在Spark Scala中生成混淆矩阵实现MLlib的随机林

我正在Scala中使用Spark的MLlib实现randomforest。我想从随机森林算法生成一个混淆矩阵

我编写了以下代码。但是什么都没有。我怎样才能得到混淆矩阵

代码：

package org.test.newrandom

import org.apache.spark.mllib.tree.RandomForest
import org.apache.spark.mllib.tree.model.RandomForestModel
import org.apache.spark.mllib.util.MLUtils
import org.apache.spark.mllib.evaluation.MulticlassMetrics
import org.apache.spark.SparkConf
import org.apache.spark.SparkContext

//org/apache/spark/mllib/evaluation/MulticlassMetrics

object RandomTest {

def main(args: Array[String]) = {

  //Start the Spark context
  val conf = new SparkConf()
    .setAppName("RandomTest1")
    .setMaster("local")
  val sc = new SparkContext(conf)

  // 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 = 5 // 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("Test Error = " + testErr)
  println("Learned classification forest model:\n" + model.toDebugString)

  MultiClassMetrics metrics = new MultiClassMetrics(labelAndPreds.rdd())
  println(metrics.precision()); //prints 0.94334140435
  println(metrics.confusionMatrix()); //prints like the following
  }
}

我在这行中遇到错误：

MultiClassMetrics metrics = new MultiClassMetrics(labelAndPreds.rdd())

上面写着-未找到：value MultiClassMetrics，但我添加了

import org.apache.spark.mllib.evaluation.MulticlassMetrics

---

在scala中，变量可以有一个类型归属，其名称如下：

val metrics:MultiClassMetrics=新的MultiClassMetrics（labelAndPreds.rdd（））

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也许您复制了从java源文件中截取的数据，其中类型位于变量名之前？

您是否在SBT中运行它？如果是，则应将依赖项添加到build.sbt

libraryDependencies += "spark-mllib" % "1.4.0"

---

如果这是问题所在，它应该仍然适用于scala shell。

您可以尝试用

val metrics=new MulticlassMetrics（labelandreds）替换该行。

@ChristianHirsch，它可以工作。（y）