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src/main/scala/com/aliyun/odps/spark/examples/makedata_ad/v20240718/makedata_ad_33_bucketDataFromOriginToHive_20250522.scala

@@ -0,0 +1,693 @@
+package com.aliyun.odps.spark.examples.makedata_ad.v20240718
+
+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, ParamUtils, env}
+import examples.extractor.{ExtractorUtils, RankExtractorFeature_20240530}
+import examples.utils.{AdUtil, DateTimeUtil, SimilarityUtils}
+import org.apache.spark.sql.SparkSession
+
+import java.time.{Instant, ZoneId, ZonedDateTime}
+import scala.collection.JavaConversions._
+import scala.collection.mutable.ArrayBuffer
+import scala.io.Source
+import scala.language.postfixOps
+import scala.util.Random
+
+object makedata_ad_33_bucketDataFromOriginToHive_20250522 {
+  val CTR_SMOOTH_BETA_FACTOR = 25
+  val CVR_SMOOTH_BETA_FACTOR = 10
+  val CTCVR_SMOOTH_BETA_FACTOR = 100
+
+  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", "20250216")
+    val endStr = param.getOrElse("endStr", "20250216")
+    val project = param.getOrElse("project", "loghubods")
+    val inputTable = param.getOrElse("inputTable", "alg_recsys_ad_sample_all")
+    val outputTable = param.getOrElse("outputTable", "ad_easyrec_train_data_v1_sampled")
+    val outputTable2 = param.getOrElse("outputTable2", "")
+    val filterHours = param.getOrElse("filterHours", "00,01,02,03,04,05,06,07").split(",").toSet
+    val idDefaultValue = param.getOrElse("idDefaultValue", "1.0").toDouble
+    val filterNames = param.getOrElse("filterNames", "").split(",").filter(_.nonEmpty).toSet
+    val filterAdverIds = param.getOrElse("filterAdverIds", "").split(",").filter(_.nonEmpty).toSet
+    val whatLabel = param.getOrElse("whatLabel", "ad_is_conversion")
+    val negSampleRate = param.getOrElse("negSampleRate", "1").toDouble
+    // 分割样本集的比例，splitRate部分输出至outputTable，补集输出至outputTable2（如果outputTable2不为空）
+    val splitRate = param.getOrElse("splitRate", "0.9").toDouble
+
+    val loader = getClass.getClassLoader
+    val resourceUrlBucket = loader.getResource("20250217_ad_bucket_688.txt")
+    val buckets =
+      if (resourceUrlBucket != null) {
+        val buckets = Source.fromURL(resourceUrlBucket).getLines().mkString("\n")
+        Source.fromURL(resourceUrlBucket).close()
+        buckets
+      } else {
+        ""
+      }
+    val bucketsMap = buckets.split("\n")
+      .map(r => r.replace(" ", "").replaceAll("\n", ""))
+      .filter(r => r.nonEmpty)
+      .map(r => {
+        val rList = r.split("\t")
+        val featureName = rList(0).replace("*", "_x_").replace("(view)", "_view")
+        (featureName, (rList(1).toDouble, rList(2).split(",").map(_.toDouble)))
+      }).toMap
+    val bucketsMap_br = sc.broadcast(bucketsMap)
+    val denseFeatureNames = bucketsMap.keySet
+    val sparseFeatureNames = Set(
+      "cid", "adid", "adverid", "targeting_conversion",
+      "region", "city", "brand",
+      "vid", "cate1", "cate2",
+      "user_cid_click_list", "user_cid_conver_list",
+      "user_vid_return_tags_2h", "user_vid_return_tags_1d", "user_vid_return_tags_3d", "user_vid_return_tags_7d",
+      "user_vid_return_tags_14d", "apptype", "ts", "mid", "pqtid", "hour", "hour_quarter", "root_source_scene",
+      "root_source_channel", "is_first_layer", "title_split", "profession", "user_vid_share_tags_1d", "user_vid_share_tags_14d",
+      "user_vid_return_cate1_14d", "user_vid_return_cate2_14d", "user_vid_share_cate1_14d", "user_vid_share_cate2_14d",
+      "creative_type", "creative_hook_embedding", "creative_why_embedding", "creative_action_embedding","user_has_conver_1y")
+
+
+    // 2 读取odps+表信息
+    val odpsOps = env.getODPS(sc)
+    // 3 循环执行数据生产
+    val dateRange = MyDateUtils.getDateRange(beginStr, endStr)
+    for (dt <- dateRange) {
+      val timeRange = MyDateUtils.getDateMinuteRange(dt + "0800", dt + "2355")
+      val recordRdd = timeRange.map { dt_hh =>
+          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)) {
+            None
+          } else {
+            Some(partition)
+          }
+        }.collect {
+          case Some(partition) => partition
+        }.map(partition => {
+          val odpsData = odpsOps.readTable(project = project,
+              table = inputTable,
+              partition = partition,
+              transfer = func,
+              numPartition = tablePart)
+            .filter(record => {
