这是我第一次使用 Spark 中的 Mlib。我尝试运行一个随机森林
model = RandomForest.trainClassifier(trainingData, numClasses=2, categoricalFeaturesInfo={}, numTrees=3, featureSubsetStrategy="auto", impurity='gini', maxDepth=4, maxBins=40)但是我得到了以下错误
Py4JJavaError Traceback (most recent call last)<ipython-input-49-5a8de04ff14b> in <module>() 4 model = RandomForest.trainClassifier(trainingData, numClasses=2, categoricalFeaturesInfo={}, 5 numTrees=2, featureSubsetStrategy="auto",----> 6 impurity='gini', maxDepth=4, maxBins=40)/opt/spark/current/python/pyspark/mllib/tree.py in trainClassifier(cls,data, numClasses, categoricalFeaturesInfo, numTrees, featureSubsetStrategy, impurity, maxDepth, maxBins, seed)377 return cls._train(data, "classification", numClasses,378 categoricalFeaturesInfo, numTrees, featureSubsetStrategy, impurity,--> 379 maxDepth, maxBins, seed)380 381 @classmethod/opt/spark/current/python/pyspark/mllib/tree.py in _train(cls, data, algo, numClasses, categoricalFeaturesInfo, numTrees, featureSubsetStrategy, impurity, maxDepth, maxBins, seed)294 model = callMLlibFunc("trainRandomForestModel", data, algo, numClasses,295 categoricalFeaturesInfo, numTrees, featureSubsetStrategy, impurity,--> 296 maxDepth, maxBins, seed)297 return RandomForestModel(model)298 /opt/spark/current/python/pyspark/mllib/common.py in callMLlibFunc(name, *args)128 sc = SparkContext.getOrCreate()129 api = getattr(sc._jvm.PythonMLLibAPI(), name)--> 130 return callJavaFunc(sc, api, *args)131 132 /opt/spark/current/python/pyspark/mllib/common.py in callJavaFunc(sc, func, *args)121 """ Call Java Function """122 args = [_py2java(sc, a) for a in args]--> 123 return _java2py(sc, func(*args))124 125 /opt/spark/current/python/lib/py4j-0.9-src.zip/py4j/java_gateway.py in __call__(self, *args)811 answer = self.gateway_client.send_command(command)812 return_value = get_return_value(--> 813 answer, self.gateway_client, self.target_id, self.name)814 815 for temp_arg in temp_args:/opt/spark/current/python/pyspark/sql/utils.py in deco(*a, **kw) 43 def deco(*a, **kw): 44 try:---> 45 return f(*a, **kw) 46 except py4j.protocol.Py4JJavaError as e: 47 s = e.java_exception.toString()/opt/spark/current/python/lib/py4j-0.9-src.zip/py4j/protocol.py in get_return_value(answer, gateway_client, target_id, name)306 raise Py4JJavaError(307 "An error occurred while calling {0}{1}{2}.\n".--> 308 format(target_id, ".", name), value)309 else:310 raise Py4JError(Py4JJavaError: An error occurred while calling o1123.trainRandomForestModel.: org.apache.spark.SparkException: Job aborted due to stage failure: Task 0 in stage 94.0 failed 4 times, most recent failure: Lost task 0.3 in stage 94.0 (TID 680, mapr5-217.jiwiredev.com): java.lang.RuntimeException: No bin was found for continuous feature. This error can occur when given invalid data values (such as NaN). Feature index: 20. Feature value: 1670.0at org.apache.spark.mllib.tree.impl.TreePoint$.findBin(TreePoint.scala:131)at org.apache.spark.mllib.tree.impl.TreePoint$.org$apache$spark$mllib$tree$impl$TreePoint$$labeledPointToTreePoint(TreePoint.scala:84)at org.apache.spark.mllib.tree.impl.TreePoint$$anonfun$convertToTreeRDD$2.apply(TreePoint.scala:66)at org.apache.spark.mllib.tree.impl.TreePoint$$anonfun$convertToTreeRDD$2.apply(TreePoint.scala:65)at scala.collection.Iterator$$anon$11.next(Iterator.scala:328)at scala.collection.Iterator$$anon$11.next(Iterator.scala:328)at org.apache.spark.storage.MemoryStore.unrollSafely(MemoryStore.scala:283)at org.apache.spark.CacheManager.putInBlockManager(CacheManager.scala:171)at org.apache.spark.CacheManager.getOrCompute(CacheManager.scala:78)at org.apache.spark.rdd.RDD.iterator(RDD.scala:268)at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38)at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:306)at org.apache.spark.rdd.RDD.iterator(RDD.scala:270)at org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:73)at org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:41)at