逻辑回归进行鸢尾花分类的案例

背景说明：

基于IDEA + Spark 3.4.1 + sbt 1.9.3 + Spark MLlib 构建逻辑回归鸢尾花分类预测模型，这是一个分类模型案例，通过该案例，可以快速了解Spark MLlib分类预测模型的使用方法。

依赖

ThisBuild / version := "0.1.0-SNAPSHOT"  
  
ThisBuild / scalaVersion := "2.13.11"  
  
lazy val root = (project in file("."))  
  .settings(  
    name := "SparkLearning",  
    idePackagePrefix := Some("cn.lh.spark"),  
    libraryDependencies += "org.apache.spark" %% "spark-sql" % "3.4.1",  
    libraryDependencies += "org.apache.spark" %% "spark-core" % "3.4.1",  
    libraryDependencies += "org.apache.hadoop" % "hadoop-auth" % "3.3.6",     libraryDependencies += "org.apache.spark" %% "spark-streaming" % "3.4.1",  
    libraryDependencies += "org.apache.spark" %% "spark-streaming-kafka-0-10" % "3.4.1",  
    libraryDependencies += "org.apache.spark" %% "spark-mllib" % "3.4.1",  
    libraryDependencies += "mysql" % "mysql-connector-java" % "8.0.30"  
)

代码如下：

package cn.lh.spark  
  
import org.apache.spark.ml.{Pipeline, PipelineModel}  
import org.apache.spark.ml.classification.{LogisticRegression, LogisticRegressionModel}  
import org.apache.spark.ml.evaluation.MulticlassClassificationEvaluator  
import org.apache.spark.ml.feature.{IndexToString, StringIndexer, StringIndexerModel, VectorIndexer, VectorIndexerModel}  
import org.apache.spark.ml.linalg.{Vectors,Vector}  
import org.apache.spark.rdd.RDD  
import org.apache.spark.sql.{DataFrame, Row, SparkSession}  
  
case class Iris(features: org.apache.spark.ml.linalg.Vector, label: String)  
  
/**  
 * 二项逻辑斯蒂回归来解决二分类问题  
 */  
object MLlibLogisticRegression {  
  
  def main(args: Array[String]): Unit = {  
  
    val spark: SparkSession = SparkSession.builder().master("local[2]")  
      .appName("Spark MLlib Demo List").getOrCreate()  
  
    val irisRDD: RDD[Iris] = spark.sparkContext.textFile("F:\\niit\\2023\\2023_2\\Spark\\codes\\data\\iris.txt")  
      .map(_.split(",")).map(p =>  
      Iris(Vectors.dense(p(0).toDouble, p(1).toDouble, p(2).toDouble, p(3).toDouble), p(4).toString()))  
  
    import spark.implicits._  
    val data: DataFrame = irisRDD.toDF()  
    data.show()  
  
    data.createOrReplaceTempView("iris")  
  
    val df: DataFrame = spark.sql("select * from iris where label != 'Iris-setosa'")  
    df.map(t => t(1)+":"+t(0)).collect().foreach(println)  
  
    //    构建ML的pipeline  
    val labelIndex: StringIndexerModel = new StringIndexer().setInputCol("label")  
      .setOutputCol("indexedLabel").fit(df)  
    val featureIndexer: VectorIndexerModel = new VectorIndexer().setInputCol("features")  
      .setOutputCol("indexedFeatures").fit(df)  
  
//    划分数据集  
    val Array(trainingData, testData) = df.randomSplit(Array(0.7, 0.3))  
  
    //    设置逻辑回归模型参数  
    val lr: LogisticRegression = new LogisticRegression().setLabelCol("indexedLabel")  
      .setFeaturesCol("indexedFeatures").setMaxIter(10).setRegParam(0.3).setElasticNetParam(0.8)  
  
    //    设置一个labelConverter，目的是把预测的类别重新转化成字符型的  
    val labelConverter: IndexToString = new IndexToString().setInputCol("prediction")  
      .setOutputCol("predictedLabel").setLabels(labelIndex.labels)  
  
    //    构建pipeline，设置stage，然后调用fit()来训练模型  
    val lrPipeline: Pipeline = new Pipeline().setStages(Array(labelIndex, featureIndexer, lr, labelConverter))  
    val lrmodle: PipelineModel = lrPipeline.fit(trainingData)  
    val lrPredictions: DataFrame = lrmodle.transform(testData)  
  
    lrPredictions.select("predictedLabel", "label", "features", "probability")  
        .collect().foreach { case Row(predictedLabel: String, label: String, features: Vector, prob: Vector) =>  
      println(s"($label, $features) --> prob=$prob, predicted Label=$predictedLabel")}  
  
    //    模型评估  
    val evaluator: MulticlassClassificationEvaluator = new MulticlassClassificationEvaluator()  
          .setLabelCol("indexedLabel").setPredictionCol("prediction")  
    val lrAccuracy: Double = evaluator.evaluate(lrPredictions)  
    println("Test Error = " + (1.0 - lrAccuracy))  
  
    val lrmodel2: LogisticRegressionModel = lrmodle.stages(2).asInstanceOf[LogisticRegressionModel]  
    println("Coefficients: " + lrmodel2.coefficients+"Intercept: " +  
      lrmodel2.intercept+"numClasses: "+lrmodel2.numClasses+"numFeatures: "+lrmodel2.numFeatures)  
  
  
    spark.stop()  
  }  
  
}