我是Pyspark的新手，我正在使用它来使用NaiveBayes分类器训练模型，但是当我训练模型并尝试获取多类评估指标时，我得到了下一个错误：
Py4JJavaError: An error occurred while calling o11342.precision.
: org.apache.spark.SparkException: Job aborted due to stage failure: Task 0 in stage 1683.0 failed 4 times, most recent failure: Lost task 0.3 in stage 1683.0 (TID 30310, server123.es): net.razorvine.pickle.PickleException: expected zero arguments for construction of ClassDict (for numpy.dtype)
at net.razorvine.pickle.objects.ClassDictConstructor.construct(ClassDictConstructor.java:23)
at net.razorvine.pickle.Unpickler.load_reduce(Unpickler.java:707)
at net.razorvine.pickle.Unpickler.dispatch(Unpickler.java:175)
at net.razorvine.pickle.Unpickler.load(Unpickler.java:99)
at net.razorvine.pickle.Unpickler.loads(Unpickler.java:112)
at org.apache.spark.api.python.SerDeUtil$$anonfun$pythonToJava$1$$anonfun$apply$1.apply(SerDeUtil.scala:150)
at org.apache.spark.api.python.SerDeUtil$$anonfun$pythonToJava$1$$anonfun$apply$1.apply(SerDeUtil.scala:149)
at scala.collection.Iterator$$anon$13.hasNext(Iterator.scala:371)
at scala.collection.Iterator$$anon$11.hasNext(Iterator.scala:327)
at scala.collection.Iterator$$anon$11.hasNext(Iterator.scala:327)
at scala.collection.Iterator$$anon$11.hasNext(Iterator.scala:327)
at scala.collection.Iterator$$anon$11.hasNext(Iterator.scala:327)
at scala.collection.Iterator$$anon$11.hasNext(Iterator.scala:327)
at org.apache.spark.util.collection.ExternalSorter.insertAll(ExternalSorter.scala:189)
at org.apache.spark.shuffle.sort.SortShuffleWriter.write(SortShuffleWriter.scala:64)
at org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:73)
at org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:41)
at org.apache.spark.scheduler.Task.run(Task.scala:89)
at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:227)
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$$failJobAndIndependentStages(DAGScheduler.scala:1433)
at org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1421)
at org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1420)
at scala.collection.mutable.ResizableArray$class.foreach(ResizableArray.scala:59)
at scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:47)
at org.apache.spark.scheduler.DAGScheduler.abortStage(DAGScheduler.scala:1420)
at org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:801)
at org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:801)
at scala.Option.foreach(Option.scala:236)
at org.apache.spark.scheduler.DAGScheduler.handleTaskSetFailed(DAGScheduler.scala:801)
at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.doOnReceive(DAGScheduler.scala:1642)
at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:1601)
at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:1590)
at org.apache.spark.util.EventLoop$$anon$1.run(EventLoop.scala:48)
at org.apache.spark.scheduler.DAGScheduler.runJob(DAGScheduler.scala:622)
at org.apache.spark.SparkContext.runJob(SparkContext.scala:1856)
at org.apache.spark.SparkContext.runJob(SparkContext.scala:1869)
at org.apache.spark.SparkContext.runJob(SparkContext.scala:1882)
at org.apache.spark.SparkContext.runJob(SparkContext.scala:1953)
at org.apache.spark.rdd.RDD$$anonfun$collect$1.apply(RDD.scala:934)
at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:150)
at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:111)
at org.apache.spark.rdd.RDD.withScope(RDD.scala:323)
at org.apache.spark.rdd.RDD.collect(RDD.scala:933)
at org.apache.spark.rdd.PairRDDFunctions$$anonfun$collectAsMap$1.apply(PairRDDFunctions.scala:741)
at org.apache.spark.rdd.PairRDDFunctions$$anonfun$collectAsMap$1.apply(PairRDDFunctions.scala:740)
at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:150)
at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:111)
at org.apache.spark.rdd.RDD.withScope(RDD.scala:323)
at org.apache.spark.rdd.PairRDDFunctions.collectAsMap(PairRDDFunctions.scala:740)
at org.apache.spark.mllib.evaluation.MulticlassMetrics.tpByClass$lzycompute(MulticlassMetrics.scala:49)
at org.apache.spark.mllib.evaluation.MulticlassMetrics.tpByClass(MulticlassMetrics.scala:45)
at org.apache.spark.mllib.evaluation.MulticlassMetrics.precision$lzycompute(MulticlassMetrics.scala:142)
at org.apache.spark.mllib.evaluation.MulticlassMetrics.precision(MulticlassMetrics.scala:142)
at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
at sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62)
at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
at java.lang.reflect.Method.invoke(Method.java:498)