+              AdUtil.isApi(record)
+            })
+            .filter(record => {
+              val appType = record.getString("apptype")
+              !Set("12", "13").contains(appType)
+            })
+            .filter(record => {
+              val adverId = record.getString("adverid")
+              !filterAdverIds.contains(adverId)
+            })
+            .filter(record => {
+              val label = record.getString(whatLabel).toInt
+              label > 0 || Random.nextDouble() < negSampleRate
+            })
+            .map(record => {
+              val featureMap = new JSONObject()
+              val ts = record.getString("ts").toInt
+              val instant = Instant.ofEpochSecond(ts)
+              // 设置时区为中国时区
+              val chinaZone = ZoneId.of("Asia/Shanghai")
+              // 将 Instant 对象转换为中国时区的 ZonedDateTime 对象
+              val zonedDateTime = ZonedDateTime.ofInstant(instant, chinaZone)
+              // 获取小时
+              val tsHour = zonedDateTime.getHour()
+              // 当前小时-刻钟（15分钟一个间隔，0～95）
+              val tsHourQuarter = zonedDateTime.getMinute() / 15 + zonedDateTime.getHour() * 4
+              val cid = record.getString("cid")
+              val mid = record.getString("mid")
+              val pqtid = record.getString("pqtid")
+              val apptype = record.getString("apptype")
+
+              featureMap.put("apptype", apptype)
+              featureMap.put("ts", ts)
+              featureMap.put("mid", mid)
+              featureMap.put("pqtid", pqtid)
+              featureMap.put("hour", tsHour)
+              featureMap.put("hour_quarter", tsHourQuarter)
+
+              val extend: JSONObject = if (record.isNull("extend")) new JSONObject() else
+                JSON.parseObject(record.getString("extend"))
+              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)
+              }
+              if (b1.containsKey("cid") && b1.getString("cid").nonEmpty) {
+                featureMap.put("cid", b1.getBigInteger("cid"))
+              }
+              if (b1.containsKey("adid") && b1.getString("adid").nonEmpty) {
+                featureMap.put("adid", b1.getBigInteger("adid"))
+              }
+              if (b1.containsKey("adverid") && b1.getString("adverid").nonEmpty) {
+                featureMap.put("adverid", b1.getBigInteger("adverid"))
+              }
+              if (b1.containsKey("profession") && b1.getString("profession").nonEmpty) {
+                featureMap.put("profession", b1.getString("profession"))
+              }
+              if (b1.containsKey("creative_type") && b1.getString("creative_type").nonEmpty) {
+                featureMap.put("creative_type", b1.getString("creative_type"))
+              }
+              if (b1.containsKey("creative_hook_embedding") && b1.getString("creative_hook_embedding").nonEmpty) {
+                featureMap.put("creative_hook_embedding", b1.getString("creative_hook_embedding").split('|').map(_.toDouble).map(_.toFloat).mkString("|"))
+              }
+              if (b1.containsKey("creative_why_embedding") && b1.getString("creative_why_embedding").nonEmpty) {
+                featureMap.put("creative_why_embedding", b1.getString("creative_why_embedding").split('|').map(_.toDouble).map(_.toFloat).mkString("|"))
+              }
+              if (b1.containsKey("creative_action_embedding") && b1.getString("creative_action_embedding").nonEmpty) {
+                featureMap.put("creative_action_embedding", b1.getString("creative_action_embedding").split('|').map(_.toDouble).map(_.toFloat).mkString("|"))
+              }
+
+              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 (extend.containsKey("region") && extend.getString("region").nonEmpty) {
+                featureMap.put("region", extend.getString("region"))
+              }
+
+              if (extend.containsKey("city") && extend.getString("city").nonEmpty) {
+                featureMap.put("city", extend.getString("city"))
+              }
+
+              if (extend.containsKey("is_first_layer") && extend.getString("is_first_layer").nonEmpty) {
+                featureMap.put("is_first_layer", extend.getString("is_first_layer"))
+              }
+
+              if (extend.containsKey("root_source_scene") && extend.getString("root_source_scene").nonEmpty) {
+                featureMap.put("root_source_scene", extend.getString("root_source_scene"))
