spark2.0中逻辑回归模型

今天上午看了下spark2.0中的逻辑回归模型，相比以前mllib版本确实改进不少，逻辑回归模型再次不再多说，原理较为简单，模型中的一些参数设定，自己要主要，代码主要是用maven跟git进行管理，数据是官方自带的数据，代码中没有模型保存的方法。

package com.iclick.ml
import org.apache.log4j.Level
import org.apache.log4j.Logger
import org.apache.spark.ml.classification.BinaryLogisticRegressionTrainingSummary
import org.apache.spark.ml.classification.LogisticRegression
import org.apache.spark.ml.classification.LogisticRegressionModel
import org.apache.spark.ml.classification.LogisticRegressionSummary
import org.apache.spark.sql.SparkSession
import org.apache.spark.sql.functions._
object LogisticTest {
  def main(args: Array[String]): Unit = {
    Logger.getLogger("org.apache.spark").setLevel(Level.WARN)
    Logger.getLogger("org.eclipse.jetty.server").setLevel(Level.OFF)
    val local = System.getProperty("user.dir")
    val spark = SparkSession.builder.master("local").appName("example").
      config("spark.sql.warehouse.dir", s"file:///${local}\\spark-warehouse").config("spark.sql.shuffle.partitions", "20").getOrCreate()
    
   val path="D:\\SPARKCONFALL\\spark-2.0.0-bin-hadoop2.6\\data\\mllib\\" +"sample_libsvm_data.txt"
   val training_dataFrame=spark.read.format("libsvm").load(path)
   println("逻辑回归模型训练模式")
   training_dataFrame.show()
//   threshold变量用来控制分类的阈值，默认值为0.5
   val lr=new LogisticRegression().setMaxIter(20).
   setRegParam(0.01).setElasticNetParam(0.2).setThreshold(0.5)
   println("计算逻辑回归系数")
   val  lrModel=lr.fit(training_dataFrame)
   println("coffiient:"+lrModel.coefficients+"Itercept"+lrModel.intercept)
   
   val summary=lrModel.summary
   
   val objectiveHistory=summary.objectiveHistory
   
   objectiveHistory.foreach(x=>print(x+","))
   
   println("计算roc曲线")
   val binarySummary=summary.asInstanceOf[BinaryLogisticRegressionTrainingSummary]
   val roc=binarySummary.roc
   roc.show()
   println("逻辑回归模型的的roc曲线为："+binarySummary.areaUnderROC)
   
   println("选择最好分类阈值默认设置为0.5")
   val fMeasure = binarySummary.fMeasureByThreshold
   fMeasure.show()
   val maxFMeasure = fMeasure.select(max("F-Measure")).head().getDouble(0)
    val bestThreshold = fMeasure.where( col("F-Measure") === maxFMeasure).select("threshold").head().getDouble(0)
    println("bestThreshold is :"+bestThreshold)
    lrModel.setThreshold(bestThreshold)
   
  }
}