org.apache.spark.scheduler.Task.run(Task.scala:89)at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:214)at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1142)at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:617)at java.lang.Thread.run(Thread.java:745)Driver stacktrace:at org.apache.spark.scheduler.DAGScheduler.org$apache$spark$scheduler$DAGScheduler$$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:47)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:236)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$$anon$1.run(EventLoop.scala:48)at org.apache.spark.scheduler.DAGScheduler.runJob(DAGScheduler.scala:620)at org.apache.spark.SparkContext.runJob(SparkContext.scala:1832)at org.apache.spark.SparkContext.runJob(SparkContext.scala:1845)at org.apache.spark.SparkContext.runJob(SparkContext.scala:1858)at org.apache.spark.SparkContext.runJob(SparkContext.scala:1929)at org.apache.spark.rdd.RDD$$anonfun$collect$1.apply(RDD.scala:927)at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:150)at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:111)at org.apache.spark.rdd.RDD.withScope(RDD.scala:316)at org.apache.spark.rdd.RDD.collect(RDD.scala:926)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$.withScope(RDDOperationScope.scala:111)at org.apache.spark.rdd.RDD.withScope(RDD.scala:316)at org.apache.spark.rdd.PairRDDFunctions.collectAsMap(PairRDDFunctions.scala:740)at org.apache.spark.mllib.tree.DecisionTree$.findBestSplits(DecisionTree.scala:651)at org.apache.spark.mllib.tree.RandomForest.run(RandomForest.scala:233)at org.apache.spark.mllib.tree.RandomForest$.trainClassifier(RandomForest.scala:289)at org.apache.spark.mllib.api.python.PythonMLLibAPI.trainRandomForestModel(PythonMLLibAPI.scala:751)at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)at sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62)at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)at java.lang.reflect.Method.invoke(Method.java:497)at py4j.reflection.MethodInvoker.invoke(MethodInvoker.java:231)at py4j.reflection.ReflectionEngine.invoke(ReflectionEngine.java:381)at py4j.Gateway.invoke(Gateway.java:259)at py4j.commands.AbstractCommand.invokeMethod(AbstractCommand.java:133)at py4j.commands.CallCommand.execute(CallCommand.java:79)at py4j.GatewayConnection.run(GatewayConnection.java:209)at java.lang.Thread.run(Thread.java:745)Caused by: java.lang.RuntimeException: No bin was found for continuous feature. This error can occur when given invalid data values (such as NaN). Feature index: 20. Feature value: 1670.0at org.apache.spark.mllib.tree.impl.TreePoint$.findBin(TreePoint.scala:131)at org.apache.spark.mllib.tree.impl.TreePoint$.org$apache$spark$mllib$tree$impl$TreePoint$$labeledPointToTreePoint(TreePoint.scala:84)at org.apache.spark.mllib.tree.impl.TreePoint$$anonfun$convertToTreeRDD$2.apply(TreePoint.scala:66)at org.apache.spark.mllib.tree.impl.TreePoint$$anonfun$convertToTreeRDD$2.apply(TreePoint.scala:65)at scala.collection.Iterator$$anon$11.next(Iterator.scala:328)at scala.collection.Iterator$$anon$11.next(Iterator.scala:328)at org.apache.spark.storage.MemoryStore.unrollSafely(MemoryStore.scala:283)at org.apache.spark.CacheManager.putInBlockManager(CacheManager.scala:171)at org.apache.spark.CacheManager.getOrCompute(CacheManager.scala:78)at org.apache.spark.rdd.RDD.iterator(RDD.scala:268)at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38)at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:306)at org.apache.spark.rdd.RDD.iterator(RDD.scala:270)at org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:73)at org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:41)at org.apache.spark.scheduler.Task.run(Task.scala:89)at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:214)at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1142)at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:617)... 1 more我正在输入一个 LabeledPoint。如果需要，我可以提供其他代码。
任何解释都将不胜感激
回答：