+              }
+
+              if (extend.containsKey("root_source_channel") && extend.getString("root_source_channel").nonEmpty) {
+                featureMap.put("root_source_channel", extend.getString("root_source_channel"))
+              }
+
+
+              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(
+                  "3h", "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
+                  // NOTE(zhoutian):
+                  // 这里cpc只是为了计算cpm的平滑的工具量，没有实际业务意义，因为cpm并非比率，本身不适合直接计算Wilson平滑
+                  // 不使用cpa的原因是未来可能出现广告采用cpc计费的情况或者无法获取转化量的情况，用点击更为稳定
+                  // 其它几组特征亦采用相同逻辑
+                  // 2025-02-17改为增加固定分母平滑，income实际已经可以直接参与cpm平滑计算
+                  val cpc = if (click == 0) 0D else income / click
+                  val f1 = RankExtractorFeature_20240530.divSmooth2(click, view, CTR_SMOOTH_BETA_FACTOR)
+                  val f2 = RankExtractorFeature_20240530.divSmooth2(conver, view, CTCVR_SMOOTH_BETA_FACTOR)
+                  val f3 = RankExtractorFeature_20240530.divSmooth2(conver, click, CVR_SMOOTH_BETA_FACTOR)
+                  val f4 = conver
+                  val f5 = RankExtractorFeature_20240530.divSmooth2(click, view, CTR_SMOOTH_BETA_FACTOR) * cpc * 1000
+                  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_x_log_view", conver * RankExtractorFeature_20240530.calLog(view))
+                  featureMap.put(prefix1 + "_" + prefix2 + "_" + "conver_x_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 cpc = if (click == 0) 0D else income / click
+                  val f1 = RankExtractorFeature_20240530.divSmooth2(click, view, CTR_SMOOTH_BETA_FACTOR)
+                  val f2 = RankExtractorFeature_20240530.divSmooth2(conver, view, CTCVR_SMOOTH_BETA_FACTOR)
+                  val f3 = RankExtractorFeature_20240530.divSmooth2(conver, click, CVR_SMOOTH_BETA_FACTOR)
+                  val f4 = conver
+                  val f5 = RankExtractorFeature_20240530.divSmooth2(click, view, CTR_SMOOTH_BETA_FACTOR) * cpc * 1000
+                  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_x_log_view", conver * RankExtractorFeature_20240530.calLog(view))
+                  featureMap.put(prefix1 + "_" + prefix2 + "_" + "conver_x_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))
+
+              if (c1.containsKey("user_has_conver_1y") && c1.getInteger("user_has_conver_1y") != null) {
+                featureMap.put("user_has_conver_1y", c1.getInteger("user_has_conver_1y"))
+              }
+
+              // 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))
+              }
+
+              val clickCidList = collection.mutable.ListBuffer[String]()
+              val converCidList = collection.mutable.ListBuffer[String]()
+              midActionList.foreach {
+                case (cid, (ts_history, click, conver, income, title)) =>
+                  if (click == 1) clickCidList += cid
+                  if (conver == 1) converCidList += cid
+              }
+              if (clickCidList.nonEmpty) {
+                featureMap.put("user_cid_click_list", clickCidList.takeRight(50).mkString(","))
+              } else {
+                featureMap.put("user_cid_click_list", "")
+              }
+              if (converCidList.nonEmpty) {
+                featureMap.put("user_cid_conver_list", converCidList.takeRight(50).mkString(","))
+              } else {
+                featureMap.put("user_cid_conver_list", "")
+              }
+              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) = funcC34567ForTagsNew(en.getString(prefix2), title)
+                      featureMap.put(prefix1 + "_" + prefix2 + "_matchnum", f1)
+                      featureMap.put(prefix1 + "_" + prefix2 + "_maxscore", f3)
+                      featureMap.put(prefix1 + "_" + prefix2 + "_avgscore", f4)
+
+                    }
+                  }
+                }
+              }
+
+              if (e1.containsKey("tags_2h") && e1.getString("tags_2h").nonEmpty) {
+                featureMap.put("user_vid_return_tags_2h", e1.getString("tags_2h"))
+              }
+              if (e1.containsKey("tags_1d") && e1.getString("tags_1d").nonEmpty) {
+                featureMap.put("user_vid_return_tags_1d", e1.getString("tags_1d"))
+              }
+              if (e1.containsKey("tags_3d") && e1.getString("tags_3d").nonEmpty) {
