Apache spark MultilayerPerceptronClassifier因ArrayIndexOutOfBoundsException而失败

时间：2016-04-06 11:47:03

标签： scala apache-spark spark-dataframe

我使用此代码尝试预测：

import org.apache.spark.sql.functions.col
import org.apache.spark.Logging
import org.apache.spark.graphx._
import org.apache.spark.{ SparkConf, SparkContext }
import org.apache.spark.SparkContext._
import org.apache.spark.sql.SQLContext._
import org.apache.log4j.Logger
import org.apache.log4j.Level
import org.apache.spark.sql.functions.col
import org.apache.spark.ml.feature.VectorAssembler

object NN extends App {

Logger.getLogger("org").setLevel(Level.OFF)
Logger.getLogger("akka").setLevel(Level.OFF)

val sc = new SparkContext(new SparkConf().setMaster("local[2]")
.setAppName("cs"))

val sqlContext = new org.apache.spark.sql.SQLContext(sc)
import sqlContext.implicits._

val df = sc.parallelize(Seq(

("3", "1", "1"),

("2", "1", "1"),

("2", "3", "3"),

("3", "3", "3"),

("0", "1", "0")))

.toDF("label", "feature1", "feature2")

val numeric = df
.select(df.columns.map(c => col(c).cast("double").alias(c)): _*)

val assembler = new VectorAssembler()
.setInputCols(Array("feature1", "feature2"))
.setOutputCol("features")

val data = assembler.transform(numeric)

import org.apache.spark.ml.classification.MultilayerPerceptronClassifier

val layers = Array[Int](2, 3, 5, 4) // Note 2 neurons in the input layer
val trainer = new MultilayerPerceptronClassifier()
.setLayers(layers)
.setBlockSize(128)
.setSeed(1234L)
.setMaxIter(100)

