构建Maven项目，托管jar包
数据格式
//0.fp_nid,1.nsr_id,2.gf_id,2.hydm,3.djzclx_dm,4.kydjrq,5.xgrq,6.je,7.se,8.jshj,9.kpyf,10.kprq,11.zfbz,12.date_key,13.hwmc,14.ggxh,15.dw,16.sl,17.dj,18.je je1,19.se1,20.spbm,21.label
(fpid_10000201 115717 (2239 173 2011-07-12 00:00:00.0 2016-08-31 15:40:37.0 4123.08 700.92 4824.0 201704 2017-04-25 N) 201706 可视回油单向阀 HYS-1Φ1.5A 只 3.0 35.8974358974359 107.69 18.31 1090120040000000000) 0)
(fpid_10000324 253389 (7310 173 2016-01-04 00:00:00.0 2017-07-24 10:01:02.0 36609.76 6223.64 42833.4 201709 2017-09-08 N) 201711 电视机 三星743寸 台 1.0 2991.4529914529912 2991.45 508.55 1090522010000000000) 0)
(fpid_10000416 126378 (5175 173 1999-01-14 00:00:00.0 2016-05-27 14:50:49.0 25337.81 4307.39 29645.2 201612 2016-12-21 N) 201706 防水涂料 null 公斤 105.0 5.225885225885226 548.72 93.28 1070101060000000000) 0)
package Test.tett1 import org.apache.spark.ml.classification.LogisticRegression import org.apache.spark.ml.param.ParamMap import org.apache.spark.ml.linalg.{Vector, Vectors} import org.apache.spark.ml.regression.LinearRegressionModel import org.apache.spark.sql.{Row, SparkSession} import org.apache.spark.ml.regression.LinearRegression import org.apache.spark.ml.regression.LinearRegression object MLDemo3 { def main(args: Array[String]): Unit = { val sess = SparkSession.builder().appName("ml").master("local[4]").getOrCreate(); val sc = sess.sparkContext; val dataDir = "hdfs://weekend110:9000/user/hive/warehouse/nsr2_xfp" //定义样例类（要分析数据的类属性） case class FP(fp_nid:String,nsr_id:String,gf_id:String,hydm:String,djzclx_dm:String,kydjrq:String,xgrq:String, je:String,se:String,jshj:String,kpyf:String,kprq:String,zfbz:String, label:String) //变换（） //0.fp_nid,1.nsr_id,2.gf_id,2.hydm,3.djzclx_dm,4.kydjrq,5.xgrq,6.je,7.se,8.jshj,9.kpyf,10.kprq,11.zfbz,12.date_key,13.hwmc,14.ggxh,15.dw,16.sl,17.dj,18.je je1,19.se1,20.spbm,21.label val fpDataRDD = sc.textFile(dataDir).map(_.split("\001")).map(f => FP(f(0).toString, f(1).toString,f(2).toString,f(3).toString,f(4).toString,f(5).toString,f(6).toString, f(7).toString, f(8).toString,f(9).toString,f(10).toString,f(11).toString,f(12).toString, f(13).toString)) import sess.implicits._ def strToDouble(str: String): Double = { val regex = """([0-9]+)""".r val res = str match{ case regex(num) => num case _ => "1" } val resDouble = res.toDouble resDouble } //转换RDD成DataFrame //1.fp_nid 2.nsr_id 3.gf_id 4.zfbz 5.hydm 6.djzclx_dm 7.je 8.se 9.jshj 10.kpyf 11.date_key 12.sl 13.dj 14.je1 15.se1 16.spbm val trainingDF = fpDataRDD.map(f => (f.label.replaceAll("[)]","").toDouble, Vectors.dense( if(f.zfbz.equals("N)")) 1 else 0, f.hydm.replaceAll("[(]","").toDouble, f.djzclx_dm.toDouble, f.kpyf.toDouble, strToDouble(f.je), strToDouble(f.se), strToDouble(f.jshj) ))).toDF("label", "features") //显式数据 trainingDF.show() println("======================") //创建线性回归对象 val lr = new LinearRegression() //设置最大迭代次数 lr.setMaxIter(50) //通过线性回归拟合训练数据，生成模型 val model = lr.fit(trainingDF) //创建内存测试数据数据框 val testDF = sess.createDataFrame(Seq( (0,Vectors.dense(3812,171,9401.71,1598.29,11000.0,201612,1)), (0,Vectors.dense(4190,173,72200.0,12274.0,84474.0,201710,1)), (0,Vectors.dense(7519,173,99999.99,3000.0,102999.99,201709,1)), (1,Vectors.dense(1951,173,19743.59,3356.41,23100.0,201612,1)), (1,Vectors.dense(5219,173,41880.35,7119.65,49000.0,201705,1)), (1,Vectors.dense(5189,173,1320.93,224.56,1545.49,201611,1)), (1,Vectors.dense(1779,173,21911.4,3724.94,25636.34,201611,0)) )).toDF("label", "features") testDF.show() //创建临时视图 testDF.createOrReplaceTempView("test") println("======================") //利用model对测试数据进行变化，得到新数据框，查询features", "label", "prediction方面值。 val tested = model.transform(trainingDF).select("features", "label", "prediction"); tested.show(); //将分析的数据导入数据库 import java.sql.DriverManager tested.rdd.foreachPartition( it =>{ var url = "jdbc:mysql://localhost:3306/data?useUnicode=true&characterEncoding=utf8" val conn= DriverManager.getConnection(url,"root","123456") val pstat = conn.prepareStatement ("INSERT INTO `test` (`label`, `pre`,`zfbz`,`hydm`, `djzclx_dm`, " +"`kpyf`,`je`,`se`,`jshj`) " +"VALUES (?,?,?,?,?,?,?,?,?)") for (obj <-it){ pstat.setString(1,obj.get(1).toString()) pstat.setString(2,obj.get(2).toString()) pstat.setString(3,obj.get(0).toString().split(",")(0).replaceAll("[\\[]", "")) pstat.setString(4,obj.get(0).toString().split(",")(1)) pstat.setString(5,obj.get(0).toString().split(",")(2)) pstat.setString(6,obj.get(0).toString().split(",")(3)) pstat.setString(7,obj.get(0).toString().split(",")(4)) pstat.setString(8,obj.get(0).toString().split(",")(5)) pstat.setString(9,obj.get(0).toString().split(",")(6) .replaceAll("[\\]]", "")) pstat.addBatch } try{ pstat.executeBatch }finally{ pstat.close conn.close } } ) } }
maven的pom.xml配置文件
4.0.0 Test tett1 0.0.1-SNAPSHOT 2008 2.7.0 scala-tools.org Scala-Tools Maven2 Repository http://scala-tools.org/repo-releases scala-tools.org Scala-Tools Maven2 Repository http://scala-tools.org/repo-releases org.apache.spark spark-mllib_2.11 2.1.0 src/main/scala src/test/scala org.apache.maven.plugins maven-surefire-plugin true org.scala-tools maven-scala-plugin compile testCompile ${scala.version} -target:jvm-1.5 org.apache.maven.plugins maven-eclipse-plugin true ch.epfl.lamp.sdt.core.scalabuilder ch.epfl.lamp.sdt.core.scalanature org.eclipse.jdt.launching.JRE_CONTAINER ch.epfl.lamp.sdt.launching.SCALA_CONTAINER org.scala-tools maven-scala-plugin ${scala.version}