at py4j.reflection.MethodInvoker.invoke(MethodInvoker.java:231)
at py4j.reflection.ReflectionEngine.invoke(ReflectionEngine.java:381)
at py4j.Gateway.invoke(Gateway.java:259)
at py4j.commands.AbstractCommand.invokeMethod(AbstractCommand.java:133)
at py4j.commands.CallCommand.execute(CallCommand.java:79)
at py4j.GatewayConnection.run(GatewayConnection.java:209)
at java.lang.Thread.run(Thread.java:745)
Caused by: net.razorvine.pickle.PickleException: expected zero arguments for construction of ClassDict (for numpy.dtype)
at net.razorvine.pickle.objects.ClassDictConstructor.construct(ClassDictConstructor.java:23)
at net.razorvine.pickle.Unpickler.load_reduce(Unpickler.java:707)
at net.razorvine.pickle.Unpickler.dispatch(Unpickler.java:175)
at net.razorvine.pickle.Unpickler.load(Unpickler.java:99)
at net.razorvine.pickle.Unpickler.loads(Unpickler.java:112)
at org.apache.spark.api.python.SerDeUtil$$anonfun$pythonToJava$1$$anonfun$apply$1.apply(SerDeUtil.scala:150)
at org.apache.spark.api.python.SerDeUtil$$anonfun$pythonToJava$1$$anonfun$apply$1.apply(SerDeUtil.scala:149)
at scala.collection.Iterator$$anon$13.hasNext(Iterator.scala:371)
at scala.collection.Iterator$$anon$11.hasNext(Iterator.scala:327)
at scala.collection.Iterator$$anon$11.hasNext(Iterator.scala:327)
at scala.collection.Iterator$$anon$11.hasNext(Iterator.scala:327)
at scala.collection.Iterator$$anon$11.hasNext(Iterator.scala:327)
at scala.collection.Iterator$$anon$11.hasNext(Iterator.scala:327)
at org.apache.spark.util.collection.ExternalSorter.insertAll(ExternalSorter.scala:189)
at org.apache.spark.shuffle.sort.SortShuffleWriter.write(SortShuffleWriter.scala:64)
at org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:73)
at org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:41)
at org.apache.spark.scheduler.Task.run(Task.scala:89)
at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:227)
at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1142)
at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:617)
... 1 more
(<class 'py4j.protocol.Py4JJavaError'>, Py4JJavaError(u'An error occurred while calling o11342.precision.\n', JavaObject id=o11345), <traceback object at 0x4a8e950>)
我执行的代码是下一个：
# This is my dataset (only show the first 20 lines):
data.show()
+--------------+------------+------+----------------------+------------+----------+--------------------+
|s_avi_defectos|s_avi_causas| ppss|indexed_s_avi_defectos|indexed_ppss| features|indexed_s_avi_causas|
+--------------+------------+------+----------------------+------------+----------+--------------------+
| 184.0| 243.0| EMB2| 0.0| 12.0|[0.0,12.0]| 0.0|
| 184.0| 243.0| EMA2| 0.0| 0.0| (2,[],[])| 0.0|
| 184.0| 243.0| X060| 0.0| 2.0| [0.0,2.0]| 0.0|
| 180.0| 243.0|M31-2V| 1.0| 11.0|[1.0,11.0]| 0.0|
| 184.0| 243.0| X062| 0.0| 5.0| [0.0,5.0]| 0.0|
| 180.0| 243.0|M31-2V| 1.0| 11.0|[1.0,11.0]| 0.0|
| 180.0| 243.0| X060| 1.0| 2.0| [1.0,2.0]| 0.0|
| 180.0| 243.0| EMA2| 1.0| 0.0| [1.0,0.0]| 0.0|
| 180.0| 243.0| X061| 1.0| 1.0| [1.0,1.0]| 0.0|
| 180.0| 230.0| X062| 1.0| 5.0| [1.0,5.0]| 4.0|
| 180.0| 243.0| X062| 1.0| 5.0| [1.0,5.0]| 0.0|
| 180.0| 230.0| X060| 1.0| 2.0| [1.0,2.0]| 4.0|
| 180.0| 230.0| EMA2| 1.0| 0.0| [1.0,0.0]| 4.0|
| 180.0| 243.0| X062| 1.0| 5.0| [1.0,5.0]| 0.0|
| 180.0| 243.0| X060| 1.0| 2.0| [1.0,2.0]| 0.0|
| 184.0| 243.0| X060| 0.0| 2.0| [0.0,2.0]| 0.0|
| 180.0| 243.0| X030| 1.0| 6.0| [1.0,6.0]| 0.0|
| 180.0| 243.0| X062| 1.0| 5.0| [1.0,5.0]| 0.0|
| 184.0| 243.0| X063| 0.0| 4.0| [0.0,4.0]| 0.0|
| 180.0| 243.0| HID| 1.0| 7.0| [1.0,7.0]| 0.0|
+--------------+------------+------+----------------------+------------+----------+--------------------+
# I prepare the data for analysis:
datos_Labeles_Point = data.map(lambda line:LabeledPoint(line[-1],line[-2]))
print(datos_Labeles_Point.take(3))
[LabeledPoint(0.0, [0.0,12.0]), LabeledPoint(0.0, (2,[],[])), LabeledPoint(0.0, [0.0,2.0])]
# I prepare a hold out experiment
(trainingData, testData) = datos_Labeles_Point.randomSplit([0.7, 0.3], seed = 123456)
trainingData.cache()
testData.cache()
model = NaiveBayes.train(trainingData, 1.0)
# I get the predictions and the Multiclass metrics:
predictions = model.predict(testData.map(lambda x: x.features))
labelsAndPredictions = testData.map(lambda lp: lp.label).zip(predictions)
predicciones = labelsAndPredictions
# Instantiate metrics object
metrics = MulticlassMetrics(labelsAndPredictions)
但是当我执行这个时：
print("Precision = %s" % metricas.precision())
我收到了我在下面写的错误。
它可能有什么问题？难道我做错了什么？它让我参与DecisionTrees，但对于朴素贝叶斯则没有。
感谢您的帮助！