Переглянути джерело

feat:修改20240729分桶脚本

zhaohaipeng 9 місяців тому
батько
коміт
1c94d7696d

+ 0 - 4
src/main/scala/com/aliyun/odps/spark/examples/makedata_ad/makedata_ad_33_bucketData_20240729.scala

@@ -1,7 +1,6 @@
 package com.aliyun.odps.spark.examples.makedata_ad
 
 import com.alibaba.fastjson.JSON
-import com.aliyun.odps.spark.examples.makedata_ad.makedata_ad_33_bucketData_20240718.getClass
 import com.aliyun.odps.spark.examples.myUtils.{MyDateUtils, MyHdfsUtils, ParamUtils}
 import examples.extractor.ExtractorUtils
 import org.apache.hadoop.io.compress.GzipCodec
@@ -10,7 +9,6 @@ import org.apache.spark.sql.SparkSession
 import scala.collection.JavaConversions._
 import scala.collection.mutable.ArrayBuffer
 import scala.io.Source
-import scala.math.random
 import scala.util.Random
 
 /*
@@ -35,8 +33,6 @@ object makedata_ad_33_bucketData_20240729 {
     val repartition = param.getOrElse("repartition", "100").toInt
     val filterNames = param.getOrElse("filterNames", "").split(",").toSet
     val whatLabel = param.getOrElse("whatLabel", "ad_is_conversion")
-    val cidSet = param.getOrElse("cidSet", "cid_3319,cid_3024").split(",").toSet
-    val cidCountThreshold = param.getOrElse("cidCountThreshold", "20000").toInt
 
     val loader = getClass.getClassLoader
 

+ 176 - 0
src/main/scala/com/aliyun/odps/spark/examples/makedata_ad/makedata_ad_33_bucketData_20240729_copy_zheng.scala

@@ -0,0 +1,176 @@
+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
+import scala.util.Random
+
+/*
+
+ */
+
+object makedata_ad_33_bucketData_20240729_copy_zheng {
+  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 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 loader = getClass.getClassLoader
+
+    val resourceUrlBucket = loader.getResource("20240718_ad_bucket_688.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)
+
+    val dateRange = MyDateUtils.getDateRange(beginStr, endStr)
+    val cidCountMap = scala.collection.mutable.Map[String, Int]()
+    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)
+        }.filter {
+          case (logKey, labelKey, features) =>
+            var key = ""
+            for (elem <- features) {
+              if (elem._1.contains("cid_")) {
+                key = elem._1
+              }
+            }
+
+            if (key.equals("cid_3319")) {
+              true
+            } else if (key.equals("cid_3024")) {
+              // 创建一个Random实例
+              val rand = new Random()
+
+              // 生成一个0到1之间的随机浮点数
+              val randomDouble = rand.nextDouble()
+
+              randomDouble < 0.01
+            } else {
+              false
+            }
+        }.flatMap {
+          case (logKey, labelKey, features) =>
+            var key = ""
+            for (elem <- features) {
+              if (elem._1.contains("cid_")) {
+                key = elem._1
+              }
+            }
+            if (key.equals("cid_3319")) {
+              val whatLabel = JSON.parseObject(labelKey).getOrDefault(whatLabel, "0").toString
+              if (!whatLabel.equals("0")) {
+                Seq(
+                  (logKey, labelKey, features),
+                  (logKey, labelKey, features),
+                  (logKey, labelKey, features),
+                  (logKey, labelKey, features),
+                  (logKey, labelKey, features)
+                )
+              } else {
+                Seq((logKey, labelKey, features))
+              }
+            } else {
+              Seq((logKey, labelKey, features))
+            }
+        }.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.contains(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)
+      }
+    }
+
+
+  }
+}