
}
|
|
|
|
|
|
+ val allFeatureV3 = "/dw/recommend/model/33_for_check_all_v3/" + readDate
|
|
|
+ if (allFeatureV3.nonEmpty && allFeatureV3.startsWith("/dw/recommend/model/")) {
|
|
|
+ println("删除路径并开始数据写入:" + allFeatureV3)
|
|
|
+ MyHdfsUtils.delete_hdfs_path(allFeatureV3)
|
|
|
+ data2.map(r => r._1 + "\t" + r._6.mkString("\t")).saveAsTextFile(allFeatureV3, classOf[GzipCodec])
|
|
|
+ } else {
|
|
|
+ println("路径不合法，无法写入:" + allFeatureV3)
|
|
|
+ }
|
|
|
+
|
|
|
}
|
|
|
|
|
|
def func(record: Record, schema: TableSchema): Record = {
|
