Question

我是mlib的新手。我想运行spark mlib的例子。我创建了新的游戏框架项目。并添加了以下依赖项：

name：=“”“EnsembleAI”“”

version：=“1.0-SNAPSHOT”

lazy val root =（项目在文件中（“。”））。enablePlugins（PlayScala）   scalaVersion：=“2.11.7”   libraryDependencies ++ = Seq（jdbc，cache，ws，
  “org.scalatestplus.play”%%“scalatestplus-play”％“1.5.1”％测试，
  “org.apache.spark”%%“spark-core”％“2.3.0”，“org.apache.spark”%%   “spark-sql”％“2.3.0”，“org.apache.spark”%%“spark-mllib”％“2.3.0”   ）

当我运行以下示例时：

package lib

import org.apache.spark.{SparkConf, SparkContext}
// $example on$
import org.apache.spark.mllib.tree.RandomForest
import org.apache.spark.mllib.tree.model.RandomForestModel
import org.apache.spark.mllib.util.MLUtils
// $example off$

object RandomForestClassificationExample {
  def main(args: Array[String]): Unit = {
    val conf = new SparkConf().setMaster("local[1]").setAppName("RandomForestClassificationExample")
    val sc = new SparkContext(conf)
    // $example on$
    // Load and parse the data file.
    val data = MLUtils.loadLibSVMFile(sc, "sample_libsvm_data.txt")
    // Split the data into training and test sets (30% held out for testing)
    val splits = data.randomSplit(Array(0.7, 0.3))
    val (trainingData, testData) = (splits(0), splits(1))

    // Train a RandomForest model.
    // Empty categoricalFeaturesInfo indicates all features are continuous.
    val numClasses = 2
    val categoricalFeaturesInfo = Map[Int, Int]()
    val numTrees = 3 // Use more in practice.
    val featureSubsetStrategy = "auto" // Let the algorithm choose.
    val impurity = "gini"
    val maxDepth = 4
    val maxBins = 32

    val model = RandomForest.trainClassifier(trainingData, numClasses, categoricalFeaturesInfo,
      numTrees, featureSubsetStrategy, impurity, maxDepth, maxBins)

    // Evaluate model on test instances and compute test error
    val labelAndPreds = testData.map { point =>
      val prediction = model.predict(point.features)
      (point.label, prediction)
    }
    val testErr = labelAndPreds.filter(r => r._1 != r._2).count.toDouble / testData.count()
    println(s"Test Error = $testErr")
    println(s"Learned classification forest model:\n ${model.toDebugString}")

    // Save and load model
    model.save(sc, "target/tmp/myRandomForestClassificationModel")
    val sameModel = RandomForestModel.load(sc, "target/tmp/myRandomForestClassificationModel")
    // $example off$

    sc.stop()
  }
}

收到以下错误：

18/03/10 15:01:55 INFO BlockManager：已初始化的BlockManager： BlockManagerId（驱动程序，192.168.42.97,34415，无）线程中的异常 “main”java.lang.ExceptionInInitializerError at org.apache.spark.SparkContext.withScope（SparkContext.scala：692）at org.apache.spark.SparkContext.textFile（SparkContext.scala：821）at org.apache.spark.mllib.util.MLUtils $ .parseLibSVMFile（MLUtils.scala：101）在 org.apache.spark.mllib.util.MLUtils $ .loadLibSVMFile（MLUtils.scala：76）在 org.apache.spark.mllib.util.MLUtils $ .loadLibSVMFile（MLUtils.scala：159）在 org.apache.spark.mllib.util.MLUtils $ .loadLibSVMFile（MLUtils.scala：167）在 lib.RandomForestClassificationExample $。主要（RandomForestClassificationExample.scala：16）在 lib.RandomForestClassificationExample.main（RandomForestClassificationExample.scala）引起：com.fasterxml.jackson.databind.JsonMappingException：不相容的杰克逊版本：2.7.8 at com.fasterxml.jackson.module.scala.JacksonModule $ class.setupModule（JacksonModule.scala：64）在 com.fasterxml.jackson.module.scala.DefaultScalaModule.setupModule（DefaultScalaModule.scala：19）在 com.fasterxml.jackson.databind.ObjectMapper.registerModule（ObjectMapper.java:730）在 org.apache.spark.rdd.RDDOperationScope $（RDDOperationScope.scala：82）。在 org.apache.spark.rdd.RDDOperationScope $（RDDOperationScope.scala） ......还有8个

这是sample_libsvm_data.txt。

注意：以上示例仅来自火花示例。

请指导我错误的地方。

线程“main”java.lang.ExceptionInInitializerError中的异常，同时执行spark RandomForestClassificationExample

注意：以上示例仅来自火花示例。

0 个答案: