feat:修改删除文件脚本

src/main/scala/com/aliyun/odps/spark/zhp/util/AdUtil.java → src/main/scala/com/aliyun/odps/spark/common/AdUtil.java

@@ -1,7 +1,10 @@
-package com.aliyun.odps.spark.zhp.util;
+package com.aliyun.odps.spark.common;
 
 import com.alibaba.fastjson.JSONObject;
 
+import java.time.Instant;
+import java.time.LocalDateTime;
+import java.time.ZoneId;
 import java.util.HashMap;
 import java.util.Map;
 
@@ -38,4 +41,16 @@ public class AdUtil {
         return newJson;
     }
 
+    public static int getHourByTimestamp(long timestamp) {
+        return LocalDateTime
+                .ofInstant(Instant.ofEpochSecond(timestamp), ZoneId.systemDefault())
+                .getHour();
+    }
+
+    public static int getDayOrWeekByTimestamp(long timestamp) {
+        return LocalDateTime
+                .ofInstant(Instant.ofEpochSecond(timestamp), ZoneId.systemDefault())
+                .getDayOfWeek()
+                .getValue();
+    }
 }

src/main/scala/com/aliyun/odps/spark/examples/makedata_ad/makedata_ad_31_originData_20240718.scala

@@ -0,0 +1,414 @@
+package com.aliyun.odps.spark.examples.makedata_ad
+
+import com.alibaba.fastjson.{JSON, JSONObject}
+import com.aliyun.odps.TableSchema
+import com.aliyun.odps.data.Record
+import com.aliyun.odps.spark.common.AdUtil
+import com.aliyun.odps.spark.examples.myUtils.{MyDateUtils, MyHdfsUtils, ParamUtils, env}
+import examples.extractor.RankExtractorFeature_20240530
+import org.apache.hadoop.io.compress.GzipCodec
+import org.apache.spark.sql.SparkSession
+import org.xm.Similarity
+
+import scala.collection.JavaConversions._
+import scala.collection.mutable.ArrayBuffer
+/*
+   20240608 提取特征
+ */
+
+object makedata_ad_31_originData_20240718 {
+  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", "2024062008")
+    val endStr = param.getOrElse("endStr", "2024062023")
+    val savePath = param.getOrElse("savePath", "/dw/recommend/model/31_ad_sample_data/")
+    val project = param.getOrElse("project", "loghubods")
+    val table = param.getOrElse("table", "alg_recsys_ad_sample_all")
+    val repartition = param.getOrElse("repartition", "100").toInt
+    val filterHours = param.getOrElse("filterHours", "00,01,02,03,04,05,06,07").split(",").toSet
+    val idDefaultValue = param.getOrElse("idDefaultValue", "1.0").toDouble
+    // 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 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 = AdUtil.getHourByTimestamp(ts)
+            featureMap.put("hour_" + hour, 0.1)
+
+            val dayOfWeek = AdUtil.getDayOrWeekByTimestamp(ts)
+            featureMap.put("dayOfWeek_" + dayOfWeek, 0.1);
+
+            if (b1.containsKey("cpa")) {
+              featureMap.put("cpa", b1.getString("cpa").toDouble)
+            }
+
+            for ((bn, prefix1) <- List(
+              (b2, "b2"), (b3, "b3"), (b4, "b4"), (b5, "b5"), (b8, "b8"), (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 apptype = record.getString("apptype")
+            val mid = record.getString("mid")
+            val headvideoid = record.getString("headvideoid")
+            val logKey = (apptype, mid, cid, ts, headvideoid).productIterator.mkString(",")
+            val labelKey = labels.toString()
+            val featureKey = featureMap.toString()
+            //6 拼接数据，保存。
+            logKey + "\t" + labelKey + "\t" + featureKey
+          })
+
+        // 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)
+        }
+      }
+
+    }
+  }
+
+  def func(record: Record, schema: TableSchema): Record = {
+    record
+  }
+
+  def funcC34567ForTags(tags: String, title: String): Tuple4[Double, String, Double, Double] = {
+    // 匹配数量 匹配词 语义最高相似度分 语义平均相似度分
+    val tagsList = tags.split(",")
+    var d1 = 0.0
+    val d2 = new ArrayBuffer[String]()
+    var d3 = 0.0
+    var d4 = 0.0
+    for (tag <- tagsList) {
+      if (title.contains(tag)) {
+        d1 = d1 + 1.0
+        d2.add(tag)
+      }
+      val score = Similarity.conceptSimilarity(tag, title)
+      d3 = if (score > d3) score else d3
+      d4 = d4 + score
+    }
+    d4 = if (tagsList.nonEmpty) d4 / tagsList.size else d4
+    (d1, d2.mkString(","), d3, d4)
+  }
+}

src/main/scala/com/aliyun/odps/spark/examples/makedata_ad/makedata_ad_32_bucket_20240718.scala

