scala - 无法在简单示例上从 spark ML 运行 RandomForestClassifier

Question

我试图运行实验 RandomForestClassifier来自 spark.ml包(版本 1.5.2)。我使用的数据集来自 LogisticRegression Spark ML guide 中的示例.

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)

当函数尝试计算列 "label" 中的类数时会出现问题。 .

正如您在 source code of RandomForestClassifier 中的第 84 行所见, 函数调用 DataFrame.schema带参数的函数 "label" .此调用正常并返回 org.apache.spark.sql.types.StructField目的。
然后，函数 org.apache.spark.ml.util.MetadataUtils.getNumClasses叫做。由于它没有返回预期的输出，因此在第 87 行引发异常。

快速浏览 getNumClasses source code 后，我想这个错误是由于 colmun "label" 中的数据造成的。两者都不是 BinaryAttribute都不是 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并提供帮助!

Answer 1

让我们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]()

对于分类，重新索引类:

val labelIndexer = new StringIndexer().setInputCol("label").setOutputCol("indexedLabel").fit(training)

使用 VectorIndexer 识别分类特征

val featuresIndexer = new VectorIndexer().setInputCol("features").setOutputCol("indexedFeatures").setMaxCategories(10).fit(training)
stages += featuresIndexer

val tmp = featuresIndexer.transform(labelIndexer.transform(training))

学习随机森林

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|
//+-----+--------------+---------------+-------------+-----------+----------+

引用文献:关于第 1 步和第 2 步，如果您想了解更多关于 Feature Transformer 的详细信息，我建议您阅读官方文档 here .