scala – 在一个简单的例子中,无法从spark ML运行RandomForestCl

这是代码：

import org.apache.spark.ml.classification.LogisticRegression
import org.apache.spark.ml.param.ParamMap
import org.apache.spark.mllib.linalg.{Vector,Vectors}
import org.apache.spark.sql.Row

// Prepare training data from a list of (label,features) tuples.
val training = sqlContext.createDataFrame(Seq(
  (1.0,Vectors.dense(0.0,1.1,0.1)),(0.0,Vectors.dense(2.0,1.0,-1.0)),1.3,1.0)),(1.0,1.2,-0.5))
)).toDF("label","features")

val rf = new RandomForestClassifier()

val model = rf.fit(training)

这是错误,我得到：

java.lang.IllegalArgumentException: RandomForestClassifier was given input with invalid label column label,without the number of classes specified. See StringIndexer.
    at org.apache.spark.ml.classification.RandomForestClassifier.train(RandomForestClassifier.scala:87)
    at org.apache.spark.ml.classification.RandomForestClassifier.train(RandomForestClassifier.scala:42)
    at org.apache.spark.ml.Predictor.fit(Predictor.scala:90)
    at $iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC.<init>(<console>:48)
    at $iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC.<init>(<console>:53)
    at $iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC.<init>(<console>:55)
    at $iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC.<init>(<console>:57)
    at $iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC.<init>(<console>:59)
    at $iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC.<init>(<console>:61)
    at $iwC$$iwC$$iwC$$iwC$$iwC$$iwC.<init>(<console>:63)
    at $iwC$$iwC$$iwC$$iwC$$iwC.<init>(<console>:65)
    at $iwC$$iwC$$iwC$$iwC.<init>(<console>:67)
    at $iwC$$iwC$$iwC.<init>(<console>:69)
    at $iwC$$iwC.<init>(<console>:71)
    at $iwC.<init>(<console>:73)
    at <init>(<console>:75)
    at .<init>(<console>:79)
    at .<clinit>(<console>)
    at .<init>(<console>:7)
    at .<clinit>(<console>)
    at $print(<console>)
    at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
    at sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62)
    at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
    at java.lang.reflect.Method.invoke(Method.java:497)
    at org.apache.spark.repl.SparkIMain$ReadEvalPrint.call(SparkIMain.scala:1065)
    at org.apache.spark.repl.SparkIMain$Request.loadAndRun(SparkIMain.scala:1340)
    at org.apache.spark.repl.SparkIMain.loadAndRunReq$1(SparkIMain.scala:840)
    at org.apache.spark.repl.SparkIMain.interpret(SparkIMain.scala:871)
    at org.apache.spark.repl.SparkIMain.interpret(SparkIMain.scala:819)
    at org.apache.spark.repl.SparkILoop.reallyInterpret$1(SparkILoop.scala:857)
    at org.apache.spark.repl.SparkILoop.interpretStartingWith(SparkILoop.scala:902)
    at org.apache.spark.repl.SparkILoop.command(SparkILoop.scala:814)
    at org.apache.spark.repl.SparkILoop.processLine$1(SparkILoop.scala:657)
    at org.apache.spark.repl.SparkILoop.innerLoop$1(SparkILoop.scala:665)
    at org.apache.spark.repl.SparkILoop.org$apache$spark$repl$SparkILoop$$loop(SparkILoop.scala:670)
    at org.apache.spark.repl.SparkILoop$$anonfun$org$apache$spark$repl$SparkILoop$$process$1.apply$mcZ$sp(SparkILoop.scala:997)
    at org.apache.spark.repl.SparkILoop$$anonfun$org$apache$spark$repl$SparkILoop$$process$1.apply(SparkILoop.scala:945)
    at org.apache.spark.repl.SparkILoop$$anonfun$org$apache$spark$repl$SparkILoop$$process$1.apply(SparkILoop.scala:945)
    at scala.tools.nsc.util.ScalaClassLoader$.savingContextLoader(ScalaClassLoader.scala:135)
    at org.apache.spark.repl.SparkILoop.org$apache$spark$repl$SparkILoop$$process(SparkILoop.scala:945)
    at org.apache.spark.repl.SparkILoop.process(SparkILoop.scala:1059)
    at org.apache.spark.repl.Main$.main(Main.scala:31)
    at org.apache.spark.repl.Main.main(Main.scala)
    at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
    at sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62)
    at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
    at java.lang.reflect.Method.invoke(Method.java:497)
    at org.apache.spark.deploy.SparkSubmit$.org$apache$spark$deploy$SparkSubmit$$runMain(SparkSubmit.scala:674)
    at org.apache.spark.deploy.SparkSubmit$.doRunMain$1(SparkSubmit.scala:180)
    at org.apache.spark.deploy.SparkSubmit$.submit(SparkSubmit.scala:205)
    at org.apache.spark.deploy.SparkSubmit$.main(SparkSubmit.scala:120)
    at org.apache.spark.deploy.SparkSubmit.main(SparkSubmit.scala)

当函数尝试计算“label”列中的类数时,会出现问题.

正如您在source code of RandomForestClassifier的第84行所见,该函数使用参数“label”调用DataFrame.schema函数.此调用正常,并返回org.apache.spark.sql.types.StructField对象.
然后,调用函数org.apache.spark.ml.util.MetadataUtils.getNumClasses.由于它没有返回预期的输出,因此在第87行引发了一个例外.

在快速浏览一下getNumClasses source code之后,我认为错误是由于colmun“label”中的数据既不是BinaryAttribute也不是NominalAttribute.但是,我不知道如何解决这个问题.

我的问题：

我该如何解决这个问题？

非常感谢您阅读我的问题和帮助！