val model = trainer.fit(data)
model.transform(data).show


}

对于我使用的数据帧（df） ("4", "1", "1")代替("3", "1", "1")我收到错误：

Java HotSpot(TM) 64-Bit Server VM warning: ignoring option MaxPermSize=256m; support was removed in 8.0
[info] Set current project to spark-applications1458853926-master (in build file:/C:/Users/Desktop/spark-applications1458853926-master/)
[info] Compiling 1 Scala source to C:\Users\Desktop\spark-applications1458853926-master\target\scala-2.11\classes...
[info] Running NN
Using Spark's default log4j profile: org/apache/spark/log4j-defaults.properties
16/04/06 12:42:11 INFO Remoting: Starting remoting
16/04/06 12:42:11 INFO Remoting: Remoting started; listening on addresses :[akka.tcp://sparkDriverActorSystem@10.95.132.202:64056]
[error] (run-main-0) org.apache.spark.SparkException: Job aborted due to stage failure: Task 0 in stage 0.0 failed 1 times, most recent failure: Lost task 0.0 in stage 0.0 (TID 0, localhost): java.lang.ArrayIndexOutOfBoundsException: 4
[error]         at org.apache.spark.ml.classification.LabelConverter$.encodeLabeledPoint(MultilayerPerceptronClassifier.scala:85)
[error]         at org.apache.spark.ml.classification.MultilayerPerceptronClassifier$$anonfun$2.apply(MultilayerPerceptronClassifier.scala:165)
[error]         at org.apache.spark.ml.classification.MultilayerPerceptronClassifier$$anonfun$2.apply(MultilayerPerceptronClassifier.scala:165)
[error]         at scala.collection.Iterator$$anon$11.next(Iterator.scala:370)
[error]         at scala.collection.Iterator$GroupedIterator.takeDestructively(Iterator.scala:934)
[error]         at scala.collection.Iterator$GroupedIterator.go(Iterator.scala:949)
[error]         at scala.collection.Iterator$GroupedIterator.fill(Iterator.scala:986)
[error]         at scala.collection.Iterator$GroupedIterator.hasNext(Iterator.scala:990)
[error]         at scala.collection.Iterator$$anon$11.hasNext(Iterator.scala:369)
[error]         at org.apache.spark.util.Utils$.getIteratorSize(Utils.scala:1595)
[error]         at org.apache.spark.rdd.RDD$$anonfun$count$1.apply(RDD.scala:1143)
[error]         at org.apache.spark.rdd.RDD$$anonfun$count$1.apply(RDD.scala:1143)
[error]         at org.apache.spark.SparkContext$$anonfun$runJob$5.apply(SparkContext.scala:1858)
[error]         at org.apache.spark.SparkContext$$anonfun$runJob$5.apply(SparkContext.scala:1858)
[error]         at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:66)
[error]         at org.apache.spark.scheduler.Task.run(Task.scala:89)
[error]         at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:213)
[error]         at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1142)
[error]         at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:617)
[error]         at java.lang.Thread.run(Thread.java:745)
[error]
[error] Driver stacktrace:
org.apache.spark.SparkException: Job aborted due to stage failure: Task 0 in stage 0.0 failed 1 times, most recent failure: Lost task 0.0 in stage 0.0 (TID 0, localhost): java.lang.ArrayIndexOutOfBoundsException: 4
        at org.apache.spark.ml.classification.LabelConverter$.encodeLabeledPoint(MultilayerPerceptronClassifier.scala:85)
        at org.apache.spark.ml.classification.MultilayerPerceptronClassifier$$anonfun$2.apply(MultilayerPerceptronClassifier.scala:165)
        at org.apache.spark.ml.classification.MultilayerPerceptronClassifier$$anonfun$2.apply(MultilayerPerceptronClassifier.scala:165)
        at scala.collection.Iterator$$anon$11.next(Iterator.scala:370)
        at scala.collection.Iterator$GroupedIterator.takeDestructively(Iterator.scala:934)
        at scala.collection.Iterator$GroupedIterator.go(Iterator.scala:949)
        at scala.collection.Iterator$GroupedIterator.fill(Iterator.scala:986)
        at scala.collection.Iterator$GroupedIterator.hasNext(Iterator.scala:990)
        at scala.collection.Iterator$$anon$11.hasNext(Iterator.scala:369)
        at org.apache.spark.util.Utils$.getIteratorSize(Utils.scala:1595)
        at org.apache.spark.rdd.RDD$$anonfun$count$1.apply(RDD.scala:1143)
        at org.apache.spark.rdd.RDD$$anonfun$count$1.apply(RDD.scala:1143)
        at org.apache.spark.SparkContext$$anonfun$runJob$5.apply(SparkContext.scala:1858)
        at org.apache.spark.SparkContext$$anonfun$runJob$5.apply(SparkContext.scala:1858)
        at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:66)
        at org.apache.spark.scheduler.Task.run(Task.scala:89)
        at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:213)
        at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1142)
        at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:617)
        at java.lang.Thread.run(Thread.java:745)

