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+package com.aliyun.odps.spark.examples.makedata
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+
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+import com.alibaba.fastjson.{JSON, JSONObject}
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+import com.aliyun.odps.TableSchema
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+import com.aliyun.odps.data.Record
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+import com.aliyun.odps.spark.examples.myUtils.{MyDateUtils, MyHdfsUtils, ParamUtils, env}
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+import examples.extractor.RankExtractorFeature_20240530
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+import org.apache.hadoop.io.compress.GzipCodec
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+import org.apache.spark.sql.SparkSession
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+import org.xm.Similarity
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+
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+import scala.collection.JavaConversions._
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+import scala.collection.mutable.ArrayBuffer
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+/*
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+ 20240608 提取特征
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+ */
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+
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+object makedata_17_bucketDataPrint_20240617 {
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+ def main(args: Array[String]): Unit = {
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+ val spark = SparkSession
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+ .builder()
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+ .appName(this.getClass.getName)
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+ .getOrCreate()
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+ val sc = spark.sparkContext
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+
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+ // 1 读取参数
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+ val param = ParamUtils.parseArgs(args)
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+ val tablePart = param.getOrElse("tablePart", "64").toInt
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+ val beginStr = param.getOrElse("beginStr", "2024061500")
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+ val endStr = param.getOrElse("endStr", "2024061523")
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+ val savePath = param.getOrElse("savePath", "/dw/recommend/model/16_train_data_print_online/")
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+ val project = param.getOrElse("project", "loghubods")
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+ val table = param.getOrElse("table", "alg_recsys_sample_all_new")
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+ val repartition = param.getOrElse("repartition", "32").toInt
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+
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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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+ 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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+ val record = if (record_.isNull("allfeaturemap")) new JSONObject() else
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+ JSON.parseObject(record_.getString("allfeaturemap"))
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+ val featuresStr = record.toMap.map(r=> r._1 + ":" + r._2.toString).toSeq
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+ val apptype = record_.getString("apptype")
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+ val pagesource = record_.getString("pagesource")
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+ val abcode = record_.getString("abcode")
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+ val level = if (record_.isNull("level")) "0" else record_.getString("level")
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+ val label = record_.getString("is_return")
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+ (apptype, pagesource, level, label, abcode, featuresStr)
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+ }).filter{
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+ case (apptype, pagesource, level, label, abcode, featuresStr) =>
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+ apptype.equals("3") && pagesource.endsWith("recommend") &&
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+ Set("ab0", "ab1", "ab2", "ab3").contains(abcode) && level.equals("0")
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+ }.map{
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+ case (apptype, pagesource, level, label, abcode, featuresStr) =>
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+ label + "\t" + featuresStr.mkString("\t")
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+ }
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+
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+ // 4 保存数据到hdfs
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+ val savePartition = dt + hh
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+ val hdfsPath = savePath + "/" + savePartition
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+ if (hdfsPath.nonEmpty && hdfsPath.startsWith("/dw/recommend/model/")){
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+ println("删除路径并开始数据写入:" + hdfsPath)
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+ MyHdfsUtils.delete_hdfs_path(hdfsPath)
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+ odpsData.coalesce(repartition).saveAsTextFile(hdfsPath, classOf[GzipCodec])
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+ }else{
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+ println("路径不合法，无法写入:" + hdfsPath)
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+ }
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+ }
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+ // 合并一天数据
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+
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+
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+ }
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+
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+ def func(record: Record, schema: TableSchema): Record = {
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+ record
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+ }
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+ def funcC34567ForTags(tags: String, title: String): Tuple4[Double, String, Double, Double] = {
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+ // 匹配数量 匹配词 语义最高相似度分 语义平均相似度分
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+ val tagsList = tags.split(",")
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+ var d1 = 0.0
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+ val d2 = new ArrayBuffer[String]()
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+ var d3 = 0.0
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+ var d4 = 0.0
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+ for (tag <- tagsList){
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+ if (title.contains(tag)){
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+ d1 = d1 + 1.0
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+ d2.add(tag)
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+ }
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+ val score = Similarity.conceptSimilarity(tag, title)
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+ d3 = if (score > d3) score else d3
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+ d4 = d4 + score
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+ }
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+ d4 = if (tagsList.nonEmpty) d4 / tagsList.size else d4
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+ (d1, d2.mkString(","), d3, d4)
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+ }
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+}
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zhangbo