+                featureMap.put("user_vid_return_tags_3d", e1.getString("tags_3d"))
+              }
+              if (e1.containsKey("tags_7d") && e1.getString("tags_7d").nonEmpty) {
+                featureMap.put("user_vid_return_tags_7d", e1.getString("tags_7d"))
+              }
+              if (e1.containsKey("tags_14d") && e1.getString("tags_14d").nonEmpty) {
+                featureMap.put("user_vid_return_tags_14d", e1.getString("tags_14d"))
+              }
+
+              if (e2.containsKey("tags_14d") && e2.getString("tags_14d").nonEmpty) {
+                featureMap.put("user_vid_share_tags_1d", e2.getString("tags_1d"))
+              }
+              if (e2.containsKey("tags_14d") && e2.getString("tags_14d").nonEmpty) {
+                featureMap.put("user_vid_share_tags_14d", e2.getString("tags_14d"))
+              }
+
+              val g1: JSONObject = if (record.isNull("g1_feature")) new JSONObject() else
+                JSON.parseObject(record.getString("g1_feature"))
+              val g2: JSONObject = if (record.isNull("g2_feature")) new JSONObject() else
+                JSON.parseObject(record.getString("g2_feature"))
+              if (g1.containsKey("cate1_14d") && g1.getString("cate1_14d").nonEmpty) {
+                featureMap.put("user_vid_return_cate1_14d", g1.getString("cate1_14d"))
+              }
+              if (g1.containsKey("cate2_14d") && g1.getString("cate2_14d").nonEmpty) {
+                featureMap.put("user_vid_return_cate2_14d", g1.getString("cate2_14d"))
+              }
+              if (g2.containsKey("cate1_14d") && g2.getString("cate1_14d").nonEmpty) {
+                featureMap.put("user_vid_share_cate1_14d", g2.getString("cate1_14d"))
+              }
+              if (g2.containsKey("cate2_14d") && g2.getString("cate2_14d").nonEmpty) {
+                featureMap.put("user_vid_share_cate2_14d", g2.getString("cate2_14d"))
+              }
+
+              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 cpc = if (click == 0) 0D else income / click
+                  val f1 = RankExtractorFeature_20240530.divSmooth2(click, view, CTR_SMOOTH_BETA_FACTOR)
+                  val f2 = RankExtractorFeature_20240530.divSmooth2(conver, view, CTCVR_SMOOTH_BETA_FACTOR)
+                  val f3 = RankExtractorFeature_20240530.divSmooth2(conver, click, CVR_SMOOTH_BETA_FACTOR)
+                  val f4 = conver
+                  val f5 = RankExtractorFeature_20240530.divSmooth2(click, view, CTR_SMOOTH_BETA_FACTOR) * cpc * 1000
+                  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")
+                featureMap.put("cate1", d3.getOrDefault("merge_first_level_cate", ""))
+                featureMap.put("cate2", d3.getOrDefault("merge_second_level_cate", ""))
+                featureMap.put("title_split", d3.getOrDefault("title_split", ""))
+              }
+              val machineinfo: JSONObject = if (record.isNull("machineinfo")) new JSONObject() else
+                JSON.parseObject(record.getString("machineinfo"))
+              if (machineinfo.containsKey("brand") && machineinfo.getString("brand").nonEmpty) {
+                featureMap.put("brand", machineinfo.getString("brand").toUpperCase)
+              } else {
+                featureMap.put("brand", "")
+              }
+              featureMap.put("vid", record.getString("headvideoid"))
+
+              /*
+            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
+              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 headvideoid = record.getString("headvideoid")
+              val logKey = (apptype, mid, cid, ts, headvideoid).productIterator.mkString(",")
+              val labelKey = labels.toString()
+              (logKey, labelKey, featureMap)
+            })
+          odpsData
+        }).reduce(_ union _)
+        .map { case (logKey, labelKey, jsons) =>
+          val denseFeatures = scala.collection.mutable.Map[String, Double]()
+          val sparseFeatures = scala.collection.mutable.Map[String, String]()
+          denseFeatureNames.foreach(r => {
+            if (jsons.containsKey(r)) {
+              denseFeatures.put(r, jsons.getDoubleValue(r))
+            }
+          })
+          sparseFeatureNames.foreach(r => {
+            if (jsons.get(r) != null) {
+              sparseFeatures.put(r, jsons.get(r).toString)
+            }
+          })