Driver stacktrace:
        at org.apache.spark.scheduler.DAGScheduler.org$apache$spark$scheduler$DAGScheduler$$failJobAndIndependentStages(DAGScheduler.scala:1431)
        at org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1419)
        at org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1418)
        at scala.collection.mutable.ResizableArray$class.foreach(ResizableArray.scala:59)
        at scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:48)
        at org.apache.spark.scheduler.DAGScheduler.abortStage(DAGScheduler.scala:1418)
        at org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:799)
        at org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:799)
        at scala.Option.foreach(Option.scala:257)
        at org.apache.spark.scheduler.DAGScheduler.handleTaskSetFailed(DAGScheduler.scala:799)
        at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.doOnReceive(DAGScheduler.scala:1640)
        at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:1599)
        at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:1588)
        at org.apache.spark.util.EventLoop$$anon$1.run(EventLoop.scala:48)
        at org.apache.spark.scheduler.DAGScheduler.runJob(DAGScheduler.scala:620)
        at org.apache.spark.SparkContext.runJob(SparkContext.scala:1832)
        at org.apache.spark.SparkContext.runJob(SparkContext.scala:1845)
        at org.apache.spark.SparkContext.runJob(SparkContext.scala:1858)
        at org.apache.spark.SparkContext.runJob(SparkContext.scala:1929)
        at org.apache.spark.rdd.RDD.count(RDD.scala:1143)
        at org.apache.spark.mllib.optimization.LBFGS$.runLBFGS(LBFGS.scala:170)
        at org.apache.spark.mllib.optimization.LBFGS.optimize(LBFGS.scala:117)
        at org.apache.spark.ml.ann.FeedForwardTrainer.train(Layer.scala:878)
        at org.apache.spark.ml.classification.MultilayerPerceptronClassifier.train(MultilayerPerceptronClassifier.scala:170)
        at org.apache.spark.ml.classification.MultilayerPerceptronClassifier.train(MultilayerPerceptronClassifier.scala:110)
        at org.apache.spark.ml.Predictor.fit(Predictor.scala:90)
        at NN$.delayedEndpoint$NN$1(NN.scala:56)
        at NN$delayedInit$body.apply(NN.scala:15)
        at scala.Function0$class.apply$mcV$sp(Function0.scala:34)
        at scala.runtime.AbstractFunction0.apply$mcV$sp(AbstractFunction0.scala:12)
        at scala.App$$anonfun$main$1.apply(App.scala:76)
        at scala.App$$anonfun$main$1.apply(App.scala:76)
        at scala.collection.immutable.List.foreach(List.scala:381)
        at scala.collection.generic.TraversableForwarder$class.foreach(TraversableForwarder.scala:35)
        at scala.App$class.main(App.scala:76)
        at NN$.main(NN.scala:15)
        at NN.main(NN.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)
Caused by: java.lang.ArrayIndexOutOfBoundsException: 4
        at org.apache.spark.ml.classification.LabelConverter$.encodeLabeledPoint(MultilayerPerceptronClassifier.scala:85)
        at org.apache.spark.ml.classification.MultilayerPerceptronClassifier$$anonfun$2.apply(MultilayerPerceptronClassifier.scala:165)
        at org.apache.spark.ml.classification.MultilayerPerceptronClassifier$$anonfun$2.apply(MultilayerPerceptronClassifier.scala:165)
        at scala.collection.Iterator$$anon$11.next(Iterator.scala:370)
        at scala.collection.Iterator$GroupedIterator.takeDestructively(Iterator.scala:934)
        at scala.collection.Iterator$GroupedIterator.go(Iterator.scala:949)
        at scala.collection.Iterator$GroupedIterator.fill(Iterator.scala:986)
        at scala.collection.Iterator$GroupedIterator.hasNext(Iterator.scala:990)
        at scala.collection.Iterator$$anon$11.hasNext(Iterator.scala:369)
        at org.apache.spark.util.Utils$.getIteratorSize(Utils.scala:1595)
        at org.apache.spark.rdd.RDD$$anonfun$count$1.apply(RDD.scala:1143)
        at org.apache.spark.rdd.RDD$$anonfun$count$1.apply(RDD.scala:1143)
        at org.apache.spark.SparkContext$$anonfun$runJob$5.apply(SparkContext.scala:1858)
        at org.apache.spark.SparkContext$$anonfun$runJob$5.apply(SparkContext.scala:1858)
        at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:66)
        at org.apache.spark.scheduler.Task.run(Task.scala:89)
        at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:213)
        at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1142)
        at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:617)
        at java.lang.Thread.run(Thread.java:745)
[trace] Stack trace suppressed: run last compile:run for the full output.
java.lang.RuntimeException: Nonzero exit code: 1
        at scala.sys.package$.error(package.scala:27)
[trace] Stack trace suppressed: run last compile:run for the full output.
[error] (compile:run) Nonzero exit code: 1
[error] Total time: 19 s, completed 06-Apr-2016 12:42:20

为什么我收到ArrayIndexOutOfBoundsException，我没有正确设置我的标签？标签不能取任何价值，因为它们只是标签吗？在这个例子中，它们似乎必须在0-3？

的范围内

2 个答案:

答案 0 :(得分：8)

输出图层使用one-hot encoding;也就是说，标签“3”转换为（0,0,0,1），其中“第三”元素为1，其余为0.当您有4个输出节点且标签为4时，LabelConverter函数（其来源可见here）将失败。 （labelCount为4，labeledPoint.label.toInt为4，因此您的错误。）

val output = Array.fill(labelCount)(0.0)
output(labeledPoint.label.toInt) = 1.0
(labeledPoint.features, Vectors.dense(output))

所以改变这一行：

val layers = Array[Int](2, 3, 5, 4) // Note 2 neurons in the input layer

到此：

val layers = Array[Int](2, 3, 5, 5) // Note 2 neurons in the input layer and 5 neurons in the output layer

我期待它能够发挥作用。

答案 1 :(得分：1)

最后一层出现（我自己编写第一个严肃的样本）来表示四舍五入到int值的标签，所以通过声明4作为这个值，你期望标签为0,1,2,3 - 显然，代码旨在创建一个神经网络，根据输入将输出分类为一系列状态 - 我正在尝试确定如何使用此功能编写t tic tac toe player

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