决策树进行鸢尾花分类的案例

背景说明：

通过IDEA + Spark 3.4.1 + sbt 1.9.3 + Spark MLlib 构建鸢尾花决策树分类预测模型，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"  
)

完整代码

package cn.lh.spark  import org.apache.spark.ml.{Pipeline, PipelineModel}  
import org.apache.spark.ml.classification.{DecisionTreeClassificationModel, DecisionTreeClassifier}  
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  
import org.apache.spark.rdd.RDD  
import org.apache.spark.sql.{DataFrame, SparkSession}  /**  * 决策树分类器,实现鸢尾花分类  */  //case class Iris(features: org.apache.spark.ml.linalg.Vector, label: String)  // MLlibLogisticRegression 中存在该样例类，这里不用写,一个包里不存在这个样例类时需要写object MLlibDecisionTreeClassifier {  def main(args: Array[String]): Unit = {  val spark: SparkSession = SparkSession.builder().master("local[2]")  .appName("Spark MLlib DecisionTreeClassifier").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")  println("鸢尾花原始数据如下：")  df.map(t => t(1)+":"+t(0)).collect().foreach(println)  //    处理特征和标签，以及数据分组  val labelIndexer: StringIndexerModel = new StringIndexer().setInputCol("label").setOutputCol(  "indexedLabel").fit(df)  val featureIndexer: VectorIndexerModel = new VectorIndexer().setInputCol("features")  .setOutputCol("indexedFeatures").setMaxCategories(4).fit(df)  //这里我们设置一个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(labelIndexer.labels)  //接下来，我们把数据集随机分成训练集和测试集，其中训练集占70%。  val Array(trainingData, testData) = data.randomSplit(Array(0.7, 0.3))  val dtClassifier: DecisionTreeClassifier = new DecisionTreeClassifier()  .setLabelCol("indexedLabel").setFeaturesCol("indexedFeatures")  //在pipeline中进行设置  val pipelinedClassifier: Pipeline = new Pipeline()  .setStages(Array(labelIndexer, featureIndexer, dtClassifier, labelConverter))  //训练决策树模型  val modelClassifier: PipelineModel = pipelinedClassifier.fit(trainingData)  //进行预测  val predictionsClassifier: DataFrame = modelClassifier.transform(testData)  predictionsClassifier.select("predictedLabel", "label", "features").show(5)  //    评估决策树分类模型  val evaluatorClassifier: MulticlassClassificationEvaluator = new MulticlassClassificationEvaluator()  .setLabelCol("indexedLabel")  .setPredictionCol("prediction").setMetricName("accuracy")  val accuracy: Double = evaluatorClassifier.evaluate(predictionsClassifier)  println("Test Error = " + (1.0 - accuracy))  val treeModelClassifier: DecisionTreeClassificationModel = modelClassifier.stages(2)  .asInstanceOf[DecisionTreeClassificationModel]  println("Learned classification tree model:\n" + treeModelClassifier.toDebugString)  spark.stop()  }  }