我尝试RandomForestClassifier从spark.ml软件包（1.5.2版）运行实验版。我使用的数据集来自Spark ML指南中的LogisticRegression示例。
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)),
(0.0, Vectors.dense(2.0, 1.3, 1.0)),
(1.0, Vectors.dense(0.0, 1.2, -0.5))
)).toDF("label", "features")
val rf = new RandomForestClassifier()
val model = rf.fit(training)
这是我得到的错误：
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)
当函数尝试计算列中的类数时，将出现问题"label"。
正如您在RandomForestClassifier的源代码的第84行看到的那样，该DataFrame.schema函数使用参数调用该函数"label"。此调用可以，并返回一个org.apache.spark.sql.types.StructField对象。然后，该函数org.apache.spark.ml.util.MetadataUtils.getNumClasses被调用。由于它没有返回预期的输出，因此在第87行引发了异常。
快速浏览一下getNumClasses源代码后，我认为该错误是由于colmun"label"中的数据都不BinaryAttribute是事实所致NominalAttribute。但是，我不知道如何解决此问题。
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并为您提供帮助！
首先修复导入以消除歧义
import org.apache.spark.ml.classification.RandomForestClassifier
import org.apache.spark.ml.feature.{StringIndexer, VectorIndexer}
import org.apache.spark.ml.{Pipeline, PipelineStage}
import org.apache.spark.ml.linalg.Vectors
我将使用您使用的相同数据：
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)),
(0.0, Vectors.dense(2.0, 1.3, 1.0)),
(1.0, Vectors.dense(0.0, 1.2, -0.5))
)).toDF("label", "features")
然后创建管道阶段：
val stages = new scala.collection.mutable.ArrayBuffer[PipelineStage]()
val labelIndexer = new StringIndexer().setInputCol("label").setOutputCol("indexedLabel").fit(training)
val featuresIndexer = new VectorIndexer().setInputCol("features").setOutputCol("indexedFeatures").setMaxCategories(10).fit(training)
stages += featuresIndexer
val tmp = featuresIndexer.transform(labelIndexer.transform(training))
val rf = new RandomForestClassifier().setFeaturesCol(featuresIndexer.getOutputCol).setLabelCol(labelIndexer.getOutputCol)
stages += rf
val pipeline = new Pipeline().setStages(stages.toArray)
// Fit the Pipeline
val pipelineModel = pipeline.fit(tmp)
val results = pipelineModel.transform(training)
results.show
//+-----+--------------+---------------+-------------+-----------+----------+
//|label| features|indexedFeatures|rawPrediction|probability|prediction|
//+-----+--------------+---------------+-------------+-----------+----------+
//| 1.0| [0.0,1.1,0.1]| [0.0,1.0,2.0]| [1.0,19.0]|[0.05,0.95]| 1.0|
//| 0.0|[2.0,1.0,-1.0]| [1.0,0.0,0.0]| [17.0,3.0]|[0.85,0.15]| 0.0|
//| 0.0| [2.0,1.3,1.0]| [1.0,3.0,3.0]| [14.0,6.0]| [0.7,0.3]| 0.0|
//| 1.0|[0.0,1.2,-0.5]| [0.0,2.0,1.0]| [1.0,19.0]|[0.05,0.95]| 1.0|
//+-----+--------------+---------------+-------------+-----------+----------+
参考：关于第1步和第2步。对于那些需要有关功能转换器的更多详细信息的人，我建议您在此处阅读官方文档。
本文收集自互联网，转载请注明来源。
如有侵权，请联系[email protected] 删除。
我来说两句