机器学习结果加ID插入数据库源码

import org.apache.spark.mllib.evaluation.BinaryClassificationMetrics
import org.apache.spark.mllib.linalg.Vectors
import org.apache.spark.mllib.regression.LabeledPoint
import org.apache.spark.mllib.tree.GradientBoostedTrees
import org.apache.spark.mllib.tree.configuration.BoostingStrategy
import org.apache.spark.mllib.tree.model.GradientBoostedTreesModel
import org.apache.spark.sql.{Row, SaveMode}
import org.apache.spark.sql.hive.HiveContext
import org.apache.spark.sql.types.{DoubleType, StringType, StructField, StructType}
import org.apache.spark.{SparkConf, SparkContext}
import scala.collection.mutable.ArrayBuffer
object v4score20180123 {
  def main(args: Array[String]): Unit = {
  val sparkConf = new SparkConf().setAppName("v4model20180123")
  val sc = new SparkContext(sparkConf)
  val hc = new HiveContext(sc)

  val dataInstance = hc.sql(s"select * from lkl_card_score.fqz_score_dataset_04vals").map {
    row =>
      val arr = new ArrayBuffer[Double]()
      //剔除label、phone字段
      for (i <- 3 until row.size) {
        if (row.isNullAt(i)) {
          arr += 0.0
        }
        else if (row.get(i).isInstanceOf[Int])
          arr += row.getInt(i).toDouble
        else if (row.get(i).isInstanceOf[Double])
          arr += row.getDouble(i)
        else if (row.get(i).isInstanceOf[Long])
          arr += row.getLong(i).toDouble
        else if (row.get(i).isInstanceOf[String])
          arr += 0.0
      }
      (row(0),row(1),row(2),Vectors.dense(arr.toArray))
  }


  val  modeltest=GradientBoostedTreesModel.load(sc,s"hdfs://ns1/user/songchunlin/model/v4model20180123s")
  val preditDataGBDT = dataInstance.map { point =>
    val prediction = modeltest.predict(point._4)
    //order_id,apply_time,score
    (point._1,point._2,point._3,prediction)
  }
  preditDataGBDT.take(5)
  //rdd转dataFrame
  val rowRDD = preditDataGBDT.map(row => Row(row._1.toString,row._2.toString,row._3.toString,row._4))
  val schema = StructType(
    List(
      StructField("order_id", StringType, true),
      StructField("apply_time", StringType, true),
      StructField("label", StringType, true),
      StructField("score", DoubleType, true)
    )
  )
  //将RDD映射到rowRDD，schema信息应用到rowRDD上
  val scoreDataFrame = hc.createDataFrame(rowRDD,schema)
  scoreDataFrame.count()
  scoreDataFrame.write.mode(SaveMode.Overwrite).saveAsTable("lkl_card_score.fqz_score_dataset_03val_v4_predict0123s")

}
}