+          (logKey, labelKey, denseFeatures, sparseFeatures)
+        }
+        .map {
+          case (logKey, labelKey, denseFeatures, sparseFeatures) =>
+            val labelObject = JSON.parseObject(labelKey)
+            val label = labelObject.getOrDefault(whatLabel, "0").toString
+            val bucketsMap = bucketsMap_br.value
+            var resultMap = denseFeatures.collect {
+              case (name, score) if !filterNames.exists(name.contains) && score > 1E-8 =>
+                val value = if (bucketsMap.contains(name)) {
+                  val (bucketsNum, buckets) = bucketsMap(name)
+                  1.0 / bucketsNum * (ExtractorUtils.findInsertPosition(buckets, score).toDouble + 1.0)
+                } else {
+                  score
+                }
+                name -> value.toString
+            }.toMap
+            sparseFeatures.foreach(kv => {
+              resultMap += (kv._1 -> kv._2)
+            })
+            resultMap += ("has_click" -> labelObject.getString("ad_is_click"))
+            resultMap += ("has_conversion" -> labelObject.getString("ad_is_conversion"))
+            resultMap += ("logkey" -> logKey)
+            resultMap
+        }.coalesce(128)
+
+      val partition = s"dt=$dt"
+      if (outputTable2.isEmpty) {
+        odpsOps.saveToTable(project, outputTable, partition, recordRdd, write, defaultCreate = true, overwrite = true)
+      } else {
+        // 固定seed以保证可重入
+        val splitRdds = recordRdd.randomSplit(Array(splitRate, 1 - splitRate), seed = dt.toLong)
+        odpsOps.saveToTable(project, outputTable, partition, splitRdds(0), write, defaultCreate = true, overwrite = true)
+        odpsOps.saveToTable(project, outputTable2, partition, splitRdds(1), write, defaultCreate = true, overwrite = true)
+      }
+    }
+  }
+
+  def write(map: Map[String, String], record: Record, schema: TableSchema): Unit = {
+    for ((columnName, value) <- map) {
+      try {
+        // 查找列名在表结构中的索引
+        val columnIndex = schema.getColumnIndex(columnName.toLowerCase)
+        // 获取列的类型
+        val columnType = schema.getColumn(columnIndex).getTypeInfo
+        try {
+          columnType.getTypeName match {
+            case "STRING" =>
+              record.setString(columnIndex, value)
+            case "BIGINT" =>
+              record.setBigint(columnIndex, value.toLong)
+            case "DOUBLE" =>
+              record.setDouble(columnIndex, value.toDouble)
+            case "BOOLEAN" =>
+              record.setBoolean(columnIndex, value.toBoolean)
+            case other =>
+              throw new IllegalArgumentException(s"Unsupported column type: $other")
+          }
+        } catch {
+          case e: NumberFormatException =>
+            println(s"Error converting value $value to type ${columnType.getTypeName} for column $columnName: ${e.getMessage}")
+          case e: Exception =>
+            println(s"Unexpected error writing value $value to column $columnName: ${e.getMessage}")
+        }
+      } catch {
+        case e: IllegalArgumentException => {
+          println(e.getMessage)
+        }
+      }
+    }
+  }
+
+
+  def func(record: Record, schema: TableSchema): Record = {
+    record
+  }
+
+  def funcC34567ForTagsNew(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 = SimilarityUtils.word2VecSimilarity(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)
+  }
+}

src/main/scala/com/aliyun/odps/spark/examples/myUtils/MyDateUtils.scala

@@ -223,6 +223,28 @@ object MyDateUtils {
   }
 
 
+  def getDateMinuteRange(beginStr: String, endStr: String, format: String = "yyyyMMddHHmm"): ArrayBuffer[String] = {
+    val ranges = ArrayBuffer[String]()
+    val sdf = new SimpleDateFormat(format)
+    var dateBegin = sdf.parse(beginStr)
+    val dateEnd = sdf.parse(endStr)
+
+    while (dateBegin.compareTo(dateEnd) <= 0) {
+      ranges += sdf.format(dateBegin)
+      // 将开始时间增加5分钟
+      dateBegin = addMinutes(dateBegin, 5)
+    }
+    ranges
+  }
+
+  // 辅助方法：增加指定分钟数
+  private def addMinutes(date: Date, minutes: Int): Date = {
+    val calendar = Calendar.getInstance()
+    calendar.setTime(date)
+    calendar.add(Calendar.MINUTE, minutes)
+    calendar.getTime
+  }
+
   def main(args: Array[String]): Unit = {
 //    var from = DateUtils.parseDate("2019-09-01", Array[String]("yyyy-MM-dd"))
 //    var to = DateUtils.parseDate("2019-09-10", Array[String]("yyyy-MM-dd"))