@@ -0,0 +1,103 @@
+package com.aliyun.odps.spark.examples.makedata_ad
+
+import com.alibaba.fastjson.JSON
+import com.aliyun.odps.spark.examples.myUtils.{MyHdfsUtils, ParamUtils}
+import org.apache.hadoop.io.compress.GzipCodec
+import org.apache.spark.sql.SparkSession
+
+import scala.collection.JavaConversions._
+import scala.collection.mutable.ArrayBuffer
+import scala.io.Source
+/*
+
+ */
+
+object makedata_ad_32_bucket_20240718 {
+  def main(args: Array[String]): Unit = {
+
+    val spark = SparkSession
+      .builder()
+      .appName(this.getClass.getName)
+      .getOrCreate()
+    val sc = spark.sparkContext
+
+    val loader = getClass.getClassLoader
+    val resourceUrl = loader.getResource("20240703_ad_feature_name.txt")
+    val content =
+      if (resourceUrl != null) {
+        val content = Source.fromURL(resourceUrl).getLines().mkString("\n")
+        Source.fromURL(resourceUrl).close()
+        content
+      } else {
+        ""
+      }
+    println(content)
+    val contentList = content.split("\n")
+      .map(r=> r.replace(" ", "").replaceAll("\n", ""))
+      .filter(r=> r.nonEmpty).toList
+
+
+
+    // 1 读取参数
+    val param = ParamUtils.parseArgs(args)
+    val readPath = param.getOrElse("readPath", "/dw/recommend/model/31_ad_sample_data/20240620*")
+    val savePath = param.getOrElse("savePath", "/dw/recommend/model/32_bucket_data/")
+    val fileName = param.getOrElse("fileName", "20240620_100")
+    val sampleRate = param.getOrElse("sampleRate", "1.0").toDouble
+    val bucketNum = param.getOrElse("bucketNum", "100").toInt
+
+    val data = sc.textFile(readPath)
+    println("问题数据数量：" + data.filter(r=>r.split("\t").length != 3).count())
+    val data1 = data.map(r => {
+      val rList = r.split("\t")
+      val jsons = JSON.parseObject(rList(2))
+      val doubles = scala.collection.mutable.Map[String, Double]()
+      jsons.foreach(r =>{
+        doubles.put(r._1, jsons.getDoubleValue(r._1))
+      })
+      doubles
+    }).sample(false, sampleRate ).repartition(20)
+
+    val result = new ArrayBuffer[String]()
+
+    for (i <- contentList.indices){
+      println("特征:" + contentList(i))
+      val data2 = data1.map(r => r.getOrDefault(contentList(i), 0D)).filter(_ > 1E-8).collect().sorted
+      val len = data2.length
+      if (len == 0){
+        result.add(contentList(i) + "\t" + bucketNum.toString + "\t" + "0")
+      }else{
+        val oneBucketNum = (len - 1) / (bucketNum - 1) + 1 // 确保每个桶至少有一个元素
+        val buffers = new ArrayBuffer[Double]()
+
+        var lastBucketValue = data2(0) // 记录上一个桶的切分点
+        for (j <- 0 until len by oneBucketNum) {
+          val d = data2(j)
+          if (j > 0 && d != lastBucketValue) {
+            // 如果当前切分点不同于上一个切分点，则保存当前切分点
+            buffers += d
+          }
+          lastBucketValue = d // 更新上一个桶的切分点
+        }
+
+        // 最后一个桶的结束点应该是数组的最后一个元素
+        if (!buffers.contains(data2.last)) {
+          buffers += data2.last
+        }
+        result.add(contentList(i) + "\t" + bucketNum.toString + "\t" + buffers.mkString(","))
+      }
+    }
+    val data3 = sc.parallelize(result)
+
+
+    // 4 保存数据到hdfs
+    val hdfsPath = savePath + "/" + fileName
+    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)
+      data3.repartition(1).saveAsTextFile(hdfsPath, classOf[GzipCodec])
+    } else {
+      println("路径不合法，无法写入:" + hdfsPath)
+    }
+  }
+}

src/main/scala/com/aliyun/odps/spark/examples/makedata_ad/makedata_ad_33_bucketData_20240718.scala

