Spark MLLIB TFIDF文本聚类Python

我是Spark的新手，尝试使用Python中的Spark API将新闻文章集群为集群。新闻文章已被爬网并存储在本地文件夹/input/中。它包含大约100个小文本文件。

作为第一步，我已经设置了我的SparkContent

sconf= SparkConf().setMaster("local").setAppName("My App")
sc= SparkContext(conf=sconf)

接下来，我创建HashingTF，并使用sc.wulleTextFiles（）加载数据。Directory是包含txt文件的文件夹的路径。

htf=HashingTF()
txtdata=sc.wholeTextFiles(directory)

现在，我想分别拆分每个Text文件，并为每个文件输出TF-IDF。第一个问题是分割函数dose不适用于txtdata。我正在使用以下功能：

split_data=txtdata.map(lambda x: x.split(" "))

我得到以下错误：

split_data=sc.wholeTextFiles(directory).map(lambda x: x.split(" "))
AttributeError: 'tuple' object has no attribute 'split'
    at org.apache.spark.api.python.PythonRDD$$anon$1.read(PythonRDD.scala:137)
    at org.apache.spark.api.python.PythonRDD$$anon$1.<init>(PythonRDD.scala:174)
    at org.apache.spark.api.python.PythonRDD.compute(PythonRDD.scala:96)
    at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:263)
    at org.apache.spark.rdd.RDD.iterator(RDD.scala:230)
    at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:61)
    at org.apache.spark.scheduler.Task.run(Task.scala:56)
    at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:196)
    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$$failJobAndIndependentStages(DAGScheduler.scala:1214)
    at org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1203)
    at org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1202)
    at scala.collection.mutable.ResizableArray$class.foreach(ResizableArray.scala:59)
    at scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:47)
    at org.apache.spark.scheduler.DAGScheduler.abortStage(DAGScheduler.scala:1202)
    at org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:696)
    at org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:696)
    at scala.Option.foreach(Option.scala:236)
    at org.apache.spark.scheduler.DAGScheduler.handleTaskSetFailed(DAGScheduler.scala:696)
    at org.apache.spark.scheduler.DAGSchedulerEventProcessActor$$anonfun$receive$2.applyOrElse(DAGScheduler.scala:1420)
    at akka.actor.Actor$class.aroundReceive(Actor.scala:465)
    at org.apache.spark.scheduler.DAGSchedulerEventProcessActor.aroundReceive(DAGScheduler.scala:1375)
    at akka.actor.ActorCell.receiveMessage(ActorCell.scala:516)
    at akka.actor.ActorCell.invoke(ActorCell.scala:487)
    at akka.dispatch.Mailbox.processMailbox(Mailbox.scala:238)
    at akka.dispatch.Mailbox.run(Mailbox.scala:220)
    at akka.dispatch.ForkJoinExecutorConfigurator$AkkaForkJoinTask.exec(AbstractDispatcher.scala:393)
    at scala.concurrent.forkjoin.ForkJoinTask.doExec(ForkJoinTask.java:260)
    at scala.concurrent.forkjoin.ForkJoinPool$WorkQueue.runTask(ForkJoinPool.java:1339)
    at scala.concurrent.forkjoin.ForkJoinPool.runWorker(ForkJoinPool.java:1979)
    at scala.concurrent.forkjoin.ForkJoinWorkerThread.run(ForkJoinWorkerThread.java:107)

最后我计划运行：

temp=htf.transform(split_data) temp.cache() idf = IDF().fit(temp)
tfidf = idf.transform(temp)