使用Spark MLlib的逻辑回归(LogisticRegression)进行用户分类预测识别

import org.apache.spark.SparkContextimport org.apache.spark.SparkConfimport org.apache.spark.mllib.classification.{LogisticRegressionWithLBFGS, LogisticRegressionWithSGD}import org.apache.spark.mllib.regression.LabeledPointimport org.apache.spark.mllib.linalg.Vectorsimport org.apache.spark.mllib.evaluation.BinaryClassificationMetricsimport org.apache.spark.mllib.optimization._/**  * Created by simon on 2017/5/8.  */object genderClassificationWithLogisticRegression {  def main(args: Array[String]): Unit = {    val conf = new SparkConf()    conf.setAppName("genderClassification").setMaster("local[2]")    val sc = new SparkContext(conf)    // 1.读取数据    val trainData = sc.textFile("file:\\E:\\test.csv")    // 2.解析数据，构建数据集    val parsedTrainData = trainData.map { line =>      val parts= line.split("\\|")      val label = toInt(parts(1)) //第二列是标签      val features = Vectors.dense(parts.slice(6,parts.length-1).map(_.toDouble)) //第7到最后一列是属性，需要转换为Doube类型      LabeledPoint(label, features) //构建LabelPoint格式,第一列是标签列，后面是属性向量    }.cache()    // 3.将数据集随机分为两份，一份是训练集，一份是测试集    val splits = parsedTrainData.randomSplit(Array(0.7, 0.3), seed = 11L)    val training = splits(0)    val testing = splits(1)    // 4.新建逻辑回归模型，并设置训练参数//    val model = new LogisticRegressionWithLBFGS().setNumClasses(2)//    model.optimizer.setNumIterations(500).setUpdater(new SimpleUpdater())//可以选择LogisticRegressionWithLBFGS，也可以选择LogisticRegressionWithSGD，LogisticRegressionWithLBFGS是优化方法    val model = new LogisticRegressionWithSGD()  //建立模型    model.optimizer.setNumIterations(500).setUpdater(new SimpleUpdater()).setStepSize(0.001).setMiniBatchFraction(0.02) //模型参数    val trained = model.run(training)  //使用训练集训练模型    // 5.测试样本进行预测    val prediction = trained.predict(testing.map(_.features)) //使用测试数据属性进行预测    val predictionAndLabels = prediction.zip(testing.map(_.label)) //获取预测标签    // 6.测量预测效果    val metrics = new BinaryClassificationMetrics(predictionAndLabels)    // 7.看看AUROC结果    val auROC = metrics.areaUnderROC    println("Area under ROC = " + auROC)  }  // 将标签转换为0和1  def toInt(s: String): Int = {    if (s == "m") 1 else  0  }}