@@ -0,0 +1,128 @@
+package com.aliyun.odps.spark.examples.makedata_ad
+
+import com.alibaba.fastjson.JSON
+import com.aliyun.odps.spark.examples.myUtils.{MyDateUtils, MyHdfsUtils, ParamUtils}
+import examples.extractor.ExtractorUtils
+import org.apache.hadoop.io.compress.GzipCodec
+import org.apache.spark.sql.SparkSession
+
+import scala.collection.JavaConversions._
+import scala.collection.mutable.ArrayBuffer
+import scala.io.Source
+/*
+
+ */
+
+object makedata_ad_33_bucketData_20240718 {
+  def main(args: Array[String]): Unit = {
+
+    val spark = SparkSession
+      .builder()
+      .appName(this.getClass.getName)
+      .getOrCreate()
+    val sc = spark.sparkContext
+
+    val loader = getClass.getClassLoader
+
+    val resourceUrlBucket = loader.getResource("20240704_ad_bucket_351.txt")
+    val buckets =
+      if (resourceUrlBucket != null) {
+        val buckets = Source.fromURL(resourceUrlBucket).getLines().mkString("\n")
+        Source.fromURL(resourceUrlBucket).close()
+        buckets
+      } else {
+        ""
+      }
+    println(buckets)
+    val bucketsMap = buckets.split("\n")
+      .map(r => r.replace(" ", "").replaceAll("\n", ""))
+      .filter(r => r.nonEmpty)
+      .map(r =>{
+        val rList = r.split("\t")
+        (rList(0), (rList(1).toDouble, rList(2).split(",").map(_.toDouble)))
+      }).toMap
+    val bucketsMap_br = sc.broadcast(bucketsMap)
+
+
+    // 1 读取参数
+    val param = ParamUtils.parseArgs(args)
+    val readPath = param.getOrElse("readPath", "/dw/recommend/model/31_ad_sample_data/")
+    val savePath = param.getOrElse("savePath", "/dw/recommend/model/33_ad_train_data/")
+    val beginStr = param.getOrElse("beginStr", "20240620")
+    val endStr = param.getOrElse("endStr", "20240620")
+    val repartition = param.getOrElse("repartition", "100").toInt
+    val filterNames = param.getOrElse("filterNames", "").split(",").toSet
+    val whatLabel = param.getOrElse("whatLabel", "ad_is_conversion")
+
+    val dateRange = MyDateUtils.getDateRange(beginStr, endStr)
+    for (date <- dateRange) {
+      println("开始执行:" + date)
+      val data = sc.textFile(readPath + "/" + date + "*").map(r=>{
+        val rList = r.split("\t")
+        val logKey = rList(0)
+        val labelKey = rList(1)
+        val jsons = JSON.parseObject(rList(2))
+        val features = scala.collection.mutable.Map[String, Double]()
+        jsons.foreach(r => {
+          features.put(r._1, jsons.getDoubleValue(r._1))
+        })
+        (logKey, labelKey, features)
+      })
+        .filter{
+          case (logKey, labelKey, features) =>
+            val logKeyList = logKey.split(",")
+            val apptype = logKeyList(0)
+            !Set("12", "13").contains(apptype)
+        }
+        .map{
+          case (logKey, labelKey, features) =>
+            val label = JSON.parseObject(labelKey).getOrDefault(whatLabel, "0").toString
+            (label, features)
+        }
+        .mapPartitions(row => {
+          val result = new ArrayBuffer[String]()
+          val bucketsMap = bucketsMap_br.value
+          row.foreach{
+            case (label, features) =>
+              val featuresBucket = features.map{
+                case (name, score) =>
+                  var ifFilter = false
+                  if (filterNames.nonEmpty){
+                    filterNames.foreach(r=> if (!ifFilter && name.startsWith(r)) {ifFilter = true} )
+                  }
+                  if (ifFilter){
+                    ""
+                  }else{
+                    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(label + "\t" + featuresBucket.mkString("\t"))
+          }
+          result.iterator
+      })
+
+      // 4 保存数据到hdfs
+      val hdfsPath = savePath + "/" + date
+      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)
+        data.repartition(repartition).saveAsTextFile(hdfsPath, classOf[GzipCodec])
+      } else {
+        println("路径不合法，无法写入:" + hdfsPath)
+      }
+    }
+
+
+
+  }
+}

src/main/scala/com/aliyun/odps/spark/zhp/makedata_ad/makedata_ad_31_originDataCheck_20240620.scala

@@ -3,11 +3,8 @@ package com.aliyun.odps.spark.zhp.makedata_ad
 import com.alibaba.fastjson.{JSON, JSONObject}
 import com.aliyun.odps.TableSchema
 import com.aliyun.odps.data.Record
-import com.aliyun.odps.spark.examples.myUtils.{MyDateUtils, MyHdfsUtils, ParamUtils, env}
-import com.aliyun.odps.spark.zhp.util.AdUtil
+import com.aliyun.odps.spark.common.AdUtil
 import examples.extractor.RankExtractorFeature_20240530
-import org.apache.hadoop.io.compress.GzipCodec
-import org.apache.spark.sql.SparkSession
 import org.xm.Similarity
 
 import scala.collection.JavaConversions._