使用PySpark和以下教程时出现Java错误

Question

我遵循以下代码：https://github.com/thinline72/nsl-kdd#8.-KMeans-clustering-with-Random-Forest-Classifiers

在带有python3的Jupyter笔记本中使用PySpark v3.6.4

我不断得到以下错误：

Py4JJavaError: An error occurred while calling o23.fit.
: java.lang.IllegalArgumentException
    at org.apache.xbean.asm5.ClassReader.<init>(Unknown Source)
    at org.apache.xbean.asm5.ClassReader.<init>(Unknown Source)
    at org.apache.xbean.asm5.ClassReader.<init>(Unknown Source)
    at org.apache.spark.util.ClosureCleaner$.getClassReader(ClosureCleaner.scala:46)
    at org.apache.spark.util.FieldAccessFinder$$anon$3$$anonfun$visitMethodInsn$2.apply(ClosureCleaner.scala:449)
    at org.apache.spark.util.FieldAccessFinder$$anon$3$$anonfun$visitMethodInsn$2.apply(ClosureCleaner.scala:432)
    at scala.collection.TraversableLike$WithFilter$$anonfun$foreach$1.apply(TraversableLike.scala:733)
    at scala.collection.mutable.HashMap$$anon$1$$anonfun$foreach$2.apply(HashMap.scala:103)
    at scala.collection.mutable.HashMap$$anon$1$$anonfun$foreach$2.apply(HashMap.scala:103)
    at scala.collection.mutable.HashTable$class.foreachEntry(HashTable.scala:230)
    at scala.collection.mutable.HashMap.foreachEntry(HashMap.scala:40)
    at scala.collection.mutable.HashMap$$anon$1.foreach(HashMap.scala:103)
    at scala.collection.TraversableLike$WithFilter.foreach(TraversableLike.scala:732)
    at org.apache.spark.util.FieldAccessFinder$$anon$3.visitMethodInsn(ClosureCleaner.scala:432)
    at org.apache.xbean.asm5.ClassReader.a(Unknown Source)
    at org.apache.xbean.asm5.ClassReader.b(Unknown Source)
    at org.apache.xbean.asm5.ClassReader.accept(Unknown Source)
    at org.apache.xbean.asm5.ClassReader.accept(Unknown Source)
    at org.apache.spark.util.ClosureCleaner$$anonfun$org$apache$spark$util$ClosureCleaner$$clean$14.apply(ClosureCleaner.scala:262)
    at org.apache.spark.util.ClosureCleaner$$anonfun$org$apache$spark$util$ClosureCleaner$$clean$14.apply(ClosureCleaner.scala:261)
    at scala.collection.immutable.List.foreach(List.scala:381)
    at org.apache.spark.util.ClosureCleaner$.org$apache$spark$util$ClosureCleaner$$clean(ClosureCleaner.scala:261)
    at org.apache.spark.util.ClosureCleaner$.clean(ClosureCleaner.scala:159)
    at org.apache.spark.SparkContext.clean(SparkContext.scala:2292)
    at org.apache.spark.SparkContext.runJob(SparkContext.scala:2066)
    at org.apache.spark.SparkContext.runJob(SparkContext.scala:2092)
    at org.apache.spark.rdd.RDD$$anonfun$collect$1.apply(RDD.scala:939)
    at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:151)
    at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:112)
    at org.apache.spark.rdd.RDD.withScope(RDD.scala:363)
    at org.apache.spark.rdd.RDD.collect(RDD.scala:938)
    at org.apache.spark.rdd.PairRDDFunctions$$anonfun$countByKey$1.apply(PairRDDFunctions.scala:370)
    at org.apache.spark.rdd.PairRDDFunctions$$anonfun$countByKey$1.apply(PairRDDFunctions.scala:370)
    at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:151)
    at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:112)
    at org.apache.spark.rdd.RDD.withScope(RDD.scala:363)
    at org.apache.spark.rdd.PairRDDFunctions.countByKey(PairRDDFunctions.scala:369)
    at org.apache.spark.rdd.RDD$$anonfun$countByValue$1.apply(RDD.scala:1208)
    at org.apache.spark.rdd.RDD$$anonfun$countByValue$1.apply(RDD.scala:1208)
    at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:151)
    at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:112)
    at org.apache.spark.rdd.RDD.withScope(RDD.scala:363)
    at org.apache.spark.rdd.RDD.countByValue(RDD.scala:1207)
    at org.apache.spark.ml.feature.StringIndexer.fit(StringIndexer.scala:140)
    at org.apache.spark.ml.feature.StringIndexer.fit(StringIndexer.scala:109)
    at java.base/jdk.internal.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
    at java.base/jdk.internal.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62)
    at java.base/jdk.internal.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
    at java.base/java.lang.reflect.Method.invoke(Method.java:564)
    at py4j.reflection.MethodInvoker.invoke(MethodInvoker.java:244)
    at py4j.reflection.ReflectionEngine.invoke(ReflectionEngine.java:357)
    at py4j.Gateway.invoke(Gateway.java:282)
    at py4j.commands.AbstractCommand.invokeMethod(AbstractCommand.java:132)
    at py4j.commands.CallCommand.execute(CallCommand.java:79)
    at py4j.GatewayConnection.run(GatewayConnection.java:214)
    at java.base/java.lang.Thread.run(Thread.java:844)

此错误出现在以下代码段：

# Loading train data
t0 = time()
train_df = load_dataset(train_nsl_kdd_dataset_path)

# Fitting preparation pipeline
labels_mapping_model = labels_mapping_pipeline.fit(train_df)

# Transforming labels column and adding id column
train_df = labels_mapping_model.transform(train_df).withColumn('id', sql.monotonically_increasing_id())

train_df = train_df.cache()
print(train_df.count())
print(time() - t0)

有人知道是什么引起的吗？我为PySpark找到了一篇关于管道的文章，一切似乎都很好。在这里找到：https://spark.apache.org/docs/2.2.0/ml-pipeline.html